blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.108 letter to the editor | open access dmms: a decentralized blockchain ledger for the management of medication histories patrick li1, scott d. nelson2, bradley a. malin3, you chen4 1computer science, saratoga high school, saratoga, ca, usa; 2department of biomedical informatics, vanderbilt university medical center, nashville, tn, usa; 3department of biomedical informatics, vanderbilt university medical center, nashville, tn, usa; 4department of biomedical informatics, vanderbilt university medical center, nashville, tn, usa blockchain in healthcare today, january 4, 2019. © the author(s). 2019 the original article was published in blockchain in healthcare today. doi: https://doi.org/10.30953/bhty.v2.38 section: use cases/pilots/methodologies keywords: blockchain ledger, decentralized, hyperledger fabric framework, medication. histories sirs, regarding the letter writer’s first point, we do not agree with the opinion the authors proposed. as we stated in our paper, medication management and exchange across health institutions can bring great benefits for patients, payers and healthcare organizations. we admin that there are barriers to achieve the goals of secure and trustworthy medication exchanges. that is also the reason why we need to conduct research to remove these barriers. as we discussed in our paper, we can make connections between nodes in dmms network and ehr systems to avoid duplicative works. https://doi.org/10.30953/bhty.v2.38 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.108 we think the authors’ proposed comprehensive and modular ehr still cannot solve the secure and trust problem raised by centralized system. if authors want to implement a decentralized ehr system, then they will face many big challenges such as a variety of healthcare processes and complex workflows. that is also the reason why we only considered secure and trustworthy medication management as a pilot study. on page 4, we acknowledged that we did not use a precise term here. instead of using the term “definition,” we can state the differences between public and private blockchain as: “there are several distinctions between public and private blockchains, which often display properties of permissionless and permissioned blockchains respectively” additionally, according to the hyperledger website “hyperledger fabric is an open source enterprise-grade permissioned distributed ledger technology (dlt) platform, designed for use in enterprise contexts, that delivers some key differentiating capabilities over other popular distributed ledger or blockchain platforms.” it is highly customizable but mostly within the scope of private business-centered applications. i believe our definition of it is correct in the paper.1 we agree and apologize on page 5 that we did not conduct an intensive investigation on etherium network during the study of the work. thank you for this critique, we can specify this further in our paper. on page 6 of our article, the reason we call patients assets is because that is the naming convention for hyperledger composer data structure. in the hyperledger fabric framework, there are three main categories as explained: assets, participants, and transactions. in this business network, patients display properties most related to those of assets described in the hyperledger composer structure, which is why we categorize patients as assets. of course, patients will not be treated in the traditional term of assets as that would be highly unethical. 2 thank you for this critique on page 7, we can change “breach” to “intrusion” blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.108 regarding the statement on page 9, as we stated in our paper, dmms is independent on the ehr systems. all the data generated and transferred within the dmms network are secure and trustworthy. to avoid providers prescribing medication the way private keys, as noted on page 10 of our article, are stored is very similar with the way the online cryptocurrency wallets managed by cryptocurrency exchange platforms. the private keys in our study are managed and maintained by nodes in the dmms network, and only permissioned nodes can join the network, thus the private keys in the dmms are more secure than the private keys of the online cryptocurrency wallets. on orders in both dmms network and ehr systems, we discussed a potential solution to connect the two systems. to the best of our knowledge, third-party ehr vendors do not actually store any ehr data generated by private healthcare organizations. if they plan to store or maintain data coming from private healthcare organizations, the data should be in an encrypted manner to avoid private information leaking. sincerely, you chen, phd references 1. introduction. hyperledger fabric. 2019. available at url: https://hyperledgerfabric.readthedocs.io/en/latest/whatis.html 2. introduction. welcome to hyperledger composer. accessed 2019: https://hyperledger.github.io/composer/v0.19/introduction/introduction https://hyperledger-fabric.readthedocs.io/en/latest/whatis.html https://hyperledger-fabric.readthedocs.io/en/latest/whatis.html https://hyperledger.github.io/composer/v0.19/introduction/introduction cenaj. editorial bhty feb 18 page 1 of 2 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.25 notes from our publisher t is with pride that i present to you the product of a team effort—bhty debut articles. we share them with you to evaluate, adopt, engage, challenge, or champion. it is up to the community to determine. blockchain in healthcare today is a platform where all can converge to share, learn, contribute, vet, and build a new framework for the future of patient care, and backbone for a new health system, created with the assistance of blockchain technology. we thank and congratulate all the authors appearing in this debut and all the authors that submitted manuscripts for review. you are pioneers and visionaries, all. personal thanks is extended to each, as you endeavored to publish in a new publication. my desire is that the initiative assists the accumulation and acceleration of validated successes using the technology. please know bhty is unique in that we hold failures and successful outcomes in equal esteem. failures are not traditionally lauded, yet those events may offer more insight to propelling success. in this instance, it is my hope that altruism and courage inspire a balanced body of work to perpetuate a trusted union within the community. as publisher, i opted to launch a traditional scholarly peer review journal anticipating the unfamiliar territory of the editorial mission. the healthcare sector, at large, must build familiarity and confidence with the technology and understand its vast applications. the bhty online journal platform provides the broadest, most rapid adoption to scale proficiency and agility with the technology for leadership, early adopters, and ultimately, all stakeholders to harness. conformity at the outset should engender acceptance for both journal and use of the technology. our plan for bhty entails transforming the journal for the blockchain and expanding its reach with international editions. in addition, we plan a global health citizenry initiative. we will announce this at the bhty conference—stay tuned. blockchain in healthcare today endeavors to be impartial, objective, egalitarian, and collegiate in essence and raison d'être. i believe our editorial board members reflect these values. board member selections are based on expertise, integrity, passion, commitment, willingness to contribute, work ethic, and ingenuity. i cannot thank each and every member enough for all their continued achievements and assistance in presenting the journal to you. peer review is a voluntary role, and these individuals, ardent enthusiasts and users of the technology, are commended. should you be interested in in joining the board, reach out. the bhty board members may not be i page 2 of 2 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.25 employed at a commercial enterprise. two board members stepped down moving to the commercial side, jim nasr and sean manion. we sincerely thank them for their service as they continue to propel a forward reaching agenda in healthcare. the original plan was to launch the journal in january. it is now march. in fairness to authors who submitted manuscripts, i wanted to include as many peer reviewed articles to present a robust issue, as the technology impacts many facets of the industry. this is reflected in the content. we encountered a software upgrade that presented some technical issues. in addition, two board members stepped down and we opted to begin the review process again for those submissions impacted. a shift in authorship and corresponding author(s) occurred, and of course, the peer review process usually takes several revisions before a paper is accepted. we will extend peer review turn around in future. we expect to publish a continuous flow of articles on a rolling basis; and this will allow reviewers more flexibility considering personal and professional schedules. you are encouraged to register as a subscriber to receive future article alerts here. the article acceptance rate was 50%. for those declined, submissions were either too general, a rehash of what already appears on the web and media at large, or not written or formatted properly. we encourage authors to review and follow general information for authors on the site. questions are welcome, and we are happy to assist where possible. we will feature a session at the upcoming bhty conference on how to write an original research manuscript. the conference is scheduled for october 24th, lerner hall, columbia university, new york city. details will be released soon. i would be remiss if i did not thank our editorin-chief for his leap of faith with bhty. i recall smiling all day the day he agreed to take on the role. in closing, our tag line is “building trust through truth.” i can’t think of a more earnest phrase or brighter beacon for a scholarly journal to aspire to, particularly for this sector. hold us to it. we look to the community to validate the initiative and build consensus behind it. thank you. thanks to bhty managing editor, john russo, pharmd, for his support and dedication to the success of bhty. mentor and friend. his guidance, commitment, diligence, kindness, and humor inspire me. i am blessed to know him, and we are thankful for his contributions to the journal. thanks too to johnny h., and joe z, (who introduced me to blockchain). tory cenaj founder and publisher blockchain in healthcare today 29-article text-267-1-2-20180602 page 1 of 2 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.29 separating signal from noise: advice for blockchain startups john d. halamka, md, editor-in-chief, bhty ow many startups have you discovered that promise to solve every outstanding computer science and informatics challenge with blockchain? as a harvard medical school professor of innovation, beth israel deaconess chief information officer, and mentor to several accelerators/incubators, i listen to startup pitches virtually every day. an increasing number of them sound like this. “we’ve got a cloud-hosted, big-data, machinelearning, api-driven (application program interface) mobile app, with blockchain!” if we are not careful, blockchain will become a meme for overpromising and underdelivering in healthcare it. here’s my rubric to distinguish blockchain signal from noise? 1. when you listen to a pitch, is there a product, a business model, and a management team? or, is it just a powerpoint created over a cappuccino that attempts to capture the frenzy around blockchain in the same way that most of us were taught about the dutch tulip mania, causing fortunes to be made and lost?1 2. is blockchain really necessary as part of their business model and architecture? blockchain is useful for many things: ensuring data integrity via consensus,2 consent management via smart contracts,3 providing a decentralized public ledger not controlled by any corporation or government.4 however, it is not a database, an analytic tool, a fast/scalable platform, an interoperability solution, or a user-friendly platform. 3. is the product or service being pitched actually in production? if so, what is the product maturity—a low volume pilot or a high-volume implementation? 4. what is the user experience? i recently heard a pitch in which patients are expected to generate cryptocurrency tokens using command line software, then cut and paste their tokens into web apps that are part of a secure medical record exchange. few patients are likely to have the technical skills and patience to do this. at the moment, most blockchain user experiences are a multi-step process.5 5. what is the scalability? remember that the worldwide throughput of the public bitcoin blockchain is around four transactions per second.6 if a startup claims it can support thousands of transactions per second, how will they do it—a private blockchain using technologies like hyperledger7 or iota?8 of all of these, the most important to me is clarifying the value that blockchain delivers h page 2 of 2 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.29 versus a non-blockchain implementation, so that we really understand the true blockchain application value. although initial coin offerings (icos) have been described as the next great wave of venture capital, they are increasingly risky. the security and exchange commission has recently provided guidance9 and been increasingly active in shutting down suspected fraud. it's highly likely that tokens are actually securities,10 and thus companies must register as a national security exchange—an expensive and time consulting process. further, tax implications of icos are still a work in progress. bottom line: any icos involving u.s. investors are best avoided at this time. i hope this is useful guidance as you work with blockchain startups. i only support startups that carefully align their products with the strengths of blockchain and avoid unregulated initial coin offerings. references 1. goldgar a. tulipmania: money, honor, and knowledge in the dutch golden age. university of chicago press, chicago, il 60637 usa. ©2008. isbn: 9780226301303. url: http://press.uchicago.edu/ucp/books/boo k/chicago/t/bo5414939.html. 2. castor a. a (short) guide to blockchain consensus protocol. coindesk. 2017. url: https://www.coindesk.com/short-guideblockchain-consensus-protocols/. 3. neisse r, steri g, nai-fovino i. a blockchain-based approach for data accountability and provenance tracking. url: ares 2017: 14:114:10. https://arxiv.org/pdf/1706.04507.pdf 4. gavril m. the financial revolution and the many benefits it brings: cryptocurrency & blockchain technology. forbes. 2017. url: https://www.forbes.com/sites/forbescom municationscouncil/2017/11/15/thefinancial-revolution-and-the-manybenefits-it-brings-cryptocurrencyblockchain-technology/#4e09fd233cc0 5. how to buy bitcoins. wikihow. accessed 5/1/18. https://www.wikihow.com/buy-bitcoins 6. transaction rate. blockchain luxembourg s.a. 2017. . url: https://blockchain.info/charts/transaction s-per-second 7. about hyperledger. the linux foundation projects. 2018. url: https://www.hyperledger.org/about 8. what is iota. iota. 2018. url: https://www.iota.org/get-started/what-isiota 9. initial coin offerings (icos). u.s. security and exchange commission. 2018. url: https://www.sec.gov/ico 10. report of investigation pursuant to section 21(a) of the securities exchange act of 1934: the dao. securities exchange act of 1934. release no. 81207. 2017. url: https://www.sec.gov/litigation/investrep ort/34-81207.pdf dropbox conv2x_181024-1600_netflixing-clinical-trials.mp3 simplify your life dropbox conv2x_181024-1500_making-telehealth-efficient.mp3 simplify your life blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.98 letter to the editor | open access dmms: a decentralized blockchain ledger for the management of medication histories patrick li1, scott d. nelson2, bradley a. malin3, you chen4 1computer science, saratoga high school, saratoga, ca, usa; 2department of biomedical informatics, vanderbilt university medical center, nashville, tn, usa; 3department of biomedical informatics, vanderbilt university medical center, nashville, tn, usa; 4department of biomedical informatics, vanderbilt university medical center, nashville, tn, usa blockchain in healthcare today, january 4, 2019. © the author(s). 2019 the original article was published in blockchain in healthcare today. doi: https://doi.org/10.30953/bhty.v2.38 section: use cases/pilots/methodologies keywords: blockchain ledger, decentralized, hyperledger fabric framework, medication, histories dear editor, we hereby appreciate you for publishing a unique journal dedicated to healthcare blockchain. we would like to mention some points on the latest paper published: “dmms: a decentralized blockchain ledger for the management of medication histories”.1 it is a valuable text prototyping one of the most case requirements of healthcare suggesting that we should to move on peer-to-peer blockchain network, facilitating prescribing and patient history access. first, when we are trying to solve an issue, specifically in the health care; it would be better to plan a more comprehensive solution,2 what is called lean management.3 the prescription is not a stand-alone piece of software discrete of other administration and admission processes in hospitals. experience has shown that stand-alone ehttps://doi.org/10.30953/bhty.v2.38 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.98 prescription has cost barriers to the system and also training of the staff.4,5 therefore, a comprehensive and modular ehr could contain an e-prescription module. on page 4, “public and private blockchain”, the first sentence parallels the public and private blockchain to permissionless and permissioned. although the definitions are close together, they are different as described in chapter 23 of this apress book6 or this website7. on page 4, hyperledger is mentioned as a private blockchain. however, it is a highly customizable platform that can be used for any type of blockchain.8–10 on page 5, research exemplified etherium next to bitcoin as a “proof of work” consensus blockchain. however, etherium has switched to a mixed method “proof of concept” called casper in recent years11,12 and has become more green and environmental friendly13 by reducing energy costs needed for mining. the other point to mention is about component naming (page 6). although the system and component names are not visible to everyone, ethically it would be better not to categorize the patients as assets. even some have suggested that we should not write the term patient in papers and that we should use the “participant” instead.14 however, as you have that for the physicians, you could simply call that “users”. on page 7 you have stated, “machines will have a pre-installed client with a prescribertype network card.” it was not clear to us what you mean at the first sight. it appears that the researcher has manufactured specific hardware that replaces computer network card for prescribers’ computer to join the developed blockchain network. the security part of the paper (page 8) starts with the sentence “in some breaches…”. although using the word “breach” is not wrong here, as the research is published in an expertise blockchain journal, we have to consider that “breach” is specifically a type of insecurity issue15 that may lead to misunderstanding here. the other ethical issue (page 9) is that in a system developed by highly secure blockchain technologies, users trust and join as they are assured their data are safe and secured in the blockchain. thus, how and in what case are the records queried and sent blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.98 to the third-party ehr vendors? in most cases, they do not store data in an encrypted format. furthermore, (as described on page 10), the same issue happens for the private keys of the users unable to access smartphones. how are their private keys stored? what has been done in the research puts the users in more danger in comparison to those that have access to smartphones. that’s the violation of ethical code “justice”.16,17 acknowledgements: the authors would like to thank shiraz university of medical sciences, shiraz, iran and also center for development of clinical research of nemazee hospital and dr. nasrin shokrpour for editorial assistance and also dr. maryam shahpasand from asia pacific university of technology & innovation for her kind help. sincerely, amir hossein zolfaghari1, mahdi nasiri2, roxana sharifian3 1msc student of medical informatics, school of management and medical information sciences, student research committee, shiraz university of medical sciences, shiraz, iran. e-mail: zolfaghari@sums.ac.ir; 2assistant professor, health human resources research center, school of management and medical information sciences, shiraz university of medical sciences, shiraz, iran; 3associate professor, school of management and medical information sciences, health human resources research center, shiraz university of medical sciences, shiraz, iran. references 1. li p, nelson sd., malin ba, chen y, chen y. dmms: a decentralized blockchain ledger for the management of medication histories. blockchain healthcare today 2019; 1. available at url: mailto:zolfaghari@sums.ac.ir blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.98 https://blockchainhealthcaretoday.com/index.php/journal/article/view/38 2. zaccai g. designing the future of healthcare. in studies in health technology and informatics 149, 49–57 (2009). 3. lawal ak, rotter t, kinsman l, et al. lean management in health care: definition, concepts, methodology and effects reported (systematic review protocol). syst rev. 2014 sep 19;3:103. doi: 10.1186/2046-4053-3-103. 4. porterfield a, engelbert k. coustasse a. electronic prescribing: improving the efficiency and accuracy of prescribing in the ambulatory care setting. perspect. heal. inf. manag. 2014;11, 1g. 5. lander l, klepser dg. cochran gl, lomelin de. morien m. barriers to electronic prescribing: nebraska pharmacists’ perspective. j. rural heal. 2013; 29:119–24. 6. drescher d. kirk l. blockchain basics : a non-technical introduction in 25 steps. apress. 2017. 7. blockchainhub.net. types of blockchains and dlts (distributed ledger technologies). https://blockchainhub.net/ available at: https://blockchainhub.net/blockchains-and-distributed-ledger-technologies-ingeneral/. accessed: 14th january 2019. 8. hyperledger. blockchain technology projects – hyperledger. https://www.hyperledger.org/ available at: https://www.hyperledger.org/projects. accessed: 14th january 2019. 9. hyperledger.org. github hyperledger. http://www.hyperledger.org available at: https://github.com/hyperledger/hyperledger. accessed: 14th january 2019. 10. moses sp. hyperledger — chapter 1 | blockchain foundation – the startup – medium. medium.com (2018). available at: https://medium.com/swlh/hyperledgerchapter-1-foundation-7ad5bd94d452. accessed: 14th january 2019. 11. evan t. types of consensus protocols used in blockchains – hacker noon. hackernoon (2018). available at: https://hackernoon.com/types-of-consensus-protocolsused-in-blockchains-6edd20951899. accessed: 14th january 2019. 12. ethereum github page. proof of stake faqs · ethereum/wiki wiki · github. github (2018). available at: https://github.com/ethereum/wiki/wiki/proof-of-stake-faqs. accessed: 14th january 2019. blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.98 13. tron live. an easy to understand guide to pow, pos, dpos, consensus mechanism and super representative. medium (2018). available at: https://medium.com/tronfoundation/an-easy-to-understand-guide-to-pow-pos-dpos-consensus-mechanism-andsuper-representative-eb1f5504a8e. accessed: 14th january 2019. 14. neuberger j. do we need a new word for patients? let’s do away with “patients.” bmj 1999; 318:1756–7. 15. kobus iii, tj. the a to z of healthcare data breaches. j. healthc. risk manag. 2012; 32:24–8. 16. goodman, kw. cambridge university press. & cambridge. ethics, computing, and medicine : informatics and the transformation of health care. cambridge university press. 1997. doi:10.1017/cbo9780511585005 17. gracyk t. four fundamental principles of ethics. minnesota state university moorhead. 2012. available at: http://web.mnstate.edu/gracyk/courses/phil 115/four_basic_principles.htm. accessed: 20th october 2018. page 1 of 3 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.122 blockchain, interoperability, and self-sovereign identity: trust me, it’s my data jim st. clair,1 ann ingraham, phd,2 dominic king,3 michael b. marchant,4 fletcher cotesworth mccraw,5 david metcalf, phd,6 john squeo, mba7 affiliations: 1dinocrates group llc; 2exponential healthtech advisors, llc; 3harmony healthcare it; 4system integration & health information exchange, uc davis health; 5blockchain & dlt practice, cognizant; 6metil, university of central florida, ist; 7chcio, accenture strategy, blockchain lead—health and life sciences, north america, accenture corresponding author: jim st.clair. jim.stclair@dinocrates.com keywords: blockchain, healthcare, identity, interoperability, tefca section: discussion the problem with industry adoption of electronic health records, provider organizations have been unable to escape the recurring challenge of establishing standards and incentives to fully enable provider-to-provider interoperability. this challenge is exacerbated by an emerging demand for allowing patients greater control and ownership over their medical records. adversarial relationships between organizations limit interoperability and impact value-based care, care coordination, and the provider–patient experience.1,2 the current interoperability processes for data exchange result in fragmentation and lack of aggregation, impacting patient identity, consent management, and access management across stakeholders. patients lack the ability to administer and transfer consent in managing their own data. payers risk sharing data with partners without consent. and, providers have identified “pain points” in data sharing in consent management and care coordination.3,4 this lack of management is critical as studies have shown that “patients only visited their primary care physicians 54.6% of the time when seeking care. where do they go for that other 45.4%? patients receive care from other organizations where that provider may not have access to the patient’s medical records.”5 the technology as was described in “pragmatic, interdisciplinary perspectives on blockchain and distributed ledger technology: paving the future for healthcare,” the foundational construct of blockchain, a type of distributed ledger technology (dlt), is stored by each node in a “permissionless” or public network https://doi.org/10.30953/bhty.v3.122 mailto:jim.stclair@dinocrates.com https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.122&domain=blockchainhealthcaretoday.com&date_stamp=2020-01-02 page 2 of 3 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.122 (i.e., one that allows anyone to participate) or may be structured as a “permissioned” or private network (i.e., whereby participation is controlled by the originator of the network).6 for each block on the blockchain, a hash code is computed as a combination of the data in the block, as well as the hash code of the previous block. in this way, hash codes are chained. hash codes are easy to compute and verify by all participants of the blockchain, enabling them to verify that the blockchain data have not been altered. deletion of a block or changing the data on a block renders the chain of hash codes on the blockchain invalid and is easily detectable by the blockchain participants. each node, or network participant, continuously synchronizes the blockchain as consensus is achieved according to the specific consensus protocol of that network. this consensus ensures the validity and consistency of each copy of the distributed ledger running on each node of the blockchain network.6 the application blockchain offers transformational opportunities in healthcare processes, including the ability to establish self-sovereign identity and a consent audit trail for the patient’s digital identity. these identity systems are used primarily for authentication and authorization.7 to date, most digital identities are issued by a company that maintains control over the identity, rather than allowing user control. this enables the service provider to control the identity and related services without the consumer’s knowledge or consent. when using self-sovereign identities, every person has authority over his or her own digital identities. self-sovereign identity can be characterized as the: • existence of a person’s identity independent of identity administrators • control of their digital identity • full access to their own data • interoperable digital identities • protection of individual rights8 blockchain as a solution the fundamental promise of blockchain is to provide a seamless method for multiple entities to share data without a single entity fully controlling all of the information.5 it has the potential to improve healthcare in innovative ways, including support for a master patient identifier (mpi) and autonomous automatic adjudication and interoperability.4,7 globally, blockchain technology could help with reliability, security, transparency of self-sovereign data, and consent management to inform the exchange of information across approved entities. as patients gain more control of their data and permissions for exchanging of that data, robust privacy and security considerations will be critical to maintain appropriate protections for protected health information (phi).9 healthcare information and management systems society (himss) is taking an active role in education regarding the appropriate use of blockchain for patient-centric information sharing. as the industry works to address the components of trusted exchange proposed outlined in trusted exchange framework and common agreement (tefca), robust patient data management will be a key component to success. blockchain technology has the potential to be a part of the solution to reach these interoperability goals.10 funding statement the authors declare that no funding was received to conduct this research. conflict of interest david metcalf disclosed his participation in johnson & johnson wellness and prevention projects; managing partner in global blockchain ventures and merging traffic. no other authors stated any conflicts of interest. https://doi.org/10.30953/bhty.v3.122 page 3 of 3 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.122 contributors all authors are members of the himss blockchain in healthcare task force. jim st. clair, ann ingraham, dominic king, michael b. marchant, fletcher cotesworth mccraw, david metcalf, and john squeo were involved in the original draft preparation. jim st. clair, ann ingraham and michael b. marchant were responsible for review and editing of the article. references 1. yoder l. care coordination and transition management: critical roles for medicalsurgical nurses. medsurg nurs. 2017 jul [cited 2019 dec 26];26(4):225–8. available from: https://www.amsn.org/ 2. dhalla ia, tepper j. improving the quality of health care in canada. cmaj. 2018 oct 01;190(39):e1162–7. doi: 10.1503/ cmaj.171045 3. leeming g, cunningham j, ainsworth j. a ledger of me: personalizing healthcare using blockchain technology. front med [internet]. 2019 jul 24 [cited 2019 dec 26];6(171):1–10. available from: https://www.frontiersin.org/ articles/10.3389/fmed.2019.00171/full. doi: 10.3389/fmed.2019.00171 4. bordersen c. blockchain: securing a new health interoperability experience. semantic scholar [internet]. 2016 [cited 2019 nov 4]. available from: https://pdfs.semanticscholar. org/8b24/dc9cffeca8cc276d3102f8ae17467c 7343b0.pdf 5. randall d, goel p, abujamra r. blockchain applications and use cases in health information technology. j health med informatics [internet]. 2017 jul 20 [cited 2019 nov 4];08(03):1–4. available from: https://www.omicsonline.org/pdfdownload. php?download=open-access-pdfs/blockchainapplications-and-use-cases-in-healthinformation-technology-2157-7420-1000276. pdf&aid=91911. doi: 10.4172/21577420.1000276 6. ribitzky r, st. clair j, houlding di, et al. pragmatic, interdisciplinary perspectives on blockchain and distributed ledger technology: paving the future for healthcare. bhty. 2018 [cited 2019 dec 26];1. available from: https://blockchainhealthcaretoday. com/index.php/journal/article/view/24. doi: 10.30953/bhty.v1.24 7. bhargav-spantzel a, squicciarini ac, bertino e. establishing and protecting digital identity in federation systems. j comput secur [internet]. 2006 jun 23 [cited 2019 nov 4];14(3):269–300. available from: http://content.iospress. com/articles/journal-of-computer-security/ jcs261 8. der u, jähnichen s, sürmeli j. selfsovereign identity − opportunities and challenges for the digital revolution [internet]. arxiv.org. cornell university; 2017 [cited 2019 nov 4]. available from: https://arxiv.org/abs/1712.01767. 9. nichol p. national onc blockchain challenge explores micro-identities to improve healthcare interoperability [internet]. the next generation of health it. cio.com; 2016 [cited 2019 nov 4]. available from: http:// www.cio.com/article/3107004/health/ national-onc-blockchain-challengeexplores-micro-identities-to-improvehealthcare-interoperability.html 10. zhang a, lin x. towards secure and privacy-preserving data sharing in e-health systems via consortium blockchain. j med syst. 2018;42(8):1–18. doi: 10.1007/s10916018-0995-5 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v3.122 https://www.amsn.org/ https://www.frontiersin.org/articles/10.3389/fmed.2019.00171/full https://www.frontiersin.org/articles/10.3389/fmed.2019.00171/full https://pdfs.semanticscholar.org/8b24/dc9cffeca8cc276d3102f8ae17467c​7343b0.pdf https://pdfs.semanticscholar.org/8b24/dc9cffeca8cc276d3102f8ae17467c​7343b0.pdf https://pdfs.semanticscholar.org/8b24/dc9cffeca8cc276d3102f8ae17467c​7343b0.pdf https://www.omicsonline.org/pdfdownload.php?download=open-access-pdfs/blockchain-applications-and-use-cases-in-health-information-technology-2157-7420-1000276.pdf&aid=91911 https://www.omicsonline.org/pdfdownload.php?download=open-access-pdfs/blockchain-applications-and-use-cases-in-health-information-technology-2157-7420-1000276.pdf&aid=91911 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covid-19 pandemic hanaa a. fatoum, mbbs1, kat kuzmeskas, mph2, john d. halamka, md, ms3, shahrukh k. hashmi, md, mph4,5 affiliations: 1college of medicine, alfaisal university, riyadh, saudi arabia; 2tamarin health, brighton, ma, usa; 3mayo clinic platform, mayo clinic, rochester, mn, usa; 4department of medicine, mayo clinic, rochester, mn, usa; 5sheikh shakhbout medical city, mayo clinic, abu dhabi, uae corresponding author: shahrukh k. hashmi, md, mph; email: hashmi.shahrukh@mayo.edu keywords: blockchain, covid-19, machine-learning, pandemic, point-of-care testing, serology-based test, test section: discussion the last catastrophic pandemic the world has seen was the 1918 h1n1 influenza pandemic, considering that it happened 102 years ago, one can perhaps leverage the technologic advancements over the past century to combat the current pandemic of the severe acute respiratory syndrome coronavirus 2 (sars-cov-2) (covid19), which has already infected more than 16 million people globally and shut down the majority of the daily activities of life. the danger and challenge of infectious diseases such as covid-19 lies in their highly contagious nature, spreading like wildfire with the potential to infect the majority of the world’s population unless drastic measures are undertaken. in some countries, the social distancing and quarantine measures are either insufficient or ineffective as the number of cases is still on the rise. a major issue causing this rise is that most covid-19 carriers appear asymptomatic. identifying infected individuals as well as healthy/immune ones is the most crucial step in halting the disease spread. this is where the role of mass screening comes in. there are currently two main methods for covid-19 testing: molecular (detection of the viral ribonucleic acid through polymerase chain reaction [pcr]) and serology (immunoglobulin g [igg] and immunoglobulin m [igm] antibodies) based. the pcr has a slow turnaround, is labor-intensive, and requires sophisticated laboratory machines, making it more suitable for high risk and severely symptomatic patients, whereas the serologicbased testing is relatively inexpensive, fast, convenient, and ideal for point-of-care testing (poct) and mass screening for covid-19, including asymptomatics. while many companies are trying to produce new, highquality, rapid testing kits such as lateral flow https://doi.org/10.30953/bhty.v3.153 mailto:hashmi.shahrukh@mayo.edu https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.153&domain=blockchainhealthcaretoday.com&date_stamp=2020-11-05 page 2 of 4 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.153 immunoassays, the united states food and drug administration only recently approved the first rapid serology-based test. while antibodies testing is pivotal in the response to the pandemic, one crucial part of the buzzle puzzle remains unclear. that is, which is whether immunity occurs postinfection and if so, for how long. the detection and interpretation of the presence and levels of various serum antibodies is a key for easing the lockdown measures, reopening borders, and restoring daily activities. for mass screening, poct can allow vast diagnostic testing at or near patients’ sites and may include emergency departments, primary care settings, outreach clinics, and mobile settings. efficient utilization of mass screening was exhibited by authorities in vó (a small town in italy). researchers implemented the masstesting strategy with the majority of vó’s 3,000 inhabitants after the lockdown. this allowed researchers to identify and isolate those who tested positive, including many asymptomatic carriers, eventually halting the spread of covid19. another technologic advancement that can be harnessed at mass scale in pandemics due to contagions is wearable devices or the internet of things, which may include body-temperature sensors. for sars-cov-2, such sensors are particularly useful since high-grade fever is a hallmark of the covid-19 disease. regardless of the screening methodology used, the obvious question is how to operationalize current technological advancements to control the pandemic on a global scale. managing data from testing and sensors, while maintaining coordination between authorities, hospitals, governments, and various poct facilities—at mass scale—without compromising privacy and security is challenging. this is where blockchain technology can be of great value. blockchain is a figure 1—the blockchain-based passport. https://doi.org/10.30953/bhty.v3.153 page 3 of 4 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.153 distributed ledger technology, where there is no central authority over the data. it enables secure, trustless, and reliable real-time storage and sharing of unalterable data, which could serve as the backbone for a health passport. using a blockchain to create a health passport that stores covid-19 test results and is available readily for information at institutions and governmental agencies is a viable avenue to curbing the spread of covid-19 on a mass scale. the contents of the passport are backed by a blockchain, so there is verifiable proof that the results were not tampered with by any party. similarly, for the frontline healthcare staff, knowing who is covid-19 immune via the health passport can be a gamechanger in combatting the disease and treating the affected patients while protecting healthcare workers. the blockchain-based passport can be taken a step further by using the test results in conjugation with digital mobile location tracking services, powered by machine learning (ml) models, to identify the disease hotspots, patterns of spread, and alert individuals who have been in contact/proximity from those who have tested positive for the disease as soon as their test results go on the blockchain (figure 1). some governments and organizations are already planning to employ blockchains and ml algorithms for combatting this outbreak. specifically, the world health organization has recently launched its blockchain-based covid19 platform called mipasa. “it is a global-scale control and communication system that enables a swift and more precise early detection of covid-19 carriers and infection hotspots through seamless and fully private information sharing between individuals, state authorities and health institutions such as hospitals and hmos (health maintenance organizations), utilizing advanced technological tools and a dedicated user app, outsmarting covid-19 using crowd data (the intelligence of the crowd), and can also help monitor and foresee local and global epidemiological trends and detect likely asymptomatic carriers by feeding big data on infection routes and occurrences to powerful artificial intelligence-based processors globally.”4 another blockchain implementation that can be useful without the need for contact tracing, location data tracking, or storing sensitive data on-chain, which all carry significant privacy and policy implications, would be a blockchainbacked unique testing id. there lacks a unified national and international response to the pandemic. each state and nation implemented their own pandemic response. in addition, there is no unifying identity to leverage across borders that shares covid-19 infection or immunity status. a testing passport that has a unique testing id for the individual can provide the necessary data without compromising unnecessary protected information. a blockchain architecture that uses on-chain smart contracts to interact with data off-chain is one the most effective methods for protecting privacy, as well as reducing data traffic, and could form the architecture of the unique testing id. a framework proposed by mit media lab researchers called medrec5 defines this infrastructure. the researchers used different layers of blockchains (a public-permissioned blockchain) and their respective smart contracts in order to securely govern access to data. implementing this technology for a covid-19 unique testing id would involve a smart contract-based key pair system or an id contract (idc) that links an existing form of id, such as a passport number or a social security number, to their respective unique testing id. then, a permissioned testing contract (tc) would function as a trustless, uniform, and private data storage ledger for all testing data from various facilities. these two would communicate via a mediation contract, which retrieves testing https://doi.org/10.30953/bhty.v3.153 page 4 of 4 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.153 information, acting as a mediator between the idc and tc blockchain without compromising neither the patient privacy nor the trustlessness (integrity) of the data. finally, it is of utmost importance to protect the privacy of patients and their data when applying digital solutions as well as maintain the integrity of the data, which is another advantage of blockchain; yet, certain limitations of blockchains should be addressed. the blocks are not 100% immune to privacy breaches, and the scalability issues matter when dealing with massive amounts of global megadata on a public blockchain. however, disease outbreak such as covid-19 is a good example to test the power of blockchains, especially public-permissioned and a key pair system, which can be global, rapid, and potentially harness real-time data to merge efforts, improve coordination, and combat any pandemic at both governmental and nongovernmental levels. we hope that the focus on technological advancements will serve as a call for the implementation of a blockchain-based mass testing strategy to eradicate covid-19. conflicts of interest none of the authors declare any relevant conflicts of interest. funding statement skh has received funding from mallinckrodt, pfizer, novartis, and janssen. skh has received travel grants from msd (merck sharp & dohme), takeda, gilead, and bms (bristol myers squibb). contributors all authors approved the final version of the draft. hf created the figure. references 1. 1918 pandemic (h1n1 virus) | pandemic influenza (flu) | cdc. cdc.gov.; 2020 [cited 2020 apr 03]. available from: https://www. cdc.gov/flu/pandemic-resources/1918pandemic-h1n1.html 2. emergency use authorizations. u.s. food and drug administration. 2020 [cited 2020 apr 05]. available from: https:// www.fda.gov/medical-devices/emergencysituations-medical-devices/emergency-useauthorizations 3. tondo l. scientists say mass tests in italian town have halted covid-19 there. the guardian. 2020 [cited 2020 apr 05]. available from: https://www.theguardian. com/world/2020/mar/18/scientists-saymass-tests-in-italian-town-have-haltedcovid-19 4. what is mipasa—mipasa. mipasa. 2020 [cited 2020 apr 05]. available from https:// mipasa.org/about/ 5. azaria a, ekblaw a, vieira t, lippman a. medrec: using blockchain for medical data access and permission management, 2016. 2nd international conference on open and big data (obd). vienna, 2016; p. 25–30 [cited 2020 oct 31]. available at https:// ieeexplore.ieee.org/document/7573685/ citations?tabfilter=papers#citations copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. see: http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v3.153 http://cdc.gov https://www.cdc.gov/flu/pandemic-resources/1918-pandemic-h1n1.html https://www.cdc.gov/flu/pandemic-resources/1918-pandemic-h1n1.html https://www.cdc.gov/flu/pandemic-resources/1918-pandemic-h1n1.html https://www.fda.gov/medical-devices/emergency-situations-medical-devices/emergency-use-authorizations https://www.fda.gov/medical-devices/emergency-situations-medical-devices/emergency-use-authorizations https://www.fda.gov/medical-devices/emergency-situations-medical-devices/emergency-use-authorizations https://www.fda.gov/medical-devices/emergency-situations-medical-devices/emergency-use-authorizations https://www.theguardian.com/world/2020/mar/18/scientists-say-mass-tests-in-italian-town-have-halted-covid-19 https://www.theguardian.com/world/2020/mar/18/scientists-say-mass-tests-in-italian-town-have-halted-covid-19 https://www.theguardian.com/world/2020/mar/18/scientists-say-mass-tests-in-italian-town-have-halted-covid-19 https://www.theguardian.com/world/2020/mar/18/scientists-say-mass-tests-in-italian-town-have-halted-covid-19 https://mipasa.org/about/ https://mipasa.org/about/ https://ieeexplore.ieee.org/document/7573685/citations?tabfilter=papers#citations https://ieeexplore.ieee.org/document/7573685/citations?tabfilter=papers#citations https://ieeexplore.ieee.org/document/7573685/citations?tabfilter=papers#citations http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 dropbox conv2x_181024-1300_x_telehealth-ready-nation.mp3 simplify your life ibm_digital_blockchain_bhty_convergyx_short v2 making blockchain real for business revisiting design principles of blockchain network: addressing security, scalability and sustainability by design inspirations from real world use cases & deployments nitin gaur – ngaur@us.ibm.com director, ibm blockchain labs mailto:ngaur@us.ibm.com 22 blockchain and digital money trust time & 3 blockchain a platform for: • trusted digital transaction system • disintermediation • co-creation models • digital marketplace • multiparty trust network • and more … 3 4page© 2016 ibm corporation blockchain and healthcare ecosystem patient lab radiology specialist payer (s) pcp hospital regulator / auditor / govt. research billing & payment resolution revenue cycle management alternative payment models provider onboarding coordination of benefits shared accumulators utilization transparency longitudinal health data blockchain-based solutions can streamline and transform processes in the healthcare industry 5 bundled payments: fastest growing alternative payment model “all-in” reimbursement price for an episode of care • goal is to reduce cost of care while improving patient outcomes • payers shift risk toward providers common bundled episodes of care • orthopedic surgery, hip/knee joint replacement, cardiac procedures program structure • two levels of contracts among stakeholders – bundle program & risk-share agreements • coordination of care across multiple healthcare providers • information exchange – claim, quality and other data shared across stakeholders • two primary models – retrospective, prospective source: forbes (fastest growing apm) ©2016 ibm corporation7 what makes this problem difficult to solve? 7 systemic challenges cause pain points contributing to limited adoption of bundled payment programs manual processes manual, inefficient and redundant processes across multiple stakeholders. lack of provenance unclear history of data associated with each transaction. delayed reconciliation protracted time frames for reconciliation of actual vs target costs. limited visibility lack of real-time visibility into bundle status across stakeholders limits proactive risk management low trust payers and providers do not share a single source of truth. multiple stakeholders cross-organization coordination required for success lack of standardization multiple program constructs and variation in episode of care definitions risk management bundled payment programs result in a major shift of risk to providers operational challenges system limitations at both payer and provider inhibit processing at scale 8 ibm confidential bundled payment solution hospital post acute care physician claims from providers payer convener/ episode initiator bundled claims payment or penalty bank bundled payment allocationgainsharing payment 1 3 4 5 6 2 bundle identification and performance calculations by smart contracts patient information from provider out of network providers 9 ibm confidential bundled payments on blockchain pre-operative risk assessment patient activation pre-operative care management referral management pre-operative efficiency management inpatient intra-operative optimization post-acute care transition and management physician engagement readmission management postdischarge care management patient engagement readmission management pac reporting pro surveys today with blockchain/dlt manual reconciliation of claims under a bundle smart contracts perform automated reconciliation of claims across contract participants months for payment reconciliation and no view into bundle performance until reconciliation real-time reconciliation and view into bundle performance fragmented data regarding claim activity shared and trusted information about claims stored on chain lack of provenance for payment decisions immutable record for provenance and auditing create the business blueprint 4 steps identify the use case map business blueprint to technology blueprint enterprise integration blockchain network step 1 use case should have: enterprise impact industry impact why: network effect is essential must justify costs of investments identify the use case step 2 understand the business process: distill existing process into blockchain model redefine as necessary narrow the focus why: discover inefficiencies uncover interaction points find dependencies create the business blueprint. step 3 business components feed into technical requirements: define the smart contract logic choose a consensus protocol format the block data data visibility rules existing system integrations why: uncover risk and total costs understand total impacts map business blueprint to technology blueprint enterprise integration step 4 consider operational integration points: ensure the trust model tenant is met eliminate redundancies of existing systems maximize savings and new efficiencies why: work with internal business processes proprietary value additions eliminate roadblocks to adoption 15 path to enterprise adoption enterprise impact and industry impact meaningful issues should revolve around significant costs to enterprise and industry use case identification business blueprint technology blueprint enterprise integration existing business process is distilled down to blockchainbased model reinventing the business based on a trust system technology to align with the business imperatives technology design decisions and deployment options integration with down stream transaction systems reflecting on critical business systems blockchain network 16 lessons learnt: 7 design principles of sustainable blockchain business networks providing network participants control of their business provision for an extensible business network – flexibility in membership permissioned but protected network – protecting competitive data open access and collaborative global network – collective innovation scalability – transaction processing and data encryption processing security – new security challenges of shared business network coexisting with existing systems of record and transaction systems recent publication: blockchain for business making blockchain real for business thank you! nitin gaur – ngaur@us.ibm.com 19 making blockchain real for business with over 600 engagements and multiple active networks 19 20 network of networks: driving nextgen economies global trade freight forwarders airlines port authorities ocean carriers customs / govt. agencies supplier s agriculture flowers & perishables cattle farms warehouses retail wholesal e consumers consumers wholesale consumers financial servicestrade finance insurance payments supply chain finance insurance payments provenance digitization shared economy consumer industry 21 going back to basics fundamental tenets – trade, trust and ownership duality of transactions – issues of clearing and settlement focus just cannot be on digital assets (tokenization of assets) is digital identity essential? • non repudiation • establish ownership • claim on instance of an digital/crypto asset what are we solving if we are only solving for reconciliation of ledger entries? digital fiat or a similar instrument is essential to solve the last mile – settlement issue digital identity is diagonally essential to digital fiat/crypto asset/ digital asset 22 digital identity foundation technology to ensure the trade and ownership digital fiat address the last-mile issue of settlement for every financial transaction asset tokenization ensure that digital manifestations reflect real-world assets security design of the blockchain system address non-repudiation, privacy, confidentiality; and verifiability of claims with consent-driven models business of blockchain business models a befitting business model to progress blockchain agenda governance model self-governance networks to consortium-defined; and semi-autonomous governance structures my focus for 2019 wrt to blockchain $ 23 what would enterprise chain infrastructure look like? integrated enterprise will need more than one specialized use case • driving synergies between blockchains • invisible blockchain infrastructure • interand intra-enterprise connections • concept introduction – interledger – intraledger • cross the trust systems for transactions • fractal visibility of ledger data • enterprise visibility – control systems enterprise chain infrastructure payments mortgage initiation securitization trade finance crowdfunding identity partner enterprise / dao interledger / ilp conditional contracts conditional contracts 24 vision – ‘interprise synergy’ enterprise chain infrastructure design that enables new business models • invisible enterprise chain infrastructure will provide foundation • use of connectors, apis to enable incumbent systems chain aware • conditional contracts between chains – ‘interprise synergy’ • new business (e.g., p2p lending, crowdfunding) solely on blockchain enterprise systems bi / data enterprise systems bi / data enterprise chain infrastructure payments mortgage initiation securitization trade finance crowdfunding identity partner enterprise / dao interledger / ilp conditional contracts conditional contracts enterprise systems bi / data enterprise systems bi / data enterprise systems 25 separating blockchain from cryptocurrencies • cryptocurrencies are one specific usage of blockchain technology • blockchain can be used to solve many more real-life business challenges without the fallacies of cryptocurrency • exchange of digital currencies using cryptography • first crypto currency = bitcoin • fully decentralized • anonymous participation, transparent activity cryptocurrency blockchain key attributes • distributed and sustainable (finality) • secure and un-editable (immutability) • transparent and auditable (provenance) • consensus-based and transactional (consensus) • flexible and orchestrated (smart contract) however, we need few key attributes for blockchain to be business ready page 1 of 4 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.139 the fourth industrial revolution of healthcare information technology: key business components to unlock the value of a blockchain-enabled solution ann ingraham,1 jim st. clair2 affiliations: 1exponential healthtech advisors, llc; 2dinocrates group llc corresponding authors: jim st. clair, jim.stclair@dinocrates.com; ann ingraham, ann@ehtadvisors.com keywords: blockchain, business models, fourth industrial revolution, global healthcare, healthcare, internet of medical things, iomt section: discussion editor’s note: this article is one of an ongoing series covering topics published in conjunction with the health information management and systems society (himss) describing the development of blockchain technology and its applicability to healthcare. as described by the world economic forum (wef),1 the fourth industrial revolution is here and is changing business models across every industry vertical. this revolution includes digital technology, big data, artificial intelligence, distributed ledger technology (dlt, or blockchain), and analytics, and represents new ways in which technology is being integrated into societies. this changing interaction with technology will impact business models. traditional business models are historically based on a centralized framework for delivery of goods and services to the consumer. the new business model is based on the decentralization of the creation and delivery of goods and services. at the core of the new model, organizations must demonstrate value-creation and value-delivery, while ensuring their solutions are secure, scalable, and interoperable to remain competitive. a decentralized business model built on a blockchain framework can provide the decentralization and security needed for this industry shift. advancements driving business models each revolution created shifts in business models. the industrial revolutions of mechanical production (first), science and mass crossmark ←click for updates https://doi.org/10.30953/bhty.v3.139 mailto:jim.stclair@dinocrates.com mailto:ann@ehtadvisors.com https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.139&domain=blockchainhealthcaretoday.com&date_stamp=2020-06-05 page 2 of 4 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.139 production (second), and the digital revolution (third) have disrupted how society operated between 1784 to this day. prior revolutions1 occurred within 100 years of each other (1784– 1870 and 1870–1969); however, the fourth revolution is on an expedited trajectory— occurring about 50 years after the third revolution in 1969. similar to past revolutions, the fourth revolution requires a shift in business models to ensure growth while maintaining resilience and sustainability. the wef argues that we are experiencing the fourth industrial revolution, in which technology and humanity merge as “cyber-physical systems,”1 and these systems may potentially incorporate distributed ledger technology as part of cryptographically secure, decentralized infrastructure. in the new world of cyber-physical systems, healthcare organizations are not exempt from the need to have a clear definition and understanding of value delivery, and will need to be ready to implement and adopt technology to remain relevant in the new paradigm. in addition, relevance and sustainability require operating within a trusted ethical framework that addresses data governance, access, security, identity management, accountability, and transaction authentication. the fourth industrial revolution and blockchain for the internet of medical things the innovation spurring the fourth industrial revolution in the healthcare industry is centered on the internet of medical technology (iomt),2 which will digitally connect approximately 50 billion3 medical sensors, devices, and machines to collect and monitor patients’ health. promoting the expansion of iomt requires an enabling framework of standards and regulations that promote scalable and secure solutions, policies that drive change, interoperable data-enabled infrastructure, incentives and investments, a skilled and capable workforce, collaborative multi-stakeholders, and continual innovation and entrepreneurship. blockchain technology can serve as a potential infrastructure to drive these capabilities for the expanded use of iomt. blockchain-enabled disruption of the status quo is primed to hit areas such as healthcare supply chains, research, payment, and care delivery systems. to date, conventional healthcare service models constrain diagnosis and care delivery geographically to the hospital, doctor’s office, or community-based clinics. on the contrary, iomt, enabled by blockchain, allows for an increasing venue from which patients can choose more cost-effective alternatives to monitor and diagnose their health conditions regardless of their geographic location. by converging bluetooth technology, wearable medical devices, and smartphones, one can enable remote health monitoring in the diagnosis and treatment of health-related conditions, while shifting the power dynamics between the patient and the provider. iomt also enables increased collaboration between the doctor and the researcher to achieve a better understanding in the treatment of clinical conditions and disease. preparing a business for new technology and models taking advantage of iomt business opportunities requires developing a blockchain strategy for implementation, evolution, and sustainability. organizations seeking to explore this should consider the following steps in the process: 1. identify the business case or use case to align business priorities 2. perform a return-on-investment (roi) analysis https://doi.org/10.30953/bhty.v3.139 page 3 of 4 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.139 3. develop a consensus or network of patientcentered services 4. identify a pilot system, location, program, etc. 5. develop the implementation framework, which includes plans to: a. establish governance b. conduct assessments c. determine budgets d. design standards and workflows e. ensure compliance with regulations such as trusted exchange framework and common agreement (tefca), health insurance portability and accountability act of 1996 (hipaa), family educational rights and privacy act of 1974 (ferpa), and the information blocking rules (or other regulations if non-us based). all new technologies require documented compliance with applicable laws, which drive information governance and information sharing. f. train and educate stakeholders g. conduct operational readiness change management exercises h. implement i. identify key performance indicators (kpis) to measure and assess impact j. maintain communication with the executive leadership and board of directors 6. implement tokenization and token ecosystems 7. assess the total cost of ownership (tco), as reflected in the budget. tco should be clearly understood for all projects, especially understanding when those costs are realized. enabling progress in healthcare the fourth industrial revolution will leave its mark on global healthcare. in spite of its size, complexity, and regulations, healthcare is poised for rapid change. news stories are published daily about disruptions to traditional industries and businesses because of its advances. telemedicine evolves into virtual care encounters. remote patient monitoring is critical to chronic disease management. artificial intelligence and machine learning supplant call centers and provide key tools in diagnostic radiology. personal devices collect and disseminate health information and provide real-time monitoring of chronic conditions. the rapid and expansive availability of health data allows for proactive interventions and transfers care into the sphere of wellness, enabled by predictive analytics. the new healthcare delivery system will have new cost modeling, advanced revenue-cycle options, and increased price transparency, all of which empowers the patient and the family to make better choices. blockchain can serve as an enabler for all of these disruptive technologies. as discussed in our previous health information management and systems society articles, blockchain provides a “transactional framework” that ties together these new processes in healthcare while enabling a new approach to identity and patient control of data and information. blockchain, through the use of crypto coins and tokens, can even support new ecosystems of value and incentivization that could drive new ways to interact with health and life sciences. as blockchain technology supports this revolution’s impact, businesses will need to be prepared to adjust their business models and governance structures to benefit from this revolution. funding statement no funding was associated with the development of this publication. conflicts of interest the authors state they have no known conflict of interest. https://doi.org/10.30953/bhty.v3.139 page 4 of 4 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.139 contributors both authors are members of the health information management and systems society blockchain in healthcare task force. jim st. clair, ann ingraham, dominic king, michael b. marchant, fletcher cotesworth mccraw, and david metcalf were involved in the original draft preparation. jim st. clair and ann ingraham were responsible for review and editing of the article. references 1. davis n. what is the fourth industrial revolution? [internet]. world economic forum; 2016 [cited 2020 mar 9]. available from: https://www.weforum. org/agenda/2016/01/what-is-the-fourthindustrial-revolution/ 2. internet of medical things revolutionizing healthcare [internet]. the alliance of advanced biomedical engineering; 2017 [cited 2020 mar 9]. available from: https://aabme.asme.org/posts/ internet-of-medical-things-revolutionizinghealthcare 3. seliem m, elgazzar k. biomt: blockchain for the internet of medical things [internet]. biomt: blockchain for the internet of medical things—ieee conference publication. ieee international black sea conference on communications and networking (blackseacom); 2019 [cited 2020 mar 9]. available from: https:// ieeexplore.ieee.org/document/8812784 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v3.139 https://www.weforum.org/agenda/2016/01/what-is-the-fourth-industrial-revolution/ https://www.weforum.org/agenda/2016/01/what-is-the-fourth-industrial-revolution/ https://www.weforum.org/agenda/2016/01/what-is-the-fourth-industrial-revolution/ https://aabme.asme.org/posts/internet-of-medical-things-revolutionizing-healthcare https://aabme.asme.org/posts/internet-of-medical-things-revolutionizing-healthcare https://aabme.asme.org/posts/internet-of-medical-things-revolutionizing-healthcare https://ieeexplore.ieee.org/document/8812784 https://ieeexplore.ieee.org/document/8812784 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 page 1 of 2 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.125 dawn of a new decade: looking forward with bhty in 2020, blockchain in healthcare today (bhty) begins its third year of publication. we thank our global community of passionate leaders and early adopters for their support and diligence in making technology innovation in healthcare attainable for both the healthcare consumers who are the key stakeholders in patient-centered care and the broader healthcare system. blockchain in healthcare today (bhty) is credited with establishing the first  international, evidence-based academic journal—creating a sector that did not exist for healthcare-based blockchain technology. our journal assists in gathering and accelerating rigorously vetted theoretical and experiential knowledge that is required for a growing sector. during 2019, market makers and leaders were busy in conducting consequential research, as well as building alliances and pilots. we expect that many of these will reveal findings  (both positive and negative) that will advance the discipline. and, candidly, we ask you (e.g., researchers, entrepreneurs, enterprise, government, and other organizations) to share these findings through submission, peer review,  and publication in bhty. academicians understand that new journals will be lacking in impact factor—an expression of viewer engagements, citations, and readership—compared with older journals. while we work toward this objective, bhty ensures that all manuscripts are reviewed critically by experts on our editorial board prior to acceptance for publication and receive broad market exposure worldwide. the market clamors for use cases or modeling innovations that advocate and respond clearly to both practical and regulatory expectations. accordingly, we challenge our colleagues in the blockchain and healthcare communities to submit use cases throughout 2020 and meet this worldwide need. furthermore, authors are invited to submit original manuscripts, unpublished research, and defensible opinions on a broad spectrum of topics, including economic, legal, regulatory, and social impact issues relevant to blockchain and converging technologies in healthcare. we are excited to share new journal features that enable us to “walk our talk.” to this end, bhty continuously pushes the boundaries of technology and innovation in scholarly publication and ecosystem practices to bring https://doi.org/10.30953/bhty.v3.125 https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.125&domain=blockchainhealthcaretoday.com&date_stamp=2020-01-13 page 2 of 2 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.125 trust, transparency, and truth to authors, readers, and distinguished peer reviewers. today, knowledge transfers include using social media to communicate with colleagues and share information. it is not surprising then that journals and innovators are asked to show impact and share insights at lightning speed. to that end, we have added the altmetric conversation badge to help authors extend the reach of their research along with a citation badge, which makes article feedback more transparent. we have also added reviewercredits (i.e., certification of peer review and conference  presentations) to reward peer reviewers along with editage, which offers peer-review support, translation services, and manuscript editing that improve the global science communication process. and there is more; bhty has partnered with collaborators to strengthen the validity of scholarly publishing and accelerate scaled research and development (r&d) and reproducibility with artifacts.ai, code ocean, orvium, and publons. over the course of our global growth, bhty has garnered the endorsement of ata and ieee sa. in addition, bhty collaborates with himss, and we are proud to launch a new peer-reviewed quarterly column written by members of the himss blockchain task force. the first in this  series is contained in this volume. we hope our audience will find it as a valued educational  feature. we express our sincere thanks to our audience, peer reviewers, ambassadors, and stalwart authors for sharing their unique perspectives and experiences through bhty. we look forward to the market using the bhty evidence-based platform to share greater knowledge worldwide and continue to enhance validation and credibility of this sector. tory cenaj partners in digital health publisher, blockchain in healthcare today copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v3.125 http://artifacts.ai http://creativecommons.org/licenses/by-nc/4.0 cyran layout. blockchain journal article_revisions_final2[1] blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.13 blockchain as a foundation for sharing healthcare data marek a cyran1 author: 1booz allen hamilton, inc., 8283 greensboro dr., mclean, va 22102, united states corresponding author: marek a cyran at cyran_marek2@bah.com keywords: blockchain, data exchange, data sharing, ehr, electronic health record, ethereum, interplanetary file system, ipfs section: use cases/pilots/methodologies blockchain technology has the potential to transform healthcare delivery by facilitating data sharing between providers and electronic health record (ehr) systems. however, significant roadblocks stand in the way of widespread implementation of this technology across the healthcare industry. our blockchainbased data-sharing solution addresses two of the most critical challenges associated with using blockchain for health data sharing: protecting sensitive health information and deploying and installing blockchain software across diverse hospital environments. since transparency is a fundamental feature of blockchain, we enabled userand group-based secret sharing by adding purpose-built software that leverages a collection of well-established cryptographic algorithms. to streamline deployment, we built a containerized solution that guarantees portability, simplifies installation, and reduces overhead maintenance costs associated with administration. to ensure ease of implementation in a hospital system, we designed our blockchain solution using a distributed microservices architecture that allows us to encapsulate core functions of our system into isolated services that can be scaled independently based on the requirements of a particular hospital system deployment. as part of this architecture, we built core components for securely handling cryptographic secrets, interacting with blockchain nodes, facilitating large file sharing, enabling secondary-index based lookups, and integrating external business logic that governs how users interact with smart contracts. the innovative design of our blockchain solution, which addresses critical data security, deployment, and installation challenges, provides the healthcare community with a unique approach that has the power to connect providers while protecting sensitive data. keywords: blockchain, data exchange, data sharing, ehr, electronic health record, ethereum, interplanetary file system, ipfs lockchain technology provides a decentralized, transparent, authenticated platform that applies a consensus driven approach to facilitate the interactions of multiple entities through the use of a shared ledger. beyond the financial sector, where much of the initial development is taking place, blockchain b blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.13 has the potential to revolutionize the healthcare system. by providing doctors, patients, researchers, and other healthcare professionals with a mechanism for the controlled exchange of sensitive, permissioned data, blockchain technology can improve data sharing and transparency between clinical and research data systems. any healthcare organization participating in a blockchain consortium would be able to share medical information, regardless of their native electronic health record system. blockchain provides significant opportunities for healthcare organizations to deliver more efficacious treatments and diagnoses through increased provider data sharing, and potentially safer and more effective clinical trials through research method tracking. however, significant challenges remain towards wide-spread implementation of this technology across healthcare systems. this report will describe how we addressed two specific challenges associated with blockchain implementation in a hospital system, namely protecting sensitive data on the blockchain, and deploying and implementing solutions across hospital systems. methods and findings in this section, we describe approaches for enabling permissioned data sharing, and deploying blockchain solutions to facilitate collaboration across hospital systems. data sharing/security solution modern blockchains are fundamentally transparent platforms where interactions between users and smart contracts, modeled by cryptographically signed but unencrypted transactions, are visible to every participant on the blockchain network. this central feature of blockchain technology results in obvious challenges to implementing solutions that share sensitive data, where only a restricted number of recipients should be given access to a piece of data, or a cryptographic artifact that can unlock a piece of data stored off the blockchain. because of this property, special purpose software designed to work alongside the blockchain must be implemented to facilitate additional layers of encryption that enforce the privacy of content embedded within transaction data. in order to enable data sharing across hospital systems, we developed a purpose-built solution based on hospital privacy and security requirements that leverages a collection of strong cryptographic algorithms to enable user and group based secret sharing. within our blockchain implementation, each piece of data has one user (owner) who can share a piece of data they own with other users or groups at varying levels of access (summary versus full data). to limit full access, and instead enable summary access, each piece of data consists of a descriptor, viewable to anyone on the blockchain network, a summary, and content, which are stratified at different access levels. therefore, having summary access gives the receiver only access to the descriptor and the summary, whereas full access provides all three components. data sharing between users is modeled by a system where users can share data with other users and groups, as well as receive data requests from other users at any access level. if a user responds to a request by granting data access, a cryptographic artifact is exposed to the receiver in a way that allows only that receiver to view data at the specified access level. our system ensures that sensitive information is never exposed on the blockchain, including both private and document keys, which is necessary in order to maintain the privacy and security of user-controlled data. as an additional security measure, our system preserves the fundamental property of revocation where the data owner may revoke access to a piece of shared data with a guarantee that even a receiver’s private key together with blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.13 the raw blockchain transaction data would not be sufficient to obtain data access. having a robust encryption scheme as part of a blockchain-based data sharing system is particularly critical from a security perspective because most blockchain implementations replicate the entire transaction ledger onto each node, therefore multiplying the potential attack surface by the number of nodes in the network. though our existing system implements access controls at the document level, at its core, we designed the underlying architecture to support attribute-based sharing. this approach would require that the structure of submitted documents is captured within the underlying smart contracts so that not all data fields are treated homogenously, and that sensitive fields are treated separately from the rest of the document. the components of our platform that deal with secret sharing are not coupled to the format or structure of the underlying sensitive data being shared. our solution utilizes the ethereum1 platform for smart contract functionality with docker containers and distributed architecture using microservices. security in our data sharing system is derived from the use of a collection of well-established cryptographic algorithms. we used elliptic curve integrated encryption system (ecis), which is a hybrid of the elliptic curve diffie-hellman (ecdh)2 algorithm, concatenation key derivation function (kdf)3, and the advanced encryption standard (aes256 in galois counter mode) block cipher4 that facilitates key encryption using elliptic curve primitives. ecdh is a well-studied algorithm and has been endorsed by the national institute of standards and technology (nist)3. of note, many of the algorithm choices we employ for this cryptosystem were informed by the standard set of algorithms utilized by ethereum. the use of these cryptographic algorithms is foundational to all blockchain implementations, particularly the use of hashing and digital signature algorithms to maintain the immutability of blockchain data and the authenticity of submitted transactions, respectively. to build a data sharing system requires additional design requirements including the use of asymmetric cryptographic algorithms and approaches to facilitate encryption and decryption operations on arbitrary data. algorithms using asymmetric cryptography utilize public and private keys to decrypt data. the keys are essentially large numbers that have been paired together but are not identical. one key in the pair can be shared with everyone, termed the “public key”. the other key is the private key and is kept secret. either of the keys can be used to encrypt a message and the opposite key is used for decryption. blockchain technology is not typically designed for large transaction data payloads, so when building a data sharing system, one approach is to store data in a separate software solution that can provide a global reference to uniquely identify a document. in our system, we chose to use the interplanetary file system (ifps)5 decentralized filesystem that we could deploy alongside our blockchain nodes to enable the storage of very large files in a way that ensures minimal duplication across the entire filesystem network. since file storage is decentralized, and has a large potential attack surface, all data is fully encrypted before being written into it. because data is stored within an external storage solution, the blockchain component of our system is responsible for executing smart contracts that, in part, refer to our data and provide information on how data is owned, retrieved, and decrypted. constraints may be added to our system to ensure that data files are standardized to healthcare relevant blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.13 specifications (i.e., fast healthcare interoperability resources hl7). figure 1 depicts the system architecture of our solution, and in particular, the containerized platform deployed across a number of hospital systems. figure 1. an illustration of the system architecture of our blockchain solution ease of deployment and implementation within a hospital system streamlining the deployment of our blockchain solution is essential for acceptance and utilization within a hospital system. often, variations in hospital information technology (it) infrastructure can impede deployment and lead to increased reliance on it administration. our system uses the concept of containerization where the blockchain solution is wrapped within a special virtual machine image. containerization guarantees the portability of our software, simplifies deployment, and reduces the maintenance overhead across a variety of infrastructure environments. an application that can be easily deployed within any infrastructure environment reduces overhead costs since the only requirement for becoming a member of the network is the provisioning of one or more computing instances and any it administration support required to make connections to the external blockchain network. ease of implementation within a hospital system requires both solution scalability and services within the blockchain architecture that perform functions necessary for data sharing including encryption, decryption, facilitation of transaction signatures, and storage of cryptographic artifacts that adhere to security best practices. additionally, since blockchain technology is not built for data queries based on a set of user criteria, our solution includes a component that enables us to build secondary indices to enable this capability. web application services act as the public endpoint into the system and contain all higher order business logic which leads to the creation of and interaction with smart contracts and data retrieval within the blockchain. we designed and developed a high-level abstraction layer for creating and interacting with smart contracts that acts as the primary interface to a blockchain node. since blockchain is not geared towards storing large data payloads, we utilized a distributed file sharing solution that enables blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.13 the decentralized sharing of documents between members of the blockchain network. we leveraged and enforced open standards on data being submitted into the system to allow for flexibility on data specifications. this flexibility allows a user to employ their own organizational specifications for formatting and downstream analytics. each of the above services encapsulate defined roles in the system, which makes them easier to develop in parallel. they may also be developed in different programming languages eliminating any tight coupling to a specific technology stack. these functionalities were enabled using a distributed microservice architecture where overall resource usage is spread across multiple computing instances and any component may be developed and scaled individually and independently depending on the requirements of the deployment site. finally, an integral part of implementation of our solution within and between hospital systems is its ability to enable semantic interoperability. as part of the larger solution, our system relies on submissions of health data that are triggered by software components embedded within electronic health record (ehr) systems that are part of our blockchain network. submissions of this data come from a variety of different ehr systems, including cerner and epic. in order for the submitted data to be computable by all consortium members, regardless of software deployed within their native environment, the data is encoded using open standards based on fhir hl7. conclusions current centralized data sharing solutions struggle to meet the scale, accessibility and security requirements of healthcare organizations. although blockchain technology provides a promising solution for addressing these issues and improving the interoperability of health data, permissioned blockchain capabilities must be combined with robust encryption components for integrity, security, and portability of user-owned data. in this paper, we shared our innovative solution for a blockchain system that supports the secure exchange of data with the addition of cryptographic algorithms that enforce the privacy of transactions. our design, which combines enhanced security measures with containerization, provides a trusted and easy to deploy solution that will drive adoption of a blockchain-based health data sharing network. our solution is just one example of a health data sharing platform. our system is unique because it is designed specifically for our use-case (data sharing across ehr systems), enables user and group-based data sharing, is fully containerized and deployable across multiple hospital it infrastructures, and is designed as a platform solution utilizing a distributed microservice architecture that can be scaled depending on deployment requirements. although blockchain technology is still in its infancy, excitement about potential applications is growing. solutions for permissioned sharing of health data, including data generated by wearables and other “internet of things” (iot) devices, will become increasingly important to individuals who want greater access to their own data. beyond the hospital, blockchain promises a solution that can empower patients, and support greater transparency between healthcare professionals. developing and testing new designs on real-world use cases, such as data sharing between hospital systems, provides the first step in demonstrating the power of blockchain to break down data siloes in healthcare. blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.13 acknowledgments i would like to thank all those involved in the development and support of this manuscript including steven escaravage, lauren neal, ernest sohn, holly stephens, and dr. henry francis. funding statement this paper is a work product of mr. cyran as part of his ongoing employment by booz allen hamilton. competing interests we have no competing interests to declare in regard to the content of this manuscript including financial interests or other situations that might raise the question of bias in the work reported or the conclusions, implications, or opinions stated. copyright ownership booz allen hamilton, inc. references 1. wood g. ethereum: a secure decentralised generalised transaction ledger. ethereum project yellow paper. 2014 apr;151. 2. allen c. evaluation of secp256k1 as popular alternate curve. lecture presented at; 2017; cfrg interim meeting, paris. 3. barker e, chen l, roginsky a, smid m. recommendation for pair-wise key establishment schemes using discrete logarithm cryptography. nist special publication. 2007:800-56a. 4. daemen j, rijmen v. aes proposal: rijndael. 5. allen c. evaluation of secp256k1 as popular alternate curve. lecture presented at; 2017; cfrg interim meeting, paris. page 1 of 10 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.18 ethics governance outside the box: reimagining blockchain as a policy tool to facilitate single ethics review and data sharing for the 'omics' sciences vaso rahimzadeh1 author: 1vaso rahimzadeh, phd candidate, vanier canada graduate scholar, centre of genomics and policy; department of family medicine, mcgill university corresponding author vaso rahimzadeh, vasiliki.rahimzadeh@mail.mcgill.ca keywords: blockchain, data sharing, ethics review, governance, irb, research, single mutual recognition section: use cases/pilots/methodologies clinical research and health information data sharing are but ripples in a growing wave of reimagined applications of distributed ledger technologies beyond the digital marketplace for which they were originally created. this paper explores the use of distributed ledger technologies to facilitate single institutional ethics review of multi-site, collaborative studies in the dataintensive sciences such as genetics and genomics. immutable record-keeping, automatable protocol amendments and direct connectivity between stakeholders in the research enterprise (e.g., researchers, research ethics committees, institutions, funders and regulators) comprise several of the conceptual and technological advantages of distributed ledger technologies to research ethics review. this novel-use proposal dovetails recent policy reforms to research ethics review across north america that mandate a single ethics review for any study that takes place across more than one research site. such reforms in the united states, canada and australia replace prior institution-by-institution approval mechanisms that contributed to significant research delays and duplicative procedures for collaborative research worldwide. while this paper centers on the common rule revision in the united states, the single ethics review mandate is a noteworthy example of regulation evolving in parallel with advances in the dataintensive sciences it governs. the informational exchange capacities of distributed ledger technologies align well with the procedural goals of streamlining the ethics review system under the new common rule ahead of its official page 2 of 10 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.18 implementation on january 19, 2020. the ethical, legal and social implications of applying such technologies to ethics review will be explored in this concept paper. namely, the paper proposes how administrative data from research ethics committees (rec) could be protected and shared responsibly, as well as interinstitutional cooperation negotiated within a centralized network of research ethics committees using the blockchain. keywords: blockchain, data sharing, ethics review, governance, irb, research, single mutual recognition n january 2017, the united states national institutes of health finalized what is arguably the most significant reform to policies of ethics review for research involving humans and their data.1 in the revised common rule, nonexempt multi-site research will undergo a mandated single research ethics review. that is, collaborative research studies that span data collection and participant recruitment across multiple institutions and state jurisdictions will no longer require separate ethics approval from each collaborating site named in the study. a policy artifact of post-nuremberg consensus, this institution-by-institution approval process served its purpose well until a few landmark scientific advances in the early 2000’s. the human genome project, for one, systemically challenged the notion of scientific discovery built on the singular contributions of ‘lone scientists’ in biomedicine.2 the current demands for data of adequate volume, veracity and validity to make sound scientific associations between the human genome and disease are far greater than any one scientist or institution can meet alone.3–5 an ethical imperative to share genomic and associated clinical data complements this scientific rationale. participants accept informational risk(s), albeit minor, as part of their involvement in genetic/genomic research.6–11 it is therefore the charge of research ethics committees (rec) to determine whether the study strikes an appropriate balance between these risks and the knowledge benefits anticipated from the collaborative study. increasing recognition of the need to marry clinical research and care in what the institute of medicine termed the ‘learning healthcare system,’12 further underscores the direct involvement of research ethics review to facilitating innovation in standards of care.13 as noted elsewhere, the “same ethics review procedures have historically applied to single-site biomedical studies as for multisitei, data-only studies despite their internationalization and data intensification.”14 the pronounced emphasis on collaboration and data sharing motivated by the human genome project (hgp) accentuated the growing incoherence between traditional models of ethics governance—namely the institution-byinstitution approach to research ethics approval—and the norms of collaborative research practice in the ‘omics’ disciplines in particular e.g., genomics, proteomics as well as broader biomedical research endeavors e.g., precision medicine of cancer15–18 and dementia.19,20 to name two. growing anecdotal and empirical evidence in the years preceding reform suggested the extent of this incoherence.21–27 taken together, the procedural inefficiency, inconsistency, high administrative burden and increasing cost of the ethics approval process under the institution-by-institution model prompted transition to a single institutional review board (sirb) approach for multi-site studies within the united states by 20201 and in other international jurisdictions.28–30 the sirb model is purported to better respond to the contemporary realities and practices of collaborative, data-intensive research typified by the emerging ‘omics’ disciplines.31 it subscribes to a principle of mutual recognition, or the ethical, legal and social legitimacy of ethics review(s) i page 3 of 10 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.18 conducted by an external institution named in a multi-site study.31,32 motivating adoption of the sirb model is the hope that centralizing ethics review will reduce—if not eliminate outright—the redundancies and inefficiencies that previously delayed the ethics approval step on the bench-tobedside continuum for genetic and genomic research. for its intuitive simplicity, the sirb model poses several challenges to implementation. without practical guidance and infrastructural support, these challenges could negate any improvement in quality and efficiency that drove the model's adoption in the first placeii. first, recs can be bureaucratically complex.33 they involve relational hierarchies both within and external to the institution. klitzman supports this in his claim that the relationship between a leading rec for a multi-site study and the local institutions at which the lead rec's decision applies will “profoundly shape the costs and effectiveness of future multi-site research involving human research participants.”24 cultures of (mis)trust in the procedures, competencies and approaches between participating recs hint at some of the relational complexities that face institutions in successfully operationalizing the sirb model. the mutual, yet secure network exchange capacities of the blockchain offer innovative solutions for meeting the procedural as well as relational complexities of a centralized sirb system based on mutual recognition. this paper proposes how the blockchain could enable e-governance with respect to ethics review approvals, particularly for multi-site studies in genetics and genomics. the technological virtues of the blockchain—including immutable documentation, timestamping and automatable updating for protocol amendments, to name but a few examples— can moreover help committees achieve the performance goals that centralizing ethics review promises for researchers and institutions alike. distributed ledger technologies (dlt) generally, and the blockchain specifically, present several solutions to some of the systemic challenges recs face in centralizing ethics review under the new common rule. this paper explores the ethical, legal and social implications of adopting the blockchain to facilitate such egovernance of ethics review in the multi-site research context. namely, it proposes how the blockchain could enable administrative rec data exchange and broker interinstitutional cooperation across research sites or jurisdictions that will participate in a sirb system. the blockchain furthermore affords new opportunities for improved decision reporting, transparency and accountability to stakeholders in the research enterprise e.g., researchers, institutions, funders and research participants for whom rec decisions chiefly impact. lastly, the paper nuances several conceptual tensions that sirb powered by the blockchain could pose for u.s. regulatory bodies moving forward. the building blocks for blockchain in ethics review blockchain is best recognized as the technological backend for cryptocurrencies, securely recording in a distributed and mutually transparent ledger (or database) all informational transactions in a peer-to-peer network. these transactions and the data parameters that enable them are stored as ‘blocks.’ each block is timestamped and added to a chain of blocks, whereby its addition is contingent on the parameters and codes set by the blocks before it. each block is validated by a third party ‘miner’ by solving a computational problem. once successfully executed, validated and timestamped, information contained in the block is immutable, incorruptible and digitally historicized on every node server. a private ledger comprised of participating institutions named in a multi-site study, using ethereum smart contracts to execute regulatory permissions and consent is proposed. figure 1 depicts a typical research page 4 of 10 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.18 ethics review workflow, from initial investigator application through to data safety monitoring and study closure. figure 2, in contrast, identifies various points where ethereum smart contracts can potentially intervene to execute regulatory permissions in the sirb workflow via the blockchain. smart contracts are proposed to replace three requisite approval documents needed during the review process, including interinstitutional reliance agreements between collaborating research sites, participant consent forms and data sharing/access agreements. not only would the blockchain serve as a common platform upon which individual recs could better oversee collaborative research studies, it could dramatically improve the consistency of rec decisions and reporting. figure 1. proposed blockchain application for enhancing mutual recognition of institutional ethics review for research involving humans figure 2—model institutional review board workflows using the blockchain consider two potential use cases of the blockchain to enhance decision-making consistency for sirbs: “blockchain-based credentialing” and “blockchain-accountable data sharing”. the former use case automates eligibility criteria for the nominated board of record, providing a transparent view of the lead rec’s page 5 of 10 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.18 credentials to be deemed lead. an rec of record, for example, could only serve as the lead rec if it met established criteria for proper representation of scientific expertise and community membership, and its membership was free of any financial or perceived conflicts of interest. in “blockchain-accountable data sharing,” the single rec of record could easily monitor and hold researchers accountable for sharing their data if the study is required to do so under mandatory data sharing conditions for funding. many federal funding agencies including the nih34 and nsf35 require researchers to make data from publicly funded projects available following a one-year publication window. few researchers, however, are sanctioned for failing to meet these data sharing requirements despite policies proposing how they could be better held accountable (see for example the global alliance for genomics and health accountability policy released in 2015).36 recs that participate in a sirb system could fill this important accountability gap using the blockchain. the blockchain could prevent approval of federally funded studies without a data sharing or management plan, and by automating annual study reporting. furthermore, smart contracts executing the accountability policy could also be integrated into the ethics review process, allowing recs to collectively monitor researchers’ data sharing activities. adapted from peterson et al,37 the benefits of the blockchain could facilitate a sirb model insofar as three general assumptions about the participating recs (or nodes) are true. first, data input and participation from institutional recs should subscribe to a shared lexicon of data protection and securities (e.g., harmonized definitions of data encryption, anonymization, pseudonymization etc. to assess data management plans for multi-site studies).37 second, without guarantees of security and auditability outlined in sirb reliance agreements, institutional recs will not trust each other to share study information from other research sites named in the protocol.37 third, individual recs should control their own records, authorize how these records may be accessed and by whom via a permissioned sirb ledger.37 blockchain and the foundations for ethics egovernance in research involving humans and their data beyond powering a shared infrastructural platform upon which a sirb system could rest, distributed ledger technologies carve a space for ethics e-governance of research involving humans and their data. this egovernance system might adopt the structure of a global solutions network (gsn) not unlike what tapscott proposes as an approach to governing existing cryptocurrencies.38 of the ten interrelated networks that comprise the gsn, three subnetworks in particular would be key to facilitating ethics review mutual recognition internationally: i) policy networks, ii) knowledge networks, and iii) global standards networks the relationship binding each of the three subnetworks is discussed below, using the global alliance for genomics and health as an exemplar case of a gsn for ethics review mutual recognition in the data-intensive sciences. like those who established early finance regulations in the united states, drafters of the original common rule did not (and could not) anticipate how disruptive biotechnologies would revolutionize the nature and scope of biomedical research. policy/guideline development in an era of rapid scientific advancement means regulators must often act on incomplete information to address specific ethical, legal and social implications such advances pose. policy networks can be most effectively leveraged when relevant knowledge is both generated and used to support evidencepage 6 of 10 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.18 based interventions. knowledge networks achieve this aim and are the sources from which new systems-based solutions emerge. the resultant science governance policies therefore should be informed by relevant empirical evidence available to date, and subsequent technical standards developed to activate the ethical principles supporting the proposed policy/guideline. the regulatory and ethics, as well as data work streams that comprise part of the global alliance for genomics and health39 exemplify how the three subnetworks (policy, knowledge and standards) can be leveraged to support ethics review egovernance of genomic and health-related data sharing. by proposing several essential elements, the ethics review recognition (err) policy32 developed a procedural roadmap for establishing sirb models based on mutual recognition. the err policy further highlighted areas of unmet need, whereby other knowledge networks within the global alliance could subsequently contribute with new empirical research. integrating knowledgeable stakeholders into the policy formulation process is the primary aim of well-organized policy networks according to tapscott, that “turn decision making from the traditional hierarchical broadcast model to one of consultation and collaboration”38(p20). many contributors to the final err policy went on to advise governmental policy bodies on how to model ethics review mutual recognition in their home jurisdictions. the essential elements outlined in the policy complemented many of the provisions that were ultimately adopted in various centralization reforms in canada, australia and most recently in the united states. conclusion blockchain technologies powering digital cryptocurrencies still remain elusive in the scientific research (governance) arena. awareness of distributed ledger technologies are, however, gradually taking hold in healthcare. this is particular true of health information systems40–44 wherein distributed ledger technologies are helping overcome two competing goals: securing sensitive research and clinical data while ensuring its usefulness and responsible access among more stakeholders in the learning healthcare system. this paper draws on the conceptual and technological virtues of distributed ledger technologies to inspire new forms of ethics (e)governance that occupies an important gatekeeping step to innovation in the learning healthcare system. improving the quality, transparency and efficiency of ethics review for collaborative multi-site studies can directly translate into quicker turn-around time. further research is needed, however, to address several pressing ethical-legal challenges in pursuing this novel-use application. first, smart contracts have yet to be used to broker reliance agreements between collaborating institutions and recs, nor their legal recognition formalized in a regulatory context. insofar as a permissioned ledger is used to enable a sirb system, attestation of the information exchanges between participating recs remains unclear. whereas third party miners conduct this integrity-validation on the bitcoin ledger, it is unlikely that the same role can be fulfilled on a permissioned ledger for sirb purposes. that is, how and who testifies to the integrity of amendments to the protocol or smart contract terms among the individual research sites (nodes)? future qualitative, and public perceptions research is planned to investigate the implementation potential of distributed ledger technologies and e-governance in the multi-site ethics review context. this paper lays the conceptual foundation from which a transition to e-governance can launch. it is furthermore motivated by a pressing need to fill a practical, infrastructural gap in inter-institutional cooperation, data sharing and collaboration among existing governance mechanisms under the revised common rule; all areas of which distributed ledger technologies can facilitate towards a more responsible page 7 of 10 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.18 governance system for biomedical research and innovation in the post genomic era. acknowledgements i wish to acknowledge the global alliance for genomics and health, ethics review equivalence task team, drs. gillian bartlett, amalia issa, tibor schuster (department of family medicine, mcgill university) and professor bartha maria knoppers (centre of genomics and policy) for their input in helping me conceptualize this paper. this work was supported in part by the vanier canada graduate scholarship (cihr#359258); genome canada, genomics and personalized health (gaph) grant, biomarkers for pediatric glioblastoma through genomics and epigenomics; and the canada research chair in law and medicine. conflicts of interest none acronyms dlt: distributed ledger technologies— elsi: ethical, legal, social implications— hgp: human genome project nih: national institutes of health nsf: national science foundation rec: research ethics committee sirb: single institutional review board references 1. department of health and human services. final nih policy on the use of a single institutional review board for multisite research. fed regist. 2016;81(119):40325-40331. doi:10.1007/s10750-004-4538-3. 2. collins fs, morgan m, patrinos a. the human genome project: lessons from large-scale biology. science (80). 2003;300(5617):286-290. doi:10.1126/science.1084564. 3. zawati mh, 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continu des projets multicentriques. unité de l’éthique, direction générale adjointe de l’évaluation, de la recherche et de l’innovation. canada; 2008. http://ethique.msss.gouv.qc.ca/fileadmi n/documents/mecanismes_multicentriq ue_2008/documents_maitres/multimec anisme20080401.pdf. 29. national mutual acceptance of ethical and scientific review for multi-centre page 9 of 10 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.18 clinical trials conducted in public health organisations. vol 1.; 2015. doi:10.1017/cbo9781107415324.004. 30. bc ethics harmonization initiative final evaluation report.; 2016. 31. dove es, townend d, meslin em, et al. ethics review for international dataintensive research. science (80). 2016;351(6280):1399-1400. doi:10.1126/science.aad5269. 32. global alliance for genomics and health. ethics review recognition policy.; 2017. 33. hedlund m. ethics expertise in political regulation of biomedicine: the need of democratic justification. crit policy stud. 2014;8(3):282-299. doi:10.1080/19460171.2014.901174. 34. national institutes of health. final nih statement on sharing research data.; 2003. http://grants.nih.gov/grants/guide/notic e-files/not-od-03-032.html. accessed march 12, 2016. 35. national science foundation. other post award requirements and considerations: chapter vi. grant propos guid. 2011;(january 2013):124. 36. global alliance. global alliance for genomics and health: accountability policy.; 2015. http://genomicsandhealth.org/aboutglobal-alliance. 37. peterson k, deeduvanu r, kanjamala p, boles k. a blockchain-based approach to health information exchange networks. nist work blockchain healthc. 2016;(1):1-10. doi:10.1016/j.procs.2015.08.363. 38. tapscott a. a bitcoin governance network: the multi-stakeholder solution to the challenges of cryptocurrency.; 2014. 39. global alliance for genomics and health. about us. https://www.ga4gh.org/aboutus/. accessed july 21, 2015. i a distinction between multi-site and multijurisdictional research should be emphasized 40. brodersen c, kalis b, leong c, mitchell e, pupo e, truscott a. blockchain : securing a new health interoperability experience. nist work blockchain healthc. 2016;(august):111. doi:10.1001/jama.2012.362.4. 41. thomason j. blockchain: an accelerator for women and children’s health? glob heal j. 2017;1(1):3-10. 42. linn la, koo mb. blockchain for health data and its potential use in health it and health care related research. us dep heal hum serv. 2014:1-10. 43. yue x, wang h, jin d, li m, jiang w. healthcare data gateways: found healthcare intelligence on blockchain with novel privacy risk control. j med syst. 2016;40(10):218. doi:10.1007/s10916-016-0574-6. 44. li b. blockchain and smart contracts in health-related mydata scenario. 2017;(april). 45. hébert p, saginur r. research ethics review: do it once and do it well. can med assoc j. 2009;180(6):597-598. doi:10.1503/cmaj.090172. 46. al-shahi salman r, beller e, kagan j, et al. increasing value and reducing waste in biomedical research regulation and management. lancet. 2014;383(9912):176-185. doi:10.1016/s0140-6736(13)62297-7. 47. the canadian clinical trials coordinating centre. new streamlined system for research ethics review launched in ontario. http://www.cctcc.ca/index.cfm/news/ne w-streamlined-system-for-researchethics-review-launched-in-ontario/. accessed may 16, 2015. 48. wagner th, murray c, goldberg j, adler jm, abrams j. costs and benefits of the national cancer institute central institutional review board. j clin oncol. 2010;28(4):662-666. doi:10.1200/jco.2009.23.2470. here. whereas multi-site research implies the project takes place across individual page 10 of 10 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.18 research sites, multi-jurisdictional refers to participating research sites across different legal jurisdictions. multi-jurisdictional research adds to the procedural complexity of multi-site studies, as recs must reconcile the regulatory as well as legal differences of the jurisdictions included ii i elaborate elsewhere that the sirb mandate in the revised common rule mandate is more a leap of faith than evidence-based policy14. although many in the ethics governance community recognize the virtues of centralizing ethics review on a conceptual basis45,46,23,17,47 limited empirical evidence demonstrates the superiority of centralization over the existing institution-by-institution approach from either a costor resource-saving perspective48. page 1 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 public health surveillance using decentralized technologies jose luis bellod cisneros, 1 frank møller aarestrup, 2 ole lund 1 authors: 1 dtu bioinformatics, kgs. lyngby, 2800, denmark. 2 dtu food, kgs. lyngby, 2800, denmark corresponding author: jose luis bellod cisneros. cisneros@bioinformatics.dtu.dk keywords: blockchain, cosmos framework, decentralized technology, public health surveillance category: use cases/pilots/methodologies this article describes how blockchain technologies can be used in the context of public health surveillance through decentralized sharing of genomic data. a brief analysis of why blockchain technologies are needed in public health is presented together with a distinction between public and private blockchains. finally, a proposal for a network of blockchains, using the cosmos framework, together with decentralized storage systems like ipfs and bigchaindb, is included to address the issues of interoperability in the health sector. keywords: blockchain, cosmos framework, decentralized technology, public health surveillance ext-generation sequencing technologies are creating new opportunities in the fields of animal and human health due to the rapid decrease in cost and high-throughput of data generation. when the size and price of sequencing devices drop significantly, public health institutions could use the technology to perform routine clinical diagnostics. these technologies have the potential to become so widespread that storing genomic information could be a problem due to the sensitivity of the data and the technological and ethical challenges arising from sharing genetic information that might be linked to future health disease for individuals. in the context of an emerging disease, data sharing becomes critical to act rapidly for fast diagnosis and better treatment. currently, the technology is still expensive so it’s not widely accessible yet to private individuals, but that may change in the near future. several public institutions like the dna data bank of japan, genbank (usa) and the european nucleotide archive (uk) n https://doi.org/10.30953/bhty.v1.17 mailto:cisneros@bioinformatics.dtu.dk page 2 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 store genomic data and provide free and open access to it. data providers, like sequencing labs and research institutions, initially keep the data private before publication of the study results, which goes against rapid-sharing protocols. new developments in technologies like oxpord nanopore1 could challenge this by offering in-place real-time analysis that can discard raw data thanks to the use of streaming algorithms.2 other factors that influence rapid and open data sharing include patenting and intellectual property, fear of losing control over the data that can be commercialized (outside their borders), reputation and economic damage. there are several initiatives that aim to create an interoperability network for data sharing like compare3 and the global microbial identifier that are planning to develop a centralized or confederated solution where all the data are stored. this creates several issues regarding the ownership of an individual’s dna and the related data that comes from the sequencing process but also how to deal with interoperability to connect data consumers and providers when needed, outside the centralized network. traditional public health surveillance approach researches have until now followed the approach of gathering genomic data locally and, after the analysis and publication are finished, release the data to the community through global repositories. aarestrup and koopmans4 address a set of barriers for the sharing of data pre-publication, freely and in real-time: lack of data standardization, political sensitivities, national regulations and laws, ethical issues and intellectual property rights. aarestrup and koopmans suggest that the decreasing cost and accelerated development of next generation sequencing (ngs) can be used as a common language for the exchange of genomic information. to avoid the legal and ethical challenges associated with data sharing, a hybrid public/private publication model will ensure real-time access to the data with the option of temporarily keeping the data private to guarantee that public health authorities can evaluate any issue regarding sensitive data. the rationale behind public health surveillance and interoperability between systems is not new. for example, in5 several unstructured event-based report systems like the global public health intelligence network, healthmap, and epispider are evaluated, concluding that those systems, even though developed separately, are highly complementary. in the european union (eu) we can find many health systems and databases that are fragmented and lack harmonization of data, methodologies, and common analysis practices due to the fact that each state has the responsibility of regulation of its own healthcare system. auffray et al.6 recommend five initiatives to provide a common framework for data interoperability in the eu: launching pilot projects on big data, promoting open access and transparency of data, methodologies and publications, creation of a multidisciplinary involvement of all stakeholders in the health care industry, use state-of-the-art mathematical and statistical methods and harmonization of european policy and regulatory frameworks. public health surveillance and privacy a new european regulatory framework is currently being addressed by the third eu https://doi.org/10.30953/bhty.v1.17 http://www.globalmicrobialidentifier.org/ http://www.globalmicrobialidentifier.org/ http://www.globalmicrobialidentifier.org/ page 3 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 health program (2014-2020) aiming, among other things, to the establishment of a common network for public health surveillance and control of emerging diseases.7 one of the aspects addressed by the eu is data protection through the general data protection regulation (gdpr). this regulation focuses on mandatory safeguards implemented by any organization in charge of storing personal health data but also, under certain circumstances, override a subject’s right to ensure that his information has been deleted. public health research falls under the same category as scientific research and several exceptions are added, like permission of data transfer outside national borders in the case of a contagious disease. use case example to give an example of how a data-sharing platform would provide a solution in the context of an emerging disease, suspected from imported cucumbers, the following use case is presented from a researcher’s point of view: a danish scientist uploads a set of dna samples from an ongoing e. coli outbreak that has been detected in denmark. the genomic data are stored in the university’s system where permissions are temporarily granted to a colleague in germany, where the infected cucumbers are suspected to come from. the german scientist discovers that the origin of the cucumbers can be traced back to spain, comparing the dna data to a supplychain study that he has access to. the supply-chain is linked to a set of human samples from several spanish hospitals where patients were admitted with gastroenteritis. the patients can be linked to a restaurant chain in spain that has locations in denmark and germany. problem definition from this very simple example it can be inferred that the described data-sharing system has several features that need to be addressed in order to stop an ongoing disease: • permission-based data sharing. • interoperability (data located in several places for political or security reasons). • common repository and data modeling, to serve as source of truth. • fast access to the data that allows a real-time surveillance system. in the following we will give a short introduction to blockchain and then discuss how it can address some of the problems identified above. what is a blockchain? a blockchain can be described as spreadsheet duplicated across a network of computers. the data that a blockchain contains is shared and no single no single authority has the “official” or unique source of the data. an analogy would be a google sheet, where the document is not stored on google’s servers but served by all the participants, with each new entry going through an agreement protocol (consensus mechanism) that establishes the reconciled state of the document that everybody believes to be the shared true state. why is a blockchain needed? decentralized technologies have the potential to increase research opportunities and clinical effectiveness by providing an open platform that addresses interoperability challenges. the following list of conditions, part of deloitte’s blockchain decision framework,8 https://doi.org/10.30953/bhty.v1.17 page 4 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 are discussed here in order to consider a blockchain solution: multiple parties generate transactions that change information in a shared repository it is estimated that the future growth of sequencing technologies could surpass the amount of data produced by online video platforms, like youtube, in the next ten years.9 this rapid production of genomic information will provide the basis for realtime comparison of pathogen data, across different countries and sectors, dramatically increasing the effectiveness of disease control. the future spread of the technology will create a heterogeneous ecosystem with many sources of data that will need a common repository for analysis and comparison. parties need to trust that transactions are valid data stored in the system needs to be validated and standardized. sensitive data like human dna or any other medical data that can be linked to a person needs to be appropriately checked and secured. intermediaries are inefficient or not trusted as arbiters of truth real-time data sharing, and availability are some of the key features of a surveillance system. any central system can be hacked, or data can be removed due to failures or system malfunctions. siloed data are slow to access, and permissions need to be manually granted, or relay on sharing passwords with other users compromising security and privacy. in the context of an outbreak, to act rapidly turns out to be crucial to stop the spread of the disease in order to save lives. enhanced security is needed to ensure integrity of the system security is crucial in all aspects of the system, especially for patients but also for other actors who store the data. if security is not guaranteed, nodes will not share or have access to the data. data could be stored, either encrypted or using a secondary structure for the most sensitive information. what blockchain will be used? public health and the health sector in general have a lot of pressure from the legislation to ensure that they comply with the current data-protection rules that ensure that people’s privacy is protected. this has had a negative effect on interoperability creating incompatibility between data records and inefficient data-sharing systems. blockchains are grouped in two in deloitte’s paper, permissioned and public. public blockchains allow anybody that wants to join the network to get access to all the information that is available. a more restricted version of permissioned blockchain is a private blockchain. yuan et al10 regard private blockchains (in their terminology, the ones controlled by a single entity) “[…] in general a bad idea.” “[a private blockchain] controlled by a single entity degenerates to a traditional centralized system with a bit of cryptographic auditability sprinkled on top.”10 private blockchains don’t get any of the benefits of a decentralized system so a traditional database, based for example on cryptographic primitives like merkle trees,1 that allows for fast verification of data integrity of large data archives, would be a better solution. a better way to describe a blockchain where nodes are known and controlled is as a federated blockchain (also called consortium blockchain). this solution offers https://doi.org/10.30953/bhty.v1.17 page 5 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 several advantages over public blockchains. federated blockchains “allow for transparent governance within the consortium only.”12 this approach avoids all problems related to public goods (i.e. basic societal goods than can’t be excluded from anybody’s use), like abuse of the system and spam. data upload and sharing in a traditional centralized system, data upload and sharing has to go through a single provider for storage and data permission. while storage services like dropbox, and aws do not rely on a single server (rather on a distributed network of servers to guarantee a certain uptime, low latency and backup) it is actually a single organization who controls and practically owns the data. genomic information is stored in large gigabyte-size files. blockchain as a data structure is not suitable for storing this information due to scalability and performance. a different type of data are the associated metadata: country, data of sequencing, provider, pathogenic attributes, species...etc. this data are small compared to the genomic data, but it needs to be searchable and organized efficiently for fast retrieval. some of these attributes are sensitive since they could be linked to patients or reveal private information. the interplanetary file system (ipfs) is one of several candidates which aim is to decentralize and improve the way data are stored on the internet, based on a paradigm called content-addressable storage, where data are addressed not by where it is located but by the content itself. ipfs is able to handle big files, which aligns very well with big genomic data, and, by using an extra layer called filecoin,13 addresses the concerns regarding data privacy using strong encryption techniques. fillecoin also adds an incentive mechanism that enables the creation of a market for data storage that rewards users for storing and providing accessibility to the information. another of the benefits of using ipfs’s content-based addressing also affects the speed of data transfers. this is where its name, interplanetary, comes to importance. if we think about a mars colony requesting data from earth, “with one-way latencies of between 4 and 24 minutes”, the first time the information is accessed will cause a significant waiting time. but after that first attempt is completed, any node closer to the mars’s colony will get it locally without having to request it through any interplanetary communication. we can translate this example to a global surveillance framework, where data needed in tanzania for analysis in an on-going disease doesn’t need to ask for it in any of the overseas databases, with the extra penalty of a slow internet connection. using ipfs, a tanzanian researcher can ask the network for the content of the data and any node, physically located closer to where is needed, will serve it faster and with less round-trip requests. bigchaindb is a “scalable blockchain database: a big-data database with blockchain characteristics including decentralization, immutability and built-in support for creation & transfer of assets.”14 bigchaindb uses mongodb, making it suitable for storing and retrieval of tabular or document-based metadata. a blockchain that timestamps genomic-sample uploads and monitors a set of parameters set up by researchers on the uploaded data, could trace a pathogen by analyzing how it spreads https://doi.org/10.30953/bhty.v1.17 https://readplaintext.com/how-ipfs-solves-the-internets-speed-of-light-problem-ab611b2a4d8e https://readplaintext.com/how-ipfs-solves-the-internets-speed-of-light-problem-ab611b2a4d8e page 6 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 from one patient to another through comparison of dna from different samples, improving the efficiency of the current disease-control systems. in this example, each sample is hashed, along with the attributes defined by the researcher (species, country of origin…etc.). a list of peers known only by the consortium can be used to create a private network. in this scenario, each user could own ipfs and bigchaindb nodes, or encrypt the information and use nodes owned by others in the consortium to store the data. data upload will be done via a common user interface that handles both genomic and metadata upload to the ipfs and bigchaindb nodes and registers and timestamps the creation of digital assets in the blockchain (figure 1). the blockchain will act as the source of truth, the shared state that all participants in the consortium agree to. while some of the information could be kept public if not important, the blockchain will contain hashes or identifiers to the uploaded genomic data. figure 1. upload of genomic data this will serve as proof that an event was registered in the system which later can be shared automatically via a set of smart contracts (figure 2) that will act as a validation mechanism (e.g., only the owner can share, sharing happens only with certain users or when certain conditions are met... etc.) the ipfs and bigchaindb nodes could https://doi.org/10.30953/bhty.v1.17 page 7 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 monitor the blockchain to listen to "shared transactions” that will whitelist the receiving nodes and replicate the data to them. this pattern creates a problem, known as data escapes. how can we prevent users from getting the data and sharing it with unauthorized users? implementing mechanisms of data-curation could solve this, where data posted would need to go through a validation step that will check if the data was previously uploaded, and then flagged for later review. since all the transaction regarding access to a genomic resource are registered in the blockchain, it would be easy to narrow down the users that could have shared data without consent of the owner. data discovery the proposed framework for a decentralized genomic infrastructure needs to address a very important question regarding data discoverability. in a centralized system there is only one place that holds the information but, in an open infrastructure, there is an unknown number of locations that the users need to access in order to retrieve the data. another issue is the question of reputation, how can we trust that the requested data are valid and reliable? a project called ocean protocol introduced a token-curated registry model “for establishing trust in network assets and services through staking and reputation.” this registry allows a data marketplace to have reputable actors that are incentivized to keep the network alive and to store the data. these actors are rewarded for providing high-quality data that then gets promoted to higher positions in the registry, signaling its value to data consumers. anybody can challenge the value, quality or source of these data, and the network will reach an agreement whether to keep or remove the challenged dataset. interoperability proposal several solutions (cosmos, plasma or polkadot) have approached the problem of interoperability between blockchains in different forms. public blockchains might exist as the arbiters of truth when conflicts arise, for example, governance of the protocol or, in the context of sidechains where bitcoin can provide extra security to the pegged blockchains, as was the case of the national currencies backed by gold in the past. off-chain storage or federated blockchains could be used to overcome the scalability and performance issues of these public blockchains, moving most of the computation off chain, resulting in cheaper and faster transactions. figure 3 shows a proposal for a network of blockchains based on the cosmos network architecture, powered by tendermint’s consensus mechanism16 (an adaptation of the practical byzantine fault tolerance17 algorithm that uses proof-of-stake18 to achieve distributed consensus). this system does not spend time solving any computationally difficult cryptographic puzzle to elect the proposer of the next block, making it environmentally efficient and faster than current proof-of-work19 blockchains. https://doi.org/10.30953/bhty.v1.17 https://oceanprotocol.com/ https://oceanprotocol.com/marketplace-framework.pdf https://cosmos.network/ http://plasma.io/plasma.pdf https://polkadot.io/ https://cosmos.network/whitepaper page 8 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 figure 2. sharing uploaded data zones belong to organizations made up of several partners (public hospitals in the same country). they can be considered as blockchains (federated or public) and are independent of each other. hubs act as exchanges that coordinate sharing information through transfer of tokens between zones and hubs external to the consortium that manages the network. the central hub would provide the basis for user-management access, and to keep a global state across all zones. the hub would take care of how data sharing of biological data are granted (tokens representing permissions to access the data), provide the infrastructure to be connected to other zones (ethereum, private chains…) and implement governance mechanism to coordinate all participants in the network. the system could work as follows. each zone would be implemented using tendermint’s application blockchain interface (abci) that uses a set of smart contracts for handling permission and access to the blockchain and file distributed storage servers (ipfs) that allows for data uploading. tendermint’s abci allows for more flexibility since the application logic can be implemented in any programming language. this flexibility is what allows for cosmos to create a common mechanism for different abcis to talk to each other. the consortium will create a template abci app that can be used or changed by other members of the consortium, granted that they keep tendermint’s communication protocol unchanged. this abci will be the basis for each zone with the following features: https://doi.org/10.30953/bhty.v1.17 page 9 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 • permission management (via monax’s implementation of the ethereum virtual machine on top of tendermint’s consensus engine), • data upload interfaces to a decentralized storage network for genomic data and associated metadata through ipfs and bigchaindb nodes, • a set of seed nodes to bootstrap the network, • smart contracts to handle registry and sharing of data, • real-time monitoring of new data (biological samples) pushed into the zone, • consortium members with validation access can access the data. the role of the central hub is critical when aiming to connect to other organization who are willing to participate in the data sharing but reluctant to agree to the governance model of a specific network or worried about privacy and security. the flexibility of tendermint’s design allows for zones and hubs to communicate state changes “via an inter-blockchain communication (ibc) protocol, a kind of virtual udp or tcp for blockchains.”20 cosmos also provides a governance mechanism based on validators and delegators. validators are the equivalent of miners and their task is to keep the network running and to commit new blocks. delegators are token holders that delegate their task to others and share the rewards of validating new blocks. each zone has an independent governance mechanism and the hub or the other zones have no control of it. validators and delegators use their tokens to vote on proposals to change or upgrade the network. decentralised data analysis some of the initiatives that are building systems for public health surveillances like compare or gmi rely on a centralized system where all the data are aggregated and compared. this allows for a very fast comparison and monitoring since all the data resides in one place and therefore there is no need to move it from one place to another. the decentralized system for data upload and sharing described above relies on data to be physically moved to different locations in order to be accessed. in the context of an outbreak, moving genomic data, possibly several or even hundreds of samples of gigabyte size in a short amount of time is not feasible, especially if data are coming from places with very low internet connectivity. other reason not to move the data would be when legislation, privacy or data protection goes against the data to be moved outside a geographical jurisdiction or public health institution. microsoft has recently released the coco framework. coco is an open source project aimed to provide confidentiality and scalability to enterprise consortiums where actors are known and controlled. one of the most interesting additions that coco brings to the enterprise blockchain space is the integration of trusted execution environments (tees) like intel sgx and windows virtual secure mode (vsm). this set of machine operations allow to create a private enclave with privileged access to memory and computation, all protected from other processes running in the same cpu by a cryptographic key. https://doi.org/10.30953/bhty.v1.17 https://raw.githubusercontent.com/azure/coco-framework/master/docs/coco https://raw.githubusercontent.com/azure/coco-framework/master/docs/coco page 10 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 figure 3. proposal for a public health network based on cosmos in figure 4-5 we can see a proposal of a system that still relies on the data being stored in a decentralized way but that has been encrypted with private keys being stored in a tee. this is controlled by an oracle (i.e. external service that monitors the blockchain) (figure 5) that listens to sharing transactions and uploads the encrypted data to the tee. the users who participate in the sharing transaction control the private keys that live in the tee, making them the only ones who can see the decrypted data. the enclave will then run the computation on the data and later encrypt the results that will be sent back to the users. this approach still relies on the transfer of data that could be prohibitive if it were big genomic data. an alternative approach to gathering the data in one place and run the analysis, is to use what it’s called federated learning where a machine learning model is sent encrypted (with keys living in the tee) to all the places that store the data. the model will improve each time, but nobody will be able to access the improved model until it has finished its learning process. only then it can be then decrypted it when it comes back to the tee and encrypted again with the public keys of the users that requested the analysis. https://doi.org/10.30953/bhty.v1.17 page 11 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 figure 4. proposal for analysis of data in a tee one caveat is that we need a method that is able to handle encrypted data. this is called homomorphic encryptio21 and allows performing mathematical operations on encrypted data that generates the right result without revealing its content. this approach can be used in training machine learning models that use simple addition, multiplication and other mathematical operations than can be encrypted using homomorphic encryption. this method has been used by the open source project openmined, combining deep learning, federated learning, homomorphic encryption (figure 6) and economic incentivization via smart contracts and cryptocurrency. conclusion a brief introduction to the importance of data sharing in the context of public health surveillance has been presented, with a proposal of a decentralized solution. the goal of such a system would be to provide interoperability between several partners that want to share data but are concerned about their privacy. there are several implications of this approach that will need to be addressed: the need of bringing institutional partners to join the system, technical challenges on how to define the interfaces to the blockchain network, the role of privacy and security for sensitive data and the impact of decentralization in the organizational infrastructure to eliminate inefficiencies for data sharing. https://doi.org/10.30953/bhty.v1.17 https://github.com/openmined page 12 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 figure 5. proposal for analysis of data in a tee (ii) figure 6. proposal for analysis of genomic data using homomorphic encryption https://doi.org/10.30953/bhty.v1.17 page 13 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 the proposed design of a network of blockchains with data upload and sharing capabilities could be built in different stages. first, a simple data-uploading interface to ipfs or bigchaindb that can be later expanded into a consortium blockchain, using cosmos, that finally adds computing capabilities using tees or federated learning via homomorphic encryption. another aspect that has not been analyzed here, but worth mentioning, is the possibility of the creation of a genomic data market place. keeping in mind the exponential progression of sequencing technologies, one can imagine a future where sequencing devices become small enough to become pervasive, making it possible to sequence “everything, everywhere.” one of the latest developments in this direction is the ocean protocol powered by bigchaindb that aims to unlock siloed data for ai research, connecting data providers and consumers. the challenge here is how to incentivize high-quality data that is public or private but that has to comply with privacy and security regulations. competing interests there are no competing interests list of abbreviations used (if any) aws = amazon web services abci = application blockchain interface eu = european union gmi = global microbial identifier ipfs = interplanetary file system ngs = next generation sequencing tee = trusted execution environment funding statement this study was supported by the center for genomic epidemiology (www.genomicepidemiology.org) grant 09067103/dsf from the danish council for strategic research and by compare http://www.compare-europe.eu/, a european union project under grant agreement no 643476. references 1. nanopore o. smidgion. url https://nanoporetech.com/products/smid gion. 2. cao, md, ganesamoorthy d, elliott ag, et al. "streaming algorithms for identification of pathogens and antibiotic resistance potential from realtime minion™ sequencing." gigascience 5.1 (2016): 32. 3. compare. collaborative management platform for detection and analyses of (re-) emerging and foodborne outbreaks in europe. url http://www.compareeurope.eu/about. 4. aarestrup fm, koopmans mg. "sharing data for global infectious disease surveillance and outbreak detection." trends in microbiology 24.4 (2016): 241-245. 5. keller m, blench m, tolentino h, et al. "use of unstructured event-based reports for global infectious disease surveillance." emerging infectious diseases 15.5 (2009): 689. 6. auffray c, balling r, barroso i, et al. "making sense of big data in health research: towards an eu action plan." genome medicine 8.1 (2016): 71. 7. burgun a, bernal-delgado e, kuchinke w. et al. "health data for public health: towards new ways of combining data sources to support research efforts in europe." yearbook of medical informatics 26.01 (2017): 235-240. https://doi.org/10.30953/bhty.v1.17 https://oceanprotocol.com/ https://www.bigchaindb.com/ http://www.genomicepidemiology.org/ http://www.compare-europe.eu/ https://nanoporetech.com/products/smidgion https://nanoporetech.com/products/smidgion http://www.compare-europe.eu/about http://www.compare-europe.eu/about page 14 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.17 8. krawiec r. white m. blockchain: opportunities for health care. url https:// www2.deloitte.com/content/dam/deloitt e/us/documents/public-sector/usblockchain-opportunities-for-healthcare.pdf. 9. stephens, zd, lee sy, faghri f, et al. big data: astronomical or genomical? plos biol. 13, e1002195 (2015). 10. yuan, b., lin, w. & mcdonnell, c. blockchains and electronic health records. mcdonnell. mit. edu. 11. merkle, ralph c. "a digital signature based on a conventional encryption function." conference on the theory and application of cryptographic techniques. springer, berlin, heidelberg, 1987. 12. monax. permissioned blockchains. url https://monax.io/explainers/permissione d_blockchains/. 13. labs p. filecoin: a decentralised storage network. url https://filecoin.io/filecoin.pdf. 14. mcconaghy t. marques r, muller a, et al. bigchaindb: a scalable blockchain database. white paper, bigchaindb (2016). 15. back a, corallo m, dashjr l, et al. "enabling blockchain innovations with pegged sidechains."url:http://www.openscienc ereview.com/papers/123/enablingblockc hain-innovations-with-peggedsidechains (2014). 16. buchman e. tendermint: byzantine fault tolerance in the age of blockchains. diss. 2016. 17. castro m, liskov b, et al. practical byzantine fault tolerance. in osdi, vol. 99, 173–186 (1999). 18. buterin v, griffith v. "casper the friendly finality gadget." arxiv preprint arxiv:1710.09437 (2017). 19. jakobsson m, juels a. "proofs of work and bread pudding protocols." secure information networks. springer us, 1999. 258-272. 20. kwon, j. & buchman, e. cosmos: a network of distributed ledgers. 2016. url https://cosmos.network/whitepaper 21. armknecht f, boyd c, carr c, et al. a guide to fully homomorphic encryption. iacr cryptology eprint archive. 2015 (2015): 1192. https://doi.org/10.30953/bhty.v1.17 https://monax.io/explainers/permissioned_blockchains/ https://monax.io/explainers/permissioned_blockchains/ https://filecoin.io/filecoin.pdf https://cosmos.network/whitepaper clauson page 1 of 12 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.20 leveraging blockchain technology to enhance supply chain management in healthcare: an exploration of challenges and opportunities in the health supply chain kevin a. clauson,1 elizabeth a. breeden,2 cameron davidson,3 timothy k. mackey4 authors 1kevin a. clauson, pharmd, associate professor, lipscomb university college of pharmacy & health sciences, nashville, tennessee, usa. 2elizabeth a. breeden, dph, ms., associate professor and director of graduate studies in health care informatics, lipscomb university college of pharmacy & health sciences, nashville, tennessee, usa. 3cameron davidson, pharmd, pharmacy curriculum developer, pioneerrx, nashville, tennessee, usa. 4timothy k. mackey, mas, phd, associate professor, uc san diego – school of medicine, san diego, california, usa. corresponding author kevin a. clauson, pharmd, associate professor, lipscomb university college of pharmacy & health sciences, one university park drive, nashville, tn, usa; kevin.clauson@lipscomb.edu, 615.966.7001 keywords: blockchain, distributed ledger, pharmacy, pharmaceutical, supply chain, section: feature background: effective supply chain management is a challenge in every sector, but in healthcare there is added complexity and risk as a compromised supply chain in healthcare can directly impact patient safety and health outcomes. one potential solution for improving security, integrity, data provenance, and functionality of the health supply chain is blockchain technology. objectives: provide an overview of the opportunities and challenges associated with blockchain adoption and deployment for the health supply chain, with a focus on the pharmaceutical supply, medical device and supplies, internet of healthy things (ioht), and public health sectors. methods: a narrative review was conducted of the academic literature, grey literature, and industry publications, in addition to identifying and characterizing select stakeholders engaged in exploring blockchain solutions for the health supply chain. results: critical challenges in protecting the integrity of the health supply chain appear well suited for adoption of blockchain technology. use cases are emerging, including using blockchain to combat counterfeit medicines, review https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v1.20&domain=blockchainhealthcaretoday.com&date_stamp=2021-08-18 page 2 of 12 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.20 securing medical devices, optimizing functionality of ioht, and improving the public health supply chain. despite these clear opportunities, most blockchain initiatives remain in proof-of-concept or pilot phase. conclusion: blockchain technology has the unrealized promise to help improve the health supply chain, but further study, evaluation and alignment with policy mechanisms is needed. ntroduction globalization, increased adoption of information systems and related technology, and a sector populated with multiple actors in various jurisdictions, have given rise to a complex and self-proliferating health supply chain. numerous efforts to protect supply chains in the broader context of all commodities and goods have been undertaken, including the united states (us) national strategy for global supply chain security1, which is a white house initiative to: “promote efficient and secure services” and “foster resilience”.1,2 while this federal strategy on supply chain security is important for any industry, a compromised supply chain in healthcare is of particular importance as it can result in a number of failures in healthcare delivery that directly impact patient safety and health outcomes. these include the threat of failing to secure and distribute lifesaving commodities, adverse events associated with supply chain breaches, and increased morbidity and mortality in the end-user or patient. the pharmaceutical supply chain is one of the verticals most prominently considered when developing technology-driven solutions and use cases.3 for example, the global market for fake, substandard, counterfeit, and grey market medicines accounts for up to $200 billion per year.4 studies have uncovered a host of pharmaceutical products, medical devices, and biologics, that have been subject to counterfeiting in world bank categorized low, middle-income, and high-income countries indicating that the entire drug supply chain is susceptible to this transnational form of pharmaceutical crime.5-8 coupled with international growth of the pharmaceutical market and a rise in global drug sales, the emergence of various forms of technology and digital health platforms has given rise not only to supply chain solutions but also vulnerabilities.3 efforts to secure and modernize the supply chain have thus far focused on technologies such as radio frequency identification (rfid) chips with ownership transfer, mobile applications to track drug pedigree (e.g., m-pedigree), and other product verification solutions.2,3 in addition to pharmaceutical falsification, improving security and mitigating vulnerabilities in the vertical space of medical commodities and devices is a priority area. the medical device industry is particularly important, given the rise in connected devices and mobile health (mhealth) applications. for example, patients with implantable cardiac devices have been rendered vulnerable due to gaping security holes, illustrating challenges associated with the growth of the internet of healthy things (ioht) and how its development and adoption has far outpaced security requirements.9,10 in response to challenges as with the cybersecurity vulnerability identified in the pacemakers, government agencies and regulators are taking steps to increase awareness of the risks to the general public and healthcare ecosystem in the ioht.9 relatedly, rising healthcare costs tied to medical supplies are forcing healthcare systems to reexamine basic operating assumptions. while these processes would allow systems to better capitalize on a health system environment with large volumes of supply chain data, it still would not fully leverage it for supply chain optimization. i page 3 of 12 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.20 hence, effective management of the health supply chain is critical to ensuring optimal patient safety and population health level outcomes—a task that paradoxically relies on but fails to fully utilize cutting-edge technology and innovation. failures in the health supply chain evidenced by the transnational trade in fake medicines, medicine shortages and stock outs, and security vulnerabilities in connected medical devices, illustrate the high stakes nature of this sector relative to some industries.3,5 as such, solutions must address and balance optimizing supply chain management and ensuring supply chain efficiency and risk reduction. in all healthcare verticals improving resilience, integrity, data provenance, and functionality of the health supply chain are essential.11 what is the common denominator to address these challenges? all of these and more critical challenges in healthcare could be addressed with superior supply chain management practices that are digitally enabled by blockchain technology.12 for the purposes of this perspective piece, we define a supply chain as the end-to-end process from sourced raw material to final product sold to a customer. areas within healthcare primed for improvements in supply chain management that we focus on in this paper include: pharmaceuticals, medical devices and supplies, ioht, and public health. this perspective aims to raise awareness of opportunities for blockchain uptake in these health supply chain areas with a particular focus on the pharmaceutical supply chain, and also ask critical questions of what blockchain elements are crucial for future adoption and implementation. “pharma-chain”: blockchain for the pharmaceutical supply chain? a serious and well recognized threat to the pharmaceutical supply chain is the infiltration of the combined category of substandard and falsified (sf) medicines; these are also referred to as counterfeit medicines but often taking on a different legal meaning.5,13-14 collectively, these different forms of compromised and fake medicines can manifest as a result of importing substandard drugs without local approval, poor manufacturing practices or improper storage, theft and diversion of drugs, and the infiltration of poor quality or fake products into grey markets (i.e., business conducted outside of legitimate channels).15 the world health organization (who) estimates this combined market at $75 billion per year,13 but estimates range up to $200 billion.4 the pharmaceutical supply chain and healthcare system are particularly susceptible to disruption in countries like vietnam, where the vast majority (i.e., 90%) of drug expenditures are contingent upon imported sources.16 however, supply chain vulnerabilities are not limited to low-income markets or those heavily reliant upon drug importation. as an example, in 2012 the us food and drug administration (fda) notified nearly 1,000 healthcare facilities and practitioners in 48 states and 2 us territories that they might have purchased and administered fake versions of the blockbuster anti-cancer drug avastin® (bevacizumab).17 the legislative response to these threats in the us is the drug supply chain security act (dscsa).18 in a 10year time frame, the dscsa requires “medication tracking and tracing; serialization, verification, and detection of suspicious products; and strict guidelines for wholesaler licensing and reporting.”15,19 outside of the us, related efforts are underway with the falsified medicines directive in the european union (eu)20 and the council of europe’s medicrime convention,21 along with local anti-counterfeiting laws in various countries. page 4 of 12 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.20 when exploring the role of blockchain in securing and optimizing supply chain management for the manufacture, distribution, and dispensing of pharmaceutical products, the initial questions that should be posed are: would blockchain technology represent improvement over existing supply chain and anticounterfeiting systems and databases? does it offer functionality or processes unavailable with centralized databases and legacy systems? how can a blockchain interact with existing supply chain data (e.g., rfid, global standards one (gs-1), electronic product code information services (epcis), etc.) and anti-counterfeiting technology? and finally, does it offer a compliance and regulatory solution that can mitigate risk but also better ensure compliance and patient safety that can benefit both manufactures and consumers? answers to these questions should be the foundation of initial evaluations of blockchain design elements and feasibility studies to develop robust use cases, while also localizing within the context of the different challenges faced by supply chains in varying jurisdictions (e.g., eu parallel trade, markets with poor pharmaceutical governance). with the example of dscsa, each regulatory component should map to blockchain capabilities for it to be a viable solution. in the case of the pharmaceutical supply chain, possible dscsa-blockchain policy and technology alignment is illustrated in table 1. several organizations are actively exploring the use of blockchain for pharmaceutical supply chain management by developing use cases, simulation models, and prototyping blockchain solutions. leading the thought process around this development is the center for supply chain table 1. blockchain applicability for dscsa key requirements key requirement blockchain applicability compatible product identification unique product identifier can be required with contributed information validated as a side chain yes product tracing allows manufacturers, distributors and dispensers to provide tracing information in shared ledger with automatic verification of important information yes product verification creates system and open solution to verify product identifier and other contributed information yes detection and response allows public and private actors to report and detect drugs suspected as counterfeit, unapproved, or dangerous yes notification creates shared system to notify fda and other stakeholders if an illegitimate drug is found yes information requirement can create shared ledger of product and transaction information including verification of licensure information yes page 5 of 12 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.20 studies (https://www.c4scs.org/), a non-profit organization created to help explore the feasibility of blockchain adoption through a virtual pilot study22 with participation of various stakeholders from across the pharmaceutical supply chain. it is also engaged in ongoing research efforts around simulating reference models of dscsa and blockchain compatibility and compliance. additionally, technical professional organizations, such as ieee standards association (http://standards.ieee.org/) have convened workshops, webinars, and now operate a supply chain/clinical trials technology implementation industry connections program to explore frameworks for standards of interoperability between blockchain and existing legacy systems to enhance patient safety in both the pharmaceutical supply and clinical trials sectors. concurrently, a number of companies are similarly working towards these same goals, but from different perspectives of developing use cases, exploring projects with manufacturers, and extending blockchain models used in other industries (e.g., food supply chains)23 to pharmaceuticals and related healthcare uses (table 2). overall, assessing how blockchain technology might better secure the pharmaceutical supply chain, while concomitantly addressing the need to combat the decades long public health challenge of sf medications, has been a case study that has received multi-stakeholder interest in the shared spheres of the technology, public health, and healthcare community of blockchain researchers and entrepreneurs. while a project like mediledger (https://www.mediledger.com/) represents a collaborative approach between multiple companies in table 2, the practical and real-world application of blockchain to this problem remains unclear and requires further maturation. beyond pharmaceutical supply chain: other potential blockchain applications in healthcare moving beyond pharmaceuticals and the drug supply chain, blockchain applications are beginning to mature in other healthcare verticals, many of which are technology-focused and heavily regulated. arguably the most mature healthcare sectors moving forward with blockchain adoption are the clinical trial stakeholders, healthcare records and data management providers and entities, and as aforementioned, the pharmaceutical supply chain. however, areas for blockchain growth that align with the fundamental principles of improving data management and integrity of the health supply chain exist in areas of medical devices and supplies, ioht, and public health applications, which are explored in brief below. medical devices and medical supplies recently, almost half a million patients with implantable cardiac pacemakers were identified as needing a vital firmware update due to a security flaw exposing their device to potential manipulation by hackers.9 this follows other instances, including the recall of the symbiq™ infusion system, after it was discovered that hospira’s smart pumps could be accessed and controlled through a hospital network by unauthorized users to change patients’ dosages.24 as the employment of connected and digitallyenabled medical devices becomes more prevalent, their opportune use as well as their vulnerabilities become more pronounced. in response to requirements for medical devices to bear a unique device identifier (udi) by the fda and the eu, blockchain has the potential to reduce costs and improve patient safety, and combat medical device counterfeiting due to its efficiencies and accountability around trust.25,26 use of blockchain could also enhance preventive maintenance of devices via deployment of page 6 of 12 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.20 automated smart contracts.27 in one universityindustry partnership involving edinburgh napier university, national health service (nhs) national services scotland and spiritus development with support by the data lab and scottish funding council, an effort is underway to use blockchain technology to support the medical device supply chain to track devices through their lifecycle.28 the aim of the pilot is also to monitor the patient care pathway for opportunities to leverage analytics to improve safety and efficiency (e.g., improved response times for device recalls and field notices issued by responsible companies and agencies).28 table 2. selected companies exploring blockchain for health supply chain management company features website block verify extending anti-counterfeit solutions from luxury valuables to medications http://www.blockverify.io chronicled partnered with the linklab for a blockchainsupported dscsa compliance platform https://www.chronicled.com ibm blockchain early work with supply chain management in food products with multiple partners https://www.ibm.com/blockchain/supplychain farmatrust uk org developing blockchain solution for pharmaceutical supply chain, initial coin offering (ico) primarily for european market https://www.farmatrust.com isolve advanced digital ledger technology, blockrx ico primarily for u.s. market http://isolve.io modum blend of blockchain and sensors, mod token initial token offering (ito) http://modum.io origintrail recognized by walmart food safety, partnered with yimishiji; trac token https://origintrail.io provenance uk org starting with chain-of-custody for food; positioned to extend https://www.provenance.org t-mining belgians partnered with nxtport for container shipping; adaptable tech http://t-mining.be the linklab knowledge resource, development partner, partnered with chronicled http://www.thelinklab.com vechain combining blockchain and iot; food/drug forays in roadmap; ven/vet token https://www.vechain.com walton early phase to use rfid and iot; goals to scale to business ecosystem; wtc token https://www.waltonchain.org dscsa: drug supply chain security act, iot: internet of things, rfid: radio frequency identification, uk: united kingdom; u.s.: united states page 7 of 12 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.20 blockchain utilization also holds the promise to improve the value of care and reduce cost via enhanced supply chain management and interconnected clinical communities. to this end, johns hopkins medicine (jhm) created a supply chain initiative focused on spine, joint, and blood management.29 focused on improving the value of care, jhm positioned this initiative as cost cutting with a “stuff not staff” philosophy to drill down on reducing supply expenditures and avoid staff reductions. the armstrong institute for patient safety and quality (aipsq) was one of the highest-profile components to come out of this jhm initiative and the coordination of these interconnected efforts was observed to be a key element in its success (figure 1). cumulatively, the communities around spine, joint and blood management helped realize $5.6 million in cost savings from their physician-led clinical communities focused on supply chain management of medical supplies.29 internet of healthy things the ioht is a subset of the internet of things (iot) focused on health and wellness.10 these “things” commonly include wearables, sensors, and standalone devices with utility including activity, sleep, cardiac function, and disease specific conditions (e.g., epilepsy). the vulnerabilities and opportunities with ioht mimic those seen with medical devices, but ioht often are associated with greater threats to data, security, and systems due to less rigorous requirements and testing relative to medical devices or devices that are less regulated (e.g., non-fda approved applications and devices) and more consumer focused. the trusted iot alliance (https://www.trusted-iot.org/), formed by multiple industry iot stakeholders, aims to help navigate these hurdles by facilitating standard setting and other efforts centering around leveraging blockchain for “connecting and securing the next generation of iot products”. perhaps the first attempt at blockchain implementation with ioht is by bowhead health (https://bowheadhealth.com/), which is based around a connected device that dispenses nutraceuticals. incentivization of patient input of health data and habits is provided in the form of anonymized health tokens (ahts). while this is a similar incentivization model as proposed by other blockchain healthcare companies including: burstiq (https://www.burstiq.com), healthcoin (https://www.healthcoin.com), scriptdrop (http://www.scriptdrop.co), and solaster (http://solasterhealth.com), bowhead health is one of the first blockchain companies in healthcare to pair with their own manufactured ioht device. assuming initial success, they also have in their roadmap to move from the less demanding wellness device to a medical device; this type of path to market entry could prove a trend in this space. public health supply chain challenges in public health include disaster and emergency mitigation and management,30 including protective supplies for healthcare workers during public health emergencies31 and access to essential medications,32 vaccines,33,34 and immunizations.35 in the context of access to essential and quality medicines, blockchain technology solutions overlap with use cases in the pharmaceutical supply chain and combatting sf medicines, but also extend to maintaining adequate supply at point of distribution (e.g., mitigating stock outs), curbing health systemsrelated corruption in medicines procurement, and catalyzing effective delivery of healthcare services and commodities. blockchain technology in public health has also manifested as cryptocurrencies (e.g., digital currency like bitcoin), which have been posited as alternative page 8 of 12 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.20 forms of currency that can be used to better effectuate foreign aid and charity and as a means to reduce fraud and corruption in global health,36 which can also intersect with disruptions and lack of resilience in supply chain integrity. figure 1. coordination of armstrong institute for patient safety and quality (aipsq) and health system supply chain through clinical communities. adapted from ishii et al.29 conclusion the purported benefits of blockchain technology for enhancing management of the supply chain include: 1) reducing or eliminating fraud and errors, 2) reducing delays from paperwork, 3) improving inventory management, 4) identifying issues more rapidly, 5) minimizing courier costs, and 6) increasing consumer and partner trust.37 however, extending these potential benefits to acute challenges in the health supply chain remains an undelivered promise. going forward, this will require greater research, investment, and deployment of solutions that can be evaluated rigorously for their actual impact on patient safety and population health outcomes. numerous use cases in the health sector will also likely emerge. two additional pharmaceutical sector examples that illustrate specific benefits that a blockchain-powered supply chain might offer are drug recall management and addressing prescription drug abuse (e.g., opioids). the capacity to utilize smart contracts to automate processes and reduce costs is also a crucial mechanism by which blockchain technology page 9 of 12 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.20 could help achieve supply chain performance enhancement. equally challenging is the need to address privacy and data protection considerations unique to the healthcare sector as illustrated by the need to comply with policy frameworks such as health insurance portability and accountability act (hipaa) in the us and the general data protection regulation (gdpr) in the eu. while many blockchain efforts for the health supply chain are still at the proof of concept (poc) or pilot stage at present, more mature deployments are being explored across other industrial sectors that can be adopted for the healthcare sector and localized to policy incentives offered by national governments (e.g., compatibility with the dscsa). possibilities and opportunities for the health supply blockchain are seemingly endless, but only time will tell if the highly regulated and complex healthcare sector can fully leverage all the possibilities blockchain technology has to offer. funding statement there was no public or private funding provided in the creation of this work. conflict of interest the authors whose names are listed immediately below report the following details of affiliation or involvement in an organization or entity with a financial or non-financial interest in the subject matter or materials discussed in this manuscript. kac: has served as a consultant for blockchain companies focused on healthcare and healthcare companies exploring blockchain solutions. eab: has served as an invited speaker for healthcare blockchain companies. tkm: serves as the co-chair for the ieee standards association supply chain/clinical trials technology implementation industry connections program, is an invited participant in the dscsa & blockchain study by the center for supply chain studies, and is a member of the advisory board for the company farmatrust a blockchain company developing technology for the pharmaceutical supply chain. cameron davidson certifies no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. contributors to fulfill of the criteria for authorship, every author of the manuscript has made substantial contributions to all of the work and participated sufficiently in the work to take public responsibility. supplemental materials none references 1. white house. national strategy for global supply chain security. 2012. available at: https://obamawhitehouse.archives.gov/sites/defa ult/files/national_strategy_for_global_supply_ch ain_security.pdf (accessed 9/7/2017) 2. burmester m, munilla j, 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10.1080/17441692.2017.1291700. 35. zehrung d, jarrahian c, giersing b, kristensen d. exploring new packaging and delivery options for the immunization supply chain. vaccine. 2017 apr 19;35(17):2265-2271. doi: 10.1016/j.vaccine.2016.11.095. 36. till bm, peters aw, afshar s, meara j. from blockchain technology to global health equity: can cryptocurrencies finance universal health coverage? bmj glob health. 2017 nov 10;2(4):e000570. doi: 10.1136/bmjgh-2017000570. 37. ibm. supply chain. 2017. available at: https://www.ibm.com/blockchain/supply-chain/ (accessed 9/7/2017) supplementary data: none this is an open access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work noncommercially, and license their derivative works on different terms, provided the original work is properly cited as first published in blockchain in healthcare today™, and the use is non-commercial. see: http://creativecommons.org/licenses/by -nc/4.0." page 1 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.120 openpharma blockchain on fhir: an interoperable solution for read-only health records exchange through blockchain and biometrics gracie carter,1 benjamin chevellereau,2 hossain shahriar,1 sweta sneha1 affiliations: 1kennesaw state university, kennesaw, georgia; 2certara, princeton, new jersey corresponding author: dr. sweta sneha, kennesaw state university, 560 parliament garden way nw, room 491, kennesaw, ga 30144. ssneha@kennesaw.edu keywords: biometric, blockchain, electronic medical records, emr, fast health interoperable resource fhir, healthcare, interoperability, openpharma section: methodologies/api the healthcare system in the united states is unique. from payor to provider, patients have the freedom of choice. this creates a complicated and profitable paradigm of care. legislation defines government expectations of data exchange; however, the methods are left to the discretion of the stakeholders. today, devices and programs are not built to unified standards, thus they do not share data easily. this communication between software is known as interoperability. we address the health data interoperability by leveraging fast health interoperable resource (fhir) standard, a viewer of fhir called openpharma, and blockchain technology. our proof of concept, called “openpharma blockchain on fhir” (obf), is interoperable by design and grants clinicians access to patient records using a combination of data standards, distributed applications, patient-driven identity management, and the ethereum blockchain. obf is a trustless, secure, decentralized, and vendorindependent method for information exchange. it is easy to implement and places the control of records with the patients. since 2009, new technologies, such as distributed ledgers (blockchains) and electronic medical records (emr), have introduced new possibilities for information exchange. the health information technology for economic and clinical health (hitech) act passed in 2009 pushed healthcare in the united states into the digital age. hitech, set aside nearly $27 billion (€24 billion) over the course of 10 years1,2 in incentives and emphasized emrs for improved care quality, efficiency, and error prevention. hitech introduced incentives among providers to digitize medical records and adopt emr systems.1–3 https://doi.org/10.30953/bhty.v3.120 mailto:ssneha@kennesaw.edu https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.120&domain=blockchainhealthcaretoday.com&date_stamp=2020-06-11 page 2 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.120 the intention of legislation was to improve information exchanges between providers.4 hitech mandated creation of an infrastructure for a nationwide health information exchange that allowed for the flow of health information electronically5 but included no systems or standards for data sharing. emrs streamlined records, but records and data still remain contained in the originating health system.5 record transmittal to the next point of care is not guaranteed because of the inability or unwillingness to share complete records.6 common hindrances cited as reasons for noninteroperability, such as vendor lock in, security, lack of data integrity, and system incompatibilities, can be overcome using openpharma blockchain on fhir (obf). system-level incompatibilities are referred to as vendor lock-in. while locked in, providers cannot migrate to another vendor without compromising revenue and/or information integrity. the most common forms of vendor lock-in for emrs are proprietary ownership, refusal to modify, competition between different vendors on the market, or cost-prohibitive fees associated with modifications.7 with these barriers, there is no guarantee that data can be transferred from one emr system to another while maintaining file integrity.7 upon purchase from a vendor, customers are offered incentives for vendor loyalty in future purchases. to ensure dependency, proprietary software may render data incompatible with third-party software.7 among providers, 49% of those surveyed stated that emrs routinely block information by intentionally limiting interoperability, charging high fees for exchanges, and making act is difficult or impossible.8 this method of lock-in originates and is controlled by the vendors themselves. it is highly effective because it makes data migration an arduous or impossible.9 vendors often require that they be the sole source of support for the software. if customers want part of their system modified, that may require an additional purchase from the vendor. more revenue can be gained through guarding information and charging systems to modify it.9 with innovation, competition among vendors increases. if a vendor is able to innovate, it becomes a powerful marketing tool. the ability for vendors to market their products in this manner attracts customers to new functions but may overshadow less functional features, which may be better with another system. additional components and features also allow for differing price tiers. however, new features10 may add information exchange complications between systems without adherence to a common data standard. having access to add-ons or plug-ins developed by trusted third parties would allow greater access to desired functionalities without purchasing a new system. as the 21st century act is enabling development of new medical devices and tools faster, there is a tremendous need to be able to share data within devices and with ehr systems. data standards using a common data standard allows systems to exchange information seamlessly. once transmitted to another point of care, the information could be directed to the appropriate field in the recipient’s system through information mapping. the obstacle is not that a standard does not exist, or the inability to implement. rather, it is the lack of willingness to commit resources to share data by emr vendors. recognizing the noninteroperability issue, the centers for medicaid & medicare services https://doi.org/10.30953/bhty.v3.120 page 3 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.120 (cms) released a request for information for possible solutions for interoperability in 2019.11 where cms funding goes, healthcare follows. in 2019, cms officially endorsed adherence to fast health interoperable resource (fhir) standards those who did not or could not risk losing.11 technology used figure 1 provides an overview of obf for an example patient, john doe, who provides data in hospital a, and eventually the same data gets shared, secured to specialist s through voice authentication, storing of encrypted url data within blockchain. because our proposed obf is a proposed architecture, adding a security layer can be achieved with an available blockchain technology such as ethereum and hyperledger. the ethereum blockchain was chosen for maturity. other choices are possible for the blockchain such as hyperledger. the current discourse in healthcare interoperability is figure 1—an overview of openpharma blockchain on fast health interoperable resource. aws: amazon web services. https://doi.org/10.30953/bhty.v3.120 page 4 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.120 centered on data ownership and transparency; we believe that a private permissioned blockchain is counter to ownership. a privately held blockchain is simply a data silo. it is only through complete transparency that data are secure. without a centralized authority and through the decentralization of data across a blockchain, fear of data hoarding or abuse are assuaged. users can be rest assured that data are accurate and safe12 through audit trails of access authorization.2 for the first time, data owners (the patients) are completely aware of who has access to their records and hold providers accountable for alteration of records.13 because a blockchain can update in near real time, the data would always be current and accessible if authorized.14,15 all ethereum transactions use solidity coding scripts to execute specified functions automatically, known as smart contracts.16 a smart contract is executed based on a predefined trigger without human action or oversight, adding to reliability and security.16 saavha is a voice authentication system. it accepts user voice and returns a unique signature, which can be used as an id, independent of phi. proposed obf framework figure 2 presents the overview of the framework of obf and further details about encryption and the process flow. the obf application includes the following for middleware: smart on fhir to launch the app in the emr, fhir resources for semantic interoperability between providers, the smart contract to store members’ ids, partner ping urls and patient resource urls, information web3.js to interact with ethereum, react for frontend and implemented in node.js. outside applications include ethereum blockchain to store and make encrypted data available to partners, saavha for biometric validation, amazon key management service (kms) for encryption, and openpharma fhir viewer to view patient files without downloading. figure 2—user flow of openpharma blockchain. aws: amazon web services. https://doi.org/10.30953/bhty.v3.120 http://web3.js http://node.js page 5 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.120 to become interoperable and retain cms funding, emrs must adopt the fhir.17 fhir allows developers to create descriptive profile framework for existing resources that any system can read.17 fhir is based on restful web services and uses modular components as resources.17,18 this allows software solutions with view only information exchange over https (hypertext transfer protocol secure). a resource may be a small packet of information that includes metadata, text, or data elements bundled to create a profile or a set of profiles with specific vocabularies for resources.19 fhir allows for standardization of urls which applications can point to and retrieve information from over an https connection rather than exchanging files between systems using the insecure file transfer protocol.17 building on fhir standards, smart on fhir is a normalization for mapping to fhir, which allows third-party applications to safely interact with an emr without the need for vendorspecific integration.19 smart extends the functionality of fhir by specifying what resources should be used when developing applications. through smart on fhir, providers can capitalize on helpful applications without emr integration, thus minimizing vulnerabilities or vendor lock in. in addition, smart on fhir provides a layer of security for patient data through a mature authorization model for third-party applications using the oauth standards.19 the coupling of multilayer encryption and tokens secures patient identifiers on a public blockchain. the use of tokens allows for a smooth transition between services such as between servers or inside and outside of a contained system. in our proposed solution, a combination of openid (which is used to authenticate the user id to the application without sharing credentials) and oauth 2.0 (used to grant authorization with only the user id) is used. this combination allows the users to verify their identity through saavha. saavha generates a separate patient-specific identifier authenticated using biometrics. the use of biometrics is revolutionary for verification and authorization. a person’s voice is unique and changes over time. this gives it great potential as an identifier not dissimilar from a fingerprint. by speaking a predefined phrase into a microphone on the patient’s device, saavha verifies their id and passes the authenticated member id information off to oauth 2.0, which grants a token that can be used to request access without passing on the user’s protected identification information. saavha uses machine learning to identify matching pass phrases. if two samples are too similar, a token will not be generated, and the account could be flagged if the threshold of failed attempts is exceeded. this safeguards against a recording of the passphrase being used to authenticate. saavha also uses gps location from the patient’s device to compare with the provider’s system gps to ensure that the patient is present with the provider requesting access. this information also establishes a trusted device pairing for faster token retrieval during future visits and provides another layer of accountability for information access through audit trails. process flow first, prior to visiting their clinician, the patients should register with saavha. upon visiting their clinician (hospital a), no special interaction with obf is needed during the exam. obf is designed to be launched as a lightweight and nonintrusive smart on fhir plug-in for the emr. if patients are to be referred out to a specialist and would like to make their file https://doi.org/10.30953/bhty.v3.120 page 6 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.120 available, hospital a would initialize obf through their fhir-enabled emr; patients would then speak the predefined phrase set by saavha and capture their voice for authentication. second, once authenticated, saavha would return a member id (e.g., 18afc2cc-8de911e9-b683-526af7764f64. this member id would never be made visible to the provider nor the patient but passed onto the obf smart contract to store member ids as a token (the saavha member id does not contain phi). only the relationship between the member id and the patient url generated by fhir are saved in the smart contract, neither of which contain phi. to ensure privacy, all information is then passed through multiple layers of encryption before being published to the blockchain. encryption figure 3 provides a summary of what data get encrypted and how the data are encrypted. although public, information on the blockchain would not be easily accessible. because of the sensitive nature of patient data, obf uses redundant data encryption. all information would be encrypted and require matching digital key pairs to access data. special permission scenarios must be established using key pairings. every participant in the blockchain would have a private and a public key that would be cryptographically connected.4 the public key would be available to view by everyone on the chain but access to any identifying data would be limited to those utilizing the corresponding private key.4,12,20 this is handled through the smart contract20 and a kms. for further security and encourage adoption, only mappings between figure 3—encryption steps. aws: amazon web services, fhir: fast health interoperable resource, kms: key management service, smart: substitutable medical applications reusable technologies. https://doi.org/10.30953/bhty.v3.120 page 7 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.120 member ids and patient resource urls are stored on the blockchain after encryption. patient records are never stored on the blockchain; these remain at the originating emr. the smart contract stores the member ids, partners’ ping urls (i.e., middleware public url for each partner to check whether their service is working and to retrieve their public key), and patient resource urls (i.e., middleware-protected urls to access patient fhir resources). while none of these contain phi, obf encrypts them using a secured encryption-as-a-service provider amazon web services (aws) kms to add additional layers of security before storing them in the smart contract and after retrieving them to use in the smart on the fhir app. obf generates and gives each provider a data key encrypted with the master key managed by kms. when a provider wants to decrypt information stored on the blockchain, they first need to decrypt the data key by connecting to kms. in the event of theft of the encryption key, the data are safe in the smart contract because of the required master key for decryption of the data key. in addition, a random initialization vector (also known as a nonce) is generated, which can be shared with all partners to be certain that the encryption process is deterministic. random initialization vectors shield multiple usages of an encryption scheme with the same key. if the data are exposed without randomized initialization vectors, any potential agent may recognize a pattern or infer a relationship between encrypted data segments, which may leave data vulnerable to decryption through dictionary attacks. once the member id is passed to obf from saavha, the middleware can encrypt it and return a value similar to: d3c8aee319239f3751407. to decrypt to decrypt information, providers would need access to the encrypted data key and have access to the master key. only then would a provider be able to use kms to obtain the encryption key in clear mode to decrypt data using the encryption key and the iv. to exchange information over the internet, the middleware needs to be exposed online. we compute the unique public url for this patient using the encrypted member id: https:// hospital_a.com/fhir/d3c8aee319239f3751407. this is the url to the patient’s data in fhir format, which any corresponding fhir server would be able to recognize. although the member id is encrypted and protected, and we further encrypt the url to return something similar to: 044a04a65cac2a88dbcde. 4. hospital a’s middleware then authenticates to ethereum and registers a new resource url: 044a04a65cac2a88dbcde for the patient: d3c8aee319239f3751407. to an observer, these values mean nothing due to encryption. 5. once the patient moves to the next point of care, a similar workflow is used to authenticate, retrieve, and encrypt the patient’s member id. however, rather than just posting to the blockchain, the specialist is also requesting to collect information. no patient data are viewable without publishing information; a provider must announce a new relationship to the patient. upon publication, the middleware then requests to collect information from all participants on the blockchain who have information for the patient: d3c8aee319239f3751407. 6. the specialist’s middleware authenticates to ethereum, and requests available resources for the following patient: d3c8aee319239f3751407 and collects the following url: https://doi.org/10.30953/bhty.v3.120 http://a.com/fhir/d3c8aee319239f3751407 page 8 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.120 044a04a65cac2a88dbcde. this information matches the information previously published by hospital a. records from multiple providers matching this unique member id could be retrieved and viewed concurrently. this ability provides a more complete overview of the patient’s medical history. 7. once the information is found, the specialist’s middleware can send a signed request (using ethereum wallet keys) directly to hospital a’s middleware. the smart contract will verify the signature and permission in the blockchain to collect the fhir resource. this request is encrypted as well, hence the signature. the specialist’s middleware can decrypt the url collected from ethereum and return a url similar to: https:// hospital_a.com/fhir/d3c8aee319239f3751407. hospital a and the specialist can now exchange fhir resources between them securely. 8. hospital a and the specialist can use the embedded fhir viewer to view the patient’s records. because the records never leave the source emr, providers are free from delay related to downloading, storing, or adding them to their records. limitations and future work obf is limited by the aspect of patient consent. a clinician cannot collect information before the patient arrives; the patient must be present to give access to the file. this interaction may increase the length of visits, which may discourage adoption simply based on inefficiency. because the patient’s file does not leave the originating system, the clinician is unable to prepare for the visit prior to the patient’s arrival. we plan to explore as to how to use mhealth app to integrate with obf framework, so that the patients are able to authenticate themselves beforehand and provide permission to share data with providers as needed without the need to stop at the doctor’s office. another limitation is the reliance of voice-based biometric for patient authentication. such method may not work for patients who cannot speak, for example. we plan to explore integrating alternative biometric such as fingerprinting to resolve this in the future. while health insurance portability and accountability act of 1996 (hipaa) was designed to protect the patients and empower them to have control over their records, it does hinder when it intends to help. the practice that no person should see more information than what is needed to do his or her job—called least privilege—is relatively common in business. obf does not adhere to least privilege in the current iteration. to become more valuable, in future versions, obf would need to have the ability to filter a patient file in order to omit viewing mental health data by the requesting clinician, as well as limit access based on user roles. conclusion there is a need for a functional and stable solution that is both lightweight, easy to operate, and relatively quick to implement with very little user prompting. a system that requires more initial investment in infrastructure such as data centers or a formalized information technology department are prohibitive both in cost and return on investment. data standards such as fhir are designed to encourage interoperability through uniform data structure. this allows for systems to organize and move relevant data more quickly.21 having standards allows for software to be designed and developed to be interoperable across multiple platforms without extra add-ons or external programs for reconciling data structures. while many functions of healthcare can be streamlined using technology, fragmentation and data transference has not been resolved in the united states. the combination of resistance, lack of mandates and hipaa guidelines equate to little https://doi.org/10.30953/bhty.v3.120 http://a.com/fhir/d3c8aee319239f3751407 page 9 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.120 movement toward interoperability. while blockchain technology could improve interoperability, the technology is still not widely adopted. just as the internet evolved rapidly and changed communication, blockchain can grow to meet the needs of healthcare. the versatility and simplicity of the concept of blockchain make it very attractive. the promise of a transparent ledger seems so simple in concept but in application it becomes difficult. without government intervention to drive interoperability, it will take consumer demand to place pressure on the healthcare establishment to improve information sharing. without the cooperation of vendors at the potential loss of profits, records may never make it to the blockchain to be shared. although interoperability is multifaceted and complex, understanding some of the human components is imperative. when there are financial incentives to be gained through hoarding data, the likelihood that information blocking will cease organically is low. without a strong directive from the government, with a mandated framework for infrastructure, interoperability may be a much longer and more painful process than is necessary. our solution requires participation of vendors at the very least to map to fhir. by using open source technology already available, the cost of implementation would be relatively low. because obf is built using smart on fhir, integration into the emr ecosystem should be unobtrusive. the only addition to the provider’s day to day workflow would be the click of a button. the query, authentication, and encryption or decryption would be handled within the app. security is at the forefront of any effort to share information. through the use of encryption, authentication tokens, unique member identifiers, and file retention at the point of origin, patient information is as secure as possible to protect privacy. funding statement: this research was partially funded by healthcare management and informatics program at kennesaw state university. conflicts of interest: none contributors: sweta sneha was the overall research supervisor contributing from the perspective of a subject matter expert, research direction, outcomes, and editorial. hossain shahriar is professor and supervisor of student research direction, technology, and manuscript development. gracie carter and ben chevellereau contributed to research, manuscript development, technical framework, and the development of proof of concept. references 1. kim d, kagel jh, tayal n, bose-brill s, lai a. the effects of doctor-patient portal use on health care utilization rates and cost savings. ssrn electronic j [internet]. 2017;(39). doi: 10.2139/ ssrn.2775261 2, linn la, koo mb. blockchain for health data and its potential use in health it and health care related research. proc onc/ nist use of blockchain for healthcare and research workshop. gaithersburg, md, usa: onc/nist; 2016:1–10. 3. nhe -fact-sheet [internet]. cms.gov centers for medicare & medicaid services. 2019 [cited 2018 mar 10]. available from: https://www. cms.gov/research-statisticsdata-andsystems/statistics-trends-andreports/ nationalhealthexpenddata/ nhe-factsheet.html 4. blumenthal d, tavenner m. the “meaningful use” regulation for electronic health records. n engl j med. 2010;363(6):501–4. 5. lapsia v, lamb k, yasnoff wa. where should electronic records for https://doi.org/10.30953/bhty.v3.120 http://cms.gov http://cms.gov/research-statistics-data-andhttp://cms.gov/research-statistics-data-andpage 10 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.120 patients be stored? int j med informat. 2012;81(12):821–7. 6. bosworth hb, zullig ll, mendys p, et al. health information technology: meaningful use and next steps to improving electronic facilitation of medication adherence. jmir med informat. 2016;4(1):e9. doi: 10.2196/ medinform.4326 7. opara-martins j, sahandi r, tian f. critical analysis of vendor lock-in and its impact on cloud computing migration: a business perspective. j cloud comput. 2016;5(1):4. 8. chang jl. the dark cloud of convenience: how the hipaa omnibus rules fail to protect electronic personal health information. loy la ent l rev. 2013;34:119. 9. salahuddin ma, al-fuqaha a, guizani m, shuaib k, sallabi f. softwarization of internet of things infrastructure for secure and smart healthcare. [internet]. 2018;arxiv preprint arxiv:1805.11011. [preprint]. 10. anjum a, sporny, m, sill a. blockchain standards for compliance and trust. ieee cloud comput. 2017;4(4):84–90. 11. cms advances interoperability & patient access to health data through new proposals [internet]. cms. center for medicaid & medicare services; 2019 [cited 2019 may 10]. available from: https://www.cms. gov/newsroom/fact-sheets/cms-advancesinteroperability-patient-access-health-datathrough-new-proposals 12. leventhal r.top ten tech trends 2017: blockchain’s promise has healthcare innovators eager. [cited 2017 mar 24]. available from: https://www.healthcareinformatics.com/article/interoperability/ blockchain-s-promise-has-healthcareinnovators-eagerpage 13. sneha s, varshney u. enabling ubiquitous patient monitoring: model, decision protocols, opportunities and challenges. decis support syst. 2009;46(3):606–19. 14. nye j. how blockchain could help boost healthcare security [internet]. 2017 [cited 2018 mar 1]. available from: healthdatamanagement.com 15. dhanireddy s, walker j, reisch l, oster n, delbanco t, elmore jg. the urban underserved: attitudes towards gaining full access to electronic medical records. health expect. 2014;17(5):724–32. 16. engelhardt ma. hitching healthcare to the chain: an introduction to blockchain technology in the healthcare sector. tech innovat manag rev. 2017;7(10):22–34. 17. bender d, sartipi k. hl7 fhir: an agile and restful approach to healthcare information exchange. in ieee 26th international symposium on computerbased medical systems (cbms), 2013 (pp. 326–331). ieee. doi: 10.1109/ cbms.2013.6627810 18. what is hl7 (health level seven international)?—definition from whatis. com. available from: https://searchhealthit. techtarget.com/definition/health-level-7international-hl7 19. chaballout bh, shaw rj, reuter-rice k. the smart healthcare solution. adv precis med. 2017;2:213. doi: 10.18063/ apm.v2i1.213 20. ahuja sp, mani s, zambrano j. a survey of the state of cloud computing in healthcare. netw commun technol. 2012;1(2):12. 21. walonoski j, scanlon r, dowling c, et al. validation and testing of fast healthcare interoperability resources standards compliance: data analysis. jmir med informat. 2018;6(4):e10870. doi: 10.2196/10870 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. see: http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v3.120 https://www.cms.gov/newsroom/fact-sheets/cms-advances-interoperability-patient-access-health-data-through-new-proposals https://www.cms.gov/newsroom/fact-sheets/cms-advances-interoperability-patient-access-health-data-through-new-proposals https://www.cms.gov/newsroom/fact-sheets/cms-advances-interoperability-patient-access-health-data-through-new-proposals https://www.cms.gov/newsroom/fact-sheets/cms-advances-interoperability-patient-access-health-data-through-new-proposals https://www.healthcare-informatics.com/article/interoperability/blockchain-s-promise-has-healthcare-innovators-eagerpage https://www.healthcare-informatics.com/article/interoperability/blockchain-s-promise-has-healthcare-innovators-eagerpage https://www.healthcare-informatics.com/article/interoperability/blockchain-s-promise-has-healthcare-innovators-eagerpage https://www.healthcare-informatics.com/article/interoperability/blockchain-s-promise-has-healthcare-innovators-eagerpage http://healthdatamanagement.com http://whatis.com http://whatis.com https://searchhealthit.techtarget.com/definition/health-level-7-international-hl7 https://searchhealthit.techtarget.com/definition/health-level-7-international-hl7 https://searchhealthit.techtarget.com/definition/health-level-7-international-hl7 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 page 1 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 evaluating blockchain for the governance of the plasma derivatives supply chain: how distributed ledger technology can mitigate plasma supply chain risks teijo peltoniemi,1 jarkko ihalainen2 authors: 1information systems science, department of management and entrepreneurship, turku school of economics, university of turku, fin-20014, finland; 2finnish red cross blood service, kivihaantie 7, fi-00310, helsinki, finland corresponding author: teijo peltoniemi: teijo.peltoniemi@utu.fi keywords: blockchain, blood, distributed ledger, medicine supply chain, pharmaceutical supply chain, plasma, plasma derivative section:research article: use cases/pilots/methodologies objective: this exploratory study examines how distributed ledger technologies could be used within the plasma derivatives supply chain. the plasma derivatives are used increasingly in the pharmaceutical market and the supply chain is global. however, there are significant risks relating to the governance of the supply. the risks include unclear origin of plasma and the propagation of contaminated or poorquality blood to the pharmaceutical production process. from an ethical perspective, the risk is that vulnerable individuals are exploited in the donation process. finally, the plasma supply chain currently depends on only a few exporters of plasma, which presents a supply chain risk. design: the blockchain technology is piloted in other areas of pharmaceutical supply chains and in this study we examine those solutions and conceptualize how a similar solution can be applied to the plasma supply chain. we identify risks within the plasma supply chain and discuss how blockchain-based solutions can mitigate those risks. results: drawing on existing literature within the pharmaceutical blockchain arena, we introduce a solution to verify the origin of plasma. we also model how the blockchain technology can be used to tackle ethical and supply chain risks. conclusions: blockchain can have a role in mitigating plasma supply chain risks. the area is, however, novel and requires more research. https://doi.org/10.30953/bhty.v2.107 mailto:teijo.peltoniemi@utu.fi https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v2.38&domain=blockchainhealthcaretoday.com&date_stamp=2019-04-24 page 2 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 plasma derivatives are defined as pharmaceutical products that contain plasma proteins, which are separated from blood.1 plasma is used to produce treatments for immunodeficiency disorders and hemophilia, for example, and the market is growing rapidly—it is anticipated to be worth us$15.5 billion by 2024.2 shortage of plasma in the market may have significant consequences, including increased mortality rates.3 plasma may be collected as a part of the whole blood donation process or by plasmapheresis where cellular constituents of blood are returned to the donor. the plasma collected as part of the whole blood is often referred to as recovered plasma, whereas the plasmapheresis product is known as apheresis or “source” plasma. in the process, whereby whole blood is donated, three main components are separated: red blood cells, blood platelets and plasma.3 there are specific plasma collection centers in which only the plasma is collected and blood cells are transfused back into the donor.4 in most countries, the collection of blood is reliant on voluntary blood donors, and it is not remunerated for. many of these countries are importers of plasma from those few countries that allow remunerating plasma donors.5 the united states is the biggest exporter of plasma at 1.6% of their total exports.6 the current plasma supply market involves various risks (table 1). one main risk is that relating to infections—in the late 80’s, contaminated blood entered into the supply system causing an hiv epidemic.6 there are also ethical risks; remunerating donors may attract those in a weaker social strata, such as the poor and drug addicts to donate frequently, while the health effects of frequent plasma donation are not clear.5 it is also commonsense that overreliance on one source—like the united states for plasma—exposes the supply chain to significant risks. europe is a significant importer of plasma, and it has been suggested that plasma collection within the region must be increased to mitigate the supply chain risks.7 another issue is the counterfeiting and falsification of medicinal products. by definition, a falsified medicinal product mimics the real product, whereas counterfeit medicinal products are illegal copies of real products, which breach the intellectual property rights.8 it is suggested that the global counterfeit drug market is valued at $200 billion.9 falsification can take forms such as improper storage and poor manufacturing practices.10 plasma derivatives are not immune table 1. plasma derivative supply chain risks. risk description ethical frequent donors are those in a socially weaker position, such as addicts or the homeless. contamination contaminated blood is transmitted to the plasma supply chain. the risk is reportedly low due to rigorous testing and cleansing process; however, falsification can undermine this. falsification falsification can take many forms (e.g., the origin of plasma can be blurred or testing procedures can be forged). supply chain relying on a single sourcing partner introduces a single point of failure in the system. regulatory regulations are getting more stringent and non-compliance can result in fines and sanctions. reputational any of the above risks materializing will lead to reputational consequences and eroded trust in the market. https://doi.org/10.30953/bhty.v2.107 page 3 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 to this, and it is suggested that counterfeiting is increasing in the plasma market.11 the testing of plasma and the viral inactivation process may be expensive.6 this may lead to false testing processes, which means plasma that is not properly tested or processed can be transmitted to the supply chain, exposing significant risks in terms of infection and poor quality of plasma. it is common sense that one of these risks materializing would have a significant reputational impact and the trust in the system would erode rapidly. regulators are paying increased attention to the counterfeiting and falsification problem, and they are launching more stringent measures to curb it. u.s. food and drug administration (fda) introduced drug supply chain security act (dscsa), which defines requirements relating to the tracing of medicinal products across the supply chain.12 dscsa is being rolled out in a phased manner between 2014 and 2023. in addition, the european union is regulating the field heavily. for example, good distribution practice (gdp) places requirements for the tracking of the logistics chain.13 directive 2002/98/ec mandates certain practices in terms of the testing of blood products, as well as tracing of the blood components, all through the donor, and maintaining this information for 30 years.14 other jurisdictions are also hardening regulations. one example is taiwan, which aims at curbing the counterfeiting of medicinal products, including plasma derivatives, with a new requirement for tracking and tracing systems across the supply chain.15 methods this is an exploratory study based on design science research (dsr) approach, whereby our intention is to create a new understanding and theoretical basis for further research and prototyping. hevner et al.16 describe the goal of design science as building artifacts to solve relevant business and organizational problems. our aim is to build a concept, which can be evaluated and implemented in further studies. the artifact in this case is the top-level analysis and the concept; this is aligned with design science guidelines and the definition of artifact.16 we also consider that the requirement for problem relevance is met due to the many risks the plasma supply chain is currently facing. failures in the plasma supply chain will undoubtedly have grave consequences. we base our concept on existing literature. in the first phase of the study, a literature review was conducted through finding sources with targeted searches, whereby we queried academic databases, google scholar and google with key words, such as “blockchain,” “medicine,” “drug,” “blood,” and “plasma.” academic literature in this context is scarce and many of our examples draw from commercial sources. this, however, suits the design approach, as real-life examples and blueprints represent tested and proven artifacts, which are adopted already in the industry. whereas there is plenty of research in the areas of blockchain and cryptocurrency, it has not been extensively studied in the context of health care. we struggled to find any previous blockchain studies in the context of plasma supply chain, which suggests this is an opening in a novel research area. in the second phase of the research, we evaluated existing blockchain models in areas relating to plasma (i.e., pharmaceutical and blood donation supply chains). we adapted these models to the plasma supply chain, building use cases and proposing technical models to solve them. in the further phases potentially following this study, these models need to be refined and documented, and evaluated further—our intention is to open up a discussion and outline further research topics in this important area. https://doi.org/10.30953/bhty.v2.107 page 4 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 paradigmatically, we move somewhere between interpretative and functionalist approaches.17 the objective of dsr, to create solutions for business, makes it essentially a vessel to solve managerial problems rather than generating radical change. however, we feel that we address important ethical questions, such as those relating to human dignity. creating new designs for an information systems (is) artifact is, on the other hand, a subjective task. as complete objectivity cannot be maintained, the researcher’s background, and in this case also interpretation of previous literature and concepts, inevitably affects the design. existing literature plasma derivative and pharmaceutical supply chains pharmaceutical supply chain is a global structure consisting of upstream excipient suppliers, pharmaceutical manufacturers, logistics providers, wholesalers, and downstream distribution channels (i.e., pharmacies and hospitals).18 plasma providers are essentially upstream suppliers in the supply chain. in summary, the plasma supply chain has three particular attributes: (1) whereas the demand is stable or increasing, the supply is irregular, (2) a certain pause is required between donations, and (3) plasma is perishable.3 plasma is often collected as a part of blood donation process, however not always—it is possible to collect only some blood components in the donation process, rather than the full blood.4 in europe, there are more than 1,350 donation centers, and annually 20 million donations are collected.4 whereas national plasma markets are often led by one leading public or private manufacturer, there are a few global plasma pharmaceutical manufacturers feeding into these markets.4 while the plasma collection sites are often provided by a pharmaceutical manufacturer, a significant portion of plasma is collected by independent organizations specializing in whole blood collection, apheresis plasma collection or both. these organizations deliver their plasma to pharmaceutical manufacturers by agreements in which financial terms as well as traceability and other quality parameters are specified. currently, plasma is tracked according to regulatory requirements defining the information on collection sites, as well as the processing and transportation steps. these are documented (e.g.,in the plasma master file [pmf]), which is a template specified by the european medicines agency (ema).19 the pmf is a compilation of all the required scientific data on the quality and safety of human plasma relevant to the medicines, medical devices, and investigational products that use human plasma in their manufacture.19 all individual donations leading to a specific plasma batch have to be identifiable in the pmf. other jurisdictions have corresponding legislations (e.g., the related code of federal regulations [cfr] in the united states). whereas today the majority of the source plasma is being collected in the united states, the largest industrial capacity for plasma fractionation resides in europe, implicating a significant logistics of labile materials across the atlantic. because of infection risks, a quarantine period (typically 60 days) is required before the plasma may enter the fractionation process. during this period, certain specified units may have to be removed from the raw plasma batch if the related donor presents signs of infection or other noncompliant characteristics during follow-up. this interim storage with strict requirements for traceability and temperature control significantly complicates the logistic chain of plasma. https://doi.org/10.30953/bhty.v2.107 page 5 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 blockchain in pharmaceutical supply chain and health care for the purpose of this article, we introduce blockchain only briefly; myriad texts are available for a more extensive introduction, including nakamoto20 seminal study. a more extensive introduction to blockchain is also published.21 broadly speaking, blockchain is a de-centralized database with cryptographic protocols, which maintains a shared ledger, and which is hosted in a network of computers.22 the blockchain consists of immutable blocks that contain information (e.g., on transactions in the ledger). these blocks are then chained together creating the ledger. an important part of the blockchain processing is the verification of a transaction (i.e., the validation of a new block). in this process, the blockchain network validates the transaction based on previous transactions, and once the network reaches consensus, the new block is linked to the chain.23 blockchain is typically conceived as the underlying technology for bitcoin and ethereum, and other cryptocurrencies.23 bitcoin is an example of cryptographic economic system, which is an autonomous and distributed economic system without any centralized governing organization or institution. the use cases for distributed ledgers and blockchain are numerous outside of cryptocurrencies. a blockchain can be private or public.24 an example of a public blockchain is that underlying bitcoin—accessible for everyone through the public internet. a particular user’s coins are protected with private key technique, which is used to prove the ownership.25 private blockchains are, in turn, closed networks restricting the access to only chosen authorized parties. blockchain has also been suggested to the health care arena to bring interoperability to the patient record area,26 and a related example is medrec,27 which is based on smart contracts, a concept introduced by ethereum.28 smart contracts allow creating logic for state transitions associated with blocks.27 in the patient record arena, this is useful when authorizing different parties to view and update patient records. smart contract is one of the key features of blockchain, as self-enforcing rules enable creating autonomous organizations.29 in logistics, this could mean, for example, how a container manages its way to the destination and negotiates optimal routes with shipping service providers. it should be noted that medrec’s blockchain is not used to store health information but rather link together service providers in a secure way.27 the immutable nature of blockchain may expose privacy issues such as data retention. even if the data are in an encrypted form, there is no certainty that the encryption would not be cracked in the future.24 in addition, blockchain has been suggested for the pharmaceutical supply chain area. tseng et al.,15 for example, suggest using gcoin blockchain to verify transaction data on sellers, buyers, and medicine deliveries. the counterfeit drugs would be identified in the supply chain through invalid data and fake drug identifiers. it is specifically the counterfeiting problem blockchain has been suggested to solve, and regulations such as dscsa has spawned various initiatives.10 these include initiatives from supply-chain-oriented organizations, such as the center for supply chain studies, whose dscsca and blockchain virtualpilot aim at exploring how blockchain can be used to share trustworthy transaction information between the participants in a supply chain, and, therefore, meet the dscsca requirements.30 mediledger is a commercial project aiming at the same. mediledger utilizes blockchain to https://doi.org/10.30953/bhty.v2.107 page 6 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 prove the authenticity of transactions.31 the idea is that supply chain trading partners can authenticate the source of the delivery through secure and private means. instead of having a centralized database to track the medicinal product delivery, the data remain distributed across the supply chain participants. mediledger’s role is to provide a “lookup” function to securely verify product identifiers between supply chain partners. mediledger utilizes a private blockchain—the blockchain nodes are hosted across the supply chain and are accessible only to authorized parties. another noteworthy point is that blockchain is not used to store any sensitive product or logistics information but rather to ensure authenticity of the products with minimum data exposed on trading partners. mediledger deploys the so-called zero-knowledge proof, exposing only the proof-of-transaction rather than any related commercial transaction data.32 within eu, the regulatory pressure has generated commercial blockchain initiatives in the pharmaceutical supply chain arena. one example is modum, which tackles eu’s gdp regulation. gdp requires supply chain parties to prove that medicinal products have been shipped in compliance to requirements (e.g., given the product requires a certain storage temperature). it must be proved that this condition holds for the duration of the delivery.13 modum’s solution is based on internet of things (iot) technology and blockchain, whereby the medicinal product delivery is monitored with sensors, with the sensor data collected during the logistics validated with blockchain.33 modum’s solution utilizes smart contracts to model required conditions and test for compliance. for example, if the temperature rises above the limit, a smart contract is triggered, and relevant parties are alerted. blockchain ensures that logs are immutable and tamper-free. according to scott et al.,18 one of the problems with pharmaceutical supply chain is the lack of standardized data models. therefore, a solution such as modum, or mediledger, which addresses a limited area of the supply chain process, could be a viable way to harness blockchain and seek for efficiency gains within the supply chain. a wider solution may be harder to develop because the supply chain participants exchange data in proprietary formats, and a wider, shared solution would require standardized formats. scott et al.18 conclude that none of the solutions has as yet proven that blockchain can scale up to meet the requirements of track-and-track regulations, although the signs are promising. blood donation is also considered a potential arena for blockchain. an example is bloodchain, an “open social blood bank” concept developed by blodon.34 this concept is intended to form an extensive solution, a new kind of market mechanism for blood donation, incorporating blood cryptocurrency. donors are remunerated with blood tokens, which can be used to acquire services in the network—the services have not been elaborated upon. another key element is the donor registry, which holds information about donors and their blood types. the network is based on public and private blockchains and an interfacing back-end system. in concept, public blockchain hosts blood tokens, and the private blockchain is used as a secure indexing mechanism for donors. the donor data are held on a back-end system, and none of the personal sensitive data are exposed in blockchain. figure 1 depicts a blockchain structure providing verification and validation within the pharmaceutical supply chain. blockchain mediates communication between various supply chain parties and enables operating trust-free within an inherently trustless network—this is https://doi.org/10.30953/bhty.v2.107 page 7 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 because it removes the need for a central middleman and decreases the need to create and maintain trust between individual parties in the supply chain.29 blockchain’s role is to guarantee a valid transaction and bring transparency to the process; the status of the transaction is visible to all participants in the blockchain network. blockchain essentially removes the requirement for a trusted middleman, an organization mediating transaction, and acts as a “trust machine.”35 this removes transaction costs drastically. at the same time, only minimum knowledge is needed of different parties, hence a high level of privacy can be maintained. in a market such as plasma and blood donation, a lot of sensitive data are on stake, and privacy is of high importance. blockchain risks it is clear that blockchain is one of the current “hype” technologies, and we should be critical when evaluating where to use it. considering risks, we can identify organizational and technologyrelated issues. from organizational point of view, risks identified by lindman, rossi and tuunainen36 in association with blockchain payment technologies apply here as well: legal, institutional and adoption related. from a legal point of view, contractual positions must be clarified and there can be completely new legal issues relating to this novel setup. institutionally, a key question is how the decentralized organization will work as there is no central authority. also, as we are discussing a platform, users, developers, and other stakeholders need to be attracted to it—it is crucial, however unclear, as to how to succeed in this. from a technology perspective, we need to deal with issues such as maintainability, performance and security.37 given the decentralized nature, change management is more complex than in a centralized, single vendor model. this can lead to chaos with multiple development branches and turf wars. it is also theoretically possible that software, such as smart contracts, is altered without other parties’ consent, which deteriorates the trust in the system.37 performance-wise, public blockchains typically suffer from high latencies, which may undermine some of the use cases.37 finally, security and privacy are highly important in the plasma supply chain field, and we need to ensure that no sensitive donor data or any sensitive business-related data are jeopardized. whereas the plasma blockchain would be a private blockchain, storing transaction-related information to the blockchain would potentially expose it to competitors. it has also been suggested that the so-called tamper-proof monitoring mechanisms during the shipping could actually be intercepted with moderate efforts.38 figure 1—blockchain in the pharmaceutical supply chain. https://doi.org/10.30953/bhty.v2.107 page 8 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 plasma derivatives blockchain in this section, we will discuss some use cases for blockchain in plasma supply chain. we intend not to outline a finalized specification but rather raise ideas for further research and prototyping. it is straightforward to infer that blockchain can be utilized across the plasma supply chain in a similar way it is used with pharmaceutical supply chain. two self-evident use cases are those relating to preventing the falsification of plasma products and the logistics of plasma. first, blockchain could be utilized in the same way mediledger is used to counter falsified medicines. the main components here would be a private blockchain, hosted by plasma supply chain actors, and nodes representing supply chain actors. a plasma delivery would be assigned an identity, stored in blockchain along with the certificate of origin. when the delivery progresses in the supply chain, each step is recorded in the blockchain complemented with other relevant information. supply chain parties can access the blockchain through their nodes and enquire the origin of the delivery and individual donations, while maintaining a high level of privacy. blockchain’s main role here would be to verify logistics transactions and provide immutable ledger, which prevents attempts to tamper any origin information or inject falsified plasma to the supply chain. the benefits would also include the enhanced traceability of plasma: for example, in the case a delivery has to be withdrawn due to a contaminated donation, it would be easy to follow the blockchain trail from the single donation to the batch and its current location. second, as plasma logistics is a major transatlantic industry, whereby the product is highly sensitive in terms of storing conditions, a solution monitoring these conditions, and raising alerts where applicable, is another area blockchain could be utilized in. the benefits would include that blockchain would verify plasma batch’s shipping conditions and provide information if there are any suboptimal conditions across the shipping chain that may cause defects in the plasma. again, this solution would be based on a private blockchain, which is accessed by nodes representing the supply chain parties. logistics transactions would be stored in the blockchain and the solution would incorporate iot sensors monitoring the batch. in addition to these two use cases, we introduce a third one, which is based on the donor’s perspective. the idea here is to provide mechanisms for incentivizing donors and allowing them to take better control of their data and donations. from a governance point of view, this would also allow monitoring individual donations and prevent too frequent donations. generally, this would entail a distributed, decentralized donor registry, whereby a donor’s data, such as health screening, would be located in one authorized donation center and donation events would be recorded in the blockchain. each of the donors would have a single identity, which would work in each of the centers. from an ethics perspective, the end customer of plasma could monitor donations and exercise corporate responsibility: for example, it could be monitored that a donor does not donate too frequently (e.g., by going to different centers). the health effects of too frequent donations are not clear, but there may be an adverse effect. the controlling could be conducted with a smart contract. for example, if a donor’s previous donation is within a certain time period, a new donation is forbidden. another key factor in this model is an incentive model, which could be tied to the system. https://doi.org/10.30953/bhty.v2.107 page 9 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 for example, rather than remunerating with cash, donors could be granted tokens they could use in public health services, public transportation or other public services. this would follow the example of blood tokens described above. in a more far-reaching manner, blockchain could contribute to a diminishing role of the middleman (i.e., the global plasma collection firms, which currently collect plasma and remunerate donors). a completely new kind of plasma supply chain networks could be built whereby donors could obtain an increased control to their donations economically. this is depicted in figure 2, which illustrates how donors donate the plasma across the network of trusted collection centers. the donor is registered in one of the collection centers and identified through blockchain. the donation transaction is verified with blockchain and recorded with the user’s data in a secure data source. a comparison here could be drawn to mydata—a concept that has recently gained traction. it aims at increasing individual’s control of his or her data and transforming it to an economic resource.39 mydata is based on a network of mydata operators, which host functionality for users to consent the use of their data for different parties; in our model, plasma blockchain is essentially carrying out this task. the philosophy is still the same: give more control to donors. the remuneration could be in the form of cash transfer or a crypto currency transfer to the donor’s crypto wallet. based on these solutions, we are now revisiting the plasma supply chain risks in table 2 and evaluating how blockchain could be used to mitigate them. figure 2—plasma derivatives blockchain. in the figure, dashed arrows depict connections to data sources, whereas solid arrows depict concrete material or remuneration flows. https://doi.org/10.30953/bhty.v2.107 page 10 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 conclusions in this study, we have examined how blockchain is used within health and pharmaceutical arenas. we have adapted these learnings in the plasma supply chain through suggesting various use cases. the intention of the paper is to provoke thoughts and outline areas for further research. the plasma supply chain in its current form imposes various risks, and it seems that blockchain-based solutions could be utilized to mitigate these risks. first, current blockchain solutions, which are designed for pharmaceutical supply chain to prevent medicine counterfeiting and monitor logistics chains, could be aligned to the plasma supply chain. this would contribute to preventing falsification of plasma and managing the risk that poor quality or contaminated plasma with uncertain origin is transmitted to the supply chain. maintaining the quality is important from reputational perspectives. furthermore, regulatory risks are increasing as regulations are becoming stricter—having a solid mechanism to confirm the origin will mitigate this risk. second, blockchain and mydata can be utilized to increase donors’ control on their data, donations, and economic benefits. this could entail new incentive models for donations, which, for example, could increase donations and therefore mitigate the supply chain risks. table 2. blockchain solutions for plasma supply chain risks. risk blockchain solution ethical blockchain could be used to implement a decentralized donor registry, which would enable monitoring of donations and prevent too frequent donations. this would discourage unethical patterns, whereby addicts or otherwise distressed individuals would be exploited through frequent donations that would jeopardize their health. furthermore, blockchain could be utilized to enable donors take control of their data and donations and related economic benefits through incentive models and diminished role of the middleman in the market. contamination whereas blockchain-based solutions cannot prevent contaminated blood being donated, they can be used to verify the origin of the plasma and ensure that it comes from a trusted source, with adequate testing and disinfection processes. this solution aims at preventing falsified plasma products being injected for delivery across the supply chain. falsification as with the contamination risk noted above, blockchain can be used to verify the origin of the plasma and ensure it is coming from a trusted source. supply chain new plasma market mechanisms and incentive models could contribute to an increased supply of plasma in regions that are currently heavy importers of plasma. regulatory blockchain-based solutions can be utilized to control and monitor the supply chain, in terms of verifying origin as well as the shipping conditions, which would contribute to the compliance to regulations. reputational improved risk management overall will mitigate the reputational risk, as increased control, monitoring, and ethicalness prevent incidents, which could have reputational impact. https://doi.org/10.30953/bhty.v2.107 page 11 of 13 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.107 utilizing blockchain or a similar solution to build a decentralized donor registry would also enable controlling the frequency of donations. from an ethical perspective, this would allow plasma industry to exercise corporate responsibility and discourage too frequent donations from addicts and other distressed individuals. these, however, are only initial findings and require further study. this study is limited to review of some of the recent developments in the field. the academic research in the arena of plasma blockchain is virtually non-existent and even in the pharmaceutical arena it is scarce, which undermines any attempt to conduct a systematic literature review. our solution is top-level, and an initial attempt to outline how blockchain could be used in the plasma supply chain. we want to initiate the discussion and understand how blockchain technology can mitigate various risks associated with the plasma industry, and the solution outlined here is to be evaluated and taken into a more detailed level. we consider outlining a top-level solution and pointing out multiple areas of further study contribute both theoretically and practically. for the former, we especially welcome research on blockchain and mydata and the related economic system as a novel area in terms of plasma blockchain. the follow-up research can be conducted from multiple perspectives, such as those of design science and technical designs, as well as considering organizational and economic impacts of such a system. for practical contribution, we consider the adaptation of the current blockchain solutions used in the pharmaceutical supply chain to the plasma industry a straightforward next step. funding statement the authors received no specific funding for this work. contributors both authors made substantial contributions in terms of literature review, market risk evaluation, solution design, and the writing of the manuscript. conflicts of interest jarkko ihalainen serves as the medical director at finnish red cross blood service, which supplies plasma to the pharmaceutical industry. teijo peltoniemi declares no potential conflicts of interest. references 1. isbt 128—the global information standard for medical products of human origin. 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with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http:// creativecommons.org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v2.107 https://doi.org/10.1371/journal.pone.0202202 https://doi.org/10.1371/journal.pone.0202202 https://www.wired.com/2017/02/moving-patient-data-messy-blockchain-help/ https://www.wired.com/2017/02/moving-patient-data-messy-blockchain-help/ https://www.wired.com/2017/02/moving-patient-data-messy-blockchain-help/ https://ethereum.org https://static1.squarespace.com/static/563240cae4b056714fc21c26/t/59112735a5790a0b1b695d54/1494296374538/dscsa%2band%2bblockchain%2bstudy%2bcharter%2b-%2bv02.pdf https://static1.squarespace.com/static/563240cae4b056714fc21c26/t/59112735a5790a0b1b695d54/1494296374538/dscsa%2band%2bblockchain%2bstudy%2bcharter%2b-%2bv02.pdf https://static1.squarespace.com/static/563240cae4b056714fc21c26/t/59112735a5790a0b1b695d54/1494296374538/dscsa%2band%2bblockchain%2bstudy%2bcharter%2b-%2bv02.pdf https://static1.squarespace.com/static/563240cae4b056714fc21c26/t/59112735a5790a0b1b695d54/1494296374538/dscsa%2band%2bblockchain%2bstudy%2bcharter%2b-%2bv02.pdf https://static1.squarespace.com/static/563240cae4b056714fc21c26/t/59112735a5790a0b1b695d54/1494296374538/dscsa%2band%2bblockchain%2bstudy%2bcharter%2b-%2bv02.pdf https://uploads-ssl.webflow.com/59f37d0583​1e850001​60b9b4/​5aaadbf​85eb6cd​21e9f0a73b_medi​ledger%202017%20​progress%​20report.pdf https://uploads-ssl.webflow.com/59f37d0583​1e850001​60b9b4/​5aaadbf​85eb6cd​21e9f0a73b_medi​ledger%202017%20​progress%​20report.pdf https://uploads-ssl.webflow.com/59f37d0583​1e850001​60b9b4/​5aaadbf​85eb6cd​21e9f0a73b_medi​ledger%202017%20​progress%​20report.pdf https://uploads-ssl.webflow.com/59f37d0583​1e850001​60b9b4/​5aaadbf​85eb6cd​21e9f0a73b_medi​ledger%202017%20​progress%​20report.pdf https://www.forbes.com/sites/samantharadocchia/2018/04/24/why-zk-snarks-are-crucial-for-blockchain-data-privacy/ https://www.forbes.com/sites/samantharadocchia/2018/04/24/why-zk-snarks-are-crucial-for-blockchain-data-privacy/ https://www.forbes.com/sites/samantharadocchia/2018/04/24/why-zk-snarks-are-crucial-for-blockchain-data-privacy/ https://www.forbes.com/sites/samantharadocchia/2018/04/24/why-zk-snarks-are-crucial-for-blockchain-data-privacy/ https://blodon.com/ https://www.economist.com/leaders/2015/10/31/the-trust-machine https://www.economist.com/leaders/2015/10/31/the-trust-machine https://aaltodoc.aalto.fi/handle/123456789/28858 https://aaltodoc.aalto.fi/handle/123456789/28858 https://www.lvm.fi/documents/20181/859937/mydata-nordic-model/ https://www.lvm.fi/documents/20181/859937/mydata-nordic-model/ https://www.lvm.fi/documents/20181/859937/mydata-nordic-model/ http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 page 1 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 toward application of blockchain for improved health records management and patient care gracie carter,1 denise white,1 anusha nalla,1 hossain shahriar,2 sweta sneha2 affiliations: 1masters of science healthcare management and informatics candidate, kennesaw state university, kennesaw, georgia, u.s.a.;2 faculty coles college of business, kennesaw state university, kennesaw, georgia, u.s.a. corresponding author: gracie carter, kennesaw state university, kennesaw, ga, usa; gcarter7@students. kennesaw.edu keywords: blockchain, emr, ethereum, healthcare information technology, patient care, records management, security, smart contract section: systematic reviews technological advancements have proven to be indispensable for improving patient care, yet they continue to present a host of problems. one of the most pressing concerns is how to improve quality of care while controlling costs. beyond clinical care, one plausible solution is to share patient information freely and efficiently. hospitals and clinics may share data internally, but external information sharing remains an issue. despite the digitization of medical records, there remains a lack of adequate computing infrastructure or unwillingness to share data among providers. care quality often suffers as a result. implementing a type of peer-to-peer distributed digital technology, known as a blockchain, to record and transmit transactional data could be a solution to these concerns. originally, blockchain was developed to record cryptocurrency transactions. however, as blockchain technologies have matured and adopted across dissimilar industries, the feasibility of possible applications of blockchain technology in healthcare is getting more attention. this article explores possible opportunities of adoption of blockchain technology to improve patient data security, privacy, and care while outlining the challenges that practitioners may encounter. this article focuses on application of blockchain technologies to address issues in electronic health records and patient care, including cost savings, security, and fraud prevention. within the last decade, in the united states, there has been a technological revolution within the healthcare community through adoption of electronic health records (ehrs). two bills were passed in 2009 that ushered in a new era in healthcare. the american recovery and reinvestment act (arra) and, within arra, https://doi.org/10.30953/bhty.v2.37 mailto:gcarter7@students.kennesaw.edu mailto:gcarter7@students.kennesaw.edu https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v2.38&domain=blockchainhealthcaretoday.com&date_stamp=2019-06-18 page 2 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 the health information technology for economic and clinical health (hitech) act, were monumental for the advancement of healthcare. hitech set aside nearly $27 billion over the course of 10 years1,2,3 in incentives and set forth the meaningful use criteria, which focused on the optimization of electronic medical records (emrs) through improved care quality, efficiency, and error prevention. in addition, providers were to digitize their medical records and implement emrs through meaningful use or risk of losing their medicaid and medicare reimbursement.1,2 the intention of meaningful use was to improve information exchanges among providers.4 hitech mandated that the office of the national coordinator (onc) for health information technology should create an infrastructure for a nationwide health information exchange that allowed for the flow of health information electronically.5 while emrs streamlined records, the caveat was that these often were contained in a single health system.5 record transmittal to the next point of care was not always guaranteed without a reciprocity agreement in place.6 this breakdown in communication was one of the unforeseen shortcomings of the hitech act. however, the 21st century cures act supported hitech by defining and setting expectations for information sharing. a survey of health information exchange organizations revealed that more than 170 regional health information organizations did not met the criteria for the comprehensive health information exchange.5 although hitech proved effective in motivating the transition to ehrs, and the cures act mandated interoperability, there has been no system mandate nor was there any requirement for data sharing. a survey of health organizations revealed that more than 170 regional health organizations did not meet the criteria for the comprehensive health information exchange.5 this cross-system information exchange is referred to as interoperability. the issue plaguing the healthcare community is how to seamlessly share these data. currently, ehr programs are a profitable business. for example, if a facility that chooses to use epic’s ehr wishes to modify or integrate features into their system, that facility must pay for the service,7,8,9 as epic maintains complete control over customization of their ehr program and charges to modify it.8 every healthcare facility or system has the freedom of choice with ehrs. perhaps, it is the intersection of choice coupled with protectionist policies by ehr developers that make interoperability a difficult task.8,9,10 this could have been avoided through a common data standard in conjunction with the hitech act. as medicine rapidly advances, healthcare information technology (hit) struggles to provide effective and affordable solutions to interoperability. multiple resolutions are available to solve the issue of interoperability, but require cooperation of stakeholders. a functional and stable program that is lightweight, easy to operate, and relatively quick to implement might be met with less resistance and greater adoption by healthcare providers. cloud-based data storage and sharing have promise, but questions of security limit desirability.11,12,13 blockchain technology would bridge the communication gaps between disparate ehr systems.2,9,14 it is more secure than cloud, no single entity controls information, and it is relatively simple to implement.8 blockchain a blockchain is a distributed ledger or an unchangeable (immutable) record of transactions. https://doi.org/10.30953/bhty.v2.37 page 3 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 blockchain is a utility that other programs are built on (see figure 1). while there are many different models of blockchain and distributed ledgers available, this article will focus on bitcoin,15 ethereum,16 and hyperledger.16 these three models were chosen for their maturity level and popularity. bitcoin is the most mature blockchain platform. bitcoin uses a public and permission-less distributed ledger. hashes on blocks on the chain are verified through proof of work, a resourceand time-intensive consensus algorithm. to ensure security, the consensus of 51% of nodes is needed to verify a hash. once a hash is verified, it is added to the chain and the original node is awarded a token. a token can be whatever the chain values, such as cryptocurrency. each block is built on the preceding block and is added to the ledger, thus creating the blockchain. the ethereum chain is next most mature technology. the ethereum chain is public and utilizes ether as a token. ethereum uses a proofof-stake consensus protocol. in proof-of-stake, validators vote on proposed blocks based on the size of their stake in the chain. all ethereum transactions utilize coding scripts to execute specified functions automatically. these are known as smart contracts.16 the advent of smart contracts added immense value and versatility to the ethereum chain. hyperledger is the last model of blockchain. it can be either a permissioned, private, or public blockchain. hyperledger was developed by the linux foundation with input from respected industry leaders such as ibm and intel,16 and aimed at cross-ledger transactions. hyperledger fabric uses its own version of smart contracts known as chain code, but does not use a cryptocurrency as a token.16 hyperledger is becoming the blockchain of choice in healthcare due to versatility, configurable consensus protocol, and multiple offerings to suit consumer needs. interoperability interoperability is the ability of two or more systems to share healthcare data for use by recipients.10 noninteroperability hinders cost figure 1.—blockchain architecture. https://doi.org/10.30953/bhty.v2.37 page 4 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 and quality of patient care. without access to complete records to fully understand a patient’s history, providers waste time and resources. according to the onc for health information technology, a functional system should include the following: a secure network infrastructure, verification of identity and authentication of all participants, and consistent proof of authorization of electronic health records.5,17 however, no ehr was chosen nor a requirement for interoperability was mandated by the us federal government. because of legislation passed at the federal level with little strategic planning, healthcare providers at the local level adopted ehrs ad hoc to meet meaningful use. this lack of foresight coupled with a disinterest in active record management by patients has further complicated interoperability.10 if issues hindering interoperability were resolved, it could result in cost reductions10 through allowing complete data to be accessed, thus improving clinical workflow.18,19 in many care settings, patient records are only obtained through telecommunications by a doctor, nurse, or the patient.15 rather than spending the time looking for records, clinicians often reorder tests to save time!20 according to a survey conducted by klas research, only 6% of clinicians found that information from other organizations was easy to obtain without interrupting their workflow, and fewer than one-third said they could access data from other ehrs easily.15 noninteroperability makes utilizing new technologies difficult moving forward.21 this is a direct result of market competition. often, providers must join multiple networks with differing interfaces due to a lack of integration or cross-system communication.20 this results in increased costs to maintain multiple vendor interfaces. blockchain is a viable alternative method for data sharing that offers a complete source of data. currently, the foundational, structural, and semantic levels of interoperability are fraught with interpretation issues. the foundational level of interoperability is the most basic in which a cache of medical records is sent (pushed) from one provider to another.15 it is assumed that the recipient of the data received it intact and can interpret it correctly.15,18,22 there is no accountability with this method.15 if the interfaces do not communicate through common data models, then the data are not usable. at the structural level, records are pulled between two systems where data structures are defined.15,18,22 in this scenario, there is no audit trail between systems,15,18,22 which allows for duplicate record requests and poses a security risk. at this level, the information is available, but not readily. semantic is the highest level of interoperability. this would be the paramount model of effective communication. providers would be able to view and pull the most accurate data without necessarily having an established relationship.18,22 blockchain users would instantly achieve semantic interoperability. the data would be readily available for viewing using a distributed ledger. healthcare providers could have coherent collaborative records without the cost of reconciling differing ehr interfaces.15 blockchain could improve continuity of care by granting patients greater access to their own health records. active involvement in the management of their healthcare data could encourage a healthier lifestyle through putting the patient at the center of their own care.2,20 it would impart an unprecedented level of knowledge and power to the patient. historically, patients were not able to revoke a provider’s access to their records. theoretically, a record was the possession of that provider permanently.15,18 https://doi.org/10.30953/bhty.v2.37 page 5 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 this resulted in fragmentation of data and increased the chances of security breaches.18 blockchain puts the power of sharing squarely in the hands of the patient using permissions. this allows the patient, not the provider, to decide who had access to their data and when.2,15,18 this concept is known as identity management. identity management places the patient at the center of their care. the primary tenet of patientcentered care is that all services are geared toward the requirements of the patient. today, most healthcare workflows are geared toward the clinician.23 although it sounds elementary, patient-centered care is a no small task. with the use of permissioned blockchains, entire records would not be stored in the chain due to current restrictions on block size. instead, records could be accessed through metadata or through pointers to off-chain secure storage.24 this would improve security and increase efficiency by eliminating the need to hunt for records. efficency is becoming an important cost control measure, as healthcare costs account for 17.9% of the gross national product in the united states, and continues to rise.3 enhanced ehr security with blockchain as ehrs were adopted, providers were suddenly inundated with digital data. to focus more on patients and less on data management, most facilities store patient health information in a cloud rather than in file rooms.25 although they are convenient, not all clouds meet the same standards of security.25 due to a lack of security standards, 43% of security breaches in the united states are related to health data. these attacks originate both internally and externally.25 every human touchpoint is a potential security risk. to ensure patient privacy, health insurance portability and accountability act (hipaa) was established in 1996.25 despite revisions and updates, hipaa cannot fully address the rapid advances in technology. any database or software can be used if it is hipaa compliant despite security risks. blockchain could revolutionize security through decentralization of data across a shared platform, all while maintaining security14 through audit trails of access authorization.26 on a blockchain, records tampering would be immediately evident through mismatch between ledgers. it is believed that blockchain could eliminate ransomware and protect patient privacy2 beyond the current hipaa requirements. permissioned blockchains would allow patients to control access to their records using digital keys (or asymmetric key cryptography that uses public and private key pairs). everyone participating in the blockchain would have a private and a public key that would be cryptographically connected.4 the public key would be available to view by everyone on the chain, but access to any identifying data would be limited to those utilizing the corresponding private key (the patient or authorized provider).4,11,14 patients could set up special permission scenarios using smart contracts.11 security would be strengthened through the transparency of the public ledger as well. without a centralized database, it is difficult to falsify or alter records. this would hold patients and providers accountable and discourage altering records.27 because a blockchain can update in near real time, the data would always be current. this could prevent human error from fewer interactions with data.26,28 building off hashes makes the chain more secure, thus making blockchains resistant to attacks.28 to be successful, hackers would have to attack https://doi.org/10.30953/bhty.v2.37 page 6 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 the target and every subsequent block built off it simultaneously to avoid any detection.2 this is cost prohibitive as well as a major challenge for hackers. cost savings just as the mechanization improved efficiency and reduced cost of production during the industrial revolution, similar cost savings could be available within healthcare. if the healthcare industry took full advantage of all available technological capabilities, trillions of dollars of efficiency could be possible through improving access to data and patients. mckinsey & company conducted a study, which concluded that more than $300 billion could be saved per year by using already available siloed data.29 by leveraging current technology data could be readily accessible, and the doctor–patient connection could be greatly improved through reliable patient histories and improved communication.28,30 because blockchains do not depend on thirdparty goods and services, the savings are immediate and require very little effort on the part of the provider.27 doctors would no longer have to pay for hard copy record storage or file rooms and would require fewer administrative staff. the cost of cloud computing, for example, is heavily reliant on the fees associated with the services. it is also the cost of performance issues, process bottlenecks around data transfers, and the risk of the having to revert to in-house data storage due to the volatility of a third-party business model.22 this uncertainty could impede productivity within a hospital or clinic. blockchain technology would allow healthcare providers to focus on their patients and worry less about availability and organization of data.29 ideally, a combination of cloud-based storage and blockchain technology would allow for stable and fully digitized records management while requiring less human administration. a reduction in staff would not be possible without automation. a revolutionary aspect of blockchain technology is the availability of automation through smart contracts. blockchains are versatile and can be combined with other technologies to allow for previously humanintensive activities like claims adjudication to be done automatically by using smart contracts.30 smart contracts allow a programmable blockchain to blossom. smart contracts are programmed to always function in the same manner and therefore can be trusted to perform exactly as specified.29 this results in fewer transactions being blocked due to disagreements between systems.31 automation allows for fewer administrative staff, thus saving significant amounts of money. practices in the united states spent $70 billion on paperwork alone in 2012, while hospitals spent $74 billion.22 smart contracts translate to less human interaction, which reduces cost of doing business and limits the possibility of data being lost, sold, or stolen. decrease fraud while rapid claims processing is important for revenue, to have an accurate claim the information must accurate. in 2016 alone, medicare lost $30 million to fraud.22 approximately 50% of fraud is from superfluous billing or charging for services never rendered.22 currently, it is difficult to know where and when an order originated due to differing data storage practices between ehrs.32 the basic functionality of blockchain technology makes fraud very difficult. to defraud is a covert operation. blockchain would alleviate the burden of proving fraud through transparent, immutable records. because each transaction is a record of who did what and on what date.32 the transparency of blockchain could also help address drug-seeking behaviors by patients through a record of access and transactions.22 https://doi.org/10.30953/bhty.v2.37 page 7 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 without the ability to view treatment information and histories, healthcare providers have little defense against “doctor shopping” by patients seeking prescriptions for controlled substances.22,33 the proof of concept blockchain nuco (a founding member and director of the enterprise ethereum alliance) is working to combat prescription drug abuse through a system of accountability.20 upon prescribing an opiate, a randomized machine-readable code is issued to that prescription to function as a specific identifier, which is associated with an information block that includes data such as the drug name, quantity, and the fully anonymized identity of the patient as well as when it was prescribed.20 this allows for accountability without stigma. blockchains would make a lasting impact on the opiate crisis through responsible prescribing and accountability. this would help patients who need pain relief without the fear of contributing to abuse. the use of a blockchain could clearly illustrate when, where, and how many opiates were prescribed to the patient or by a physician. accountability could discourage doctor shopping as well as overprescribing. blockchain adoption challenges challenges facing blockchain include interoperability, scalability, vulnerability, security of patient data, data ownership, and information blocking. interoperability questions persist regarding interoperability. for example, will providers be on the same blockchain? will it be public or private? will each patient have their own personalized private blockchain? however, multiple models of digital ledgers create a new interoperability issue. technologies are burgeoning; allowing communication cross-chain and off-chain open communication is still a work in progress.34 even with blockchain, the data structure issue persists between private and public chains.21 heterogeneous structures pose challenges for effective communication and data analysis.21 for any model, data would be cryptographically secure, irrevocable, and easily exchanged.22 currently, the capacity for storage within a blockchain is limited. the technology is not advanced enough to accommodate large files, so storing multitudes of complete medical records is not possible.34 private blockchains could address privacy and security issues, but they bring new risks of vendor lock-in and not utilizing the same open standards for data exchange.17 without an agreed upon coding standard, the same issues that currently plague interoperability would transfer to the blockchain. scalability as previously mentioned, blockchain would have to function as an index due to issues with scalability. “the factors influencing scalability are bit rate, the frequency of monitoring and transmission, and the amount of information transmitted per patient.”35 if healthcare records utilized the bitcoin blockchain, every node on the chain would have a copy of the ledger, which would not only cause issues with latency and block size limits, but also bring questions of security to light.17,36 in addition, throughput is limited in the number of transactions and computing power of a node to increase efficiency of transaction processing.22 the existing infrastructure of blockchain focuses on security and integrity over scalability and plasticity so addressing lag due to volumes of transactions would be difficult.29 also, each node would have a copy of every patient record stored on the chain across the country—a concept that is neither currently feasible nor secure. https://doi.org/10.30953/bhty.v2.37 page 8 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 what is possible is to use permissioned or private blockchains that utilize scalable data repositories called data lakes to store records off chain.17 data lakes would allow more functionality such as interactive queries, text mining, and machine learning.2 data lakes would be maintained and located at point of origin (provider nodes), assuming providers already function on secure networks.24 all records would be cryptographically signed to guarantee authenticity and file integrity.2 vulnerability blockchains are still vulnerable to malfunction and human errors.22 the decentralized autonomous organization (dao) attack proved that blockchain is not unassailable. dao is a system that exists on the ethereum blockchain whose processes can be modified if certain criteria encoded in a smart contract are met.37 the dao was a crowd-funded initiative that allowed investors to vote on and potentially invest in project proposals by startups on the chain.38 quickly, $168 million in ether was amassed from potential investors.37,38 hackers were able to siphon off $50 million in ether simply by exploiting a flaw in the code of the smart contract.37 codes originate from humans and therefore are subject to human error and exploitation by proxy. even beyond the blockchain itself, vulnerabilities exist. in any situation, a reliance on the security of the originating ehr or personal computer would be unavoidable.9,24 blockchains are not antiviral in nature, thus systems are still open to ransomware attacks.34 up to 43% of system attacks were carried out from within the system, while 27% was due to external attacks such as hacking and ransomware.22 the risk for security breaches only grows as the chain ages. without proper maintenance of code used to implement the chain, the chain becomes more vulnerable to attacks.28 furthermore, the encryption used could be exploited and leave any information open for decryption.18,39 to combat this, a third party would have to audit the system; this defeats the purpose of the public blockchain. it would be more accurate to say that blockchains are resistant to tampering rather than completely secure or tamper proof. security of patient data the primary concern with sensitive data is responsible handling and security. on a public blockchain records would be disseminated across every node without cryptographic signatures; data would be available for anyone to access. due to the immutable nature of the blockchain, all data attached to the blockchain would remain open to indefinite public viewing.28 thus, the lack of security due to uncontrolled access not only makes the bitcoin blockchain inappropriate,17 but it would also be noncompliant with hipaa standards for handling protected health information (phi). any public blockchain that stored patient records would have to insure complete privacy. this could be accomplished by using pseudonyms, but this does not guarantee complete anonymity. connections could still be drawn about the identity of the patient from the metadata of another node.9,24 this is a direct violation of hipaa, which expressly states that all phi must be expunged from all medical data before sharing.30 data ownership hipaa states that patients own their data and should have unfettered access to them.29 compliance aside, many systems feel they own the data because it originated with them,40 and any patient ownership is negligible.19 in traditional models, patients are not able to revoke access to https://doi.org/10.30953/bhty.v2.37 page 9 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 their records. thus, data are siloed and hoarded for permanent possession.18 a patient may see many different doctors over their lifetime. if every provider feared to share data due to losing competitive advantage or decreasing patient retention,21 the resulting data fragmentation could leave the data vulnerable to attack.18 hipaa is counterintuitive to interoperability. it states that as little information as possible should be shared to protect patient data while still conveying the methods of care.41 this sum of the refusal or reluctance to share data is known as information blocking. information blocking information blocking has been defined by law to be “any practice that ... is likely to interfere with, prevent or materially discourage access, exchange or use of electronic health information.” proving intent to keep information (information blocking) is difficult to prove. being greedy with data is not limited to systems; 49% of providers surveyed stated that ehrs routinely block information through intentionally limiting interoperability, charging high fees for exchanges, and making third party access difficult or impossible.41 blockchain could combat information blocking by making patients the stewards of their own records,30 but de-soling data may prove difficult. in 2016, the cures act became law, which directed the onc to create a framework of rules and enforcement agencies to prevent information blocking.27 included in the legislation was the ability to wager stiff penalties for instances of information blocking, up to 1 million per violation.27 in a time where data are capital, ehr vendors are routinely opaque in their practices. huge competition exists within the ehr market. ehr vendors gain more revenue through guarding information and charging systems to modify it,30 so much so that the vendors are oftentimes more likely to engage in blocking.41 without a log to understand when and how this happens, it is difficult to prove the information was knowingly blocked. blockchain could change that, but would require cooperation from vendors. discussion and conclusions while many functions of healthcare can be streamlined using technology, fragmentation and data transference have not been resolved in the united states. the combination of resistance, lack of mandates, and outdated hipaa guidelines mean there is very little movement toward interoperability. while blockchain technology could revolutionize or at least improve interoperability, the technology is still not fully developed. issues of scalability and privacy mean that developing platforms or useful applications are difficult. it is evident that within 10 years the blockchain technology will evolve to meet the needs of consumers. just as the internet evolved rapidly and changed communication, blockchain can grow to meet the needs of healthcare. the versatility and simplicity in the concept of blockchain make it a very attractive answer to conundrums faced by businesses today. the promise of a transparent ledger seems so simple in concept, but in application it becomes difficult. without government intervention to force interoperability it will take consumer demand to place pressure on the healthcare establishment to improve information sharing. blockchain technology in its current form will not be able to live up to its potential for record sharing. it will take time, focus, and more use cases to bring blockchain to the forefront of healthcare. blockchain might be used successfully in healthcare globally before https://doi.org/10.30953/bhty.v2.37 page 10 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 it is able to be used on a large scale in the united states. the very ideas of governance and profitability that have driven the united states to be a superpower may be the biggest hindrance to the adoption and interoperability. without hipaa reform and/or improved data security surrounding privacy, governance will continue to stifle potential adoption. similarly, without the cooperation of vendors who may risk the potential loss of profits, records may never make it to the blockchain to be shared. healthcare is a business, thus it strives to maintain a profit. blockchain could help improve profits and improve the quality of care for patients through decreasing fraud and increasing savings for doctors. as technology advances, more solutions to current problems will arise with blockchain. currently, the three models of blockchain discussed in this article have very distinct advantages and disadvantages depending upon the situation. for example, the bitcoin chain works well when privacy is not a concern. the ethereum chain would be ideal for situations where automation is required. it is still not well suited for data storage and throughput may be an issue for large volumes of claims though. hyperledger’s private design is more suited for phi, but current limitations such as data silos and legal constraints make it difficult to implement. the future of blockchain in healthcare is undeniably bright. as demand drives innovation, the current limitations can be overcome. to resolve current issues facing healthcare, it will take reconciliation between technologies such as blockchain, cloud storage, and emerging technologies to deliver higher quality patientcentered care. currently, advances are being made in open sourced distributed ledgers that utilize different consensus protocols to verify transactions and improve verification time. these solutions allow for greater throughput and are more scalable; however, a more detailed explanation is beyond the scope of this article. the blockchain and distributed ledger transformation of healthcare are close, but nothing is achievable overnight and not without the effort of the healthcare community as a whole. financial statement: the authors received no financial support to conduct this study. contributors: gracie carter was the principal author and conducted the research. denise white was the contributing author and did the research. anusha nalla conducted the research. hossain shahriar performed editorial and advisory roles for this study. sweta sneha played an advisory role in the conduct of this research. conflicts of interest: the authors declare no competing interests with respect to research, authorship, and/or publication of this article. references 1. kim d, kagel jh, tayal n, bose-brill s, lai a. the effects of doctor-patient portal use on health care utilization rates and cost savings. ssrn electronic j. 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(39) 2017. doi:10.2139/ssrn.2775261 2. linn la, koo mb. “blockchain for health data and its potential use in health it and health care related research.” proc onc/ nist use of blockchain for healthcare and research workshop. gaithersburg, md, usa: onc/nist; 2016:1–10. 3. nhe -fact-sheet [internet]. cms.gov centers for medicare & medicaid services. 2019. available from: https://www. cms.gov/research-statistics-data-andsystems/statistics-trends-and-reports/ nationalhealthexpenddata/nhe-factsheet.html (accessed march 10, 2018). 4. blumenthal d, tavenner m. the “meaningful use” regulation for electronic health records. n engl j med. 2010;363(6):501–4. https://doi.org/10.30953/bhty.v2.37 https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html page 11 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.37 5. lapsia v, lamb k, yasnoff wa. where should electronic records for patients be stored?. int j med informat. 2012;81(12):821–7. 6. bosworth hb, zullig ll, mendys p, et al. health information technology: meaningful use and next steps to improving electronic facilitation of medication adherence. jmir med informat. 2016;4(1):e9. doi:10.2196/ medinform.4326 7. koppel r, lehmann cu. implications of an emerging ehr monoculture for hospitals and healthcare systems. jamia. 2014;22(2):465–71. 8. azaria a, ekblaw a, vieira t, lippman a. medrec: using blockchain for medical data access and permission management. in 2016 2nd international conference on open & big data (obd) [internet] 2016 aug 22 (pp. 25–30). ieee. 9. studeny j, coustasse a. personal health records: is rapid adoption hindering interoperability? 2019 winter. perspect health inf manag. 2019;16(winter):1a. 10. ahuja sp, mani s, zambrano j. a survey of the state of cloud computing in healthcare. netw commun technol. 2012;dec 1;1(2):12. 11. kuo amh. opportunities and challenges of cloud computing to improve health care services. jmir. 2011;13(3):e67. 12. zhang r, liu l. security models and requirements for healthcare application clouds. in 3rd international conference on cloud computing (cloud).) 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[internet] arxiv preprint arxiv:1706.03700.2017 june 5. 37. adler-milstein j, pfeifer e. information blocking: is it occurring and what policy strategies can address it? the milbank quarterly. 2017 mar;95(1):117–35. 38. christidis k, devetsikiotis m. blockchains and smart contracts for the internet of things. ieee access. 2016;4:2292–303. 39. mehar mi, shier cl, giambattista a, et al. understanding a revolutionary and flawed grand experiment in blockchain: the dao attack. jcit. 2019 jan 1;21(1):19–32. 40. ivan d. moving toward a blockchain-based method for the secure storage of patient records. inonc/nist use of blockchain for healthcare and research workshop. gaithersburg, md, united states: onc/ nist; 2016. 41. chang jl. the dark cloud of convenience: how the hipaa omnibus rules fail to protect electronic personal health information. loy. la ent. l. rev. 2013;34:119. copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. see: http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v2.37 https://www.sciencedirect.com/science/article/pii/s0167923608002030 https://www.sciencedirect.com/science/article/pii/s0167923608002030 https://www.sciencedirect.com/science/article/pii/s0167923608002030 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0  page 1 of 7 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.8 applications of blockchain within healthcare liam bell,1 william j buchanan,1 jonathan cameron,2 owen lo1 authors 1the cyber academy, edinburgh napier university, edinburgh, uk, 2nhs nss, edinburgh, uk. corresponding author prof. william (bill) buchanan, obe, phd, fbcs, fiet, ceng, bsc (hons), cisco regional instructor, professor of computing, edinburgh napier university, 10 colinton road, edinburgh. eh10 5dt. b.buchanan@napier.ac.uk. keywords: asset tracking, blockchain, drug tracking, ethereum, healthcare, internet of things, iot section: review there are several areas of healthcare and well-being that could be enhanced using blockchain technologies. these include device tracking, clinical trials, pharmaceutical tracing, and health insurance. within device tracking, hospitals can trace their asset within a blockchain infrastructure, including through the complete lifecycle of a device. the information gathered can then be used to improve patient safety and provide after-market analysis to improve efficiency savings. this paper outlines recent work within the areas of pharmaceutical traceability, data sharing, clinical trials, and device tracking. lockchain is a distributed ledger technology, with the potential to disrupt many industries. indeed, with $1.4 billion invested in blockchain related startups in 2016;1 and with this projected to grow further in 2017 the hype cycle shows no sign of slowing. at the time of writing, a lot of the attention around blockchain has centered on cryptocurrency, predominantly bitcoin2 and the effect that blockchain is predicted to have on the financial sector. this effect led to the established consultancy accenture labeling it as one of three technologies that will change the financial services world.3 despite this focus on financial services, there are many other areas prime for disruption including voting,4 real estate,5 supply chain management,6 and of course, healthcare.7 healthcare is prime for disruption as it has a variety of problems in the industry that blockchain can solve through its immutability, fraud prevention and capability to share data between organizations without requiring trust. current issues within modern healthcare industry are listed in table 1. a key challenge, as identified by frost & sullivan,8 is to tag medical equipment with a usable id and in integrating trust in device identification and tracking. when a device, such as an infusion pump is shown to have malfunctioned, the tracking of the device can reveal the source of the problem and prevent unnecessary repurchasing in the case of lost devices. a strong trust infrastructure based around the identification of medical devices is likely to reduce these threats. the report estimates that only 20% to 30% of medical devices are connected within hospitals due to security and privacy issues. within the pharmaceutical industry, blockchain can help overcome the increasing risks around counterfeit and unapproved drugs. as with device tracking, it is possible to define smart contracts for drugs and then identify pill containers, with integrated gps and chain-of-custody logging. b page 2 of 7 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.8 table 1. current issues within the modern healthcare industry issue activity healthcare data interchange data must pass between healthcare providers to necessary third parties, insurers, and patients while meeting data protection regulation in the healthcare sector. nationwide interoperability having a single standard for patient data exchange allows for ease of passing data between healthcare providers, which legacy systems often do not provide. medical device tracking medical device tracking from supply chain to decommissioning allows for swift retrieval of devices, prevention of unnecessary repurchasing, and fraud analytics. drug tracking as with medical devices, blockchain offers the capability to track the chain of custody from supply chain to patients, allowing for frictionless recalls and prevention of counterfeit drugs. within clinical trials, blockchain can be used to overcome the problems of fraudulent results and removal of data, which do not support the researcher’s bias or funding source’s intention. this will enforce integrity in clinical trials. in addition, it allows an immutable log to be kept of trial subject consent. it is thought that the pharmaceutical industry could benefit with savings of $200 billion in defining a chain-ofcustody in the supply chain.8 with health insurance, many areas could benefit from a trusted record of events around the patient pathway, including improved reporting around incidents and automating underwriting activities. contracts could also be clearly defined and then enacted, such as automated payments for parts of the patient pathway. current implementations blockchain implementation a blockchain is ostensibly a chain of blocks secured by cryptographic techniques. one of the features most appealing about this to many industries is its immutability. data added to the blockchain cannot be modified; and therefore, a consensus-based, verifiable and correct ledger of data can be created. this makes blockchain particularly suited to tasks where data integrity is of utmost importance, a practical example of this immutability is provchain,9 an architecture built on the blockchain for providing chain-of-custody for data objects on the cloud. there are multiple implementations of the blockchain, these include bitcoin,2 the cryptocurrency token implemented on the blockchain; ethereum,10 the blockchain-based ledger that features a turing-complete virtual machine allowing execution of code on the blockchain using smart contracts; and jp morgan’s juno,11 an ethereum fork using a different consensus method known as quorum as well as many other blockchain implementations. consensus methods is one of the ways in which blockchain implementations differ. bitcoin, for example, uses a proof-of-work algorithm known as hashcash,12 and is an intentionally expensive algorithm originally designed prevent denial-of-service attacks. all bitcoin miners validate the blockchain by performing this proofof-work algorithm as a vote towards the consensus on the blockchain. ethereum also uses a proof-of-work algorithm, ethash,13 as addressed in the ethereum yellow paper,10 based on the dagger-hashimoto algorithm.14 ethereum will, however, move to a proofof-stake algorithm, casper, in the future. this is to address the extreme energy requirements of proof-ofwork, made clear by both ethereum and bitcoin using similar amounts of electricity to all of ireland.15 ethereum also differs from bitcoin due to its implementation of smart contracts. smart contracts are pieces of code executed on every node on the blockchain. they are self-executing contracts in which the agreement is enforced on all members of the blockchain. they set out the benefits, obligations and penalties associated with behavior related to the contract like the way a traditional contract works. as they mimic traditional paper contracts and laws, they can be used, for example, to model the hipaa healthcare personal page 3 of 7 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.8 health information (phi) workflow to meet regulatory and audit requirements, such as is implemented within patientory.16 a different type of blockchain trust model is also emerging, that of trust in the consortium. microsoft has recently developed a framework named coco which allows for blockchain agnostic consortiums to be created.17 these consortium models rely on a pre-defined group of trusted parties. in healthcare this may be multiple hospitals or in the uk, nhs trusts, as well as medical device manufacturers and third parties. by executing smart-contracts solely on these trusted partner’s hardware, consensus is generated without the need for miners. this has resulted in a significantly greater performance with a coco-optimized blockchain instance able to process 1600 transactions per second, a performance improvement bringing blockchain much closer to the big payment processors.18 coco is also agnostic of trusted execution environments allowing the use of intel software guard extensions, windows virtual secure mode and arm trustzone among others. clinical trials clinical trials and the management of trial subject consent are an area where blockchain has the potential to increase transparency, auditability and accountability of medical practitioners and researchers.19 by maintaining an immutable log of patient consent, regulators can easily monitor clinical trial standards, ensuring that the trial meets the country’s informed consent regulations. this is particularly important as fabricated informed consent forms have been among the most common type of clinical fraud.20 this includes editing records and falsifying patient consent, which indicates that a level of trial subject authentication would be required to prevent this. this system could be further augmented; as proposed by benchoufi, porcher and ravaud,21 implementing a smart contract system that prevents clinicians from using patient data until a key has been released at the end of an auditable smart contract process requiring consent at each stage of the trial. this process should also allow for the revocation of patient consent. implementing a blockchain clinical trial consent log gives clinical trial subjects ownership of their own data while providing an audit trail for clinical staff, researchers, and regulators. data sharing data sharing represents one of the greatest opportunities for improvements in healthcare but also one of the largest privacy challenges. indeed, powles and hodson22 address the need to provide transparency on how patient data are shared with 3rd parties using the case study of the deepmind collaboration with royal free london nhs foundation trust. the lack of patient consent in the previous case study is addressed as one of the most significant issues, despite the positive effect google’s product suite had on patient diagnosis and treatment. on the opposite end of the spectrum, ibm and the american sleep apnea association23 are using ibm’s watson supercomputer to study sleep apnea in thousands of americans at home, with clear and informed consent from patients to solve major challenges in healthcare. it is important to have a nationwide standard for interoperability in it services in healthcare. this has been underlined in a uk nhs white paper written by wachter and hafter24 in a comparison with the u.s. healthcare system, which underlined the importance of interoperability in allowing access to patient electronic health records (ehrs) across multiple hospitals, as many trusts have different systems built by different vendors for accessing these records. and as shown in the u.s., this creates issues for doctors and nurses. social care and mental health are reported as having suffered as in both the u.s. and uk it is still mostly paper-based. medical device asset tracking is a current problem within the healthcare sector. a report by harland simon25 on a project justifying rfid tagging in nhs cambridgeshire asserted that 15% of a hospital’s assets are lost every year, representing a significant cost in repurchasing items the hospital already has. additionally, according to a report published by ge healthcare [26] nurses spend an average of 21 minutes per shift searching for devices and beds that have been misplaced with many hospitals, according to the study, defining any device below $5000 consumable and to be repurchased if they can’t be found representing page 4 of 7 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.8 significant cost in the sector. by adopting radiofrequency identification (rfid) standards for medical device tracking, nhs forth valley in scotland, according to another study published by harland simon27 were able to save almost £400,000 in cost avoidance due to not having to purchase significant medical devices that would have otherwise been lost to the system. drug tracking is a different problem entirely to medical device tracking, as the main concern is counterfeit drugs. a study by the who28 identified that as many as 10% of the pharmaceutical supply in the u.s. is counterfeit. the food and drug administration (fda) in the us recently endorsed the use of rfid to track pharmaceuticals from the supply chain to the patient. this allows the complete chain of custody to be monitored, ensuring that the hospitals have bought the pharmaceuticals from a legitimate source. pfizer29 was the first pharmaceutical company to adopt rfid “e-pedigree” where patients and doctors could trust the source and capabilities of their flagship medicine, viagra, as they identified that it was among their most counterfeited drugs. this system has allowed wholesalers and pharmacists to verify the authenticity of their viagra using a simple rfid scanner with the cost to pfizer being low due to the use of lowcost passive rfid tags & barcodes. patient records blockchain has significant power to disrupt healthcare and put data in the hands of patients. one particularly interesting move towards this is medrec,30 which gives patients and doctors an immutable log of healthcare records. it takes a different approach to incentivization for miners by giving access to anonymized healthcare data in exchange for sustaining the network. medrec uses smart contracts to map patient-provider relationships (pprs) where the contract shows a list of references detailing the relationships between nodes on the blockchain. it also puts pprs in the hands of the patient, giving them the ability to accept, reject, or modify relationships with healthcare providers such as hospitals, insurers, and clinics. blockchain offers an opportunity for interoperability in healthcare systems as having a decentralized ledger of accepted fact in medical records where all healthcare providers have access to this ledger. this means that though the user-interfaces may be different, their central ledger will be identical across all providers. a challenge that exists relates to the current state of health records across providers, which contain significant amounts of the same information under different identifiers that may not be linked. this creates duplication and as the blockchain grows, the performance degrades and this level of replication of data across records would require deduplication to maintain a reasonably performant system with unique, anonymized identifiers to identify patients across all services. this is a business challenge in and of itself of adopting a blockchain health record, it is important to note that health records would not start from zero as they would have to replace the existing system which creates challenges. additionally, the sheer volume of data generated in healthcare environments, which is only set to increase further, with kaiser permanente believed to have between 26 and 44 petabytes of data on its 9 million members from ehrs and other medical data in 2014.31 the volume of data logged and referenced to will only add to this scalability problem. drug tracking drug tracking on the blockchain is another opportunity as it leverages the immutability of the blockchain to develop tracking and chain of custody from manufacturer to patient. chronicled is a technology startup company developing their product, discover,32 which creates a chain of custody model showing where the drug was manufactured, where it has been since, and when it has been disbursed to patients, leveraging the immutability of the blockchain to prevent fraud and theft of pharmaceuticals. this allows healthcare providers to meet current healthcare standards regarding pharmaceutical supply security, again with an emphasis on interoperability between healthcare providers. hyperledger,33 the open-source blockchain working group, recently launched the counterfeit medicines project34 focusing on the problem of counterfeit pharmaceuticals. using blockchain, the origins of counterfeit medicines can be traced and removed from the supply chain. page 5 of 7 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.8 the advantage of blockchain in drug tracking over traditional means is the decentralization of trust and authority inherent in the principles behind the technology, where central authorities can be bribed or faked it is much harder to bribe a consensus of those on the blockchain. for this reason, the current industry standard in pharmaceutical tracking, epedigree,29 which currently uses rfid and a traditional database is moving towards their own blockchain solution. if pharmaceuticals can be modified and tracked using blockchain’s inherent anti-tampering capabilities at the point of manufacture, counterfeit pharmaceuticals can be completely removed from participating supply chains. device tracking medical device tracking is another opportunity for the blockchain in disrupting healthcare from manufacturer to decommissioning. the monetary savings created by asset tracking are clear, nhs east kent hospital found that as a result of a case study by harland simon35 in which they implemented active rfid trackers on their high waste equipment they found 98 infusion pumps they had no idea they still owned across three sites. at a cost of $1,500 each they saved $147,000 due to this single case study. the use of the blockchain along with this technology offers the opportunity for an immutable ledger, which shows not only where the device is but where it has been in its lifecycle, as well as which manufacturer, reseller, and the serial number are associated with the device, aiding regulatory compliance. this capability was addressed by deloitte7 in a white paper as one of the potential game-changers for blockchain in the healthcare sector. indeed, an ibm study36 showed that 60% of government stakeholders in healthcare believe that medical device integration and asset management are the greatest areas for disruption in the sector. a blockchain approach offers several benefits over traditional location tracking products. the most obvious of which is the immutability and tamper-proof qualities of the blockchain. this prevents a malicious user from changing the location history of a device or deleting it from record. this is particularly important factor considering that medical device theft and shrinkage has become a multi-million-dollar problem both in the us and the uk.37,38 as well as traditional theft, this immutability also prevents devices being lost and reordered, which incurs a significant cost both in terms of the care provided and in actual equipment costs. this system should not add significant additional workload to a nurse, porter or support worker, as it would only require a tap on the device with the mobile phone or scanner and then entering of the current location of the device. while the application of blockchain within the internet of things (iot) is fast developing, huh39 defines a way for devices to intercommunicate through an ethereum blockchain and use an rsa public key system. in this way, a device stores their public key on the blockchain and the associated private key is stored on the device. conclusions proofs of concept have been developed which bring blockchain technologies into the healthcare industry however there are still many barriers to adoption. one of the most significant barriers will be the inherent resistance of the healthcare industry to change its current practices,40 especially relating to organizational, structural, technological, and human factors. funding statement there was no public or private funding provided in the creation of this work. conflict of interest: none copyright holder: edinburgh napier university, 10 colinton road, edinburgh. eh10 5dt references 1. courbe j. financial services technology 2020 and beyond: embracing disruption. pwc. 2016. p. 48. 2. nakamoto s. bitcoin: a peer-to-peer electronic cash system. www.bitcoin.org, 2008. p. 9. 3. accenture. three technologies that are changing the financial services game. how visionaries are exploiting digital technology to reshape the future of financial services. 2016. page 6 of 7 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.8 4. boucher p. what if blockchain technology revolutionised voting? european parliament. 2016. 5. deloitte. blockchain in commercial real estate. the future is here! 2017. 6. korpela k, hallikas j, dahlberg t. digital supply chain transformation toward blockchain integration. proc. 50th hawaii int. conf. syst. sci. 2017. p. 4182–91. 7. krawiec r, barr d, j. killmeyer j, et al. blockchain: opportunities for healthcare. nist work. blockchain healthc., august, pp. 1–12, 2016. 8. f. & sullivan, “why healthcare industry should care about blockchain?,” 2017. [online]. available: https://ww2.frost.com/files/8615/0227/3370/why_h ealthcare_industry_should_care_about_blockchain _edited_version.pdf. [accessed: 28-may-2018]. 9. liang x, shetty s, tosh d, et al. provchain: a blockchain-based data provenance architecture in cloud environment with enhanced privacy and availability. 17th ieee/acm int. symp. clust. cloud grid comput. 2017. pp. 468–77. 10. wood g, ethereum: a secure decentralised generalised transaction ledger, ethereum proj. yellow pap. 2014. pp. 1–32. 11. martino w, popejoy s, schroeder b, and kent l, juno distributed cryptoledger. cib new product development. 2016. 12. back a. hashcash a denial of service countermeasure. adam beck. 2002, http://www.hashcash.org/papers/hashcash.pdf. 13. ethereum. ethash. 2017. 14. ethereum. dagger hashimoto algorithm. 2015. 15. o’dwyer kj, malone d. bitcoin mining and its energy footprint. 25th iet irish signals syst. conf. 2014 2014 china-irel. int. conf. inf. communities technol. (issc 2014/ciict 2014), 2014. pp. 280– 5. 16. mcfarlane c, beer m, brown j, prendergas n. patientory: a healthcare peer-to-peer emr storage network v1.0. 2017. 17. microsoft. the coco framework. 2017. 18. microsoft. microsoft announces the coco framework to improve performance, confidentiality and governance characteristics of enterprise blockchain networks. 2017. 19. roma p, quarre f, israel a, et al. blockchain: an enabler for life sciences and healthcare blockchain: an enabler for life sciences healthcare. delotte. 2016. pp. 1–16. 20. barrett j. fraud and misconduct in clinical research. princ pract pharm med. second ed. 2007;4(2):631–41. 21. benchoufi m, porcher r, ravaud p. blockchain protocols in clinical trials: transparency and traceability of consent. f1000research. 2017;6:66. 22. powles j, hodson h. google deepmind and healthcare in an age of algorithms. health technol (berl). 2017;7(4):351-67. 23. fraser h, kwon y, neuer m. the future of connected health devices. life sci. 2011. p. 20. 24. wachter k. making it work: harnessing the power of health information technology to improve care in england. report of the national advisory group on health information technology in england. 2016. 25. booth c, jarritt p, dawkins s. the role of rfid in managing mobile medical devices. harl. simon, 2013. pp. 1–28. 26. horblyuk r, kaneta k, mcmillen gl, et al. white paper: out of control. how clinical asset proliferation and low utilization are draining healthcare budgets. ge healthc. 2012. 27. hynd b, physics m, valley nhsf. healthcare science final report on test of change passive rfid tracking of mobile medical devices within forth valley royal hospital. 2017;1:1–8. 28. who, international medical products anticounterfeiting taskforce (impact). 2010. 29. bacheldor b. pfizer prepares for viagra e-pedigree trial. rfid j. 2007. 30. azaria a, ekblaw a, vieira t, lippman a. medrec: using blockchain for medical data access and permission management. proc. 2016 2nd int. conf. open big data, obd 2016. pp. 25–30. 31. raghupathi, r raghupathi v. big data analytics in healthcare: promise and potential. heal. inf. sci. syst. 2014;2(1):3. 32. chronicled, chronicled supply chain compliance. 2017. 33. hyperledger. hyperledger. 2017. . 34. taylor p. applying blockchain technology to medicine traceability. 2016. page 7 of 7 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.8 35. simon h. case study tracking medical devices with rfid. 2014. 36. ibm institute for business value. healthcare rallies for blockchains. 2016. 37. adt services. preventing shrinkage and equipment losses in hospitals, 2011. 38. auditor general for scotland. equipped to care managing medical equipment in the nhs in scotland. 2001. 39. huh s, cho s, ki s., managing iot devices using blockchain platform. 2017, 19th international conference on advanced communication technology (icact), 2017. pp. 464–467. 40. sligo j, gauld r, roberts v, villa l. a literature review for large-scale health information system project planning, implementation and evaluation. int. j. med. inform. 2017;97:86–97. supplementary data: none this is an open access article distributed in accordance with the creative commons attribution noncommercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work noncommercially, and license their derivative works on different terms, provided the original work is properly cited as first published in blockchain in healthcare today™, and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 page 1 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 dmms: a decentralized blockchain ledger for the management of medication histories patrick li,1 scott d. nelson,2 bradley a. malin,2,3,4 you chen2 authors: 1computer science, saratoga high school, saratoga, ca, usa; 2department of biomedical informatics, vanderbilt university medical center, nashville, tn, usa; 3department of biostatistics, school of medicine, vanderbilt university medical center, nashville, tn, usa; 4department of electrical engineering & computer science, school of engineering, vanderbilt university, nashville, tn, usa corresponding author: you chen, phd, department of biomedical informatics, vanderbilt university medical center, 2525 west end ave, suite 1475, nashville, tn 37203, usa. email: you.chen@vumc.org section: use cases/pilots/methodologies background: access to accurate and complete medication histories across healthcare institutions enables effective patient care. histories across healthcare institutions currently rely on centralized systems for sharing medication data. however, there is a lack of efficient mechanisms to ensure that medication histories transferred from one institution to another are accurate, secure, and trustworthy. methods: in this article, we introduce a decentralized medication management system (dmms) that leverages the advantages of blockchain to manage medication histories. dmms is realized as a decentralized network under the hyperledger fabric framework. based on the network, we designed an architecture, within which each prescriber can create prescriptions for each patient and perform queries about historical prescriptions accordingly. finally, we analyzed the advantages of dmms over centralized systems in terms of accuracy, security, trustworthiness, and privacy. results: we developed a proof of concept to showcase dmms. in this system, a prescriber prescribes medications for a patient and then encrypts the prescriptions via the patient’s public keys. patients can query their own prescriptions from different histories across healthcare institutions and then decrypt the prescriptions via their private keys. at the same time, a prescriber can query a patient’s prescription records across healthcare institutions after approval from the patient. analytic results show that dmms can improve security, trustworthiness, and privacy in medication history sharing and exchanging https://doi.org/10.30953/bhty.v2.38 mailto:you.chen@vumc.org page 2 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 across healthcare institutions. in addition, we discuss the potential for dmms in e-prescribing markets. conclusions: this study shows that a distributed secure ledger can enable reliable, interoperable, and accurate medication history sharing. keywords: blockchain ledger, decentralized, hyperledger fabric framework, medication histories it is important to provide prescribers with the most recent knowledge about the set of medications that a patient is taking, has taken in the past, and those he/she may be allergic to. such knowledge influences  the decision-making process during a patient encounter, as medications can interfere with laboratory tests, as well as informs which, if any, additional medications to prescribe. yet, medication errors are common, which is unfortunate because incomplete medication lists increase the risk of medication errors and adverse drug effects (ades).1 notably, 3% of errors correspond to the omission of life-saving medications and 41% of errors have the potential to cause moderate to severe harm.2 more than 770,000 injuries or deaths occur annually due to ades, which often arise as a result of errors in medication lists.3–8 moreover, when medication histories are incomplete at the time of patient admission, they can be the source of complications, including longer hospital stays.9,10 electronic health records (ehrs) are composed of private, highly sensitive information, including medication records. however, the process of storing, transferring, and sharing data (e.g., historical prescriptions) across multiple entities is complicated and inconvenient. large healthcare systems often rely on third-party systems (e.g., epic, cerner, and surescripts) to handle the sharing and transfer of medication records. these systems rely on private centralized databases, which is problematic because they are susceptible to costly intrusions, such as ransomware attacks or data leaks. in 2016, the health records of 16.6 million americans were leaked, a number that increased by 26% in 2017.11 the main drawback of centralized systems is their reliance on a central server to perform all network functions, which allows for a single point of failure. the moment the central server is compromised, the entire network is suspended and becomes susceptible to alterations. beyond security risks, private centralized systems are also extraordinarily costly, often requiring hundreds of millions of dollars to install, integrate, and manage.12 even with the assistance of ehr vendors, medication lists are often outdated between encounters with the healthcare system, especially when a patient sees multiple care providers. since most healthcare institutions (hi) maintain an internal copy of a patient’s ehr, if a care provider from hospital a makes changes to the patient’s medication list, hospital b is unlikely to be aware of these changes. the situation increases in complexity as patients work with an increasing number of care providers and pick up their prescriptions from different pharmacies. care providers usually obtain information about a patient’s medication history through an initial interview,13 but this can be unreliable due to human error and patients being poor historians (i.e., not knowing the medications they take) or having low health literacy. given the deficiencies of the status quo, we  believe that a cross-institution, private, immutable ledger of personal patient medication records has the potential to address the aforementioned issues, especially in an https://doi.org/10.30953/bhty.v2.38 page 3 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 environment where no single person takes responsibility for maintaining an accurate medication list. this network can be realized with decentralized systems because a distributed ledger can solve roadblocks with medication record transfer and sharing. moreover, the security protocols of a distributed ledger are more reliable than centralized systems. thus, we propose a decentralized medication management system (dmms), which leverages blockchain technology to improve security, trustworthiness, and privacy in the sharing and transfer of medication histories. we will be focusing on us histories across healthcare institutions. this article is organized into two primary sections. first, we depict the dmms architecture and illustrate its advantages in terms of security, trustworthiness, and privacy. second, we present a dmms prototype, investigate its potential effect on e-prescriptions, and expand on the future implications of our framework. decentralized network a decentralized network, also known as a peer-to-peer platform, is a distributed architecture that allocates its resources to a host of nodes, functioning together to make decisions on behalf of the network. in a decentralized system, no centralized authority acts as an agent for all communications; instead, each node is free to perform peer-to-peer functions known as transactions (figure 1). blockchain is a decentralized architecture that features a distributed immutable ledger in which all transactions are recorded. more generally, blockchain is a secure and decentralized datastore of ordered records, including events, called blocks.14 each block consists of a group of transactions and a hash that binds it to the preceding block. these blocks are added to the blockchain through a majority node verification  process known as a consensus protocol. the specific consensus protocol varies depending on  the network. once verified, the ledger is updated  across all nodes in the network. the blockchain datastore is controlled by peers in the network and is independent of any third-party central management systems. although blockchain originated as the foundational technology that powers cryptocurrencies, such as bitcoin and ethereum,14 it has since expanded to various figure 1—network structures of centralized system (left) and decentralized system (right). page 4 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 other use cases, such as decentralized apps (dapps), blockchain voting, contract management, and identity management.15 public and private blockchains there are several distinctions between public and private blockchains, also known as permissioned and permissionless blockchain implementations. public networks are accessible to every internet user and do not discriminate based on credentials, location, or affiliation.16 all participants are either pseudonymous or anonymous, and may add new blocks to the distributed ledger.17 any machine (with internet access and the required storage criteria) can become a node in the network, perform transactions, or view the public ledger. an example of a public blockchain is a cryptocurrency, such as bitcoin and ethereum. by contrast, private networks are centered around permissioned access of individual nodes. users need credentials to connect to the network and these credentials are often provided for by a node already inside the network. users are often labeled and identified and have restricted levels  of access in the network based on its identification. there is a main identity provider  that manages access control within the network, including control over users’ ability to participate in the consensus protocol, query ledger data, perform certain transactions, and add new nodes. an example of a private blockchain is hyperledger (https://www.hyperledger.org/). recent movements in pharmaceutical applications of blockchain there has been some discussion on the application of blockchain technology being in the pharmaceutical sector. according to the world health organization estimates, fake drug sales were worth as much as $75 billion in 2010, which makes the monitoring of drug transportation paths vital.18given this situation, lo and colleagues underscore the advantages of managing drug supply chains using a decentralized ledger technology.18 they describe how blockchain implementations can provide visibility of vulnerabilities in the drug supply chain, where points of drug ownership transfer between pharmaceutical manufacturers, while other stakeholders have little visibility for tracking the authenticity of products. engelhardt and colleagues19 also suggested leveraging blockchain technologies to prevent prescription fraud by using it as a monitoring program to flag suspicious purchasing patterns and alert  prescribers and pharmacists. finally, accenture recently released a white paper citing cold chain management as a target for blockchain implementations and how a decentralized ledger system could help with the complicated and expensive process of “temperature-controlled, refrigeration, production and distribution of products.”20 while the literature depicts opportunities for blockchain in the pharmaceutical sector, there are several deficiencies. first, there has been little focus on  secure and trustworthy exchanges of personalized medication histories across healthcare institutions. second, most of the work to date provided conceptual designs but did not provide the proof of concept. creating a business network via hyperledger fabric hyperledger fabric is one of the hyperledger projects founded by the linux foundation in 2015. it is an open-source blockchain framework tailored toward enterprise implementations. the fabric development community currently has approximately 35 organizations and 200 developers.21 a key advantage of fabric is its modular architecture, which allows flexibility  in a broad range of implementations including banking, finance, insurance, and healthcare.  its features provide support for pluggable https://doi.org/10.30953/bhty.v2.38 https://www.hyperledger.org/ page 5 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 consensus protocols, general-purpose programing languages for writing smart-contracts, and independence from native cryptocurrencies that require competitive mining. fabric’s smart contracts are implemented through chaincode, which is the business logic for transaction processes in the network. chaincode is highly programmable and can be structured for a variety of functions in the network. fabric uses the practical byzantine fault-tolerant consensus protocol,22 which has several advantages over other protocols. first, nodes in a practical byzantine fault-tolerant system communicate with each other to agree on the state of the system at a specific time, such as  verifying a new block. second, it does not require a large amount of computational power to solve an intensive hashing algorithm, which is required in most public blockchain implementations and accomplished through proof of work. proof of work has been widely used in cryptocurrencies (e.g., bitcoin and ethereum) to confirm transactions submitted to the network.  in these cryptocurrency networks, machines participate in a process called cryptomining in order to generate the computational power needed for proof of work. by contrast, the practical byzantine fault-tolerant system consensus protocol does not rely on costly mining. most notable for our implementation, fabric is permissioned, meaning every user is vetted and therefore trusted in the network. in addition, permissioned chains use small consensus groups, resulting in a more efficient  process of confirming the state of a new block.  hyperledger composer is an open development toolset for creating blockchain applications. the composer supports the fabric infrastructure and runtime and allows for quicker business network modeling, application implementation, and integration with existing systems.23 the business network definition is exported as  an archive (.bna file) when it is ready to be  deployed. the definition of the network is made  up of four main files: model, script, access  control, and query (figure 2). the model file is responsible for outlining the structure of the network. it has three main components: assets, participants, and transactions. assets are often the variables stored in the network. participants are the nodes of the network and can interact with assets and other participants through transactions. transactions are the functions of the network and are invoked to update the network (e.g., transferring an asset). the script file defines the various transaction  functions in the network. it is written in javascript and handles the transaction logic, including which types of participants interact (different categories of participants have different levels of access in the network) and which types of assets are transferred. the access control file delineates the specific scopes of access users have in the  figure 2—a framework to create a network via hyperledger composer. https://doi.org/10.30953/bhty.v2.38 page 6 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 business network. this is where the role of the user (participant) is described, determining their role in creating, reading, updating, or deleting elements of the network. the query file defines the structure and function  of queries from this network. queries can be defined to extrapolate transactions from the  historian, which is a ledger of all past transactions in the network. once the network is defined, it can be exported  as an archive, downloaded, and run on another machine. a network card is used to connect to the network. network cards can take the form of a participant type or an admin (figure 2). participant cards generally have a more controlled scope of access in the network, while the admin can perform more high-clearance functions such as adding new participants or deleting participants. this card type defines the node that  uses the card to connect to the network and, thus, outlines what kind of role the node plays. building components of the business network three main components of our hyperledger fabric network are shown in figure 3. the network will be structured into participants, assets, and transactions. the network involves three parts: (1) prescribers who prescribe medications; (2) patients who receive the prescription; and (3) the details of the medication, such as the name, ingredients, and specific instructions for use. because prescribers  need to send the prescriptions to the network, prescribers will act as the nodes/participants in the network. patients will not have permission to document prescriptions, but will have the right to provision access to their medication history to the prescriber/institutions of their choice. as such, patients and medication prescriptions will be assets and transactions in the network, respectively. dmms architecture figure 4 provides a high-level architectural depiction of dmms. each healthcare institutions has an administrator, a local network consisting of participants’ accounts (prescribers), and asset accounts (patients). the global network is composed of all of the local networks, each of which is a part of the distributed ledger. a set of randomized nodes within each local network contains a copy of blockchain, which consists of all medication prescriptions ordered by participants within the global network. an healthcare institutions administrator (admin node) can create new participants and assets, which need to be validated by other institutional figure 3—three components of the hyperledger fabric-based business network. https://doi.org/10.30953/bhty.v2.38 page 7 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 administrators. this ensures that the network cannot be tampered with even if an admin node is compromised. the newly created participants and assets will be updated across all nodes in the global network. each institution admin holds an administrative card to connect to the network. there may be multiple admin nodes in a single healthcare institution. each prescriber will have a hospital computer associated with them, each of which will function as a participant node in the network. machines will have a pre-installed client with a prescriber-type network card. clients and network cards will be supplied by the institution admin. prescribers will interact with the client interface, and the client will handle all the network connections and verification. the client holds a  pair of keys to perform secure communications between prescribers and patients. patients with an account issued by the admin will be provided a pair of private and public keys. the public key will be invoked to encrypt medication prescriptions, while the private key will be applied to decrypt the medication prescriptions they receive after querying the network’s ledger. creating transactions in a medication prescription process, the patient will provide their public key to the prescriber to encrypt the prescription transaction. in a realworld implementation, patients will not need to memorize their public keys, but instead they use an online health portal to communicate their public key to the prescriber’s machine or, alternatively, let the prescriber scan a quick response (qr) code (which will point to the public key). when a prescriber prescribes a medication, the prescriber client will assemble a transaction that consists of the prescriber id, patient id, details of medications (e.g., generic and brand names), their ingredients, instructions figure 4—architecture of decentralized ledger system applied across several healthcare institutions. page 8 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 for how to use the medications (e.g. dosages and times per day), and the time the transaction was created. after the transaction is assembled, the client will use the patient’s public key to encrypt the transaction and submit it to the ledger network. the network will package the transaction along with other new transactions to form a block and randomly select a set of nodes in the network to confirm the newly formed block. the workflow  for submitting a medication prescription to the ledger network is depicted in figure 5. conducting queries in a query process, the prescriber will query all records under a patient using the patient id. the records will include those submitted by the prescriber and all other healthcare providers who interacted with the patient. all the returned transactions will be decrypted with the patient’s private key and the client will show the decrypted patient records. the patient’s private key should not be seen by or known to anyone else. to protect a patient’s private key when transferred from one device to another, the following steps are taken. when a prescriber needs to query a patient’s medication history, the prescriber sends an invitation to the patient through patient client. the patient must approve this invitation through their patient client (e.g., online health portal). the patient client generates a random salt value, combines it with their private key, and then encrypts the string with the prescriber’s public key using an asymmetric encryption algorithm (e.g., rivest, shamir, and adelman encryption (rsa)). next, the encrypted string is transferred to the prescriber client where it is decrypted with the prescriber’s private key. the decrypted private key is then parsed from the string and used to decrypt the queried transactions. the process for a prescriber to query all medical prescriptions associated with a patient is depicted in figure 6. an important distinction between the patient and prescriber clients constitutes the user interfaces. patient clients will be built into their patient portals, while prescriber clients will be individual applications on their healthcare institution machines. interpretations of dmms in security, accessibility, and privacy a decentralized ledger system has several advantages over a traditional third-party centralized system: security, accessibility, and privacy. security in some breaches, intruders hold medical centers functionally hostage until a ransom is paid. these figure 5—workflow for the submission of a medication prescription to the ledger network. page 9 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 are known as ransomware attacks and are growing in prevalence.24 our decentralized network is resilient against ransomware and similar security breaches. this is because the decentralized network topology does not have a single point of failure or central repository for intruders to infiltrate. in the case where intruders infiltrate a  single node in the network, they will be unable to read the ledger due to it being encrypted. furthermore, the use of a private blockchain– hyperledger fabric adds an additional level of security because nodes must be approved from the institutional administrator, making it more difficult for invaders to create malicious nodes  in a majority attack (e.g., where pool operators obtain control over the network once it injects over 50% of malicious nodes).25 to learn health information, an attacker would need to bypass the initial institution firewall, infiltrate a majority  of peer nodes, and decrypt industry standard encryption. accessibility the dmms should allow for easier access to medication records. patients often have the burden of recalling their past medication history by memory or carry around physical copies of their medication records. using the decentralized ledger system, prescribers can easily update medication histories through a simple client user interface. when patients visit different medical institutions, prescribers can query medication histories easily with the approval of the patient. the decentralized network eliminates the need to cooperate with a set of privatized central repositories. privacy barrows and colleagues explain that increasing reliability on centralized health data repositories leads to greater privacy risks.26 decentralized networks reduce the need for trust between the prescribers, patients, and the network. integration with existing electronic health record systems there are several ways by which our system can be integrated into existing ehr infrastructure. for medication histories across healthcare institutions that use third-party ehr vendors (e.g., epic or cerner), fast health interoperability resource (fhir) application programming interface can be used as bridges between the blockchain and ehr clients. specifically, we  recommend a data inquiry application program interface to pull health data from the patient’s ehr and serve as initial entries for their medication histories. future prescribed medications could then be pushed from our client to the ehr using the same fhir application programming interface. a javascript object notation (json) format with key:value pairs could be used to define dynamic  number of fields. fields can include, but are  figure 6—a workflow for a prescriber to read all medical prescriptions associated with a patient. page 10 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 not limited to, the product code, product code terminology (e.g., rxnorm), strength, dose form, quantity, quantity units, patient directions (sig), start and stop dates, number of refills, structured  sig fields (e.g., dose, route, frequency, and pro re  nata (prn) indication), product status (e.g., active, on hold, completed, or canceled), and adherence. on the prescriber’s end, they only need to submit updates once because the client will handle the rest of the communications between the existing ehr and our dmms network. this system would use standardized terminologies for coding drugs, including an identifier for which terminology was  used. for example, care providers from some histories across healthcare institutions can use rxnorm ids and names, others may use anatomical therapeutic chemical (atc) classification system  codes, while some others may use national drug code (ndc) codes. regardless of which terminology is used, each transaction will contain original categories (rxnorm, atc, or ndc). patient and prescriber acceptance a potential barrier to this system is the requirement of patient health portals. certain patient demographics (e.g., elderly or the mentally handicapped) may not have access to smartphones or may find it difficult to work with  such systems. this may limit their ability to communicate with hospital clients for medication prescriptions. we believe that this problem can be addressed through in-hospital machines, where patients can log in to their account (possibly through the assistance of care providers) and manage their patient portals from there. another solution could be for hospitals to include a qr code on the printed (or electronic) copy of the medication list at the end of an encounter for each patient. in doing so, a patient could browse and check his/her medication list. the qr code could store a patient’s public key, which can be used by prescribers to access the medication list associated with the patient. if a patient has no internet access at home or smartphone to manage security and privacy setting of their account, they can use hospital computers to manage them onsite. in addition, for patients who are unable to manage or use their health portals, current features in health portals such as allowing access to delegates or surrogates of patients address this issue. one of the barriers to prescribers adopting our client is the potential increase in their workload. however, as alluded to earlier, the integration of fhir application programming interface will alleviate this problem because it will allow for the simultaneous updating of the blockchain system and the ehr. our prescriber client will be preinstalled in computers in each participating healthcare institutions, which will allow access to our client anywhere within an healthcare institutions. still, it should be recognized that prescribers may need additional training when they first use our client. the training and learning  process may add additional financial costs for  healthcare institutions; however, there are many benefits to healthcare institutions for using our  system. beyond providing access to an accurate medication history, our system can also offer specific decision support to prescribers to  avoid adverse drug reactions and replicated prescriptions. this could justify the time and cost spent on learning how to use and manage our client. proof of concept as a proof of concept, we engineered a system to showcase the preliminary parameters involved in the prescriber client prescription process and a working decentralized network. the software is available as a github project.27 as noted, hyperledger fabric serves as the network, while hyperledger composer handles page 11 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 simpler network modeling and integration with client applications. we used an angular application hosted on a local machine to simulate the prescriber machine client and connected our client to the network through a restful application programming interface. the hyperledger network was booted using command line. figure 7 shows the prescriber client prescription and query process. the user interface is rudimentary and is only used to show the basic inputs needed by the prescribers for a prescription. the demo highlights the simplicity of this system—in just two steps, a prescriber can prescribe medication and then query the record regardless of institution affiliation.  implications of dmms in e-prescribing the rate of e-prescribing increased drastically when the medicare improvements for patients and providers act began offering financial incentives  for institutions to use e-prescribing in 2008. by 2014, 70% of prescribers were e-prescribing on the surescripts network,28 and now e-prescribing is almost ubiquitous. the current e-prescription process relies on centralized third-party systems to connect pharmaceutical companies with healthcare institutions. figure 8 shows the workflow of a typical e-prescription process.  to begin, prescribers write prescriptions in their institution’s ehr system. the prescription is then e-prescribed to a pharmacy via an e-prescribing central network such as the surescripts network, and the patient picks up their medication at the pharmacy. the pharmacy dispensing and pharmacy benefit manager (pbm)  claims data are then sent back to the e-prescribing central entity (e.g., surescripts) by participating pharmacies, payers, and pbms; figure 7—screenshots from the prescriber client view in our demo system. page 12 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 however, not all pharmacies, payers, or pbms participate in dispense data sharing.29 reliance on centralized networks allows for a single point of failure in the e-prescription process, imbues high costs on histories across healthcare institutions, and generally complicates the e-prescription process. for instance, surescripts needs to coordinate between histories across healthcare institutions and pharmacies to ensure that their information can be exchanged with each other. when the number of involved institutions increases, the complexity to deal with such coordination will exponentially increase, and subsequently the costs will rise. another limitation is that healthcare institutions and pharmacies must place trust in surescripts that the e-prescriptions are accurate; however, errors such as the misidentification of patients are not uncommon  when using such services.30 in addition, pharmacy claims data (and commonly pharmacy dispense data by surescripts) do not include dose, route, frequency, or additional patient instructions.31 as shown in figure 9, a decentralized solution can greatly simplify the e-prescription process by eliminating the middleman and allowing safe communications directly between histories across healthcare institutions and pharmacies. our ledger solution can be expanded to include e-prescriptions by adding an e-prescription transaction function and installing a client in all participating pharmacies. our solution ensures that e-prescriptions can be accessed and exchanged among participants (e.g., prescribers and pharmacists) without relying on any central servers. at the same time, participants do not need to rely on a central system to build trust between each other. each e-prescription managed in our blockchain is inherently trustworthy. finally, patients can control which participants have accesses to their e-prescriptions because each e-prescription transaction would be encrypted using a patient’s public key such that only the patient’s private key could be used to decrypt the transaction. it is notable that each transaction submitted to the network and confirmed by peers cannot be altered  by anyone else, which would likely reduce e-prescription errors (e.g., misidentification and  content alterations) during the transactions. additionally, this would greatly facilitate prescription transfers between pharmacies and reporting to controlled substance prescription drug monitoring programs (pdmps). our solution can also overcome problems of trustworthiness and security raised by electronic prescriptions for figure 8—e-prescription process with centralized system. page 13 of 15 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.38 controlled substances (epcs), which was legalized by the us drug enforcement administration (dea) and aims to address the problem of prescription drug abuse in the united states.32 it is not uncommon for prescriptions to be forged or stolen under the current epcs technology, which heavily relies on a centralized network to require authentication of prescribers, and audit epcs.33 as mentioned earlier, e-prescription errors such as misidentification of patients and prescribers are  hard to prevent in centralized systems; thus, our decentralized ledger solution provides a great opportunity to satisfy epcs’s requirements of trustworthiness and security. conclusions in this article, we introduced a framework for a decentralized ledger system to medication history management to be more robust, secure, and convenient. we further detailed the architecture of our framework, showcased a demonstration network as a proof of concept, and analyzed ways for its implementation in the e-prescription industry. we highlighted the current difficulties in  transferring medication data and the unsecure nature of centralized networks and explained how our solution can address these issues. this framework is notable but has room for expansion in several ways. first, our system can be integrated with existing ade research to provide decision support for prescribers based on the history of a patient’s medications. second, in addition to tracking prescriptions, our network can also be used as a standardized system for patient reported results. this can be achieved by increasing the functions of a patient’s client to allow them to submit transactions (e.g., ades or medication consumption confirmations). funding statement this research was funded by the vanderbilt academic support program. contributors patrick li performed literature review, decentralized ledger solution design, architecture design, proof of concept development, evaluation and interpretation of the proof of the concept, and writing of the manuscript. scott d. nelson performed literature review, architecture design, evaluation and interpretation of the proof of the concept, and revising of the manuscript. bradley a. malin performed literature review, architecture design, evaluation and interpretation of the proof of the concept, and revising of the manuscript. you chen performed literature review, decentralized ledger solution design, architecture design, proof of concept development, evaluation and interpretation of the proof of the concept, and writing of the manuscript. conflicts of interests the authors declare no competing interests with respect to research, authorship and/or publication of this article. figure 9—e-prescription process with decentralized system. page 14 of 15 blockchain in healthcare todaytm issn 2573-8240 online 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electronic prescriptions for controlled substances. federal register 2010. available from: http://www. federalregister. gov/articles/2010/03/31/2010-6687/ electronicprescriptions-for-controlledsubstances accessed august 10, 2018 33. hufstader gm, yang y, vaidya v, wilkins tl. adoption of electronic prescribing for controlled substances among providers and pharmacies. am j manag care. 2014 nov;20(11 spec no. 17):sp541–6. this work is licensed under a creative commons attribution-noncommercial 4.0 international license. authors retain copyright of their work, with first  publication rights granted to blockchain in healthcare today (bhty). https://www.pharmaceutical-technology.com/digital-disruption/blockchain/blockchain-pharma-opportunities-supply-chain/ https://www.pharmaceutical-technology.com/digital-disruption/blockchain/blockchain-pharma-opportunities-supply-chain/ https://www.pharmaceutical-technology.com/digital-disruption/blockchain/blockchain-pharma-opportunities-supply-chain/ 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http://login.proxy.library.vanderbilt.edu/login?url=https://search.proquest.com/docview/1792354247?accountid=14816 http://login.proxy.library.vanderbilt.edu/login?url=https://search.proquest.com/docview/1792354247?accountid=14816 http://login.proxy.library.vanderbilt.edu/login?url=https://search.proquest.com/docview/1792354247?accountid=14816 http://referaat.cs.utwente.nl/conference/22/paper/7473/preventingthe-51-attack-a-stochastic-analysis-of-two-phase-proof-of-work-in-bitcoin.pdf http://referaat.cs.utwente.nl/conference/22/paper/7473/preventingthe-51-attack-a-stochastic-analysis-of-two-phase-proof-of-work-in-bitcoin.pdf http://referaat.cs.utwente.nl/conference/22/paper/7473/preventingthe-51-attack-a-stochastic-analysis-of-two-phase-proof-of-work-in-bitcoin.pdf http://referaat.cs.utwente.nl/conference/22/paper/7473/preventingthe-51-attack-a-stochastic-analysis-of-two-phase-proof-of-work-in-bitcoin.pdf https://doi.org/10.1136/jamia.1996.96236282 https://doi.org/10.1136/jamia.1996.96236282 https://github.com/lipatrick/decentralized-med-network https://github.com/lipatrick/decentralized-med-network https://doi.org/10.2146/ajhp140082 https://doi.org/10.2146/ajhp140082 http://www. federalregister.gov/articles/2010/03/31/2010-6687/electronicprescriptions-for-controlled-substances http://www. federalregister.gov/articles/2010/03/31/2010-6687/electronicprescriptions-for-controlled-substances http://www. federalregister.gov/articles/2010/03/31/2010-6687/electronicprescriptions-for-controlled-substances http://www. federalregister.gov/articles/2010/03/31/2010-6687/electronicprescriptions-for-controlled-substances 1 (page number not for citation purpose) blockchain in healthcare today 2022. © 2022 this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0. citation: blockchain in healthcare today 2022, 5: 184 http://dx.doi.org/10.30953/bhty.v5.184 *correspondence: mohamed a. maher. email: m.maher2@outlook.cardiffmet.ac.uk; mohamed.maher@balsamee.co.uk from sharing to selling: challenges and opportunities of establishing a digital health data marketplace using blockchain technologies mohamed a. maher, mba1,2* and imtiaz a. khan, phd1 1cardiff school of technologies, cardiff metropolitan university, cardiff, united kingdom; 2balsamee ltd, cardiff, united kingdom abstract during the covid-19 pandemic, we witnessed how sharing of biological and biomedical data facilitated researchers, medical practitioners, and policymakers to tackle the pandemic on a global scale. despite the growing use of electronic health records (ehrs) by medical practitioners and wearable digital gadgets by individuals, 80% of health and medical data remain unused, adding little value to the work of researchers and medical practitioners. legislative constraints related to health data sharing, centralized siloed design of traditional data management systems, and most importantly, lack of incentivization models are thought to be the underpinning bottlenecks for sharing health data. with the advent of the general data protection regulation (gdpr) of the european union (eu) and the development of technologies like blockchain and distributed ledger technologies (dlts), it is now possible to create a new paradigm of data sharing by changing the incentivization model from current authoritative or altruistic form to a shared economic model where financial incentivization will be the main driver for data sharing. this can be achieved by setting up a digital health data marketplace (dhdm). here, we review papers that proposed technical models or implemented frameworks that use blockchain-like technologies for health data. we seek to understand and compare different technical challenges associated with implementing and optimizing the dhdm operation outlined in these articles. we also examine legal limitations in the context of the eu and other countries such as the usa to accommodate any compliance requirement for such a marketplace. last but not least, we review papers that investigated the short-, medium-, and long-term socioeconomic impact of such a marketplace on a wide range of stakeholders. keywords: blockchain; ehr; marketplace; gdpr; general data protection regulation; incentives section: narrative/systematic reviews/meta-analysis received: 22 september 2021; revised: 27 september 2021; accepted: 30 december 2021; published: 28 january 2022 since the early introduction of digital health, information and communications technology (ict) developers have been under the impression that the use of digital technology in handling and processing health information will generate a wealth of data that can transform the healthcare industry. feeding these data to machine learning algorithms will enable us to de-skill medical practice and propose new diagnostics and treatment processes. projects like deepmind health is a recent example where an artificial intelligence company based in london and owned by alphabet developed mobile app streams (1) that use london royal free hospital’s ehr data to predict and identify patients about to get acute kidney injury—a condition linked to 100,000 deaths in the uk every year (2). in addition, portals like patientslikeme (3) through which patients with similar medical conditions and/or concerns can share information regarding their treatments and have demonstrable benefits to their users (4, 5). as these projects started to show the value of data sharing, the general data protection regulation (gdpr) (6) of the european union (eu), introduced in 2018, has fundamentally changed the paradigm of sharing and using patient data by repositioning ownership and stewardship http://creativecommons.org/licenses/by-nc/4.0 http://dx.doi.org/10.30953/bhty.v5.184 mailto:m.maher2@outlook.cardiffmet.ac.uk mailto:mohamed.maher@balsamee.co.uk https://orcid.org/0000-0003-2130-7717 https://orcid.org/0000-0001-7624-1319 citation: blockchain in healthcare today 2022, 5: 184 http://dx.doi.org/10.30953/bhty.v5.1842 (page number not for citation purpose) mohamed a. maher and imtiaz a. khan of medical data from service provider to the patient, along with bestowing the following rights as listed in table 1. had deepmind not initiated before the introduction of gdpr or patientslikeme were within the european economic area (eea) jurisdiction, neither of the aforementioned projects could even be initiated. it is now more evident that the data have a single owner and gatekeeper regarding access or distribution rights. even anonymously, only the patient has the right to grant access to their data and allow using the information in a way that will benefit everybody. ironically, the patients have not yet recognized the value of this new ownership right and how to manage its stewardship. this is because there are no direct wins created to serve them, as well as indirect wins, which are not clear enough to overcome the legitimate worries of data leakage and subsequent privacy exposure. therefore, the target is to find a novel approach and tools that balance individual privacy and transparent data access for research purposes (7). this paradigm shift introduced by gdpr to the service providers also brought opportunities for individuals to monetize their medical data by selling data to medical researchers or tech companies. similar to airbnb, which enabled individuals to monetize their spare accommodations, patients with their new ownership and other rights bestowed by gdpr, can now monetize their personal health data through a “digital health data marketplace” (dhdm) using a shared economic model. figure  1 illustrates the dhdm operational workflow. however, with the current centralized data management framework where ehrs are fragmented across different service providers where regulations differ across organizations and geographical jurisdictions, access and stewardship will be challenging to manage, especially the microtransactions in such a distributed environment. in this context, blockchain and associated smart contracts have been considered a game-changing technology, with an inbuilt distributed architecture and the ability to administer information governance in a decentralized manner for diverse types of transaction-based digital services. the rest of the paper is organized as follows: first, we reviewed 36 papers concerning the technical challenges, focusing on three areas such as data ownership and access control, data interoperability, and data security. then, we reviewed seven papers concerning legal issues, and finally, nine papers related to socioeconomic issues. technical challenges data ownership and access control who owns the healthcare data? a question that has always been intriguing and creating debates from a technical, legal, and philosophical perspective. kostkova et al. have argued the subject and questioned if health data should be open for research use in an attempt to balance between individuals’ privacy and the value of data-backed research on the life of millions around the world (7). the authors concluded by urging policymakers at the international level to develop a regulatory framework that safeguards personal information, limits business exploitations, yet enables the use of data for research and commercial use. a significant amount of work has been done in proposing blockchain-based access control tools and models, table 1. general data protection regulation of the eu fundamentally changed the paradigm of sharing and using patient data by repositioning ownership and stewardship of medical data from service providers to the patient, along with bestowing the following rights (6) general data protection regulation defined gdpr art 12 and 13 the right to be informed • individuals’ right to be informed about the collection and any usage of their data. gdpr art 15 the right of access: • individuals have the right to access their data. gdpr art 16 the right to rectification: • individuals’ right to have not correct personal data amended or completed if it was incomplete. gdpr art 17 the right to erasure: • individuals’ right to have personal data erased, that is, usually called “the right to be forgotten.” gdrp art 18 the right to restrict processing: • individuals’ right to request the restriction or suppression of their data. gdpr art 20 the right to data portability • allows individuals to carry, move, copy, or send their data easily from one it system to another safely and securely, without affecting its usability. gdpr art 21 the right to object: • individuals have the right to object to the processing of their data in certain circumstances. gdpr art 22 rights concerning automated decision-making and profiling: • rules to protect individuals, if an organization is carrying out automated decision-making that has significant effects on them http://dx.doi.org/10.30953/bhty.v5.184 citation: blockchain in healthcare today 2022, 5: 184 http://dx.doi.org/10.30953/bhty.v5.184 3 (page number not for citation purpose) from sharing to selling which places the patient in the driver seat and gives them all the control to grant and deny access to part or the whole of their ehr. most of the studies were trying to give such control to the patient for clinical and operational benefits. however, the same proposed models can also benefit data control from an asset management perspective. bahar et al. offer a specific literature review covering most of the work in this domain. in this survey, the authors mainly covered and discussed work covering digital identity records management and the self-sovereignty of ehr data (8). they produced a list of social data solutions implemented using ethereum smart contracts and compared them based on incentive, data market, enabling phr, decentralized asset tracking, web/mobile application, iot, ehr compatibility/interoperability, and proof-of-concept implementation. as discussed above, nguyen presented a model for secure access control for ehr stored in an interplanatery file system (ipfs) configuration (9). the model proposed an ehr manager based on a smart contract to administer access and data transactions requests while providing the patient with a blockchain interface-based mobile app to exercise their rights to control access. although this model might be a fascinating solution tackling the decentralization nature of ehr and yet providing secure and traceable tools for data access control and entry audit trail, all the practical trials of this configuration showed very high latency in operation. rifi et al. addressed the same concept of using blockchain to administer transactions in ehr(10). the authors handled the control of data acquired from personal medical devices and sensors and proposed a dapp ehealth blockchain to control read/write in the ehr database being cloud or ipfs. nortey et al. offer another example of a blockchain framework proposal for ehr privacy management by giving patients control over who accesses their ehrs (11). the authors introduced a channeling mechanism that ensures that patients authorize entities within the distributed network to access their information. others take a different approach, in which the authors aimed to build a consent model for data sharing (12, 13). they proposed a transactions workflow and created ethereum smart contracts based on luce (14). a blockchain solution for monitoring data license accountability and compliancee and followed on it by building a consent-based architectural model then implemented it on d1namo data sets (15) of 29 participants. in addition, it is proposed a semi-decentralized approach as the permissioned blockchain network is distributed across organizations (13). the access control rules are coded into smart contracts that are distributed across the blockchain network. the same path is presented by guo et al. but proposed a hybrid blockchain-edge architecture (16). figure 1. four interfaces of the operational workflow of dhdm: through the patient interface (top left) the patient sets their accounts, completes their electronic health record (ehr), and manages access control via giving consent to sharing their data with researchers of their choice. through the data producer interface (bottom left), care providers and other data producers can create their accounts and manage linking patients to their local files through the caregiver’s information technology system. researchers will set up their accounts, search for datasets, request access to data, and make payments for the data they access through the data consumer interface (top right). the back-end administration of the marketplace will be carried out through the dhdm interface, epr (electronic patient record) and dlt (distributed ledger technologies). http://dx.doi.org/10.30953/bhty.v5.184 citation: blockchain in healthcare today 2022, 5: 184 http://dx.doi.org/10.30953/bhty.v5.1844 (page number not for citation purpose) mohamed a. maher and imtiaz a. khan ehr data is stored on edge nodes that impose attribute-based access control policies. the authors used the hyperledger composer fabric blockchain programmed with smart contracts and access control lists policies to evaluate the performance by measuring the transaction processing and response time against unauthorized retrieval attempts. the experiments showed that the system provides results in milliseconds, making it suitable to be incorporated in real time and secured ehr data access control frameworks. the most significant result is that the implementation showed consistency over the different sizes in all trials. this result indicates that this architecture could be the most scalable model presented in ehr consent management. others also provided attribute-based blockchain signature models to achieve confidentiality, integrity, and authentication of the patient data while supporting data sharing between concerned parties (17–20). seol et al. prepared their study in a way that sees the model from the view of different policymakers and built their model in two stages (access control and digital signature), allowing smart contracts to impose each policy rule in turn (20). yang et al. (21). built on the attribute-based model by wang et al. (17) and built a demonstration to measure performance, especially encryption and search time, and proved that time was independent of the number of attributes. guang et al. provided a model that depends on the care provider to control the ehr transactions (22). what is interesting in this model is that the authors propose an architecture that implements blockchain technology with the existing ehr system. considering that an ehr system has to have a multiple access system and that health providers individually maintain records as per the authors’ process design, the model gave providers primary responsibilities, including creating, verifying, and appending new blocks. the design uses smart contracts, where this architecture is independent of any specific blockchain platforms, and its variations can potentially apply to any ehr system. data interoperability data interoperability is one of the critical challenges for health informatics due to the heterogeneous nature of the data and the lack of standardization in different ehr systems. medrec (23) was the base that many researches in blockchain used to securely exchange/transfer data from distributed systems into a unified patient ehr. medrec issued an industrial white paper that explains an opensource blockchain model to handle the secure transfer of ehr data entries from healthcare provider systems to patient nodes and vice versa. the aim is to securely collect the data created in a local patient file at any number of hospitals and aggregate them in a consolidated file under the patient’s control. being open source, encouraged many researchers to use it in trial implementations and similarly encouraged industrial pilots to adopt their model. this white paper model is one of the very few blockchains in healthcare models that have been implemented. the work by yang et al. is an example of an academic build upon the medrec framework (24). medshare (25) is one of the early proposed ehr data exchange control models using blockchain. the authors started by suggesting a processing layer to administer the exchange of information between existing healthcare providers’ cloud infrastructure. however, the simulation showed that latency is relatively high and increases with the increase in the number of users. medblock (26) is a similar model. here the authors proposed to use nontraditional blockchain entities, such as authentication servers and certificate authorities, to provide means to issue identities and secure the cryptographic material, which will be used to encrypt all data on the blockchain. although medbloc was designed to match the healthcare it infrastructure in new zealand, the researcher could not spot a uniqueness that would hinder its implementation elsewhere. xiaoguang et al. (27) presented a recent adaptation of the medrec model. this time the aim was to provide and implement a tamper-resistant medical data sharing scheme—a delegated proof of state mechanism to act as the lightweight and reliable consensus mechanism. the analysis results proved the scheme satisfactory and had a low computational and communication cost. this scheme is a perfect match to the scope of the data marketplace research, except that it is a non-payment scheme. zhuang et al. (28) provided another framework that differs from the medrec model. although it targets the same purpose, to achieve patient-centric health information exchange, this framework focused on empowering patient control with tools. the framework then created a dapp for the patient where they can adjust parameters in the smart contracts by giving permissions, allowing touchpoints, and managing access requests through linkage and request modules. this framework offers practical traits to the system: a blockchain adapter set up for communication, sending/receiving healthcare records, and create a graphical presentation for users with easy interaction, two security layers to ensure only authorized smart contract functions execution, minimize the risk of a data breach, hashing for data consistency, data segmentation that allows partial data sharing, and touchpoint selection for clinicians to select the relevant data segment to the specialty. data security de-identify the patient record is fundamental to ensure privacy and security. this needs to be tackled on two http://dx.doi.org/10.30953/bhty.v5.184 citation: blockchain in healthcare today 2022, 5: 184 http://dx.doi.org/10.30953/bhty.v5.184 5 (page number not for citation purpose) from sharing to selling fronts in parallel. one is segregating the identifiable patient parameters from the clinical data. segregation must be done in the application, communication, and storage layers. the other is in the clinical data itself. for example, by design, any digital imaging and communications in medicine (dicom) image will have identifiable data such as patient name, date of birth, the referring body. therefore, de-identification and anonymization must be carried out before placing the data on an immutable blockchain network. several researches adopted the model of storing the ehr in blockchain (29, 30) this approach was discounted over time for technical and legal reasons. technically, this was because of the size of the block and the capacity to store a large amount of data in a chain that is replicated over many nodes. legally, it barely adheres to the requirements of gdpr article 17 (6) concerning the right widely known as the right to be forgotten, as it is not possible to amend or delete a record once it is stored on the chain. one of the studies that adopted the ehr on the chain approach is by tang et al. (30). naturally, it would not have been of relevance to this research. however, the authors proposed an interesting model for authentication by designing an identity-based signature scheme with multiple authorities for the blockchain-based ehr system. the scheme offers what could be efficient signing and verification algorithms. a large number of publications proposed what has mostly been referred to as cloud-assisted blockchain ehr security. wang et al. (31) presented a cloud-assisted secure and privacy-preserving ehr sharing protocol based on a consortium blockchain. in other words, ehr is stored on the cloud while ehr indexes (log keeping) are kept on the blockchain. in their work, the authors proposed a blockchain-based ehr sharing scheme with conjunctive keyword searchable encryption and conditional proxy re-encryption to realize data security and privacy preservation of data sharing between different medical organizations. in addition, kim et al. (32) provided a model and a  simulated trial for a secure protocol for a cloud assisted  ehr system using blockchain. they demonstrated the safety of the proposed scheme against man-in-the-middle (mitm) and replay attacks using automated validation of internet security protocols and applications (avispa) simulation. similarly, vora et al. (33) proposed a model that uses blockchain to enhance the security of ehr databases. here the authors capitalized on ethereum smart contracts to manage consensus, permissions, classifications, and services. the model looks promising and suggested six algorithms to address transaction security and privacy preservation. nevertheless, the model has shown that it would be implausible to completely hide all information and maintain an  accessible and interoperable system. however, by using smart contracts to separate information, the proposed model still offers significant privacy preservation and data integrity. furthermore, with a smart contract, one can determine the information access level, but in public blockchain, integration with the smart contract is challenging and not practical. although being a hungarian study, magyar et al. (34) presented a blockchain signature-based model that adopts the american health insurance portability and accountability act (hipaa) regulations. the model uses smart contracts and the innovations of the cryptography industry, blind signatures, multisignatures, hierarchical signatures, and other security procedures that ensure access to the information. at the same time, on the route, no one can read any open text data. the above studies dealt with the ehr as a single database, either local or cloud stored, and discussed different approaches to using blockchain to securely adding, deleting, and modifying entries in the ehr. however, one of the main reasons why blockchain is identified as a potential technology to increase the robustness of ehr and its related transaction is that ehrs by nature are decentralized. a typical patient will have different ehrs at primary, secondary, and tertiary care. just these three levels over a patient’s lifetime can generate tens of thousands of records that need to be combined together to form a whole patient ehr. in contrast, ayesha et al. (35) discussed an alternative architecture that also challenged the principle of ehr storage on the cloud. the authors suggested a framework that proposes measures to ensure the system tackles the problem of data storage as it utilizes the off-chain storage mechanism of the ipfs. their paper evaluates the performance of the different topologies over execution time, throughput, and latency. it proposes a framework that is a combination of secure record storage along with blockchain access rules for ehrs. another model by nguyen et al. (9) targets secure access control for ehr that also proposes an interplanetary file system (ipfs) configuration for the ehr storage. the idea is to form an ipfs node at each care provider and create an ehr manager (server) that will play the role initially played by cloud ehr. the model then uses blockchain to index the transactions trail and deal with the ehr manager as the cloud service. internally, the ehr manager is responsible for aggregating the patient record from all ipfs nodes upon request and create more nodes as the patient moves between different care providers. the model suggests that the ehr manager itself be based on a smart contract to administer access and data transactions requests while providing the patient with a blockchain interface mobile app to exercise their rights to control the access. http://dx.doi.org/10.30953/bhty.v5.184 citation: blockchain in healthcare today 2022, 5: 184 http://dx.doi.org/10.30953/bhty.v5.1846 (page number not for citation purpose) mohamed a. maher and imtiaz a. khan legal and ethical challenges the legal argument is always started by who owns the data? ownership is often confused with access. kostkova et al. (7) aimed to distinguish between data ownership and right of access and finding novel balanced approaches to satisfy business interests and actively engage the public while securing transparent data access for research needs and large-scale integrations preserving individual privacy. a study by castillo et al. (36) works to identify barriers for information exchange within the context of the health information technology for economic and clinical health (hitech) act to create a more efficient and effective healthcare system. the findings suggest that a hospital is more likely to be exchanging clinical summaries with hospitals outside its health system when the other hospital uses the same ehr vendor. the authors highlight the importance of ehr vendor neutrality and thus the importance of ehr systems interoperability. in a critical survey by yadav et al. (37) about mining clinical data from patient’s ehr, the authors explore, discuss, and present novel insights on how data mining techniques have been utilized for ehrs. in this systematic review, they discuss application, study design, and data mining methodology of a large number of initiatives for clinical data mining. furthermore, the authors discuss the barrier to the widespread use of data mining in clinical practice. the review itself does not cover the dhdm legal and regulatory needs. however, it tackles the ethics and compliance of the data mining research (ai, ml, etc.) facilitated through the dhdm. mello michellem presented a comprehensive manual for the barriers to the growth in health data exchange within the context of the north american laws (38). the authors analyzed the federal and state health information privacy statutes and regulations and secondary materials then concluded that some critical legal barriers persist, but many issues that care providers acknowledge as obstacles are somewhat illusory. the authors emphasized that healthcare providers perceive health information privacy laws to be obstructing the growth of electronic health data exchange and blamed several factors such as the inconsistency in the patient consents laws, the special treatment for sensitive health data, and failure to establish a unified patient indexing system. a techno-regulatory document compares differences in health data transmission standards (iso/ieee 11073, ihe pcd-01, and hl7 dof) and suggests the most suitable environment to use each of them (39). the authors conclude that iso/ieee 11073 messages cannot contain patient information, ihe pcd-01 messages have limited device information, and that hl7 dof has the most comprehensive information coverage in all four parameters of the study (human readability, learnability, implementation, and extensibility). socioeconomic challenges the term “creative destruction” coined by joseph schumpeter explains how the process of industry transformation revolutionizes the economic structure from within by destroying the existing one and simultaneously creating a new one (40). with disruptive technologies like blockchain and industries like health care, the structure is extremely complicated in terms of stakeholder engagements and economic impetuses. although nonmedical, the model presented by a study (41) provided a promising marketplace implementation based on an existing model used for commercial vehicles data marketplace from japan. id-link was a successful model when the government of japan initiated the construction of an information infrastructure to share data in different business areas. one of these areas was sharing data from individual ehr. in their paper, the authors replace automotive data such as speed, time, range, emission, and so with medical data from the ehr. the study discussed engagement options (opt-in vs. opt-out), access control privileges, and data standardization, especially adopting specific formats such as hl7 (paper suggested v2.5., however, fhir hl7-v3.0 is currently widely in use all over the world), who icd10, and snomed-ct as a clinical terminology library. the paper also provides a medical adaptation to the automotive id-link process workflow into a feasible seven-step model from patient consent, doctor interaction, id check, commercial use, payment, and profit share. the id-link is built in four architectural layers business, functional, data, and technological layers. guo et al. (42) criticize the processes that digital health innovators follow to draw results for their solutions and implementations. the authors also emphasize a lack of implementation when it comes to digital health solutions, and therefore it is not easy to draw any evidence-based results. the study analyzed some of the major digital health solutions implementations against selecting nonexclusive relevant regulatory standards and the methodologies that innovators adopted in evaluating their solutions. nevertheless, the authors acknowledge that the innovators do not create barriers and that innovators are stuck in the “no evidence, no implementation—no implementation, no evidence” paradox in digital health. the authors suggest that approaches, such as simulation-based research, can generate higher-quality, lower-cost, and more timely evidence. affinito et al.’s survey (43), in contrast, is to understand the digital means that physicians are using to engage with their patients and the effect physicians perceive on clinical health outcomes. the survey results suggested that the main success factors in achieving patient empowerment with digital tools and improving health outcomes are clinical evidence and actual users’ (patients and caregivers) involvement in designing the digital solutions. the study http://dx.doi.org/10.30953/bhty.v5.184 citation: blockchain in healthcare today 2022, 5: 184 http://dx.doi.org/10.30953/bhty.v5.184 7 (page number not for citation purpose) from sharing to selling concludes that the use of digital tools would do improve patient empowerment. nevertheless, to date, there is no evidence of an improvement in health outcomes. having established that there is no evidence to support that patient outcomes improve with the adherence to using digital tools, angeline and sharon (44) conducted a study to investigate whether the level of digital literacy among healthcare staff is to be blamed. this study demonstrated that the majority of staff showed confidence in using ict. however, it is understood that the location of the study (australia) might have affected the results of the study and that we should anticipate other results in other territories. electronic health records for clinical research ehr4cr is a european project that aimed to enhance the patient-centric trials by developing a platform that allows access to existing patients’ ehr (45) making the project a lot similar to the dhdm research project. except that it does not handle the patient compensation for the usage of his or her ehr data. dupont et al. (46) is a study that assesses the financial results of the project. the study compared ehr4cr to existing practices and concluded that ehr4cr solutions seem to be cost saving for primary sponsors of clinical trials. the study results suggest that the potential for savings would increase with the broader adoption of ehr4cr solutions in europe and beyond. the results, in turn, suggest that a medical data marketplace where patients can sell access to their ehr records for their own benefit would in the long run save cost in industrial and clinical trials. in a paper, timo and harri (47) aimed to develop an ecosystem evaluation framework (eef) for understanding the chances of survival of a digital business platform. the authors described the eef model in six parameters (the platform, the problem that the platform is trying to reduce, the purpose of the platform, the ecosystem, the transactions enabled by the platform, and the revenue model of the platform). the authors highlighted the importance of considering the compensation model, which perfectly matches the goals of our study. they relate missing the incentive component to be the main reason for the failure of the regional health information system rhis to reach critical masses in the pirkanmaa region in finland, where they applied their model. alina and jose luis (48) discussed what the authors called a fair marketplace. they identified the attributes for the data to be findable, accessible, interoperable, and reusable, and this is where fair came from. the authors presented an architecture that accommodates layers to gather information from patients, care providers, and other platforms such as ehr4cr. credit score has always been the biggest constraint to the dhdm research. in almost every survey, poll, or even friendly chatting, this issue has been raised. people wonder if the project will make them exposed as the credit score does with their finances. people are always worried about being denied or paying more for services they are now getting without significant exposure to health history, such as renewing motor insurance. they have concerns about higher premiums once the insurance knows more details about their health or, worse, being denied services if they do not allow access to their records as it happens with the credit score. however, credit scoring architecture is a perfect example of data aggregation and permissioned sharing from a techno-commercial perspective. dumitru and gatti (49) discussed the constraints and the opportunities related to sharing health data and the usage of the data for credit scoring purposes. the authors have proposed an architecture for a trusted data marketplace that can be very useful to act as the weighing system in the dhdm project. the weighing system is what calculates the contribution of each ehr into an entire data set. it shall be used to equitably distribute the payments from medical researchers between the data owners in a way that incentivizes the ehr based on their commitment to wellbeing and their commitment to keeping the ehr up to date. a study conducted by roman and stefano (50) is a practical example of capitalizing on the successful credit scoring model in calculating the weight/value of every ehr entry. the weighing component has massive value in a fair distribution of wealth between ehr owners based on the contribution of each entry and each ehr in the research to which the wealth has been paid. ryuji’s study (41) is another practical example that could benefit the dhdm research project. it provides a workable model of commercial exchange of funds against data that capitalize on already implemented techniques in the automotive industry. conclusions although billions of dollars are spent on making the current health data management systems more efficient, data sharing remains an elusive goal in a health sector. as gdpr has introduced a paradigm shift on data ownership and control along with blockchain-like technologies, providing the technological capability of decentralized data management; it is the right time to change the underpinning incentivization model through a dhdm-like open market model. based on this review, it is evident that blockchain-based solutions like medrec (23) can be implemented as a separate layer and integrated with native databases through application programming interfaces (apis) without perturbing native data management systems and culture, which will definitely benefit the technology adaptation process. moreover, being open-source, medrec-like solutions will play a significant role in secure data collection from existing data management systems and combining http://dx.doi.org/10.30953/bhty.v5.184 citation: blockchain in healthcare today 2022, 5: 184 http://dx.doi.org/10.30953/bhty.v5.1848 (page number not for citation purpose) mohamed a. maher and imtiaz a. khan an aggregated ehr under the patient’s control. smart contract and ipfs/cloud storage systems will provide patients the control to securely grant access over different types and duration of de-identified data. the review demonstrated different proposals for data access and secure sharing between data producers and consumers. however, further studies need to be performed on digital data reproduction and how to secure the producer rights if the consumer reproduces the data beyond their consent. further studies are also needed to identify how to adapt the data sharing process with varied regulations across different geographical jurisdictions and time. the shared economy-based incentivization model that is deemed most appropriate for the dhdm context also needs to be evaluated extensively. although a company like airbnb has demonstrable economic benefits for both provider and consumer, sharing personal health data may have a different social and emotional context than personal accommodation. despite these concerns, it is almost certain that an open marketplace will 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attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons. org/licenses/by-nc/4.0. http://dx.doi.org/10.30953/bhty.v5.184 https://dx.doi.org/10.1109/access.2017.2730843 https://dx.doi.org/10.1109/access.2019.2937685 https://dx.doi.org/10.1109/access.2019.2937685 https://dx.doi.org/10.1109/jbhi.2020.2993072 https://dx.doi.org/10.1109/access.2019.2904300 https://dx.doi.org/10.1109/access.2019.2943153 https://dx.doi.org/10.1109/access.2019.2943153 https://dx.doi.org/10.3390/s20102913 https://dx.doi.org/10.1109/nc.2017.8263269 https://dx.doi.org/10.1109/nc.2017.8263269 https://dx.doi.org/10.1109/access.2019.2946373 https://dx.doi.org/10.1109/access.2019.2946373 https://dx.doi.org/10.1186/s12913-018-3230-7 https://dx.doi.org/10.1186/s12913-018-3230-7 https://dx.doi.org/10.1145/3127881 https://dx.doi.org/10.1111/1468-0009.12313 https://dx.doi.org/10.4258/hir.2018.24.1.46 https://dx.doi.org/10.4258/hir.2018.24.1.46 https://dx.doi.org/10.1109/icdmw.2016.0117 https://dx.doi.org/10.1038/s41746-020-00314-2 https://dx.doi.org/10.1007/s10389-020-01370-4 https://dx.doi.org/10.1177/1460458219839613 https://www.imi.europa.eu/projects-results/project-factsheets/ehr4cr https://www.imi.europa.eu/projects-results/project-factsheets/ehr4cr https://dx.doi.org/10.4172/2157-7420.1000235 https://dx.doi.org/10.1109/cbi.2017.6 https://dx.doi.org/10.1109/cbms.2018.00040 https://dx.doi.org/10.1109/obd.2016.21 https://dx.doi.org/10.1109/obd.2016.21 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 page 1 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v1.28 a pragmatic solution to a major interoperability problem: using blockchain for the nationwide patient index michael l. gagnon1 and grant stephen2 affiliations: 1senior executive and executive director of healthie nevada, qualis healthinsight, 2ceo of bprescient keywords: blockchain; master patient index; mpi; distributed ledger; patient identity; hie; health information exchange. corresponding author: grant stephen, gstephen@bprescient.com section: cases/pilots/methodologies associating the health-related records and transactions of patients with their numerous “identities” as they interact with different healthcare providers, payers, pharmacy benefit managers and other entities is an expensive and complex problem. with many years of experience addressing this issue in different healthcare systems and health information exchanges (hies), it is apparent that there is now a compelling and relatively straightforward technical solution for this problem. presented here is a broadly feasible and technically compelling argument for a blockchain-based approach to addressing these issues. at the same time, challenges ahead and potential strategies to address them are discussed. ssociating the health-related medical records and patient transactions with their numerous “identities” as they interact with different healthcare providers, payers, pharmacy benefit managers (pbms), and other entities is a complicated problem.1,2 failure to efficiently make such associations impacts care and is responsible for considerable wasted time and resources across the u.s. a blockchain-based approach could be used to index patient identities and locations of their health records in health information exchanges (hie) and other networks of patient data across the country. accordingly, the objects for this article are listed here. 1. set out the various problems of patient identity and their costs. 2. explore the traditional approaches to tackling the problem, and their limitations. a mailto:gstephen@bprescient.com page 2 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v1.28 3. convey a conceptual design that utilizes blockchain to address the problem of patient identity on a national level. 4. explore the weaknesses in the concept that will need to be tackled along with strategies for doing so. 5. lay the basis for a broader conversation to evolve the concept. blockchain’s characteristics as a decentralized, highly resilient, and secure log of transactions make its architecture a strong fit for the problem of maintaining an accurate association between patients and their records. blockchain offers a high integrity mechanism for locating data and monitoring precisely how it changes over time. the problem of patient identity how to collect patient data and make it accessible to any organization that performs a service for that patient is a long-standing challenge. in the early 2000’s, the conceptual architecture of a master patient index (mpi) coupled with a record locator service (rls) started being considered for hies. by the mid2000’s regional health information organizations (or rhios as hies were originally called) started springing up as regional or state-wide data aggregators. almost all of them used some form of the original mpi/rls model. in some of the early models “edge servers” were put into place at each participating organization.3 under this system, an edge server was a server appliance located within the security perimeter of each hie-participating organization. the edge server staged the data that each organization shared with the hie. the record locator service maintained an index of the information but did not house clinical data. the edge server model quickly fell out of vogue as staging data in this special server required significant resources, did not improve security, and did not scale well to small organizations like provider offices. next, hie’s started aggregating data, indexing patients through a master patient index, and storing the data in individual repositories for each organization. the mpi effectively became the rls, and hies could share data with treating providers and other participating organizations. connecting networks however, this did not address the issue of how hies would communicate with each other, which was increasingly important as individuals relocated around the country, visited multiple providers perhaps in different states, or simply changed insurance. the result was they became disconnected from their health information that was stored in their previous health system ehr or hie. the idea of a “network of networks” was long discussed,4 but it never gained much traction due to implementation costs and lack of an organizational entity that was ready to create this full “open” network. instead the concept of point-to-point network connections was promoted by organizations like the ehealth exchange (sequoia project) using the “integrating the healthcare enterprise” (ihe) concepts of patient demographic queries (xcpd) and cross community access (xca) to retrieve documents.5 while this has been a good first step, it has significant limitations in terms of identifying locations where all patient data exist and scaling the number of connections required for connecting to these sources. limitations of current connecting methodologies one thing that seems certain is that a completely federated set of patient data repositories which depend on a brokered broadcast query (xca) to look up patients using demographics (xcpd) page 3 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v1.28 will not scale. this is made clear by looking at the basic math regarding the number of connections required to integrate a fully meshed network of hies or other clinical data repositories.6 assume we level out at 200 aggregated clinical data repositories that could be hie’s, integrated delivery networks (idns), and other networks such as commonwell health alliance. to connect all 200 networks would require (n*n1)/2 connections or (200*199)/2=19,900 connections. each hie would have 199 connections to query. having federated repositories (hies) of data is not the main problem. the idea that we can do this without some type of index is the core issue. the shiec project once such effort to create a network of hies is the existing shiec patient centered data home (pcdh) project. pcdh routes data using hl7 v2 messages to other hies when you receive care away from your "home" hie. the basic idea is that if you become ill or are injured away from home and visit an emergency department or hospital, the "away" hie sends an admission discharge transfer (adt) message to the "home" hie based on the zip code of your home address. the "home" hie responds with a care summary. once your visit is complete at the "away" hie, it creates a visit summary (either a v2 or v3 message) and returns that to the "home" hie so your primary care provider has these clinical data for future follow-up. this is a straightforward approach that can be implemented by almost all hies quickly. it has already shown promise in the western region and other regions of the pcdh. in essence, it eliminates the issue of having to query all hie’s by having a predetermined zip code index. using this zip code index within the pcdh is better than xcpd/xca but it could be improved. zip codes as the "marker" for a true patient data index leaves "holes" in all data for a patient. consider the millions of patients who summer in the north and winter in the south, or those who recently moved: most of their clinical data do not exist in their current "home" hie. the preferred solution is to develop a nationwide patient index. however, having a single mpi in the cloud also suffers from issues such as scalability, data quality and accessibility. a national network of patient identity brokers to address the problems identified above, we propose the following concept: a national network of patient identity brokers with a blockchain-based record locator. implement a limited number (~6) of patient identity brokers (pibs) nationwide. each regional hie, idn or other “network” would connect to one or two pibs for performance and redundancy and send all their patient demographics via adt messages. each pib would have a master patient index. current patient identity matching logic has its issues and limitations, but it is far better than demographic queries, and it will improve over time. instead of storing the index in the patient identity brokers, the pib’s can manage the required business logic while the index itself would be stored in a single permissioned patient identity blockchain. the blockchain would not contain any protected health information (phi) but would be the index to all the locations where the patient’s clinical data exist (nationwide rls). while the index would be in the blockchain, clinical data would remain off-chain. the pib would be the broker to the patient identity page 4 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v1.28 blockchain and would perform specific business functions such as patient matching, adt message processing, patient private key management, managing relationships of patients to healthcare organizations, and managing the blockchain index. in our core use case described below the pib would process adt messages from participating organizations which would “register” this organization as having a relationship with the patient, update patient demographics, and grant access to the patient identity blockchain by managing public and private keys.7 any organization (hie, idn, or other network) with proper authorization (private keys) could get access to the index. healthcare organizations could request access (keys) by sending an adt message to the pib, which would return the patient’s private key to the organization. the organization would then have access to the index. in our model the pibs manage the public and private keys that control access to the patient identity blockchain. a future consideration would allow patients to control their own private keys to the location of their medical records. a user facing application could be developed to allow a patient to manage their consent for certain providers to view their data. while the design of such an application is outside the scope of this paper, the underlying architecture of pibs coupled with a patient identity blockchain would likely be very conducive to an application of this type. one can imagine how access to sensitive data such as substance abuse data, which is governed by 42 cfr part 2, could be managed and controlled in this model with the aforementioned consent application. when a local hie or healthcare organization wanted to perform a query, it would perform an indexed broadcast query using a regional patient identifier which would greatly improve identification of all patient data sources. the pib could perform other services. for example, the pib could implement the standardization of transactions and the normalization of the data required to implement the blockchain’s smart contracts. the smart contracts would effectively be things like the data use requirements and authorization for access. after performing a query, the pib could consolidate the responses into a single patient summary (c-cda) to return to the requesting organization. hipaa compliance would be managed in our model in much the same way that it is managed in the patient-centered data home or sequoia ehealth exchange. participants sign a participation agreement, which would include a business associate agreement and describe the rules of participation and the data use standards. access to the data locations would be managed by the pibs as described above but consent to access the actual medical records would still be managed by the source organizations and subject to their patient consent and data use requirements. the proposed architecture of the patient identity blockchain is displayed in figure 1. limitations of current matching methods there are many issues with our current deterministic and probabilistic matching methods in mpi's. the pibs will need to employ the best matching algorithms and methods available, including using data sources in an appropriate way (e.g., phone numbers, credit reports, previous addresses). core use case the core use case for loading patients into the blockchain and using the index for querying patients is as shown in table 1. page 5 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v1.28 figure 1. proposed architecture of the patient identity blockchain (ehr: electronic health records) there will be false negatives (duplicates); and each pib might employ biometrics to improve matching. matching solutions that use facial recognition and iris scans to improve patient matching already exist.8 regional mpis at hies will need to start "scoring" demographics from source organizations to ensure that patient data sent to the pib and then put into the blockchain meet the smart contracts’ data standards. this part of the solution clearly requires continuous quality improvement. the recently released office of the national coordinator (onc), trusted exchange framework common agreement (tefca) could help in this regard with a set of required patient demographics and the framework for participation. the framework agreements could propose permitted uses and other policies, which could utilize chaincode and become blockchain smart contracts. perhaps this could be the purview of the recognized coordinating entity (rce) as proposed by tefca. advantages of the blockchain approach to mpi the advantages to using blockchain as the architecture for solving the patient indexing problem at the hie level are listed in table 2. page 6 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v1.28 table 1. core use case for loading patients into the blockchain and using the index for querying patients event activity healthcare event 1. patient encounter at a connected provider organization. system activation 2. that organization sends an adt message with patient demographics to a broker; which includes a master patient index. a. it also submits a “consent” flag indicating whether this patient’s data can be queried at this time. subsequent messages may turn consent on or off. 3. the master patient index uses deterministic and probabilistic logic to determine if the patient already exists in the index. the patient is scored as a match, a probable match, or a non-match. 4. if the patient is a non-match, then a new patient private key and universal id are created. if the patient is a match, these already exist. reply 5. a message is returned to the original provider organization with the patient’s private key and a universal identifier. ensuring privacy 6. the originating organization can store the patient’s private key and universal id in their local mpi. 7. the originating organization then passes a url or ip address to the broker, which is the node acting as the responding gateway for queries from other participants. 8. the broker then performs two functions: a. add an index to the blockchain for this organization’s responding gateway for that patient, in effect identifying that this organization has data on this patient. b. update the patient’s best demographics “golden record” with new information. the broker mpi determines whether the new data should supersede what is already stored. 9. the originating organization opens the chain to identify all locations where a patient’s data may be located (or use the broker). 10. using an existing hl7 standard xca query the originating organization queries these locations and retrieves clinical documents. 11. depending on the capabilities of the originating organization’s systems, the broker may consolidate steps 9 and 10 into a single clinical document to be returned to the originating organization. audit trail 12. when a query is performed, and data exchanged, the blockchain stores the audit trail of what data went to what organization. adt: admission discharge transfer; hl7: health level seven international; mpi: master patient index; xca: cross-community access page 7 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v1.28 issues to be addressed there are issues with using blockchain, specifically when it comes to performance and maturity. performance of a large scale blockchain, which must be used to retrieve clinical data, often in real time, can be a significant issue. requiring each node to process each transaction may make it resilient to cyberattacks, but it also limits transaction processing speeds. in addition, the chain nature of a blockchain requires that each transaction be serialized, which can slow updates. in this regard, the design of the index can definitely improve the performance of the system. table 2. advantages to using blockchain as the architecture for solving the patient indexing problem at the hie level advantage comment 1. reliability most importantly, it will directly impact our ability to care for patients. 2. cost effectiveness currently, large amounts of money and resources are wasted due to limitations of alternative approaches. 3. scalability efficient scalability is a crucial advantage of the blockchain approach, especially on a regional or national level. 4. responsiveness an appropriately designed blockchain approach will provide a fast mechanism to associate patients with their up-to-date records. 5. simplicity blockchain avoids many of the governance and access issues that plague other solutions to these problems. we can envision two different chains for each patient, one for the transactions performed against the index such as patient registration events or queries, and a second chain which would be an index of the locations of patient data. the first chain could become very large as adt registration events are quite verbose and queries would become routine. the second chain of locations where a patient’s data are stored, would probably remain quite small as long as hies, integrated delivery networks or other integration networks perform the initial integration. for the vast majority of patients, this number would probably be ten or less. technical options as for the two most prevalent blockchain platforms, hyperledger and ethereum are both relatively immature which can lead to unforeseen issues with deployment and software bugs.9 over time both of these platforms will continue to improve and make enhancements such as ethereum’s concept of “sharding” which requires a far smaller number of nodes to process each transaction.10 then there are entirely new concepts for encrypted ledgers such as iota’s tangle. the iota tangle was originally proposed as a solution for connecting the internet of things (iot) and uses a more interesting underlying data structure called a directed graph.11 instead of a very simple chain, which is effectively a secure linked list, the directed graph only requires each transaction entering the tangle to approve two previous transactions. any unapproved transaction is called a “tip” and the more transactions that approve any given transaction the more confidence the system has with this transaction. ultimately the directed graph structure or ethereum’s sharding could be more effective as the model for our patient transactions, but a page 8 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v1.28 traditional blockchain would be sufficient for at least during the first phase of the patient index. this is because, as mentioned above, the total size of any patient’s chain of all the locations where their medical information exists will be modest (perhaps <10) especially when hies play the role of initially aggregating the information. at this size a traditional blockchain should perform adequately. conclusion this paper presents a high level conceptual design for a blockchain approach to the patient indexing problem and an outline of the clear advantages it has over other approaches. however, developing a nationwide patient index is not a simple task. it will involve overcoming many design challenges. at this point, we are limiting our concept of an index of patient medical data locations to the u.s., but we envision this expanding to other countries over time. the models for the necessary us data use agreements are already in place through the shiec patient centered data home and careequality. however, to implement this approach internationally will be more complex. the goal is not to make mpis free from issues but, instead, to recognize that patient id queries based on demographics will not scale and that, in the interests of patient care and cost control, a nationwide patient index is required. we should not make the perfect be the enemy of the good with regards to the patient identity brokers. the fundamental point is that, through collaborations with other disciplines and stakeholders, blockchain offers the opportunity to finally ensure that a complete record of a patient’s clinical data is truly available to the patient and clinician regardless of where the patient received care. acknowledgments: the authors thank andrew hinkle for his technical advice. contributors to fulfil all of the criteria for authorship, every author of the manuscript has made substantial contributions to all of the work and participated sufficiently in the work to take public responsibility. funding statement: the authors confirm that no third-party funding supported or influenced the development of this paper. conflict of interest: the authors confirm that there are no known conflicts of interest associated with this publication. copyright ownership: michael l. gagnon and grant stephen references 1. wang x, ling j. multiple valued logic approach for matching patient 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[online] disqus, 2017. https://www.geeksforgeeks.org/network -topologies-computer-networks/. 7. adams c, lloyd s. understanding pki, concepts standards and deployment considerations. boston : addison wesley, 2003. 0-672-32391-5. url: https://www.pearson.com/us/highereducation/program/adamsunderstanding-pki-conceptsstandards-and-deploymentconsiderations-2ndedition/pgm216259.html 8. trader j. iris recognition vs. retina scanning—what are the differences? m2sys blog on biometric technology 2012 url: http://www.m2sys.com/blog/biometrichardware/iris-recognition-vs-retinascanning-what-are-the-differences/. 9. ray j. sharding faqs. ethereum/wiki. [online] github, june 15, 2018. https://github.com/ethereum/wiki/wiki/s harding-faqs. 10. singh n. ethereum or hyperledge fabric. quillhash. 2018. url: https://medium.com/quillhash/ethereumor-hyperledger-fabric-259f3c9b8da6 11. popov, serguei. the tangle. berlin, germany : iota foundation, 2018. this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0. http://www.m2sys.com/blog/ page 1 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.114 implementation considerations for blockchain in healthcare institutions ketan paranjape,1 mitchell parker,2 david houlding,3 dr. josip car4 affiliations: 1f. hoffmann-la roche ltd., roche diagnostics, indianapolis, in, usa; 2information security and compliance, indiana university health, indiana university health university hospital, indianapolis, in, usa; 3industry experiences, cloud + ai, microsoft corporation, microsoft, redmond, washington, dc, usa; 4centre for population health sciences (cephas), lee kong chian school of medicine, nanyang technological university, singapore, singapore corresponding author: ketan paranjape, vice president, roche diagnostics, 9115 hague rd, indianapolis, in 46256, usa. email: ketan.paranjape@roche.com keywords: blockchain technology, healthcare institutions, implementation, immutability, liquidity, patient records, protected health information, tutorial section: use cases/pilots/deployment objective: this article aims to provide a primer on blockchain technology and implementation considerations for blockchain at healthcare institutions. results: after research and interviews, we developed a primer and a high-level implementation guide for healthcare systems exploring the use of blockchain technology. conclusions: the use of blockchain technology in health care is at a promising stage in development but blockchain-based applications are yet to be demonstrated as a viable platform for exchanging and reviewing information. healthcare systems should be cautiously optimistic regarding the potential of blockchain and do a thorough business and technical diligence that is driven by targeted use cases to be successful. health care is undergoing a transformation worldwide.1 in many developed countries, mature but antiquated national healthcare services are burdened by an aging population, payment reforms, worker shortages, and rising costs.2 the emergence of innovative technologies like artificial intelligence (ai),3 however, has made many healthcare systems optimistic about solutions and ready and eager for change. another key technology leading this trend is blockchain,4 which can help healthcare providers automate medical record mining to aid in making more accurate diagnoses5 or reduce medical errors6 by enabling more https://doi.org/10.30953/bhty.v2.114� mailto:ketan.paranjape@roche.com https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v2.114&domain=blockchainhealthcaretoday.com&date_stamp=2019-07-04 page 2 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.114 accurate and tailored treatment, while simultaneously reducing the financial burden. after success in industries like financial services7 and retail,8 if blockchain can be scaled and moved into mainstream health care, it can help alleviate many concerns over security and privacy of health data and help stitch together a longitudinal history of health data that are fragmented and locked away in disparate locations in the healthcare system today. sophisticated use of blockchain technology will contribute to improved health outcomes, improved healthcare quality, and lower health care costs—the three overarching aims that the united states is striving to achieve (improving care, improving health, and reducing costs).9 blockchain blockchain is a foundational platform to keep secure data in a distributed, encrypted, shared ledger and control access to that data. blockchain technology is based on distributed ledger technology10,11,12,13,14 (dlt), which is a type of secure database that is implemented among a group of participants, without a central authority or administration. members or contributors can create, modify, or remove transactions in the database by observing rules that are enforced by the ledger. for example, the ledger may ensure that you cannot spend money you do not have. immutability15 is an important aspect of blockchain for building trust and protecting the integrity of data stored on the blockchain. once data are stored on the blockchain, it cannot be changed. modifications and deletions can be accomplished through appending new records to the blockchain that supersede the originals. however, the older records on the blockchain remain intact. distributed ledgers are used for building a broad class of applications and services like secure, robust cryptocurrencies (e.g., bitcoin); for providing verifiable ownership of assets; and for managing access rights to personal data. these services can be provided without the requirement that a single organization be trusted with the data. another important consideration is ownership of assets. this is accomplished by digital keys, addresses, and digital signatures. a pair of digital keys are generated at a time—one public and one private.16 comparing this to a bank account, the public key is the bank account and the private key is the secret personal identification number (pin) to access that account. the address is similar to the bank routing number that can be shared with anyone wanting to send money to you. finally, the digital signature is like a real signature and is used to prove one’s identity, except that blockchain uses cryptography, which is more secure than hand signatures that can be easily forged. building secure, accessible, and longitudinal patient records using blockchain today’s patients demand a more personalized,17 seamless, and coordinated approach to their care, where providers amalgamate health data from multiple different siloed data sources (e.g., medical records, payer systems, genomics, clinical trials, and government sites) to come up with a diagnosis. unfortunately, two major issues limit this approach: significant security and privacy concerns18 that impede sharing of health records, and the fact that patients interact with a large number of healthcare providers, leaving a scattered trail of information.19 much of the apprehension surrounding data security and patient privacy is fueled by recent high-profile security breaches in patient healthcare records. a recent study revealed that healthcare data breaches are rapidly growing in https://doi.org/10.30953/bhty.v2.114 page 3 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.114 scale and impact to healthcare institution and patients.20 the primary concern of patients is that they have little or no control over their information after it has been provided to a payer, provider, or healthcare exchange. patients want greater insight into how their data are used, who has access to it, and when it is being modified.21 to complicate matters, patients have medical histories from a variety of caregivers, such as a pediatrician, a university physician, a dentist, an employer health plan provider, or a medical specialist. over the years, they leave data scattered across many healthcare systems that lock them away in silos.22 the result is a trail of health records that are hard to collect, are difficult to piece together, and are under primary ownership of the healthcare provider.23 table 1 summarizes the other pain points in health care today and how blockchain technology can be applied. blockchain applications24 offer opportunities to address privacy and security concerns and bring together a longitudinal patient record from the patient’s perspective. a key benefit of using blockchain is that it can be used to empower patient to control access their health records. the patient can now give permission to their clinician to review their health record; grant access to another clinician for a second opinion; or provide read-only access to a guardian, doctor, pharmacy, insurance company, as needed via their private key. a subset of the patient data (metadata) that is represented in formats like the continuity of care document (ccd) can be stored on the blockchain together with a link to the actual data location, and a hash code can be used to verify the integrity of the record stored off the blockchain. in this way, only minimal but sufficient (for the defined use case) personally identifiable information (pii) and protected healthcare information (phi)25 need be stored on the blockchain. the bulk of the pii and phi can remain in the secure enterprise systems where they currently reside. in this manner, blockchain can facilitate discovery of information about a patient, and actual records may subsequently be securely exchanged via secure, direct (i.e., not via blockchain), peer-topeer communications between the sending and table 1. summary of healthcare pain points and potential blockchain opportunities pain points blockchain opportunities healthcare “liquidity”—data silos, lack of trust, ownership and incentives to inter-operate • dlt enables a distributed trust framework • allows for secure data access and aggregation with robust auditing capabilities healthcare costs and quality of delivery • enables better utilization and risk management through creating a holistic physician–patient centered view • supports value-based care by enabling analytics for quality reporting process complexity • enables smart contracts, pre-authorizations to speed up payments. • traceability and time-stamps help eliminate fraud and abuse patient/consumer engagement • enables patients to share and control their data, better engagement, and personalized care privacy and security • encryption and cryptography-enabled security • better integrity due to peer-to-peer accountability and ability to enforce granular permissions. dlt: distributed ledger technology. https://doi.org/10.30953/bhty.v2.114� page 4 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.114 receiving healthcare organizations. confidentiality of patient information can further be protected using private consortium blockchains, accessible only to well-known and highly trusted healthcare organizations that require access to it. similarly, mechanisms like sidechains26 that can allow tokens from one blockchain to be securely used in a separate blockchain can be used to isolate blockchains. last, but not least, information on the blockchain can be encrypted to protect data confidentiality. public key infrastructure (pki) asymmetric encryption techniques can be used to empower patients with their private key and the ability to authorize access and use of their healthcare information. individuals are granted access using smart contracts.27 this is accomplished when certain conditions listed by the patient are met (see figure 1). blockchain provides the structure for health data that enables it to be analyzed but remain private. taking advantage of the pseudonymous29 nature (i.e., coded to a digital address rather than to a patient name) of blockchain technology and its privacy, personal health records could be linked securely through the blockchain.30 blockchain then provides a novel way to securely create a virtual lifelong longitudinal health record by storing encrypted access links to individual figure 1—example of blockchain ecosystem in health care28. https://doi.org/10.30953/bhty.v2.114 page 5 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.114 records from disparate health systems into a distributed ledger application and make the links accessible to authorized users. another important healthcare stakeholder, the payers (e.g., insurance companies, centers of medicare and medicaid services) also recognize the potential of blockchain. blockchain can help create and maintain an accurate, comprehensive, longitudinal and secure up-to-date view of patient revenue cycle and clearinghouse activities across the payer–provider network. this allows payers to reduce operational burden, quickly validate a claim, handle pre-authorizations, and develop more sensitive risk stratification practices. change healthcare validated this model with its purchase of the assets of pokitdok.31 finally, to take blockchain mainstream in healthcare, multiple healthcare leaders (payers, providers, and diagnostics laboratory) are coming together to create a common data sharing platform called synaptic health alliance.32 challenges with blockchain the use of blockchain technology in health care is at a promising stage in development, but blockchain-based applications have not yet been demonstrated as a viable platform for exchanging and reviewing information. one key challenge with blockchain is the immutability15 of data (i.e., once data are entered, they cannot be removed). from trust building and anti-fraud perspectives, immutability has great value, but from a legal perspective it introduces challenges, especially in the context of data subject to “right to be forgotten” requirements—such as in the recently released general data protection regulation (gdpr) rules33—since pii on the blockchain cannot be erased. blockchains can also introduce challenges with compliance where nodes span multiple regulatory or data protection law jurisdictions. any data stored on the shared distributed ledger of the blockchain flow to each copy maintained consistently by each node of the blockchain, and this can introduce data sovereignty and trans-border data flow challenges. a second challenge with blockchain relates to implementation.34 for blockchain technology to succeed, it must be integrated with current healthcare applications and processes. care delivery processes may need updating to make use of new capabilities enabled by blockchain, including those for new patient-centric use cases. in these types of use cases, patients could gain more control over who has access to their health records, and the healthcare industry would have to enable this. enabling patients to manage their healthcare data can be risky, and with multiple parties contributing, managing security keys could be difficult or prove impractical. cybersecurity challenges with blockchain also remain prevalent. blockchain has significant features that strengthen security, in particular in the protection of data integrity with immutability, and improved protection of the availability of the network since blockchains are decentralized and have no single point of failure. however, protecting the availability of each blockchain node remains the responsibility of the associated healthcare organization, and this will become more critical as blockchains are used for mission critical healthcare services. further, protecting the confidentiality of data stored on the blockchain remains the responsibility of the blockchain consortium of healthcare organizations. fortunately, there are many well-established multi-layered, defense-in-depth strategies that can be employed to achieve effective security with blockchain. https://doi.org/10.30953/bhty.v2.114� page 6 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.114 there also remains a need for guiding principles and controls to establish security in the context of existing risk management programs. the white paper advancing blockchain cybersecurity: technical and policy considerations for the financial services industry35 by microsoft illustrates eight core principles and controls needed to effectively implement security controls for permissioned blockchains. checklist for blockchain implementation the healthcare information and management systems society (himss) blockchain work group is in the process of identifying and analyzing business and technical factors that would facilitate blockchain implementation in healthcare.36 the group has developed an initial checklist that healthcare institutions can use to help set up and/or augment their existing blockchain initiatives. key activities on the business side include identifying use cases, business models, incentives and return on investment (roi). careful thought has to be given to privacy, security and compliance. the it team will have to consider the right technology, architecture, along with performance, throughput and scalability implications. finally, the institute will have to prototype and pilot the use cases with the ultimate goal of deploying a solution that can improve patient care. conclusions the healthcare industry values many of the basic underlying tenets of blockchain technology, such as trusted execution, non-repudiation of data, auditable trails and records for transactions, full replications of data in a secure environment, consensus on data changes, and decentralization of authority/data. blockchain technology holds high promise of being a widely adopted mechanism in the healthcare system for resolving issues that have long concerned the industry. at the same time, there are many areas of blockchain that are relatively untested in a healthcare environment, such as the need for a service level agreement, viability of privacy, scalability of a system to handle large numbers of participants, control and restrictions around access to patient data, and issues of patient record ownership. despite its tremendous potential, healthcare systems should be cautiously optimistic regarding blockchain technology and maintain a healthy skepticism toward the hype surrounding it today. as healthcare systems embark on securing and digitizing their infrastructure, they should focus on introducing novel clinical decision support systems using analytics and ai. blockchain shows great potential in providing a foundation to support and advance ai. as use cases for blockchain are identified that have compelling value to healthcare—from reducing cost to improving patient outcomes, engagement, and experiences—they can be prototyped with attention to privacy, security, and compliance from the start, and piloted with de-identified test data across consortiums of participating healthcare organizations to test, improve, and evolve the solutions for optimal effectiveness. funding statement: the author(s) received no financial support for the research, authorship, and/or publication of this article. conflict of interest: none of the authors declare any conflicts of interest. contributors’ contributions: each author contributed to the conception, writing, and revisions of the article. https://doi.org/10.30953/bhty.v2.114 page 7 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.114 references 1. shaping the future of health and healthcare &gt; initiatives | world economic forum [internet]. 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[cited 2019 may 27]. available from: https://www.synaptichealthalliance.com/ https://doi.org/10.30953/bhty.v2.114 http://joiv.org/index.php/joiv/article/view/109� http://joiv.org/index.php/joiv/article/view/109� http://www.ncbi.nlm.nih.gov/pubmed/25592594� http://www.ncbi.nlm.nih.gov/pubmed/25592594� http://www.ncbi.nlm.nih.gov/pubmed/17946702� http://www.ncbi.nlm.nih.gov/pubmed/17946702� http://www.ncbi.nlm.nih.gov/pubmed/25880362� http://www.ncbi.nlm.nih.gov/pubmed/25880362� https://www.ponemon.org/local/upload/file/sixthannualpatientprivacy%26datasecurityreportfinal6.pdf https://www.ponemon.org/local/upload/file/sixthannualpatientprivacy%26datasecurityreportfinal6.pdf https://www.ponemon.org/local/upload/file/sixthannualpatientprivacy%26datasecurityreportfinal6.pdf https://www.healthit.gov/sites/default/files/onc_records-request-research-report_2017-06-01.pdf� https://www.healthit.gov/sites/default/files/onc_records-request-research-report_2017-06-01.pdf� https://www.healthit.gov/sites/default/files/onc_records-request-research-report_2017-06-01.pdf� http://www.ncbi.nlm.nih.gov/pubmed/11157533� http://www.ncbi.nlm.nih.gov/pubmed/11157533� http://www.healthinfolaw.org/comparative-analysis/who-owns-medical-records-50-state-comparison� http://www.healthinfolaw.org/comparative-analysis/who-owns-medical-records-50-state-comparison� http://www.healthinfolaw.org/comparative-analysis/who-owns-medical-records-50-state-comparison� https://www2.deloitte.com/us/en/pages/public-sector/articles/blockchain-opportunities-for-health-care.html#� https://www2.deloitte.com/us/en/pages/public-sector/articles/blockchain-opportunities-for-health-care.html#� https://www2.deloitte.com/us/en/pages/public-sector/articles/blockchain-opportunities-for-health-care.html#� https://www2.deloitte.com/us/en/pages/public-sector/articles/blockchain-opportunities-for-health-care.html#� https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html� https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html� https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html� https://medium.com/coinmonks/sidechains-solving-the-blockchain-scaling-problem-b3847918b44� https://medium.com/coinmonks/sidechains-solving-the-blockchain-scaling-problem-b3847918b44� https://medium.com/coinmonks/sidechains-solving-the-blockchain-scaling-problem-b3847918b44� http://www.ncbi.nlm.nih.gov/pubmed/28357041� http://www.ncbi.nlm.nih.gov/pubmed/28357041� http://www.rjkrawiec.com/blog/2016/9/15/blockchain-opportunities-for-healthcare� http://www.rjkrawiec.com/blog/2016/9/15/blockchain-opportunities-for-healthcare� http://www.rjkrawiec.com/blog/2016/9/15/blockchain-opportunities-for-healthcare� http://www.ncbi.nlm.nih.gov/pubmed/29854130� http://www.ncbi.nlm.nih.gov/pubmed/29854130� http://shop.oreilly.com/product/0636920037040.do http://shop.oreilly.com/product/0636920037040.do https://www.healthcaredive.com/news/change-healthcare-snaps-up-blockchain-startup-pokitdok/544698/� https://www.healthcaredive.com/news/change-healthcare-snaps-up-blockchain-startup-pokitdok/544698/� https://www.healthcaredive.com/news/change-healthcare-snaps-up-blockchain-startup-pokitdok/544698/� https://www.synaptichealthalliance.com/� page 9 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.114 33. can blockchain’s immutability survive gdpr’s right to be forgotten? 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[cited 2018 aug 15]. available from: https:// www.himss.org/news/part-2-healthcareblockchain-path-success-2018 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. see: http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v2.114� https://diginomica.com/2018/05/09/can-blockchains-immutability-survive-gdprs-right-forgotten/� https://diginomica.com/2018/05/09/can-blockchains-immutability-survive-gdprs-right-forgotten/� https://diginomica.com/2018/05/09/can-blockchains-immutability-survive-gdprs-right-forgotten/� https://hitinfrastructure.com/news/himss-advises-layered-approach-to-healthcare-blockchain� https://hitinfrastructure.com/news/himss-advises-layered-approach-to-healthcare-blockchain� https://hitinfrastructure.com/news/himss-advises-layered-approach-to-healthcare-blockchain� https://www.microsoft.com/en-us/cybersecurity/content-hub/advancing-blockchain-cybersecurity� https://www.microsoft.com/en-us/cybersecurity/content-hub/advancing-blockchain-cybersecurity� https://www.microsoft.com/en-us/cybersecurity/content-hub/advancing-blockchain-cybersecurity� https://www.himss.org/news/part-2-healthcare-blockchain-path-success-2018� https://www.himss.org/news/part-2-healthcare-blockchain-path-success-2018� https://www.himss.org/news/part-2-healthcare-blockchain-path-success-2018� http://creativecommons.org/licenses/by-nc/4.0� http://creativecommons.org/licenses/by-nc/4.0� 1 (page number not for citation purpose) blockchain in healthcare today 2021. © 2021 the authors. this is an open access article distributed under the terms of the creative commons attribution-noncommercial 4.0 international license (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. citation: blockchain in healthcare today 2021, 4: 175 http://dx.doi.org/10.30953/bhty.v4.175 discussion a proposal for decentralized, global, verifiable health care credential standards grounded in pharmaceutical authorized trading partners victor dods and ben taylor* ledgerdomain, las vegas, nv, usa abstract the twin forces of privacy law and data breaches have fundamentally challenged how we collect, store, and share sensitive information. within this landscape, healthcare information is sacrosanct – and intimately tied to identity and data ownership. building on prior work with ucla health, genentech (a member of the roche group), sanofi, amgen, biogen, and others, we offer this opinion piece to promote the development of a standard for decentralized verifiable credentials (vcs). this will empower authorized trading partners (atps) in the pharmaceutical supply chain to trade and exchange information in compliance with the us federal law. starting with credentialing and interoperability for the atp community, our ultimate goal was to chart a path to a global standard for all health care vcs – providing individuals and health-care professionals control over their own data. by sharing our results and releasing essential components of the work to the public domain, we hope to align and connect with other foundational efforts, thus evolving standards within a truly open framework with broad stakeholder involvement. keywords: verifiable credentials; identity; dscsa; pharmaceutical supply chain; interoperability received: 23 february 2021; accepted: 18 march 2021; published: 28 april 2021 the rise of covid-19 and the solarwinds hack have exposed deep and systemic vulnerabilities in our health-care system (1).1 as the world converges around solutions to the pandemic, ‘the largest and most sophisticated [cyber]attack the world has ever seen’ remains largely unresolved. both have far-reaching implications on how we prevent and mitigate future crises. back in our school days, the janitor had a big key ring that provided access to any office or drawer. if that key ring was held by somebody with bad intentions, bad things could happen. this is the case today with any system that manages your identity on your behalf, including government 1 for instance, as the covid-19 credentials initiative wrote in its hello world, ‘without a holistic perspective from the onset, many covid-19 technology solutions may introduce unintended results (e.g. surveillance, abuse of personal data, social inequalities). in order to avoid such outcomes, we have committed ourselves to open collaboration with a diverse range of experts, embracing open standards, and protecting the fundamental privacy and personal data rights of all stakeholders’ (2). systems and technology enterprises. the administrator always has a backdoor (3, 4). by contrast, in a decentralized system you decide who holds your private key.2 while governments, privacy advocates, and security professionals redraw the lines around anonymity and data protection (5), personal health care information remains sacrosanct. just as the solarwinds hack provoked important questions about information security practices, so we must ask whether there is any rationale to centrally administer personal health care information. we assert the answer is no. the time has come for self-sovereign health-care privacy, in which individuals and health-care professionals have some measure of control over their data and identities with their own private credentials (6). this provides the bedrock for stakeholders to carry only their own data 2 with bitcoin, you can hold your own wallet, or you can hire and fire coinbase. either way, you are in control. *correspondence: ben taylor. email: ben.taylor@ledgerdomain.com https://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.30953/bhty.v4.175 mailto:ben.taylor@ledgerdomain.com citation: blockchain in healthcare today 2021, 4: 175 http://dx.doi.org/10.30953/bhty.v4.1752 (page number not for citation purpose) victor dods et al. and leverage authenticated identities, interacting safely with select parties of their interest. this would impact everything from drug supply assurance to cell and gene therapies and clinical studies, not to mention the needs of underserved communities. the need for atp vcs while health care is an enormously diverse and complex ecosystem, the secure and interoperable management of identity and private data is a common challenge.3 the starting point in our effort to address this lies in an emergent class of identities with relatively clear boundaries and a strong motivating use case: authorized trading partners (atps) as defined by statute in the us pharmaceutical supply chain. the drug supply chain security act (dscsa) imposes particular requirements on five groups (‘entity types’) of stakeholders: manufacturers, repackagers, wholesale distributors, third-party logistics providers (3pls), and dispensers (e.g. pharmacies) (11). one such requirement is an extended ‘know your customer’ rule, according to which each atp is required to confirm that their trading partners are also authorized. in many cases, the law requires interactions between entities without any direct business relationship.4 to enable near-real-time interoperability within the atp community, stakeholders have identified the value5 of decentralized ledger technologies (dlts), such as blockchain and decentralized identifiers (dids). together they provide all parties with a ‘single source of truth’ to address challenges, such as master data management and counterfeit detection.6 at a more fundamental level, atps must be able to identify other atps using vcs for compliant transactions and information disclosures; the same necessity motivated the development of the extended authorized trading partner (xatp) framework (16, 17). 3 other foundational efforts to develop and deploy credentials for health care include the vaccination credential initiative (7), decentralized identity foundation (8), commonpass by the commons project and the world economic forum (9), and the smart health cards framework (10). our aim is to bring together stakeholders to address an atp-specific w3c vc scheme, and look towards interoperability and expansion. 4 under certain circumstances, drug packages are required to be verified with the manufacturer or repackager in order for transactions to proceed. one such circumstance is the saleable returns process, in which dispensers with surplus drug return the drug to their wholesaler, or sell it to another atp. this process represents 2–3% of the overall volume of the us pharmaceutical supply chain, or 59 million units annually (12). manufacturers and dispensers typically have no former business relationship; yet, there must be a framework for these parties to interact within the broader requirements of the ‘fully electronic, interoperable system’ (13) mandated by the law. 5 ‘data-informed technologies, such as distributed ledger solutions like blockchain, will be critical to support fda’s track-and-trace priorities’ (14). 6 much of this effort would not be possible without the near-universal adoption of the gs1 dscsa standard (15) within the us pharmaceutical supply chain. while there are many hurdles in standards development and adoption, we believe that alignment can be more rapidly achieved in well-defined communities with clear and present needs. any framework addressing this challenge must satisfy the following two requirements: 1. it must prove that a given credential holder is a valid and current atp of a given atp entity type, and 2. it must provide mechanisms to comply with privacy laws, such as gdpr and the california privacy act (calprivacy), which concerns personally identifiable information (pii). to realize this mission, a coalition of stakeholders must come together to provide interoperable tooling. collaborative proposal for a common atp vc standard our vision is an interoperable system employing cryptographic schemes, which allow for the selective disclosure of credential elements. there is also a need for interoperability regarding workflows mandated by law.7 the w3c vc data model (17) presents a flexible and extensible credential scheme leveraging decentralized identifiers (dids),8 which meet the needs of atps.9,10 in this case, we propose an atp-specific scheme anchored in w3c standards, which would allow interoperation between all atp software solutions.11 to chart the path of an atp vc standard, several key questions must be addressed by stakeholders.12 these, in turn, provoke further questions around implementation, which can be broadly divided into software and non-software domains. key questions 1. who are the stakeholders (atp entities, relevant governance groups, solution providers, and accreditors)?13 7 by its nature, interoperation implies authentication and the corresponding need for a mutually understood credential scheme, which allows each atp’s software to verify the validity of transactors within the ecosystem. 8 a portable url-based identifier associated with an entity, most often used in vcs. they allow vcs to be easily ported from one repository to another without the need for reissuing the credential (18, 19). 9 the extensibility of the w3c vc data model comes in the form of allowing context-specific credential definitions, a natural complement to dids. 10 beyond the healthcare ecosystem, w3c dids have also seen adoption by the international technology standards organization object management group (20) and the sovrin foundation (21). 11 these ideas may justifiably be termed ‘old wine in new bottles’, but collective security and interoperability always improve as more parties adopt and then adhere to best security practices. at the same time, we are certainly not discouraging any ongoing efforts to bolt w3c credentials onto identity systems that are anchored in traditional centralized registries. these efforts can certainly enhance interoperability, but it should nonetheless be noted that they neither support privacy nor address those systems’ inherent single points of failure. (painting spots on a house cat doesn’t make it a leopard!) 12 as different atp entities will have a stake in different parts of the standard, participation in the standard development should reflect those roles. 13 the fda is a key indirect stakeholder, but best practice is for regulators to work through atps to avoid creating a single point of failure. http://dx.doi.org/10.30953/bhty.v4.175 citation: blockchain in healthcare today 2021, 4: 175 http://dx.doi.org/10.30953/bhty.v4.175 3 (page number not for citation purpose) a proposal for decentralized, global, verifiable health care credential standards 2. what are the stakeholders’ workflows?14 3. what workflows will be, or are likely to be, needed for compliance with future laws? 4. what are the requirements do those workflows impose on the vcs?15 5. how best to develop standards needed to meet those requirements? non-software implementation 1. what should the trust model be? in other words, how does a verifier determine who is authorized to issue atp credentials? this might take one of two forms: a. a hierarchical public key infrastructure (pki) architecture supported by the u.s. department of health and human services (hhs), food and drug administration (fda), national institute of standards and technology (nist), a consortium of atps, and other stakeholders (such as pdg (24)) defining the trust anchors16 for issuing credentials to atps.17 this would be less fragile for purposes of vc verification, but would still require a central authority (or consortium) to define the trust anchors. b. choose your own trusted authorities where each organization defines its own trust anchors (perhaps based on some minimal as-needed whitelisting). this would be more fragile for purposes of vc verification, but would not require a central authority (or consortium) to define the trust anchors. 2. for each atp type and its delegates, what is needed in the vc to meet the workflow needs of different organizations? a. which schema defines what a vc looks like for each atp? b. what are the rules and requirements for how an atp can be issued a vc? 14 the xatp application framework incorporates one potential workflow for an enhanced verification. because this involves interaction between dispensers and manufacturers, particular emphasis has been placed on credentialing for those entity types (16, 17). a credentialing model for dispensers is currently in place, with manufacturer credentialing on the roadmap. 15 given the need for protecting pii and minimizing its disclosure, we recommend that the atp vc use a cryptographic scheme, which allows zero-knowledge proofs and selective disclosure. in particular, we recommend the use of bbs+ signatures, which allow a flexible and minimal disclosure of information in credentials (22), thus enabling compliance with data privacy laws and best practices, while still providing powerful and meaningful credentials. bbs+ is a cryptographic signature across multiple messages that also support selectively disclosing any subset of messages, while the remainder are withheld when presented to a relying party. the implementation is written in rust in hyperledger ursa, which supports compiling to mobile devices, servers, and webassembly (23). 16 ‘an authoritative entity represented by a public key and associated data. the public key is used to verify digital signatures, and the associated data is used to constrain the types of information or actions for which the trust anchor is authoritative” (25). 17 a precedent for this is mozilla’s approach (26). c. what are the rules and requirements for a company to be an issuer of vcs? d. what are the presentation requirements for vcs? i what are the contexts that require a presentation?18 ii which claims must be shown in each context?19 iii which cryptographical model is acceptable to participants (e.g. ecdsa vs. pairing-based crypto with zero-knowledge proofs (zkps))? iv what are acceptable methods of revocation checks (e.g. zkps, bit-vectors, and certificate chains)? e. which formats are mandated, and which are acceptable (e.g. bare message format (binary), json-ld, and jwt)? f. which systems will be used to create presentations and accept them? 3. what are the rules and requirements for defining where schemas, identifiers, and keys can be stored and secured?20 4. which schema will define what a vc looks like for drug provenance? 5. how is this system bootstrapped? 6. how does a new atp in the space become certified? 7. which are capabilities needed regarding the hiding or minimizing revelation of pii, especially with respect to relevant privacy laws, such as gdpr and calprivacy? software implementation 1. what is the scope of interoperability with different w3c-vc-compliant formats?21 2. how many different cryptographic schemes need to be supported by each atp vc implementation?22 18 for example, drug receipt, returns, and master data access. 19 ‘a verifiable claim is a qualification, achievement, quality, or piece of information about an entity’s background, such as a name, government id, payment provider, home address, or university degree. such a claim describes a quality or qualities, property or properties of an entity which establish its existence and uniqueness’ (27). claims can be grouped into ‘bundles’. for example, a pharmacist requesting drug verification from a manufacturer would present a bundle, indicating that they are a currently licensed pharmacist and a pharmacist in charge at a particular pharmacy. together, the did and the bundle of claims constitute a vc. we may assume that the higher the stakes, the more claims must be revealed in the presentation of the vc. 20 in the case of xatp, identity information is held on the user’s mobile device instead of the service, and control over the identity lies with the keys stored in the device (16, 17). 21 json has emerged as a widely used data format, including jwt-based vcs used by spherity’s atp vcs (28), and json-ld-based vcs, which are generally recommended by the w3c vc data model and employed by the covid-19 credentials initiative hosted by linux foundation public health (29). alternatively, a compact, binary encoding scheme (such as protobufs [30]) might be preferred in many machine-to-machine workflows, with jwt/json-ld employed for interoperability between organizations (29). 22 the schema for w3c vcs (32) specifies four digital signature schemes. for the purpose of minimizing pii revelations, we are exploring the use of zkps and selective disclosure. it should be noted that zkp imposes certain restraints on the data structure of attributes, requiring mapping nested content to a list (31). http://dx.doi.org/10.30953/bhty.v4.175 citation: blockchain in healthcare today 2021, 4: 175 http://dx.doi.org/10.30953/bhty.v4.1754 (page number not for citation purpose) victor dods et al. 3. which mechanisms for credential revocation would be mandated or supported?23 4. how shall guidance and resources for creating a compliant atp vc implementation be developed? 5. what are the specifications for test cases and test environments for verifying compliance of an atp vc implementation?24 for a standard to be adopted, the vc should not be a ‘black box’. there should be sufficient transparency for understanding how issuance and verification work, the context necessary for vc usage and a path to including a possible human in the loop.25 recommendations and next steps within the atp context, many of the key questions raised by this paper have already been partially resolved by stakeholders joining together to define a trust framework and governance structure (35, 36).26 this proposed solution is a complete, fully transparent, open-source reference implementation that satisfies the requirement for interoperability across all atp types. we are looking to work with a coalition of the willing to design and contribute key components of this effort, including an initial implementation that should be adequate for everyday use and upgradeable over time. technical considerations • we see the overall effort constituting the definition of a global atp schema and development of a reference implementation, which will be entirely open source and include protocols for revocation. • we believe that the schema should be global, in that the contents of an atp vc should be global, but that different formats for representing the vc should be allowed.27 • as a strawman, we suggest that a reference implementation should be done in rust with a golang wrapper (standard) and a webassembly wrapper (desired).28 23 there is only some high-level guidance in the vcs’ data model. 24 for example, each atp vc vendor could provide their own ‘test network’ (or instructions for how to run one locally, in the case of open source implementation) against which denizens of the atp software-verse can test their code. 25 for instance, easy discovery of the atp’s website or other relevant contact information. json-ld is a good candidate for this purpose as it enables clients to uncover linked data based on a principle known as follow your nose (33). 26 these include the internet identity workshop headed by doc searls, phil windley, and kaliya young (34), and more recently sovrin foundation, spherity, the center for supply chain studies, and the fda’s dscsa pilot project program. 27 note that for much of this article, we discuss vcs for atp entities, which may be distinguished from a personal vc that specifically relates to a person’s identity (e.g. equivalent to their driver’s license and containing their personal information). a pharmacist might have an atp pharmacist credential that attests to his or her pharmacist license number, validity date, and other details that would not contain unrelated identity information, such as their home address. there are also vcs for entities meant for automated systems, such as authentication systems and server backends. 28 webassembly (wasm) is an assembly-like language with a compact binary • we do not see any need for anchoring standards, so a diversity of anchoring points (e.g. ethereum or sovrin) should be acceptable. similarly, using an x509 did method to hook into existing web pki for trust anchors should be acceptable (39). • we support technical decisions that are future proof. governance considerations • any role-based privilege supported by a vc should be factually grounded and accredited under a trust framework with well-defined governance.29 • we believe that the global schema should ultimately be an iso standard. as for the atp schema itself, november 2022 should be targeted, as the interoperability requirements take full effect by november 2023. • the reference implementation should be released under an open-source license (e.g. apache 2), and it should be open to global collaboration. much as a driver’s license has become a standard form of physical identification outside its original use case, so a vc schema grounded in the atp community would address a stipulated need within the us pharmaceutical supply chain, while also having far-reaching implications for other health care domains. we aim for a future in which the vc schema developed for atps could be seamlessly extended to other open-source credentials to serve patients, caregivers, and all participants in the health-care system. in the same world, patients could then share vcbacked laboratory results and prescriptions with pharmacies and clinics.30 due care would be needed in adherence to best practices as promulgated by hipaa, gdpr, and calprivacy, so that patients carry only their own data31 and disclose it to select parties of their choice. a secure, interoperable, and privacy-preserving world would mean lower costs, better care outcomes, format that runs with near-native performance and provides languages, such as c/c++, c#, and rust with a compilation target, so that they can run on the web (37). in 2019, the specification became a w3c recommendation (38), and as such, may have particular applicability to interoperability. given that webassembly is the avenue for compiled code to run within the browser, at the very minimum, a webassembly port of the open-source solution should provide a means to easily verify vcs from within the browser. however, with regard to holding and issuing credentials, the browser is inherently less secure than non-browser platforms, and thus, poses additional challenges. 29 within a trust framework model, the governance layer establishes business, legal, and technical policies, and manages membership and participation. governance is typically handled by organizations created by constituent members to administer the activities associated with operating an identity federation. they may be government programs, corporate entities, not-for-profit membership organizations, or industry associations (38, 40). 30 this change simply digitizes the information movement currently achieved by paper. paper versions of credentials can still be given simultaneously until on-theground workflows adapt to the new vcs. 31 for principles concerning the use of biometrics within a self-sovereign identity solution, see callahan et al. (41). http://dx.doi.org/10.30953/bhty.v4.175 citation: blockchain in healthcare today 2021, 4: 175 http://dx.doi.org/10.30953/bhty.v4.175 5 (page number not for citation purpose) a proposal for decentralized, global, verifiable health care credential standards greater rate of clinical innovation, and an abundance of opportunity for private companies to add value by leveraging the schema. over the coming weeks and months, we will be working with stakeholders in the health care and identity space to chart the best path forward for atp w3c vcs, and beyond. in the interest of public and transparent conversation, specific schemas, supporting documentation, and updates to this effort will be published at zoogma.org. acknowledgements authors would like to personally thank mike lodder for his insights regarding dids and cryptographic schema. the authors would also like to thank brian behlendorf (linux foundation); connie jung, rph, phd (fda’s center for drug evaluation and research); melanie nuce, gena morgan, and peter sturtevant (gs1); eric marshall (partnership for dscsa governance); bob celeste (center for supply chain studies); and max sills, jd. the authors would also like to acknowledge the xatp working group for their contributions to the framework that informed this proposal. these include ghada l. ashkar, pharm, kalpan s. patel, pharmd, mba, and josenor de jesus, pharmd, mba, fache (ucla health); vidya rajaram, mark karhoff, nirmal annamreddy, and kathy daniusis (genentech, a member of the roche group); nikkhil vinnakota, natalie helms, and alina grigorian (amgen); arthi nagaraj (sanofi); greg plante (iqvia); todd barrett, rph (providence health); and ben nichols, will jack, and will chien, pharmd, mba (ledgerdomain). the foregoing acknowledgements do not imply consensus nor endorsement. conflict of interest and funding the authors declare no potential conflicts of interest. this proposed study represents the views of the authors and has no external funding. contributors both authors contributed to the conception, development, and writing of the proposal. victor dods led the technical considerations and recommendations around dids and cryptographic schema. references 1. heath b. solarwinds hack was ‘largest and most sophisticated attack’ ever – microsoft president [internet]. financial post; 2021 [cited 22 february 2021]. available from: https://financialpost.com/pmn/business-pmn/solarwinds-hack-was-largest-and-most-sophisticated-attack-ever-microsoft-president 2. covid-19 credentials initiative. hello world from the covid-19 credentials initiative [internet]. medium; 2020 [cited 22 february 2021]. available from: https://cci-2020.medium.com/hello-worldfrom-the-covid-19-credentials-initiative-6d45534c4b3a 3. bossert tp. i was the homeland security adviser to trump. we’re being hacked [internet]. the new york times; 2020 [cited 22 february 2021]. available from: https://www.nytimes. com/2020/12/16/opinion/fireeye-solarwinds-russia-hack.html 4. krebs b. at least 30,000 u.s. organizations newly hacked via holes in microsoft’s email software [internet]. krebs on security; 2021 march 5 [cited 18 march 2021]. available from: https://krebsonsecurity.com/2021/03/at-least-30000-u-s-organizations-newly-hacked-via-holes-in-microsofts-email-software 5. newton c. warning signal: the messaging app’s new features are causing internal turmoil [internet]. the verge; 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[internet]. slideshare; 2019 [cited 22 february 2021]. available from: https://www.slideshare.net/evernym/ what-are-decentralized-identifiers-dids 20. object manage ment group. object manage ment group issues request for information for disposable self-sovereign identity standard [internet]. object management group; 2021 [cited 22 february 2021]. available from: https://www.omg.org/news/releases/pr2021/01-21-21.htm 21. lodder m, hardman d. sovrin did method specification [internet]. sovrin foundation; 2021 [cited 22 february 2021]. available from: https://sovrin-foundation.github.io/sovrin/spec/ did-method-spec-template.html 22. looker t, steele o. bbs + signatures 2020 draft community group report [internet]. w3c community group; 2021 [cited 22 february 2021]. available from: https://w3c-ccg.github.io/ldp-bbs2020/ 23. hyperledger ursa. github [internet]; 2021 [cited 18 march 2021]. available from: https://github.com/hyperledger/ursa 24. usfda. drug supply chain security act public-private partnership [internet]. fda: 2021 [cited 15 april 2021]. available from: https://www.fda.gov/drugs/drug supply-chain-security-act-dscsa/drug-supply-chain-security act-public-private-partnership 25. housley r, ashmore s, wallace c. trust anchor format [internet]. internet engineering task force (ietf); 2010 [cited 22 february 2021]. available from: https://tools.ietf.org/html/rfc5914 26. thayer w. why does mozilla maintain our own root certificate store? [internet]. mozilla security blog. mozilla; 2019 [cited 22 february 2021]. available from: https://blog.mozilla.org/security/2019/02/14/ why-does-mozilla-maintain-our-own-root-certificate-store/ 27. otto n, lee s, sletten b, burnett d, sporny m, ebert k. verifiable credentials use cases [internet]. w3c working group. w3c; 2019 [cited 22 february 2021]. available from: https:// www.w3.org/tr/vc-use-cases/ 28. spherity. entities [internet]. spherity; 2021 [cited 22 february 2021]. available from: https://docs.spherity.com/spherity-api/ verifiable-credentials-api/entities 29. 2021.02.17 general meeting agenda – healthcare sig [internet]. hyperledger foundation; 2021 [cited 22 february 2021]. available from: https://wiki.hyperledger.org/display/ hcsig/2021.02.17+general+meeting+agenda 30. google. protocol buffers – google’s data interchange format [internet]. github; 2008 [cited 22 february 2021]. available from: https://github.com/protocolbuffers/protobuf 31. young k. verifiable 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available from: https://connectis.com/wp-content/uploads/2018/05/oix-white-paper_trust-frameworks-for-identity-systems_final.pdf 37. webassembly [internet]. mozilla developer network (mdn) web docs; 2021 [cited 18 march 2021]. available from: https:// developer.mozilla.org/en-us/docs/webassembly 38. rossberg a. webassembly core specification [internet]. w3c working group. w3c; 2019 [cited 18 march 2021]. available from: https://www.w3.org/tr/wasm-core-1/ 39. kaptijn b, gort s, stöcker c. x.509 did method [internet]. web of trust info. github; 2019 [cited 22 february 2021]. available from: https://github.com/weboftrustinfo/rwot9prague/blob/master/topics-and-advance-readings/x.509-didmethod.md 40. sovrin governance framework working group. sovrin governance framework v2. sovrin foundation; 2019 [cited 22 february 2021]. available from: https://sovrin.org/wp-content/ uploads/sovrin-governance-framework-v2-master-document-v2.pdf 41. callahan j, vescent h, young k, duane d, appelcline s, othman a, et al. six principles for self-sovereign biometrics. web of trust info. github; 2019 [cited 22 february 2021]. available from: https://github.com/weboftrustinfo/rebooting-the-web-of-trust-spring2018/blob/master/draft-documents/biometrics.md http://dx.doi.org/10.30953/bhty.v4.175 https://www.xatp.org/whitepaper https://www.xatp.org/whitepaper https://doi.org/10.30953/bhty.v4.168 https://www.w3.org/tr/vc-data-model/ https://www.w3.org/tr/vc-data-model/ https://www.slideshare.net/evernym/what-are-decentralized-identifiers-dids https://www.slideshare.net/evernym/what-are-decentralized-identifiers-dids https://www.omg.org/news/releases/pr2021/01-21-21.htm https://www.omg.org/news/releases/pr2021/01-21-21.htm https://sovrin-foundation.github.io/sovrin/spec/did-method-spec-template.html https://sovrin-foundation.github.io/sovrin/spec/did-method-spec-template.html https://w3c-ccg.github.io/ldp-bbs2020/ https://github.com/hyperledger/ursa 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https://developer.mozilla.org/en-us/docs/webassembly https://developer.mozilla.org/en-us/docs/webassembly https://www.w3.org/tr/wasm-core-1/ https://github.com/weboftrustinfo/rwot9-prague/blob/master/topics-and-advance-readings/x.509-did-method.md https://github.com/weboftrustinfo/rwot9-prague/blob/master/topics-and-advance-readings/x.509-did-method.md https://github.com/weboftrustinfo/rwot9-prague/blob/master/topics-and-advance-readings/x.509-did-method.md https://sovrin.org/wp-content/uploads/sovrin-governance-framework-v2-master-document-v2.pdf https://sovrin.org/wp-content/uploads/sovrin-governance-framework-v2-master-document-v2.pdf https://sovrin.org/wp-content/uploads/sovrin-governance-framework-v2-master-document-v2.pdf https://github.com/weboftrustinfo/rebooting-the-web-of-trust-spring2018/blob/master/draft-documents/biometrics.md https://github.com/weboftrustinfo/rebooting-the-web-of-trust-spring2018/blob/master/draft-documents/biometrics.md https://github.com/weboftrustinfo/rebooting-the-web-of-trust-spring2018/blob/master/draft-documents/biometrics.md page 1 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 trust by design: evaluating issues and perceptions within clinical passporting will abramson,1 dr nicole e. van deursen,2 william j buchanan1 affiliations: 1blockpass id lab, school of computing, edinburgh napier university, edinburgh, uk; 2national cyber security centre, the hague, the netherlands, edinburgh napier university, edinburgh, uk corresponding author: william j buchanan, blockpass id lab, school of computing, edinburgh napier university, edinburgh. w.buchanan@napier.ac.uk keywords: digital credentials, trust, healthcare, passporting, ssi, design section: original clinical research a substantial administrative burden is placed on healthcare professionals as they manage and progress through their careers. identity verification, pre-employment screening, and appraisals: the bureaucracy associated with each of these processes takes precious time out of a healthcare professional’s day. time that could have been spent focused on patient care. in the midst of the covid-19 crisis, it is more important than ever to optimize these professionals’ time. this article presents the synthesis of a design workshop held at the royal college of physicians of edinburgh (rcpe) and subsequent interviews with healthcare professionals. the main research question posed is whether these processes can be re-imagined using digital technologies, specifically selfsovereign identity? a key contribution in the article is the development of a set of user-led requirements and design principles for identity systems used within healthcare. these are then contrasted with design principles found in the literature. the results of this study confirm the need and potential of professionalizing identity and credential management throughout a healthcare professional’s career. introduction while the covid-19 crisis has brought the challenges of staff mobility into the spotlight, the administrativ e burden placed on a healthcare professional throughout their career has always been present. over the years, healthcare service providers have increased the minimum standard for identity verification and pre-employment checks in line with new regulations.1,2 as a result, the time spent on these processes has increased. a house of lord’s report, for example, estimated that https://doi.org/10.30953/bhty.v3.140 mailto:w.buchanan@napier.ac.uk https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.140&domain=blockchainhealthcaretoday.com&date_stamp=2019-06-25 page 2 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 25,000 junior doctor days a year were currently being spent on these administrative tasks.3 in addition to this, digitization of healthcare services has further increased the time and complexity associated with managing one’s career. in a 2011 us survey, 87% of physicians stated that the leading cause of stress was down to administration,4 and a study of finnish physicians found that poorly functioning it systems continue to be a major cause of stress, particularly for those in highly time pressured roles.5 in a crisis like the covid-19 outbreak, the need for a healthcare service to react to rapidly evolving, location-specific stresses at a trust or hospital level cannot be clearer. different locations may hit their peak at different times, while some areas may only be minimally affected.6 however, consultation with a royal college of physicians of edinburgh (rcpe) trainee suggests that on-boarding into a new trust or hospital can take up to 2 days. this is 2 days of precious time that could potentially have been spent saving lives. technological solutions have regularly been heralded for their ability to reduce inefficiencies and streamline patient care. blockchain technology is just one of the more recent innovations predicted to have a disruptive impact.7 often though, the reality in the hospitals is different to the design assumptions made by technologists, and the productivity benefits are not always obvious.8 this article thus presents an initial set of design principles for any technical solution attempting to reduce the administrative burdens currently placed on healthcare professionals. an analysis of discourse about digital identities, verifiable claims, and trust has led to a theoretical set of trust and design principles. these principles were validated in a workshop with healthcare organizations held at the rcpe. this research takes an initial step towards understanding the problem space from the perspective of those currently experiencing it and lays the foundation for future quantitative studies in this area. research questions this article evaluates a use case in which a person can digitally obtain, manage, and present his/her professional credentials and personally identifiable data within an healthcare system. we limited the scope of the work to healthcare professionals, as these are identified as being burdened with administrative tasks associated with identity verification and pre-employment checks, as well as recording and managing their credentials as they progress through their career, a burden which is generally expected to take place in people’s personal lives. the following research questions were identified: • what are the identity interactions that a healthcare professional must manage throughout their career? • how might self-sovereign identity technology be used to simplify a healthcare professional identity administration? • how do the design principles of selfsovereign identity stated in the literature and technical sphere meet the requirements of the healthcare professionals who would actually be using these systems? related literature berwick, nolan, and whittington define the triple aim focusing on improving the care, health, and cost when accessing healthcare performance in the united states.9 they point out that these goals are interdependent so must be considered together when planning and evaluating healthcare changes. it has been https://doi.org/10.30953/bhty.v3.140 page 3 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 suggested that this framework should be extended to consider a fourth aim, care for the provider,10 due to reports that staff burnout and dissatisfaction are widespread. as care providers are on the frontline when it comes to achieving the triple aim for healthcare services, including their well-being into this assessment makes sense. healthcare professional credentials healthcare providers have a requirement to maintain strong identity verification checks to ensure that their employees are who they claim to be and that they have the required skills and training for the job.2,11,12 unfortunately, there have been examples throughout the world of doctors practicing without licenses. this puts patients’ lives at risk and reduces the trust in the profession as a whole. examples include the uk general medical council recently having to recheck credentials of 3,000 doctors after a fraudulent psychiatrist was found to have practiced for 23 years without proper credentials,13 the case of a social worker in canada involved in more than 100 child protection cases,14 and the notorious us case of christopher duntsch a.k.a dr death.15 as a consequence, credentialing healthcare professionals is a crucial process in healthcare systems throughout the world. however, the current practices of many systems add huge overheads to both the administrators and the healthcare professionals. in a report on healthcare and digital credentials, the us federation of state medical boards (fsmb)11 analyze the use of digital credentials in healthcare, looking at the potential for both current technology and future technology to streamline the process and enhance trust in the system. the implementation of the federation credentials verification service (fcvs) in 1996,16 an ncqa-certified platform providing a centralized service for obtaining primary source, verified education information for medical practitioners applying for licensing in the united states. as the report11 outlines, the fcvs reduced the time to obtain a license from 60 days to 25 days, a significant reduction. however, efforts to improve the fcvs highlighted the underutilization of technology in the process. furthermore, 66% of this time is driven by parties outside of the control of the fcvs.11 these credential verification organizations are often redundant and increase the cost of the whole process. the report highlights the movement to disintermediate the creation and management of credentials, hinting at a movement toward individual ownership of credentials. along with this, there is an increasing need for clinical staff to provide digital evidence of their training, skills, and experience. read et al.17 investigated the usage of a passporting system for surgery clerkship and found that those involved often found that it improved student’s reporting of their performance in basic clinical skills. self-sovereign identity digital identifiers—and the trust entities place in them—enable many modern societal interactions. they thus allow organizations to perform critical activities with increased levels of trust. another way of looking at it is from a risk perspective and where digital identifiers and account information correlated with an identifier that helps organizations make risk-based decisions associated with a particular interaction and value exchange. unfortunately, traditional identity management systems continue to have security risks,18 such as: credential theft or loss, biometric impersonation, document forgery, and identity theft. https://doi.org/10.30953/bhty.v3.140 page 4 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 ssi uses a new type of identifier currently going through standardization at the w3c, a decentralized identifier (did).19 a did is an identifier under the sole domain of an entity, typically the entity that created it. it is cryptographically verifiable and independent of any central authority. rather than being assigned an identifier on account creation, dids let individuals provide their own identifiers for their digital relationships. systems built following an ssi architecture could offer the opportunity to rethink the entire credential process for physicians. before the electronic transmission of credentials can be put forward as a viable option for healthcare professionals, it must be considered if such a process would break any of the rules and regulations currently governing this area. the key things identity verification and authentication process must satisfy for most healthcare services are2,11: • is it possible to verify the authenticity of the claim? • is the claim a primary source attestation. for example, is your degree certification a certificate from the university you attended? • was the credential securely delivered from the credential holder to the verifier? • is there a clear, verifiable audit trail that can trace the origin of this credential? these points can, in fact, all be satisfied digitally through the use of digital signatures. the problem of scaling digital identities into healthcare systems has led many people to explore alternative methods to identify and authenticate people and things in the digital sphere. one of the proposed architectures is commonly referred to as self-sovereign identity (ssi). connor-green identifies that ssis could be one of the core use cases of blockchain in health.20 liang et al. outline a blockchain approach within a healthcare management system, where the distributed nature of ssis supports a scalable infrastructure that moves away from the centralized control of identity within many existing healthcare infrastructures (figure 1).21,22 method the goal of the workshop was to gather a set of values and principles that can be used to evaluate emerging technology for identity systems within healthcare. terminology and definitions are often much contested in different contexts. we used definitions provided in the selected papers as much as possible, but some definitions were adapted to better fit our goal to measure the value or principle. the next step in our research was a workshop organized at rcpe. it involved 14 participants with a wide ranging experience of different aspects of the healthcare system. the participants for the workshop were selected through consultation with the rcpe, and who were able to use their contacts to invite a diverse range of attendees. this included clinicians, rcpe trainees, and rcpe staff involved in data management, digital transformation, and education, as well as a representative from the general medical council (gmc). while no personal data was captured during the workshop and all attendees remain anonymous, explicit consent was obtained and the research aims were explained at the beginning of the workshop. during the workshop, the participants evaluated the fit of ssi within the healthcare domain. first, a process mapping exercise was used to develop an understanding of the current system, https://doi.org/10.30953/bhty.v3.140 page 5 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 looking at the identity data exchanges and key entities that verify or attest to identity attributes of healthcare professionals throughout their career. then, these identity moments were re-imagined within an ssi-enabled ecosystem, and this story was told in an interactive manner using physical props and audience participation in order to convey the capabilities that ssi systems could provide for healthcare professionals, without going into unnecessary technical detail. finally, the workshop participants were asked to evaluate the positive and negative aspects of the identity management system. the participants expressed their requirements, values, and expectations. the list that was distilled from these workshop discussions was compared against the list of design principles from the desk research. design principles for selfsovereign identity systems the success of any ssi system depends not only on the technical feasibility but also on the user acceptance and trust in the system. with users we mean all stakeholders (entities) within a specific context that will use that system together. trust is harder to define, as there exist more than 70 definitions in academic literature.23 trust is seen as a human strategy to cope with uncertainty, such as those we face in relations, actions, and innovation.24 in a digital context, trust is often transferred to cybersecurity measures such as technological controls, certificates, and organizational compliance frameworks. in ssi systems, at least some aspects of this trust shifts from trust between people toward confidence that is placed in cryptographic systems. as smolenski25 frames it: trust is being depersonalized. designing new digital identity figure 1—patient-centric personal health data management system.21 https://doi.org/10.30953/bhty.v3.140 page 6 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 systems means mimicking real-life situations in a digital way. human values, such as ethics and trust, need a digital equivalent that users need to accept and understand. how do we design trust from the start? which design principles are most valued by users and most likely to establish trust in these systems? there are many papers that refer to the laws of identity (coined by cameron in 2005)26 as the foundation for design principles. these laws explain the dynamics causing digital identity systems to succeed or fail in various contexts. although written before the era of ssi, cameron himself finds the laws still relevant, also for identity systems on the blockchain and decentralized identity.27 he points out, for instance, that the first four of the laws are also requirements within the gdpr. in 2016, christopher allen28 wrote 10 principles inspired by (among others) the work of cameron. his aim was to ensure that user control is at the heart of ssi. allen pointed out that identity can be a double-edged sword: it can be used for both beneficial and maleficent purposes. therefore, he states: an identity system must balance transparency, fairness, and support of the commons with protection for the individual. the sovrin foundation29 adopted allen’s principles and arranged them in three sections, but this causes some confusion as they used one principle twice and made another principle a section above other principles. other researchers and developers have used cameron’s or allen’s principles for inspiration and adapted them to their own lists of features. however, testing of ssi systems against these features and design principles is still rare. dunphy and petitcolas30 evaluated three identity management solutions (uport, sovrin, and shocard) against cameron’s laws of identity. their overview shows that none of the solutions meets all seven laws, and none of them meets the law of human integration: usability, user understanding, and user experience. they state that none of the schemes they evaluated are accompanied by an evidence-based vision of user interaction. one of the limitations is usable end-user key management for nontechnical users that remains unaddressed. furthermore, they express concern about tightening regulation, such as the gdpr, that sometimes contradicts the transparency of data storage in these solutions. finally, most solutions provide only ad hoc trust, as trust relies on integration between participating entities, and methods to achieve trust in the context of identity attributes are still evolving. ferdous et al.31 elaborated on the principles of allen and designed a taxonomy of essential properties for ssi. then, they compared four blockchain-based ssi systems (uport, jolo, sovrin, blockcerts) against the properties, and through desk research, they found that most of the systems satisfy most of the properties. similar work was done in a student project32 where students compared eight blockchainbased (idchainz, uport, everid, sovrin, lifeid, selfkey, shocard, sora) and three non-blockchain-based ssi systems (pds, irma, reclaimid) against each of allen’s principles with one additional principle.33 they concluded that some of the blockchain-based solutions fulfill all properties, but that some of the non-blockchain-based implementations meet most of the criteria as well. interestingly, their conclusions as to whether the properties are met do not always match the conclusions of ferdous et al.31 for two systems (uport and sovrin) that both projects evaluated. toth and andersonpriddy34 validated nine properties from earlier sources (e.g., allen and sovrin foundation) and https://doi.org/10.30953/bhty.v3.140 page 7 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 added new properties. they applied these properties to their architecture for digital identity and reasoned how these apply to their solution (nexgenid). to the best of our knowledge, published evaluation of values and principles with users in a specific ssi context is very rare. one project that focused on citizens and digital identity systems in general (not ssi specific) was the digital identity lab in the netherlands. in several interactive sessions with citizens, they found which values matter the most for digital identities.35 the research methods included interviews in the streets, meet-ups, expert sessions, and design sprints. the results include evaluation quadrants to plot digital identity providers and an overview of values that citizens find important and that can be used as input for ethical design and trust of digital identity systems. another project focusing on user experience is the irma made easy project.36 irma (i reveal my attributes) is a selfsovereign identity solution with a digital wallet. the irma made easy project works on the design of the app and website with a focus on accessibility. the developers of irma point out that user experience design affects how users handle the control over their information. from their experience, they share three lessons: 1. in order for new technology to be adopted, they require a smooth user experience. 2. user experience design for privacy is not the same as general user experience design. 3. a system that puts people in control over their data does not always lead to people using that control to protect their privacy: it can even lead to the opposite when they are tricked by others. from the literature study, we learned that there is a gap in academic research that includes evaluation of proposed ssi solutions from a user perspective with domain knowledge of the ecosystem. furthermore, to the best of our knowledge, the most commonly used design principles have not been validated by users for importance and priority. projects that included consumers focus on identity management in general, and studies on ssi systems tend to focus on the evaluation by technical experts through desk research. furthermore, when ssi design principles and features indeed are evaluated, the researchers re-use existing frameworks or lists of principles without user elicitation for principles that technology experts have not imagined yet. if we do not understand the requirements of end users, then we run the risk of creating digital tools that no one wants to use, or worse introducing unintended consequences through the deployment of these systems to domains with poorly understood requirements. there are countless examples of technology being introduced into healthcare only to make the jobs of those working alongside this technology worse, like the 15 logins needed to access different nhs systems.37 we compared the different lists of principles, features, and values that we found and created an overview of different and overlapping principles. the results are presented in table 1. the overview of principles was input for the next stage in our research, where we invited future users to express their opinion on principles and values. in the next section, we describe the workshop that we held with representatives of different entities in an ecosystem, in order to contribute to the knowledge of end-user perception and trust of ssi systems. workshop description after a brief introduction, participants were asked to complete a warm up exercise where they https://doi.org/10.30953/bhty.v3.140 page 8 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 table 1. list of design principles. cameron26 allen28 ferdous et al.31 de waag35 toth anderson priddy34 existence existence user control and consent control and consent consent control control and consent access access access access transparency transparency transparency pluralism of operators and technologies portability portability portability consistent experience across contexts interoperability interoperability interoperability persistence persistence persistence minimal disclosure for a constrained use minimalization minimalization data-minimalization protection protection security secure transactions and identity transfer autonomy autonomy justifiable parties choosability human integration ease of use usability disclosure ownership directed identity single source standard cost availability trust privacy integrity decentralization inclusivity reliability counterfeit prevention identity verification disclosure identity assurance recorded different identity interactions that occur throughout a typical day in their life. this included using a rfid card, logging into a digital system with a username and password, and authenticating to a mobile device, bank card payments, anything that involved some form of identification and authentication. participants were also asked to record times when https://doi.org/10.30953/bhty.v3.140 page 9 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 authentication failed, for example, through a rushed or forgotten password attempt. the aim of the exercises was to get attendees thinking about how often they interact with digital systems, how many different username and passwords they currently manage, and the number of different authentication devices that they have to carry. the majority of participants recorded over 25 separate identity interactions, all in a single day. healthcare ecosystem process mapping the next stage of the workshop focused on eight core identity moments that captured at a high level the typical experiences of a doctor throughout their career. participants were asked to create process maps identifying key organizations involved in each of these stages and the identity information that a doctor is required to present to them. additionally, participants were asked to capture frustrations that a doctor might experience while navigating these identity moments. the general identity moments identified and validated prior to the workshop to provide some structure were as follows: • doctor graduating from university. • doctor applying for a job. • doctor joining a hospital. • doctor training. • doctor rotation. • doctor begins rcpe accreditation. • doctor qualifies as a physician. • doctor moves abroad. figure 2—healthcare professional’s identity moments. https://doi.org/10.30953/bhty.v3.140 page 10 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 the workshop participants were split into groups, and each group focused on four of the identity moments. the results were then presented back to the group providing a detailed overview of each of the stages in a doctor’s career, including recurring and trusted ecosystem entities such as the gmc (general medical council). these maps were combined and synthesized into a gantt chart, showing the time burden and repetition associated with healthcare professionals as they progress through their career, see figure 2. this was developed through follow-up communication with a final year trainee at the rcpe who attended the workshop. re-imagining identity moments using ssi the next stage of the workshop involved an interactive session on self-sovereign identity and the capabilities it could provide healthcare professionals when applied to the key identity moments that participants previously mapped out. the goal was to give attendees a high level understanding of how this technology works and where it might fit into existing processes within healthcare. physical props were used to represent different aspects of the ssi system, and a number of workshop attendees were asked to play roles within the healthcare ecosystem. specifically, six participants acted as the key entities and trust providers identified in the process mapping stages: • a medical school—before becoming a licensed doctor, individuals must first complete a degree at a medical school. • the general medical council—the doctor licensing body in the united kingdom. • the royal college of edinburgh—royal colleges are involved with training and examination procedures for junior doctors as they gradually specialize in a medical discipline. • edinburgh hospital—this hospital was used as the initial place of employment once the fictional doctor in our scenario graduated. • glasgow hospital—this entity represented the doctor rotation process within the scenario modeled. • health education scotland—a body involved with continuous training and education of doctors. the initial setup of the ssi healthcare ecosystem was represented by asking each actor in the scenario to generate a public/private key pair. a red (private) and green (public) card was used to show the two halves of a public/private key pair. actors then attached their public key to a white card, which was used to represent a decentralized identifier (did). all actors were asked to place their did onto the wall, representing the act of registering a public did on a distributed ledger such that the public keys for these trusted entities could be resolved by anyone. after the initial setup, a member of the research team played the role of doctor and walked through each of the identity moments discussed and mapped in the process mapping session. this interactive approach was used for a couple of purposes: • to educate workshop attendees about the capabilities that a self-sovereign identity (ssi) architecture enables. • to illustrate how ssi could be applied to the healthcare domain to streamline identity interactions. throughout this interactive session, a flipchart was used to represent the doctors’ digital wallet. the wallet gradually collected verifiable https://doi.org/10.30953/bhty.v3.140 page 11 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 table 2. the list of design principles was validated in a workshop. design principle definition existence the identity of a person exists independent of identity administrators or providers. control: the person is in control of their digital identity and is able to choose what personal data to share. autonomy a user is independent on creating identities, as many as required, without relying on any party and be able to update/remove it. disclosure a user must have the ability to selectively disclose particular attributes. ownership the user is the ultimate owner of an identity, including the claims. consent data must be released only after the user has consented to do so. access the person has full access to their own data. single source a user is the single source of truth regarding the identity. transparency systems and algorithms are transparent and anyone should be able to examine how they work. standard an identity must be based on open standards. cost costs must be kept to minimum. portability information and services about identity must be transportable to other services. interoperability digital identities are continually available and as widely usable as possible. persistence an identity must be persistent as long as required by its owner. minimalization when data is disclosed, that disclosure should involve the minimum amount of data necessary to accomplish the task at hand. for example, if only a minimum age is called for, then the exact age should not be disclosed, and if only an age is requested, then the more precise date of birth should not be disclosed. protection the rights of users must be protected. when there is a conflict between the needs of the identity network and the rights of individual users, then the network should err on the side of preserving the freedoms and rights of the individuals over the needs of the network. availability an identity must be available and accessible from different platforms when required by its owner. human welfare the identity system must contribute to human well-being. non-maleficence the system will not cause harm to others. justice systematic unfairness (false negatives/positives) is avoided. trustworthiness expectations to act with good will towards others. privacy the user has the right to decide what data is shared and to set boundaries. dignity dignity is intertwined with emotional identity. technological solutions have a responsibility to uphold human dignity. solidarity respectful cooperation between stakeholders. environmental welfare the solution should cause no environmental harm. https://doi.org/10.30953/bhty.v3.140 page 12 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 credentials represented as large post-it notes and digital relationships, formed through peer did connections, were represented as small blue post-it’s within the wallet (see figure 3). within an ssi system, there are generally four core interactions: • establishing a peer did connection to initiate a digital relationship. • credential issuance. • proof request. • presentation of credential attributes in response to a proof request. each of these interactions was represented in this scenario as follows: 1. establishing a peer did connection: for this interaction, the action used was simply a handshake between the doctor and the party the doctor wished to connect with. for example, during the doctor qualifies from medical school process mapping, the doctor had to establish a connection with the gmc. while the handshake took place, participants were explained that this represented forming a private peer-to-peer communication channel across which message integrity and authenticated origin can be verified. it was illustrated that the connection was stored and managed by the digital wallet using a small blue post-it note. the fact that these connections could be formed either face to face, or through a website, was additionally discussed. explaining that you probably have more trust in a connection formed face to face and that trust can be built across these connections by sharing verifiable information. this relationship once formed can last indefinitely, until one individual or the other decides to break it off. this means that the gmc or any entity could form more personal relationships with the doctors they license, enabling them to push them relevant communication across their secure communication channel. figure 3—a illustration of the workshop’s physical representation of an ssi ecosystem. https://doi.org/10.30953/bhty.v3.140 page 13 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 2. credential issuance: credential issuance was a recurring ssi interaction that was represented in the scenario. the medical school issued the doctor their medical degree, the gmc issued their license, and the rcpe issued the doctors a qualified physician credential. all these credentials exist in the current system. they are attestations about the attributes and qualifications of a doctor, made by entities with the authority to make such claims. before any credential issuance interaction, a secure connection must have been formed as discussed above this was highlighted through a handshake action. then workshop participants acting as the different trusted entities within the scenario were asked to write the name of the credential (a large post-it) and sign the credential using their private key—the red card they received as part of the setup. in reality, they used a wet signature to represent this. this helped convey the concept that once a credential has been signed it is impossible to change the contents of that credential without invalidating the signature—providing integrity to the credential. for simplicity, the scenario did not represent individual attributes that credentials contained—for example, a gmc credential might contain the doctors’ name and their gmc license number. this was conveyed verbally instead. during the session, the signature type—cl signatures, and the way that they work in the context of verifiable credentials was briefly touched upon, due to the importance to understand why a doctor couldn’t easily share a credential. specifically, the doctor contributes some secret information in a blind manner into the signing protocol as one of the credential attributes. such that when the credential is signed and given to the doctor, only they can prove they know the secret value that was signed by the issuer. even the issuer does not know this. the wall of trusted dids and their corresponding public keys was used to discuss how the doctors, or rather their wallets, could verify the signatures on any credentials they were issued to ensure they were valid. the doctor is also capable of refusing a credential or suggesting changes before accepting—for example, if the issuer had spelt his/her name wrongly. 3. proof request: a proof request is an ssi interaction whereby an entity, generally referred to as a verifier, requests proof of certain identity information from a holder—in this scenario the doctor. the verifier is able to additionally specify the credential schema that the identity information should come from and the credential issuer if they wish. for example, in the ecosystem modeled there were only two hospitals—let’s call them glasgow hospital and edinburgh hospital for simplicity, edinburgh may request proof of a doctor’s name and dob contained within an identity verification credential and only accept this proof if it was issued by glasgow hospital (represented by its did). this was a complex interaction that was challenging to represent within the scenario, so verbal communication was used to outline the majority of it. the actor representing the entity asking for a proof from the doctor wrote this request on a piece of card which was then passed to the doctor. it was made clear that this communication went across an already established did connection and that proof requests could include a subset of attributes from across multiple credentials. https://doi.org/10.30953/bhty.v3.140 page 14 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 4. credential presentation: a credential presentation was the final ssi interaction used repeatedly throughout the scenario. this is the process by which a doctor, through the use of their digital wallet, responds to a proof request by creating a cryptographic presentation from one of more verifiable credentials within their wallet. within the scenario, this was illustrated by filling out a card and creating a wet signature on this card. the card was then passed to the entity/actor who requested the proof request. again, a lot of the complexity was conveyed verbally. it was explained that a credential presentation is not the same as giving the credential within a digital wallet to the entity requesting it. a presentation is a new and distinct cryptographic object that is created from one or more credentials and can contain any number of attributes from these credentials. this has both privacy and security benefits. going back to the hospital example, edinburgh may request a doctor to prove his/her name, date of birth, and gmc license number when initially employing a new doctor. this proof request can be responded to in a single credential presentation that combines the attributes from two separate credentials originally issued by different entities into a new object that is still cryptographically verifiable using the public keys of the issuers, the keys that are publicly available and were stuck on the wall during the initial setup. evaluating ssi design principles the last session of the workshop involved presenting ssi in the context of traditional identity management systems such as federated and user-centric identity management systems. then asking for positive and negative aspects of the ssi system presented to them, challenges to its implementation were also collected through this process. after this, eight design principles and their definitions selected from the literature were presented to the group. mentimeter, a tool for audience engagement, was used to gauge how the participants valued these design principles. a couple of additional questions were also asked. before we introduced the design principles from the literature to our audience, we asked them what they thought was the most important feature of future technology that would help them trust it. the list included the following: • use all over the ecosystem, all entities need to participate. • attention for end users, usability, convenience, workable. • buy-in from government and nhs. • future proof. • resilient, reliable, fraud resilient, protection, security. • control. • transparent data sharing, clear, clarity. comparing this list with the list of design principles that we distilled from the literature demonstrates that our audience adds two specific principles to the generic list. the first is that they find it important to know that all entities will be involved in the ssi ecosystem, including the government and the nhs. the success of the system depends on buy-in of all of the involved entities and that is something that should be developed from the start. second is the attention for usability and convenience. user engagement is important from the start and throughout the development process. we selected eight principles from the literature and explained the definitions to the participants. https://doi.org/10.30953/bhty.v3.140 page 15 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 then, we asked them to rank the principles in order of importance. the majority selected protection as the most important, followed by control and consent and interoperability. then, we asked to rank importance for each individual principle. again, the highest scores were for protection, control and consent, and interoperability. figures 4 and 5 show the results of the mentimeter polls. this was used to get a sense of the room and the figures should be interpreted with that in mind. workshop results the workshop led to a number of key learning elements about identity management for healthcare professionals. this can be generally summarized as being complex, fragmented, and time-consuming in its current form. the process mapping session led to an increased understanding of the identity landscape within healthcare. we began the session with eight identity moments that we framed as being chronological; the idea behind this was to provide a rough skeleton for participants to expand on in their process maps. it came to light that we missed a key identity moment within a doctor’s career, namely appraisal and re-validation. a process whereby healthcare professionals must prove to relevant authorities that they have gone through the relevant training to keep their skills up to date such that they can still practice. this is a repetitive process that occurs every 3 years. it was interesting to find out that even the top level professionals in attendance typically spent a couple of days every 3 years getting their documents in order for this procedure. another point of clarification was that while we positioned the eight identity moments as chronological, a lot of them happen in parallel. for example, a medical student typically gets identity checked by the gmc prior to graduating, and they also spend their final year applying for jobs so that on graduation they are ready to begin their career immediately. it was pointed out that there is no strict temporal relationship between the eight stages initially identified and a large part of the frustrations come from the fact that the majority of these stages are repeated over the course of a doctor’s career, each time requiring the same time-consuming procedure. a good example of this comes from what we broadly termed doctor rotation, something commonly experienced within the healthcare system, especially for young doctors who can rotate to a new location and role as often as every 4 months. while this reduces significantly as doctors progress in their career, it is still a common occurrence. every time a doctor moves to a new hospital, there are a number of tasks that figure 4—principles ranked by importance. figure 5—principles individually rated. https://doi.org/10.30953/bhty.v3.140 page 16 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 the doctor needs to complete in order to on-board into the institution: • they must complete a full identity verification check to the nhs standards. • they must provide evidence supporting all the claims they made in their application. this is typically verified by a consultant, and there are estimates that this takes up to a full day of their time. • they must complete an induction session, taking between 1 and 2 days. this induction is required for both new locations (e.g., hospitals) and new trusts and generally includes repeated content due to lack of standardization across locations and trusts. • they must organize an appointment with occupational health, to prove they have up-to-date vaccinations. if they don’t or are unable to prove they do, then doctors must have their vaccinations refreshed, often leading to needless re-vaccinations. another big bugbear of the group, particularly from attendees still going through training, was keeping track of all the training events they had attended, including the need to enter this information into multiple distinct silos. this was further exacerbated by frustrating user experiences, different document format requirements, and even reviewers specifically asking for physical copies due to the added burden that reviewing digitally uploaded documents entailed, a clear example of how digital tools have failed healthcare professionals by increasing rather than reducing the burden placed on them to manage their professional careers. to summarize, the process mapping and ensuing discussions highlighted numerous frustrations experienced by healthcare professionals just to meet the requirements for managing their career. a phrase that came up was the need to professionalize the digital experience throughout a doctor’s career. the method for conveying the capabilities of ssi and how these might change the identity moments a doctor experiences throughout their career was a success. the majority of participants were engaged and achieved a good level of understanding as shown through the questions that this generated. many of the participants indicated that they would be receptive to these changes being implemented, in particular a trainee at the rcpe was very supportive. the workshops additionally surfaced a number of challenges that attendees thought would need to overcome in order to roll this out within a healthcare system. many participants were senior professionals within healthcare, so had experiences of other attempts to digitize aspects of healthcare. the three core challenges identified were: • funding and business model: who is paying for these tools and what is in it for them. there is no clear path to monetization of the system; however, for this to work, it needs to be well funded in order to produce something that can scale. it was suggested that part of a doctor’s annual fees and membership to organizations like the gmc and the royal colleges could be allocated toward a system like this. • adoption: an ssi system works only when it achieves large-scale adoption. in order for this to happen, it needs to be led on a national level and show the benefit to the entire ecosystem. it was also pointed out that while benefit to doctors is relatively easy to show it needs to be able to show benefit to the individual trusted entities. they need to be the ones advocating for this system not https://doi.org/10.30953/bhty.v3.140 page 17 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 the doctors. all stakeholders must be clear on why this shift is happening and what the benefit is to them. • overreaching: this technology is new and relatively untested at scale. an interesting point was made that it is important to take small steps to prove its value and build human trust before expanding the scope. attempting anything too large too quickly and failing could be disastrous for the trust placed in the system and underlying technology. conclusion to conclude, participants were largely in favor of the technology described in the workshop, at least from the viewpoint of its worth exploring further. a number of them were keen to be involved in further iterations, offering support finding additional doctors and medical students to further explore the requirements of any technical solution from their perspective. this research has further reinforced the importance of developing user-driven systems, ensuring that any solution that does get rolled out is meeting the actual needs of those it is designed for. the research identified overlooked design principles and also showed that the design principles commonly referred to within the academic literature, when explained, were also considered important by the system users within a healthcare context. next steps include developing a proof of concept which can then be validated with real users; this will include validating it against the properties deemed important by participants: usability and security. additionally, a medical student’s identity interactions in themselves seem complex, and it would be valuable to run another workshop specifically focusing on this area. this should provide a different perspective from that gained throughout this workshop. the professionalization of the digital experience within a doctor’s career is long overdue. furthermore, the covid-19 crisis has highlighted just how important these solutions can be for the nhs and its individual trusts. staffing needs fluctuate widely across the country, as different regions and hospitals hit their peaks of this crisis at different times. the ability to redeploy doctors to highly stressed areas within the nhin minutes rather than in days could greatly improve a health services ability to cope—see trust induction figure 2. while a portion of these induction processes can perhaps be ignored, trusts still have to meet strict legal requirements around identity verification2—this all takes time which could be spent saving lives. this seems to be the opportune moment to develop and deploy an ssi solution. however, it is imperative that any solution that is developed, especially if rushed through during a crisis, is clear about what it wants to achieve and for whom. furthermore, these solutions should identify a process by which to validate how well they are meeting the predefined aims and requirements of those who are actually going to be using it—healthcare professionals. acknowledgments the authors would like to extend their thanks to dr manreet nijjar, an infectious disease consultant and co-founder of truu.id, a company working to realize much of what is discussed throughout this article. his attendance at the workshop was invaluable. also thanks to professor derek bell and pernille marqvardsen without whom this workshop would not have been possible. conflicts of interest there are no conflicts of interest. https://doi.org/10.30953/bhty.v3.140 http://truu.id page 18 of 20 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.140 funding statement there are no related funded projects. references 1. regulatory overload. tech. rep. 10. american hospital association; 2017 [internet] [cited 2020 may 01]. available from: https://www.aha.org/system/ files/2018-02/regulatory-overload-report. pdf 2. ruscoe m. identity verification and authentication standard for digital health and care services. health and social care information centre; 2018, p. 19. 3. holmes c. distributed ledger technologies for public good: leadership, collaboration and innovation. house of lords; 2018. 4. keswani rn, taft th, coté ga, keefer l. increased levels of stress and burnout are related to decreased physician experience and to interventional gastroenterology career choice: findings from a us survey of endoscopists. am j gastroenterol. 2011;106(10):1734. 5. heponiemi t, hyppo¨nen h, 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https://www.bbc.co.uk/news/health-50972123 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 dropbox conv2x_181024-1620_oren-mechanic.mp3 simplify your life 1 (page number not for citation purpose) original research: proof of concept/pilots/methodologies clinical, organizational and regulatory, and ethical and social (cores) issues and recommendations on blockchain deployment for healthcare: evidence from experts john robert bautista, rn, mph, phd1 ; muhammad usman, ms2 ; daniel toshio harrell, phd3 ; eric t. meyer, phd1 ; and anjum khurshid, md, phd3 1school of information, the university of texas at austin, usa; 2department of electrical and computer engineering, the university of texas at austin, austin, tx, usa; 3dell medical school, the university of texas at austin, usa corresponding author: john robert bautista, email: jrbautista@utexas.edu keywords: blockchain, focus group, health, medilinker, socio-technical abstract objective: while existing research by our team has demonstrated the feasibility of building a decentralized identity management application (“medilinker”) for health information, there are implementation issues related to testing such blockchain-based health applications in real-world clinical settings. in this study, we identified clinical, organizational and regulatory, and ethical and social (cores) issues, including recommendations, associated with deploying medilinker, and blockchain in general, for clinical testing. methods: cores issues and recommendations were identified through a focus group with 11 academic, industry, and government experts on march 26, 2021. they were grouped according to their expertise: clinical care (n = 4), organizational and regulatory concerns (n = 4), and ethical and social issues (n = 3). the focus group was conducted via zoom in which experts were briefed about the study aims, formed into breakout groups to identify key issues based on their group’s expertise, and reconvened to share identified issues with other groups and to discuss potential recommendations to address such issues. the focus group was video recorded and transcribed. the resulting transcriptions and meeting notes were  imported to maxqda 2018 for thematic analysis. results: clinical experts identified issues that concern the clinical system, clinical administrators, clinicians, and patients. organizational and regulatory experts emphasized issues on accountability, compliance, and legal safeguards. ethics and social-context experts raised issues on trust, transparency, digital divide, and health-related digital autonomy. accordingly, experts proposed six recommendations that could address most of the identified issues: (1) design interfaces based on patient preferences, (2) ensure testing with diverse populations, (3) ensure compliance with existing policies, (4) present potential positive outcomes to top management, (5) maintain clinical workflow, and (6) increase the public’s awareness of blockchain. conclusions: this study identified a myriad of cores issues associated with deploying medilinker in clinical settings. moreover, the study also uncovered several recommendations that could address such issues. the findings raise awareness on cores issues that should be considered when designing, developing, and deploying blockchain for healthcare. further, the findings provide additional insights into the development of medilinker from a prototype to a minimum viable product for clinical testing. future studies can use cores as a socio-technical model to identify issues and recommendations associated with deploying health information technologies in clinical settings. received: december 15, 2021; accepted: february 2, 2022; published march 14, 2022 https://orcid.org/0000-0002-4892-9543 https://orcid.org/0000-0001-5788-5784 https://orcid.org/0000-0002-5085-1183 https://orcid.org/0000-0002-1998-7162 https://orcid.org/0000-0002-8946-0622 mailto:jrbautista@utexas.edu citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.1992 (page number not for citation purpose) john robert bautista et al. introduction medilinker is a prototype blockchain-based decentralized identity management system that is designed to provide patients autonomy and interoperability in managing personal health information.1,2 it was developed as a web application in 20202 and as a mobile application in 2021.1 it features a digital wallet that contains six different types of credentials: health id, insurance, medication, credit card, research consent, and medical power of attorney (mpoa). for patients to start using medilinker, they need to present a valid physical identification card to the receptionist at a participating clinic (e.g., preferably a government-issued id, such as passport, driver’s license, or resident id). the receptionist can then issue a digital identity on the blockchain. this blockchain-verified digital identity can then be shared by the patient with other participating clinics to verify their identity. after this, the patients do not need to show their physical identification card. with medilinker, patients can share or revoke their medical information with clinics, have the option to allow information to be shared for clinical research, and allow a guardian or legal representative to act on their behalf to make health decisions through the issuance of a digital mpoa. field studies were conducted in 20203 and 20214 to test medilinker’s usability and identify participants’ views on it. in both studies, university students were recruited to act as simulated patients of a simulated clinic where they navigated medilinker’s features using synthetic data. both studies provided valuable insights that allowed us to improve medilinker’s usability. more importantly, these studies demonstrate that medilinker has reached level four of the technology readiness level (i.e., trl4) since it has been tested and validated to be operational in a laboratory environment (i.e., simulated clinics).5 although developed primarily to assess the maturity of outer space exploration technologies,6 trl has been adopted as a means of measuring the maturity of technologies utilized for healthcare, such as blockchain.7,8 trl has nine stages with trl1 (i.e., basic principles observed and reported) being the lowest and trl9 (i.e., actual system proven in operational environment) being the highest.5 considering that medilinker has reached trl4, it is natural for us to set our sights on trl5 (i.e., technology validated in relevant environment). a 2020 review8 of current blockchain use cases shows only one similar system9 like medilinker that is also at trl4. to reach trl5, medilinker needs to be tested and validated to be operational in a relevant environment. a relevant environment of interest for testing medilinker would be primary care clinics since these facilities were simulated in earlier tests. moreover, such testing requires the participation of primary care patients who will be using their actual personal health information instead of simulated patients who are using synthetic data. to prepare for activities to reach trl5, it is crucial to identify issues associated with deploying medilinker in a clinical setting. although the technical side of medilinker has been dealt with by correcting multiple bugs and preparing a well-designed mobile application, there are several issues beyond these technical aspects that need to be recognized. in fact, medilinker needs to be thought of not only as a technical system but as a socio-technical system10,11 because its deployment in clinics will involve the interaction of the technology with the clinics’ relevant stakeholders as well as prevailing policies. the resulting interaction of these entities often produces issues that can have intended and unintended consequences that might cause harm.12,13 therefore, there is a need to identify these issues and come up with potential ways of addressing them before deployment. previous studies suggest a myriad of issues associated with the use of blockchain for healthcare.14–19 in general, these issues can be classified into three main groups we refer to as cores: clinical (e.g., uncertain health outcomes and patient information literacy),15,17 organizational and regulatory (e.g., accountability and legal compliance),14,18 and ethical and social (e.g., autonomy and trust).16,19 to date, such studies are in the form of literature reviews and focus on identifying clinical as well as organizational and regulatory issues14,15,17,18 with less emphasis on ethical and social issues.16,19 although these reviews offer a good overview of relevant issues associated with deploying blockchain technologies for healthcare, additional research is needed to identify context-specific issues associated with deploying a specific blockchain-based health information technology, like medilinker. moreover, additional research is needed to identify potential solutions to address such issues. in general, this study aims to identify clinical, organizational and regulatory, and ethical and social (cores) issues as well as corresponding recommendations that could address such issues. research objectives medilinker is currently at trl4 and moving it to trl5 requires testing it in a relevant environment, such as clinics. however, before such testing, it is crucial for us to understand issues associated with deploying medilinker in clinics. this is needed because deploying health information technologies in clinical settings often have intended and unintended outcomes that might be detrimental to patients, providers, and health administrators.12,13 guided by literature on the use of a socio-technical lens to identify unintended consequences of health information technologies,12,13 this study aims to achieve two objectives. first, it aims to identify cores issues associated with deploying medilinker in clinics. second, based on identified cores issues, the study aims to outline recommendations that could address these issues. figure 1 shows a diagram that summarizes this study. http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.199 3 (page number not for citation purpose) clinical, organizational and regulatory, and ethical and social (cores) methods study design and ethics approval a qualitative research design was conducted through a focus group with domain experts. previous research shows that expert focus groups are useful to obtain rich insights that can be used to identify issues and recommendations associated with the development and deployment of health information technologies.20,21 prior to data collection, the study received exempt approval from the institutional review board of the university of texas at austin. experts’ profiles potential experts from various academic, industry, and government organizations in the united states were invited to participate in a focus group. a total of 11 experts from different disciplines attended the focus group. table 1 shows their profile. they were grouped according to their domain expertise: clinical care group (n = 4), organizational and regulatory concerns group (n = 4), and ethical and social issues group (n = 3). data collection procedure a 90-min focus group was held virtually via zoom on march 26, 2021. it was divided into four segments: introduction, small group discussion, overall discussion, and closing remark. appendix 1 provides the segments of the focus group. the introduction segment provided experts with an overview and purpose of the study including a brief presentation of medilinker’s development and features. after the introduction, the experts were placed into small breakout groups that correspond to their expertise. each small group is composed of three or four experts and a moderator. the purpose of the small group discussion was to identify issues associated with deploying medilinker in clinics. appendix 1 lists the guide questions asked by the moderators during the small group discussion. subsequently, the experts were reconvened in the main zoom room to start the overall discussion where they discussed issues identified per group and propose potential recommendations to address such issues. furthermore, the overall discussion allowed the experts to engage in intergroup discussion to identify overlapping issues and recommendations. the focus group ended with a closing remark where the moderators provided a summary of the focus group and thanked the experts for their participation. data analysis a video recording of the focus group was transcribed for qualitative analysis. the resulting transcriptions and meeting notes were imported to maxqda 2020 for thematic analysis. we used tracy’s guide in analyzing qualitative data for thematic analysis.22 first, primary-cycle coding was conducted by breaking down data into small analytical units through line-by-line open coding where codes were freely assigned to the data. next, axial coding was performed during secondary-cycle by grouping primary-cycle codes to generate meaningful themes and sub-themes. finally, the themes and subthemes were categorized whether they are related to issues or recommendations within specific cores categories. appendix 2 shows the coding tree. in the entire coding process, memos were generated to provide a preliminary characterization of the themes. likewise, the coding process was conducted in consultation with the research team to resolve disagreements and refine the themes. to ensure trustworthiness in qualitative research, we followed the principles of credibility (e.g., moderators established rapport and used iterative questioning), transferability (e.g., selecting participants that come from a variety of fields and disciplines), dependability (e.g., following the approved study protocol), and expert focus groups • clinical care group (n = 4) • organiza�onal and regulatory concerns group (n = 4) •ethical and social issues group (n = 3) cores issues associated with deploying medilinker in clinics clinical • clinical system • clinical administra�on • clinicians • pa�ents organiza�onal and regulatory • accountability • compliance • legal safeguards ethical and social • trust • transparency • digital divide • health-related digital autonomy iden�fy… most can be addressed by… recommenda�ons that could address cores issues ✓ design interfaces based on pa�ent preferences ✓ ensure tes�ng with diverse popula�ons ✓ ensure compliance with exis�ng policies ✓ present poten�al posi�ve outcomes to top management ✓ maintain clinical workflow ✓ increase the public’s awareness of blockchain fig. 1. study summary. cores: clinical, organizational and regulatory, and ethical and social. http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.1994 (page number not for citation purpose) john robert bautista et al. confirmability (e.g., presenting quotes that best represent themes or subthemes) in conducting the study.23 cores issues the following sections present cores issues raised by experts. aside from experts’ insights, we discuss these issues in light of relevant literature. clinical issues clinical issues associated with the deployment of medilinker in clinics include those that concern the clinical system, clinical administration, clinicians, and patients. clinical system experts noted two clinical system issues associated with deploying medilinker in clinics that are consistent with previous work.15,17 the first issue concerns the integration of medilinker with existing clinical systems that are used in clinics. one expert explains why data integration is a crucial part of clinical systems: “thinking about integrating the data, it is nice to then have all of the personal and medical information show up within the software [medilinker] at the clinic. but then, i imagine the first question would be ‘ok, how do we get that into the ehr [electronic health record]?’ we’re going to have someone copy and paste all that stuff in or what kind of integration would there be?” (c2) even when medilinker is integrated in the clinical system of one clinic, another challenge is how to integrate it with other health institutions where heterogenous clinical systems are used. this is a pertinent issue when scaling up medilinker to work across clinics. one expert explains how this is a concern since health facilities often have different clinical systems: “[health institution a] uses compass. [health institution b] uses athena. [health institution c] use next gen and now transitioning to epic. [health institution d] uses their homegrown system that they don’t share with anyone, then they’re using meditech now. [health institution e] uses nextgen. so, there’s no unified system.” (c3) clinical administration there is a consensus among experts that the success of deploying medilinker in a health facility depends on clinical administrators’ support. however, garnering their support would be challenging because of three relevant issues. consistent with recent reports,24,25 experts noted that health organizations prefer to control patient data considering that they view such data as their property, and it serves as leverage for financial power. as a result, clinical administrators are less likely to support initiatives that would allow patients to have full control of their data (e.g., such as medilinker). “i think hospitals and health systems, they view that their patients’ data belongs to them and that’s power. there’s financial power there. there’s financial gain to it. so, this notion that it seems so obvious that patients’ data should belong to patients is completely at odds with how health systems operate.” (c3) another deterrent in obtaining support from clinical administrators is the uncertainty associated with blockchain technology. although blockchain has been used by some early adopter hospitals in the united states to improve health services and outcomes,26 for the majority, it is a relatively new and immature technology that clinical administrators may be hesitant to integrate with their clinical systems.27 one expert noted that clinical administrators could deliberately table 1. experts’ profile group/id gender domain expertise clinical care group c1 male academic accessibility of technologies for people with disabilities c2 male academic clinical informatics and systems engineering c3 male academic access, quality, and equity in healthcare c4 female academic aging, technology, and health organizational and regulatory concerns group or1 male industry application of blockchain in health and life sciences. or2 male academic blockchain governance or3 female academic corporate governance or4 male government strategy development of prehospital medicine ethical and social issues group es1 male academic sociotechnical systems in healthcare es2 male academic social and environmental processes that affect aging es3 female academic medical sociology, social justice, and medical ethics http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.199 5 (page number not for citation purpose) clinical, organizational and regulatory, and ethical and social (cores) avoid adopting blockchain as part of their systems by justifying strict adherence to the health insurance portability and accountability act of 1996 (hipaa)28: “organizations could easily use hipaa as a way [to avoid blockchain implementation in healthcare] … i mean, you’re trying to use blockchain to say that we can protect patients’ data. sure. but organizations may not just understand, or they may not want to understand, and they just want to use hipaa as a weapon to go against this new technology.” (c4) in situations where clinical administrators agree to deploy medilinker as part of their clinical systems, one pertinent challenge to be considered is the time and financial costs associated with its implementation. one review considers this issue as one of the major challenges of implementing blockchain for healthcare.15 one expert provides a clear explanation of this issue: “the frustrating thing that we’ve experienced with sharing of data across organizations is that organizations are not demonstrating that they are willing to commit the initial cost and initial work of adoption and implementation with their own systems to get that savings down the road. i guess you really have to make a case that this is either going to save them significantly or make them money somehow.” (c2) clinicians aside from integrating medilinker with existing clinical systems, experts also pointed out another facet of integration that has been described also in blockchain literature15,17: workflow integration for clinicians. research shows that even for existing health information technologies, such as electronic health records, workflow issues among clinicians are associated with stress and burnout.29,30 thus, emphasizing workflow integration is important because this could reduce clinicians’ workload when incorporating new technologies: “people really don’t want to have that [new technology] added to their workflow. are we integrating this as a part of the workflow or is this an additional piece? [the latter] would require tremendous amount of time, especially on nurses who would probably end up having to enter the data. which brings up the point of not just the patients who would need to work with the system, it’s also the clinicians.” (c4) patients since medilinker is also designed to be used by patients (or their guardians) for health information management, experts pointed out several issues associated with its use. first, experts emphasized the need for appropriate safeguards within medilinker to ensure appropriate information disclosure because not everyone has the same level of ehealth literacy to navigate complex information for decision making. for instance, options within medilinker can be added to show patients less information or more information, depending on how medically inclined they are. as one expert noted, inappropriate information disclosure can lead to patient misinformation. “let’s assume that you get these medical institutions on board with adopting this application [medilinker]. the trick would be to ensure that whoever is entering the data makes it understandable to the patient. because if you have a low level of ehealth literacy and you get a piece of information pushed to your phone and you can’t understand what it is, and you go to webmd … all of a sudden you think you’re dying of cancer, and then you could have a whole bunch of misinformation problems.” (c1) the second patient-related issue that experts noted is usability. since medilinker’s usability has only been evaluated in a study involving university students (young and well-resourced) who were acting as simulated patients,2 there is a need to test its usability among other population groups so that additional usability issues can be uncovered. as noted by one expert, asking vulnerable patient groups to use technologies for healthcare presents several challenges: “as soon as you start moving out from a young, equipped, and well-resourced student population, whether it’s the older population or population with disabilities… i work with the homeless or any of the other vulnerable populations, these technologies potentially breakdown because they don’t know how to use it, or they don’t have the phones, or they don’t like it. it’s a myriad of different reasons.” (c3) the third patient-related issue that experts noted is accessibility. although smartphones are already widely available, there is a need for medilinker to be accessible in both new and old smartphones and in different operating systems while achieving the same level of security across versions. according to one expert, this is particularly important among older adults who are likely to use old smartphones and are typically hesitant to perform updates: “i know of older individuals that have an old smartphone. for example, an iphone and they don’t update the ios at all. to make the program [medilinker] universally usable, you’d have to consider if the population isn’t as technically literate. what can we do to build in legacy support for older versions of phone operating systems to ensure that the system works as intended and we have the same level of security and usability of the program?” (c2) http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.1996 (page number not for citation purpose) john robert bautista et al. the final patient-related issue that experts noted is the verification of the patient’s identity in the absence of identity documents. since medilinker relies on the presentation of a government-issued identity document to create the patient’s account,2 those who are not able to present such a document would not be able to use medilinker and that would contribute to the proliferation of inequitable healthcare.31 one expert provides an explanation for this: “my patients often don’t have their original vital documents. for [undocumented] immigrant populations, obviously this is a major issue. so, overcoming just that sort of simple barrier of having to prove who you are without using archaic paper, plastic cards, and paper documents time and time again, i think, it’s a big win.” (c3) organizational and regulatory issues organizational and regulatory issues associated with the deployment of medilinker in clinics include accountability, compliance, and legal awareness and safeguards. accountability although blockchain provides a secure means of storing data for patients,17 experts noted that it is unclear how organizations will be accountable with health information that is stored on blockchain, especially during a data breach.14 one expert noted the complexity of accountability when data on the blockchain is handled by multiple entities: “we have organizations that are going to be represented on the blockchain, right? for those transactions to take place, what is the representation of a clinic on a blockchain? will the clinic specify whether is it the front desk person, the ceo, or somebody else who is just assigned for this function? and the same is true for a payer [insurance] who has to approve these claims. if we were to test this in the real [clinical] environment, how would organizations assign their persona on the blockchain?” (or1) aside from mapping out organizational entities that are accountable for patient-related data on the blockchain, one expert also highlighted the need for organizations to be familiar with device identity14 since devices are part of the ecosystem by which information passes to and from the blockchain: “there’s also a lot of groups looking at different standards and identity. not only identity of individuals and patients, but identity of providers, identity of organizations, and then even down to identity of internet of medical things and identity of a particular device that may be contributing information to this system… the information being tracked via blockchain.” (or4) compliance there is a need to work with the clinics’ administrators to ensure medilinker’s compliance with existing regulations and standards.14,17 although this may seem to be straightforward, one expert noted that the challenge to achieve full compliance is that regulators are still in the process of interpreting existing laws whether it is applicable for blockchain: “with many of these regulations, they’re not set for blockchain. yet they’re still being interpreted for it. the healthcare regulators don’t know this technology, so it’s a very slow conversation.” (or4) while compliance can be attained by complying with laws that are in place,14,17 experts noted the need to be mindful of relevant laws where medilinker will be deployed. in the united states, aside from federal laws such as hipaa and federal trade commission act, medilinker’s deployment requires compliance with state laws also. to illustrate this, if medilinker will be deployed in a clinic in california, it needs to comply with the california consumer privacy act (ccpa). as one expert noted: “generally, we have federal rules, but also since i assume we are going all over the country, so we also need to think about the relevant states and there are states with different rules. for each state, we add something that opens the door to more compliance.” (or3) legal safeguards considering that blockchain technologies are relatively new and regulators are still in the process of providing guidance toward full compliance,14 experts pointed out the difficulty of legally safeguarding the software (i.e., medilinker) and the organization (i.e., the clinic). one expert highlighted the difficulty of setting up medilinker’s terms and conditions in the absence of clear legal guidance: “if something goes wrong, if there is a dispute, we need to protect the program. for an example, legislators and lawyers have to think about what happens if things don’t go well. what if there is a glitch or something was not properly recorded? in case we are negligent, what do we do with the problem? how do we resolve it? what type of indemnification? we are trying to limit the [organizational] liability in situations like that.” (or3) experts also highlighted the need to legally safeguard clinicians when patients deliberately withhold health information that prevents clinicians from providing appropriate services.32 one expert asks who will be liable in such a situation: http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.199 7 (page number not for citation purpose) clinical, organizational and regulatory, and ethical and social (cores) “what if patients start censoring some of the data that they share, which could give a totally different picture because they want to hide their addiction? there is information that they [the patient] think is not relevant, but actually the physician thinks it’s very relevant. who has the liability for that?” (or1) ethical and social issues ethical and social issues associated with the deployment of medilinker in clinics include trust, transparency, digital divide, and health-related digital autonomy. trust new health information technologies are usually met with skepticism and obtaining people’s trust is a strong driving force for acceptance and adoption.33,34 although blockchain is essentially a technology meant to protect people’s data,14,17 persuading people to use a technology that they are not familiar with can result in trust issues (e.g., will it really protect my data from hacking? is this another scheme to secretly collect my data?). one expert emphasized the need to overcome trust issues, especially among oppressed groups: “how do you overcome suspicion or trust issues? we pointed out that there are specific populations [african americans, hispanics, rural people, and older people] that have been abused by surveillance and law enforcement and other mechanisms of society. so how do we not reiterate those kinds of abuses in the technological tools that we make.” (es1) transparency experts have pointed out that people’s tendency to distrust technologies may be rooted in the lack of transparency on how such things are developed and utilized.35 this is particularly true for blockchain where most people might have not heard of it, especially on how it can be used for healthcare.36 for one expert, there is a need for developers and implementers to explain how blockchain stores and protects data to demonstrate transparency: “[sharing a perspective of a potential user of medilinker] i might be a slow adopter in some ways because i’m concerned about where my original data that i’m going to share actually lives. i’m not comfortable with my credit card being on here [medilinker] and doing this.” (es3) digital divide another ethical and social issue associated with deploying medilinker in clinics is the digital divide. as medilinker requires an internet-connected smartphone, those who do not have such a device will be left behind which then contributes to inequity in health.37,38 one expert shares the link between the digital divide and inequity: “not everyone has a cell phone or a cell phone that’s capable of mobile data. my experience at the va [veteran affairs health facility] was they have a lot of veterans that are vulnerable. they don’t have any kind of internet access or mobile phones. has there been any thought to using this system [medilinker] without owning a mobile phone?” (es1) health-related digital autonomy experts have raised the issue of health-related digital autonomy (i.e., health-related decisions of individuals in the digital context)39 when patients decide to share information with clinics and institute an mpoa through medilinker. although the chapter 166 of the texas health and safety code40 approves the use of digital or electronic signatures, especially when signing an mpoa, certain institutions may prefer that patients sign forms using a wet signature rather than a digital signature that is created within an application. as such, patients’ health-related digital autonomy may not be fully acknowledged by institutions when using medilinker. as one expert noted: “our default is still relying on signing paper forms [with wet signatures] and faxing them between institutions. if you had a tool [medilinker] that allowed for a quick ‘yes, i grant access’, there will be hurdles with getting the organizations to accept patients’ authorization that doesn’t include a handwritten signature.” (es2) recommendations to address cores issues experts pointed out several recommendations to address cores issues associated with deploying medilinker in clinics. table 2 summarizes applicable recommendations that could address specific cores issues. design interfaces based on patient preferences experts noted that for medilinker to have good usability as a health information management application, it is important to design its interface that even those with limited ehealth literacy can use it. this means that its design must be based on accommodating multiple patient preferences that can contribute to reducing the digital divide. for example, allowing patients to set their preferred amount of control to their data (from little to full control) not only fosters good usability but also digital autonomy. one expert emphasized how medilinker’s interface should accommodate various user’s configuration regarding the control of data: “maybe you can make different types of interfaces or different gradients of interfaces so that people can have some control of their data and then move to maximum control of data depending upon how they want or how they graduate to that. about 20% of patients http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.1998 (page number not for citation purpose) john robert bautista et al. right now want control of their data. 30% would move there if the interface is pretty good or there’s an incentive. 50% are like ‘let my kids deal with that or my doctor deal with that, i don’t want to do it’.” (c4) ensure testing with diverse populations to be able to design medilinker that has good usability (regardless of the user’s ehealth literacy and type of device owned) and to be able to predict most, if not all, issues associated with deploying it in clinics, experts highlighted the need to conduct testing with diverse populations. to date, medilinker has only been tested by university students on a simulation-based field study.2,3 although the results of that study3 uncovered important user-related issues that can improve medilinker’s usability, experts noted that testing beyond university students is needed because actual patient populations have different needs and preferences. as one expert noted: “it would be really helpful to broaden up the testing base. you should test on users with diverse backgrounds, needs, skills, and preferences because that will be really helpful to make the technology usable by everyone.” (c4) ensure compliance with existing policies although there is still unclear regulatory guidance on the use of blockchain in healthcare, experts noted that, at the very least, we should anticipate all issues of its implementation and determine which standards or regulations will such an issue be covered. for example, it would be a good start if medilinker can attain accessibility compliance based on section 508 of the us rehabilitation act of 1973.41 although section 508 only applies to federal government-owned or funded information and communication technology, section 508 has been used as a benchmark by institutions to determine whether their technologies (e.g., websites and mobile applications) are accessible for people with disabilities. attaining such a compliance would promote accountability, minimize the digital divide, facilitate consumer trust, and promote transparency. one expert summarizes the need to be forward thinking in terms of how compliance should be attained: “you need to figure out how it would be interpreted by any of the governing bodies, not just how you interpret it, which is sometimes the shortcoming of new technologies. the people who developed them are thinking all the good things, but the regulators think of all the bad things that could happen. so, you need to kind of put yourself in those shoes [regulator’s mindset] as well.” (or1) present potential positive outcomes to top management top management support is needed for medilinker to be deployed in clinical settings. as experts noted earlier, garnering top management support for its deployment is a challenge mainly because of the perceived uncertainty and lack of clear guidance with using blockchain for healthcare. to overcome these barriers, there is a consensus among the experts that we should engage in a dialogue with the top management to identify and address context-specific issues associated with medilinker’s deployment. one expert noted that: “you’ve done a lot of work on the end user side. you need to start having focus groups and interviews with the c-suite executives and the administrators who we’re all assuming may be opposed to this for financial table 2. cores issues to be addressed by the recommendations recommendation issues to be addressed by the recommendations clinical organizational and regulatory ethical and social 1. design interfaces based on patient preferences  ehealth literacy  usability  health-related digital autonomy  digital divide 2. ensure testing with diverse populations  ehealth literacy  usability  digital divide 3. ensure compliance with existing policies  accessibility  accountability  compliance  legal safeguards  trust  transparency  digital divide 4. present potential positive outcomes to top management  uncertainty with blockchain  compliance 5. maintain clinical workflow  workflow integration 6. increase the public’s awareness of blockchain  uncertainty with blockchain  trust  trçansparency http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.199 9 (page number not for citation purpose) clinical, organizational and regulatory, and ethical and social (cores) reasons or for data control reasons. and maybe we’re wrong or it’s an oversimplified assumption.” (c3) in that dialogue, we should lay out potential positive outcomes of its adoption to garner support. these outcomes should not only emphasize positive health outcomes (e.g., reduced length of stay or mortality), but more importantly, positive organizational outcomes (e.g., reduced operation cost or positive public image). as one expert noted: “if you could make a case to say, ‘by introducing our technology, we can help you save in xyz’. i think that would be a great way to present… also think about the potential changes in the organization’s image. if you are able to put out this new sort of technology-based image to show your patients that you are cutting edge, that might potentially bring in patients who might be going after that.” (c4) maintain clinical workflow experts emphasized the need to integrate medilinker with clinical systems that are already being used in target clinics. such integration is needed to achieve the smallest possible disruption in the clinical workflow. this would ensure that the deployment of medilinker in clinics will not be a source of burden for clinicians who will be using it. moreover, since blockchain runs in the background when using medilinker, it is possible to maintain the clinician’s workflow. as one expert noted: “the best answer will be nothing changes for the people doing the work and this is an infrastructure in the background that is facilitating what we want to happen. the transactions that happen on the chain happen between parties and these parties can be individuals, organizations, devices, and we leave that up to the parties. and we simplify the definition of the transactions as being derived from the workflow as it happens. we can’t think of it these things as we do traditional it systems.” (or2) increase the public’s awareness of blockchain considering that most people are unaware of what blockchain is, let alone its use for healthcare purposes,36 experts recommend using medilinker as a means to educate people on what blockchain is and how it can enhance health data privacy. experts hoped that with more people getting familiar with the role of blockchain in healthcare, such familiarity would reduce uncertainty, foster trust, and inculcate transparency. as one expert noted: “i think there’s going to have to be a fair amount of education and even advocacy around this. blockchain is still like this scary unknown novel crazy thing to most people. there’s going to have to be a fair amount of education to patients, providers, of system leaders, of administrators on what it is, what it isn’t, and the security aspects.” (c3) conclusion and future work this study identified a myriad of cores issues associated with deploying medilinker in clinics. moreover, the study also uncovered several recommendations that could resolve cores issues and mitigate the occurrence of negative consequences (e.g., becoming a source of high clinician workload and contribution to the digital divide). in general, the findings raise awareness of cores issues that should be considered when designing, developing, and deploying blockchain for healthcare. with these findings, we can further improve medilinker from a prototype to a minimum viable product for clinical testing and make appropriate preparations for clinical testing to reach trl5. aside from the practical contributions of the study, it also contributes to theory by demonstrating the usefulness of utilizing a socio-technical perspective when uncovering blockchain-related issues. although a socio-technical perspective has been used to identify issues surrounding blockchain in general,11,42 this is the first study that explicitly used such a perspective to uncover issues and recommendations associated with deploying blockchain for healthcare. hence, future studies can use cores as a socio-technical model to identify issues and recommendations associated with deploying health information technologies in clinical settings. the study has several limitations that will guide future work. first, the study involved a focus group of 11 experts only. although we were able to obtain rich insights from these experts, future work can be geared toward inviting more experts. second, because of the first limitation, we were able to allocate experts in three groups only (i.e., clinical care, organizational and regulatory concerns, and ethical and social issues). future work can invite more experts so that there will be five expert groups that represent each aspect of cores (i.e., clinical care, organizational concerns, regulatory concerns, ethical issues, and social issues). third, the focus group was conducted within 90 min only because of scheduling constraints. it would have been ideal if this event was conducted as a full-day workshop so that the experts could have more time to brainstorm ideas within and outside their designated group. finally, although experts provide a unique perspective towards issues and recommendations associated with deploying medilinker in clinics, future work can also involve groups that are represented by lay people since their perspective can provide consumer insights in designing and deploying medilinker in clinics. competing interests the authors have no relevant financial or nonfinancial interests to disclose. http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.19910 (page number not for citation purpose) john robert bautista et al. funding the university of texas blockchain initiative provided partial funding 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https://www.wsj.com/articles/hospitals-give-tech-giants-access-to-detailed-medical-records-11579516200 https://www.theverge.com/2021/6/23/22547397/medical-records-health-data-hospitals-research https://www.theverge.com/2021/6/23/22547397/medical-records-health-data-hospitals-research https://www.forbes.com/sites/andreatinianow/2019/09/23/blockchain-technology-is-already-improving-lives-at-22-hospitals/?sh=2e27129a6c7d https://www.forbes.com/sites/andreatinianow/2019/09/23/blockchain-technology-is-already-improving-lives-at-22-hospitals/?sh=2e27129a6c7d https://www.forbes.com/sites/andreatinianow/2019/09/23/blockchain-technology-is-already-improving-lives-at-22-hospitals/?sh=2e27129a6c7d https://www.ibm.com/downloads/cas/bbrqk3wy https://www.ibm.com/downloads/cas/bbrqk3wy citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.199 11 (page number not for citation purpose) clinical, organizational and regulatory, and ethical and social (cores) 1996 [cited 2021 nov 30]. available from: https://aspe.hhs.gov/ reports/health-insurance-portability-accountability-act-1996 29. harris da, haskell j, cooper e, crouse n, gardner r. estimating the association between burnout and electronic health recordrelated stress among advanced practice registered nurses. appl nurs res. 2018;43:36–41. https://doi.org/10.1016/j. apnr.2018.06.014 30. kroth pj, morioka-douglas n, veres s, babbott s, poplau s, qeadan f, et al. association of electronic health record design and use factors with clinician stress and burnout. jama netw open. 2019;2(8):e199609. https://doi.org/10.1001/ jamanetworkopen.2019.9609 31. sanders c, burnett k, lam s, hassan m, skinner k. “you need id to get id”: a scoping review of personal identification as a barrier to and facilitator of the social determinants of health in north america. int j environ res public health. 2020;17(12):4227. https://doi.org/10.3390/ijerph17124227 32. kaplan b. how should health data be used?: privacy, secondary use, and big data sales. camb q healthc ethics. 2016;25(2):312– 29. https://doi.org/10.1017/s0963180115000614 33. xie h, prybutok g, peng x, prybutok v. determinants of trust in health information technology: an empirical investigation in the context of an online clinic appointment system. int j human–computer interact. 2020;36(12):1095–109. https://doi. org/10.1080/10447318.2020.1712061 34. or ckl, karsh b-t. a systematic review of patient acceptance of consumer health information technology. j am med inform assoc. 2009;16(4):550–60. https://doi.org/10.1197/ jamia.m2888 35. wolff jl, darer jd, larsen kl. family caregivers and consumer health information technology. j gen intern med. 2016;31(1):117–21. https://doi.org/10.1007/s11606-015-3494-0 36. lee k, lim k, jung sy, ji h, hong k, hwang h, et al. perspectives of patients, health care professionals, and developers toward blockchain-based health information exchange: qualitative study. j med internet res. 2020;22(11):e18582. https://doi.org/10.2196/18582 37. campbell br, ingersoll ks, flickinger te, dillingham r. bridging the digital health divide: toward equitable global access to mobile health interventions for people living with hiv. expert rev anti infect ther. 2019;17(3):141–4. https://doi.org/1 0.1080/14787210.2019.1578649 38. sieck cj, sheon a, ancker js, castek j, callahan b, siefer a. digital inclusion as a social determinant of health. npj digit med. 2021;4(1):52. https://doi.org/10.1038/s41746-021-00413-8 39. laacke s, mueller r, schomerus g, salloch s. artificial intelligence, social media and depression. a new concept of health-related digital autonomy. am j bioeth. 2021;21(7):4–20. https:// doi.org/10.1080/15265161.2020.1863515 40. texas health and safety code chapter 166—advance directives (2019) [internet]. [cited 2021 nov 12]. available from: https://statutes.capitol.texas.gov/docs/hs/htm/hs.166.htm 41. general services administration. section508.gov [internet]. 2017 [cited 2021 nov 23]. available from: https://www.section508.gov/ blog/do-section-508-accessibility-standards-apply-to-mywebsite/ 42. ehrenberg aj, king jl. blockchain in context. inf syst front. 2020;22(1):29–35. https://doi.org/10.1007/s10796-019-09946-6 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0. http://dx.doi.org/10.30953/bhty.v5.200 https://aspe.hhs.gov/reports/health-insurance-portability-accountability-act-1996 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https://www.section508.gov/blog/do-section-508-accessibility-standards-apply-to-mywebsite/ https://doi.org/10.1007/s10796-019-09946-6 http://creativecommons.org/licenses/by-nc/4.0 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.19912 (page number not for citation purpose) john robert bautista et al. appendix 1. focus group segments 1. introduction (10 mins) • introduction to medilinker and project motivation • overview of medilinker application (video presentation) • current research findings • discussion instructions and questions from participants 2. small group discussion (30 mins) • move experts into three groups with three to four people each for detailed discussion. • moderator: in final 10 min, ask each group to come up with actionable next steps. • questions for each expert group: clinical care group moderator: you have seen the project so far. we would like to get your expert views on which of the issues that we have not yet addressed (and there are many) are top priorities as this project moves forward. you have been invited because you have expertise in real-world clinical settings and the experience of patients. we would like this group to focus particularly on issues relating to: ◦ what parts of the clinician and patient experience and information needs would a system like this help or alternatively make more difficult? ◦ what elements might be attractive or off-putting for particular types of patients, caregivers, and providers? ◦ what sort of messaging might make this appealing to patients and their providers? what might turn people away? ◦ what challenges that you experience have not been discussed, but that you hope a system like this might be able to help with? organizational and regulatory concerns group moderator: you have seen the project so far. we would like to get your expert views on which of the issues that we have not yet addressed (and there are many) are top priorities as this project moves forward. you have been invited because you have expertise in the law, regulations, governance, health organizations, and related areas. we would like this group to focus particularly on issues relating to: ◦ what regulations might influence a system like this, positively or negatively? ◦ are there legal or regulatory barriers to a system like this? ◦ what are the organizational practices and governance issues that might influence the implementation of this system? ◦ what challenges that have not been discussed should we be thinking about? ethical and social issues group moderator: you have seen the project so far. we would like to get your expert views on which of the issues that we have not yet addressed (and there are many) are top priorities as this project moves forward. you have been invited because you have expertise in social and ethical issues around data and information. we would like this group to focus particularly on issues relating to: ◦ what does your knowledge of social behavior and ethics tell you would be either appealing or likely to find resistance to patients, citizens, organizations, or society more generally? ◦ what social and ethical issues are most crucial to address? ◦ what privacy issues are most important to understand? ◦ are there equity issues that are particularly apparent or important? ◦ what challenges that have not been discussed should we be thinking about? 3. break (10 mins) 4. overall discussion (30 mins) • convene all groups for an overall discussion http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.199 13 (page number not for citation purpose) clinical, organizational and regulatory, and ethical and social (cores) • each group will be given 5 min to present their top priorities and issues as medilinker moves from a minimum viable product to a product for real-world clinical testing. • facilitate intergroup discussion to identify overlapping issues and recommendations. • moderators: in the final 10 min, ask experts to come up with actionable next steps or recommendations 5. closing remark (10 min) • moderators provide a summary of the discussion and additional messages to the experts (e.g., potential discussions in the future). • thank participants for their time. http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 199 http://dx.doi.org/10.30953/bhty.v5.19914 (page number not for citation purpose) john robert bautista et al. appendix 2. coding tree 1. issues 1.1. clinical issues 1.1.1. clinical system 1.1.1.1. integration with clinical system 1.1.1.2. heterogenous systems among health institutions 1.1.2. clinical administrators 1.1.2.1. health institutions’ preference to control data 1.1.2.2. time and financial costs 1.1.2.3. uncertainty with blockchain 1.1.3. clinicians 1.1.3.1. workflow integration 1.1.3.2. clinician workload 1.1.4. patients 1.1.4.1. ehealth literacy 1.1.4.2. usability 1.1.4.3. accessibility 1.1.4.4. identity verification 1.2. organizational and regulatory issues 1.2.1. accountability 1.2.1.1. organizational entities 1.2.1.2. device identity 1.2.2. compliance 1.2.2.1. regulators are still in the process of interpreting existing laws 1.2.2.2. compliance with existing federal and state laws 1.2.3. legal safeguards 1.2.3.1. when clear legal guidance is absent 1.2.3.2. when patients deliberately withhold health information 1.3. ethical and social issues 1.3.1. trust 1.3.1.1. because of technology skepticism 1.3.1.2. among oppressed groups 1.3.2. transparency 1.3.2.1. rooted from technology distrust 1.3.2.2. unfamiliarity with the use of blockchain for health 1.3.3. digital divide 1.3.3.1. medilinker requires an internet-connected smartphone 1.3.3.2. inequity 1.3.4. health-related digital autonomy 1.3.4.1. sharing information with clinics 1.3.4.2. medical power of attorney 2. recommendations to address cores issues 2.1. design interfaces based on patient preferences 2.2. ensure testing with diverse populations 2.3. ensure compliance with existing policies 2.4. present potential positive outcomes to top management 2.5. maintain clinical workflow 2.6. increase the public’s awareness of blockchain http://dx.doi.org/10.30953/bhty.v5.200 proof of origin this document has been stamped and recorded on the hedera hashgraph ledger solution on 10th february 2022 at 4:40:31 pm utc. the content of the stamp includes the hash / fingerprint of this document: 383aca14ff9f9e3758d48b662afdc21435ef182a5951a618d5c5d37eebf4abf4 (sha-256) as well as the following associated metadata: file name: 252.pdf title: use of blockchain technology for electronic prescriptions author(s): ryan w. seaberg, tyler r. seaberg, david c. seaberg published in: blockchain in healthcare today published on: 2021-10-22 format: pdf size: 1171370 bytes (1.17 mb) number of pages: 5 doi: 10.30953/bhty.v4.183 technical details: hedera transaction id: 0.0.34776@1644511223.284233749 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u.s.a. 5 brian ahier, aetna, washington, dc, u.s.a. 6michael gould, independence blue cross, philadelphia, pennsylvania, u.s.a. 7heather l. flannery, obesity ppm, washington, dc, u.s.a. 8erik pupo, accenture health client service group, miami, florida, u.s.a. 9kevin a. clauson, lipscomb university college of pharmacy & health sciences, nashville, tennessee, usa. corresponding author: ron ribitzky, r&d ribitzky, 1929 beacon street, newton, ma 02468 ron@rdribitzky.com, 617.599.2200 keywords: adoption, blockchain, global, healthcare, innovation, interoperability section: opinion/perspective/point of view background: blockchain and distributed ledger technology is a disruptive force in healthcare. methods: this article provides a globally relevant, interdisciplinary perspective intended to aid disparate group of actors, participants, and users that represent the diverse stakeholders of an increasingly complex and technologically reliant healthcare system. domain expertise reinforced by literature published via industry, technical, and academic venues was used to inform these perspectives. results: key characteristics of blockchain and distributed ledger technology are highlighted and framed for a readership ranging from healthcare executive to policy makers to researchers. antecedent application of blockchain in the financial sector is explored followed by the technical, security, and interoperability considerations specific to healthcare. conclusion: blockchain remains an emerging technology both fraught with unanticipated challenges and the promise of unrealized potential in healthcare. review https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v1.24&domain=blockchainhealthcaretoday.com&date_stamp=2021-08-18 page 2 of 15 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.24 keywords: blockchain, healthcare, innovation, adoption, global, interoperability lockchain technology is designed to establish trust, accountability, traceability, and integrity of data sharing. leveraging these design goals to secure distributed data across traditional organizational and national boundaries is drawing enormous attention and resources around the world. healthcare is no exception.1-7 blockchain characteristics blockchains are currently the most popular form of distributed ledger technology (dlt) being adopted today.1-3 the foundational construct of blockchain, a dlt, is stored by each node in a ‘permissionless’ or public network (i.e., one that allows anyone to participate) or may be structured as a ‘permissioned’ or private network (i.e., whereby participation is controlled by the originator of the network). for each block on the blockchain, a hash code is computed as a combination of the data in the block as well as the hash code of the previous block. in this way, hash codes are chained. hash codes are easy to compute and verify by all participants of the blockchain, enabling them to verify the blockchain data has not been altered. deletion of a block or changing the data on a block renders the chain of hash codes on the blockchain invalid and is easily detectable by the blockchain participants. each node, or network participant, continuously synchronizes the blockchain as consensus is achieved according to the specific consensus protocol of that network. this consensus ensures the validity and consistency of each copy of the distributed ledger running on each node of the blockchain network. the data distribution model is a defining characteristic of the technology: centralized authorities do not communicate updates to records. instead, each node executes peer-topeer communication to trigger updates and achieve consensus among the nodes. subject to the type of network (i.e., whether private or public), and the corresponding network design, certain nodes may or may not process some or all the transactions. respectively, each processing node reaches its own conclusions, and then votes on those conclusions to verify that the majority are in consensus.3 several clear differences exist between dlt and traditional database technology as they were designed to support fundamentally different hypotheses of access to and control of assets. first, in traditional information technology (it) architectures, each organization manages and secures its own data; blockchain and dlts represent a departure from this approach. under this new model, a subset of an organization's total data set becomes a shared asset among network participants, continuously synchronized, and managed via consensus protocols and business rules encoded within the blockchain. confidentiality, privacy, integrity, and contractual rights are enabled through strong cryptographic techniques including hash codes, and public and private keys.3,8 a distributed ledger necessitates processes including maintenance and validation, which are performed by a network of communicating nodes.9 these nodes operate software which synchronizes the copies of the distributed ledger among a peer-to-peer network of participants, making all transactions auditable and sequentially traceable via cryptographicallygenerated digital fingerprints, or hash codes. data recorded in this type of ledger are b page 3 of 15 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.24 categorized as pervasive and persistent; it creates a transparent, nearly-immutable record. blockchain vulnerabilities and strengths blockchains are also intrinsically longitudinal data structures, enabling the verification of transactions, as well as capturing the specific sequence of transaction execution within the distributed ledger [8]. yet contrary to a widely propagated hypothesis asserting that entries in blockchain are immutable, (i.e., they can be altered only by appending a change to a record, but not by deleting or modifying the original record) it is unlikely that any technology is absolutely secure. for example, the integrity of blocks and the data they contain may be vulnerable while being evaluated by the participating nodes; or in the event there is a new consensus protocol for evaluating blocks. this is referred to as a ‘consensus fork’, a technology event analogous, in principle, to a software update. therefore, blockchain technology is more correctly characterized as offering strong resistance against tampering. this pragmatist perspective is based upon it theory and operations, emerging reports on blockchain data vulnerability, quantum resistant cryptography, vulnerabilities inherent in consensus-based proof-of-work (e.g., mining) model, and other threats to integrity.10-15 as all industries are increasingly subject to criminal hacking and related attacks resulting in compromised networks and data, it is imperative that blockchain and dlts be evaluated in the context of cybersecurity. traditional database technology typically has security features, including the use of encryption technology, intended to protect the entirety of the data store (i.e., all records, all data elements). therefore, if those security features are breached, the entirety of the data becomes accessible. by contrast, distributed ledgers can separately encrypt each discreet transaction stored on the blockchain, which represents a superior differentiating characteristic in regard to security. distributed storage in combination with the append-only, linear, sequential characteristic of blockchain technology, all parties with full nodes have full copies of the blockchain resulting in deliberate redundancy of data. although this redundancy serves a purpose (e.g., transparency, resilience, verifiability) it also comes with financial costs (e.g., capital equipment, energy consumption, other operating cost, etc.), and compliance risks (e.g., meeting legal and regulatory requirements, government issued advisories, policies, procedures governing institutional participants, etc.). multiple factors drive what data are stored on a blockchain. essentially an optimization-class consideration, the answer depends largely on: (1) what problem(s) the blockchain-enabled solution is designed to solve; (2) which usecases are in scope of this solution, and; (3) what is the minimally required scope of data to be collected and processed throughout these usecases (i.e., data minimization principle). limiting the scope of data stored on a blockchain serves to comply with data protection directives such as the european union’s (eu) general data protection regulation (gdpr), as well as achieving desired performance of the system. based on the append-only feature of blockchain, it is nearly impossible to alter previous blocks in the blockchain undetected. this may be at odds with certain rights of people whose data are processed, such as the right to correction if data are recorded inaccurately and the right to be page 4 of 15 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.24 forgotten. therefore, special attention should be paid to rights of data subjects, when applying blockchain technology. privacy and blockchains from a privacy perspective, whether the blockchain is accessible to the general public or only accessible to parties that are privy to a specific blockchain network is significant, including in the context of compliance with the principle of data minimization. blockchain technology enables granular and traceable permissions unique to each party participating in a given blockchain network. controlled access can be achieved via encryption to view or process specific data elements within certain time windows and under predetermined circumstances. in non-permissioned blockchain applications, all participants in that specific blockchain network are free to add data. by contrast, in permissioned blockchain applications, parties are able to add data to the blockchain only in accordance with their unique set of privileges encoded within that application. because a trusted intermediary is needed to assign and encode such privileges, the allocation of control over the system is not evenly distributed among the network participants. therefore, the party that is determining the functions and objectives of the application must make design decisions constrained by specific privacy rules, elevating the significance of the choice between permissioned and non-permissioned approaches. blockchain and cryptocurrency using blockchain as the underlying technology has given rise to a new type of currency, or system of value tokens that can be managed on the blockchain.16 today, this concept is referred to as cryptocurrency. cryptocurrency is encrypted code used to signify a value of transfer. alternate coins (altcoins) and tokens are also derived from cryptocurrency. although regarded as cryptocurrency, the difference between an alt coin and a token depends on the origin. alt coins are units of value and modes of exchange that originate from its parent blockchain. a token, or tokenization, represents a digital item or asset that is built on top of another such digital item (e.g., erc-20 token on top of ethereum, an ether-powered smart contracts blockchain platform.) tokens generally represent any type of asset class that is traded. another way to think about the diverse types of currency blockchain supports is as follows is shown in table 1. when considering industry uses for tokens, the value proposition relies on the type of transaction that needs to occur. any medium of exchange including medical records, healthcare data, and information can be facilitated using tokens. alternately, cryptocurrencies can be a medium of exchange to support large financial transactions. while the best use case of blockchain is based on the cryptocurrency bitcoin, the use of blockchain technology does not necessarily imply or require involvement of cryptocurrencies. blockchains may be used to enable collaboration and secure data exchange across networks, and thereby deliver value to without the use of cryptocurrencies. blockchain consensus protocols blockchain networks are heavily influenced by their consensus algorithms and the associated network protocols. these algorithms and protocols are used by the blockchain nodes in the network to coordinate collaboration to ensure the validity and consistency of decentralized ledgers. page 1 of 15 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.24 table 1. types of currency blockchain supports currency description coins or cryptocurrencies • general purpose units of digital funds that can be used as a means of payment, investment, or exchange with other coins or cryptocurrencies utility tokens • special purpose units of digital funds that are intended to be used in exchange for pre-defined goods or services. • nevertheless, certain utility tokens may be exchanged with other types of cryptocurrencies via cryptocurrency exchange services tokenized securities • special purpose units of digital funds that are tied to tradeable assets the particular consensus algorithm in a given blockchain network is a function of the specific blockchain technology used to implement the blockchain network. the performance, throughput, and scalability of blockchain networks are generally network bound, depending heavily on the consensus algorithm and associated network protocol, as well as the latency and bandwidth of the network connecting the blockchain nodes. consensus algorithms are typically more conservative and lower performance in untrusted public blockchain networks (e.g., bitcoin). while in trusted private/consortium blockchains they assume organizations connecting to the network are well-known and trusted, and therefore streamline the consensus algorithm and associated network protocols to improve performance. blockchain adoption—from fintech to healthcare financial technology financial technology (fintech) is broadly defined as any technological innovation in financial services and was the first conventional sector to explore and adopt blockchain technology. those engaged in the fintech industry develop new technologies to disrupt traditional financial markets.17,18 while bitcoin, crypto-currencies, and blockchain technology have evolved in parallel with fintech innovations, blockchain is instrumental in over a dozen fintech disruptive technologies.19 blockchains facilitate peer-to-peer, global value exchange in near real time, using mechanisms that are cryptographically secured.20 this creates a large-scale method of processing for value exchange in financial. healthcare healthcare is a system comprised of numerous components, foremost being patients, and including facilities to provide care, suppliers of medicines or equipment, the healthcare workforce to deliver services, educational and research institutions to train the workforce, and payers and government financing mechanisms. yet to better understand the relevance of blockchain, we propose to expand the definition of the healthcare industry beyond traditional delineation. in this we mean the convergence with life sciences, consumerism, precision medicine, and emerging technologies.21-26 the healthcare system in the united states (u.s.) is more decentralized and private than that of other countries.27 it is in this ecosystem that all the components of healthcare generate information and knowledge to improve health services, healthcare operations and cost, and patient outcomes. most data in the healthcare industry today exist in silos within enterprise applications deployed within individual healthcare organizations. yet page 2 of 15 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.24 blockchain technology does not cause decadesold problems of interoperability across healthcare data silos to magically go away. rather, blockchain creates net new, albeit solvable interoperability problems. examples include on-chain and off-chain use-cases, smart contracts across two or more blockchain platforms, consensus protocols, utility token and coin value exchange, and more. there is great latent potential to share healthcare data across networks of healthcare organizations to both improve the quality of patient care and reduce costs. the healthcare industry already has multiple types of networks of healthcare organizations from clearinghouse networks, to drug supply chains, provider credentialing networks, health information exchanges, and more. these existing networks represent near term opportunities for blockchain. in these networks blockchain will likely augment existing enterprise systems and enable secure data sharing to improve patient care and reduce costs. once blockchain proves its value to healthcare in the near term, this will pave the way for radical new healthcare use cases, types of networks, and values to enable further major improvements in healthcare longer term.3-6,18,2124,28-33 considerations in blockchain models for healthcare an infinite number of variations is possible when applying blockchain technology to the healthcare industry. to help identify the most compelling use cases in healthcare, the following common characteristics in the application of blockchain technology are of notable importance. health data storage with blockchain, all sorts of data can be stored, or referenced, such as data specific to conditions, lab results, medications, allergies, and myriad other clinical attributes. healthcare data also include operational and administrative data (e.g., attributed primary care providers, insurance coverage eligibility, copays, premiums, out of pocket limits, and spending account transactions). it is significant whether data relate to persons or only to organizations from a privacy perspective. data that can identify, locate, or be used to contact a person represents personally identifiable information (pii). where personal data and pii are concerned, the privacy rules are applicable. if more sensitive data are also processed (e.g., health data or citizen service numbers) more stringent requirements apply. health data combined with pii are commonly referred to as protected health information (phi) and is strictly regulated by the health insurance portability and accountability act of 1996 (hipaa) and data protection laws abroad, which can vary by location. conceivably, a blockchain-enabled solution would provide innovative design opportunities to harden pii and phi protection tied to smart contracts, data provenance, optimizing on-chain and off-chain data storage, and data minimization; coupled with individual’s governance over others access to and use of their data—in addition, of course, to the data and metadata encryption inherent in blockchain. security in the healthcare blockchain a patient’s healthcare information must represent trusted, authoritative evidence of care provided, decisions made, treatments and medications prescribed, and identities of participants in the care cycle.21 blockchain provides the assurance that data were not tampered with and confirms details of the provenance of the data. through cryptographic page 3 of 15 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.24 techniques, such as public and private key pairs and the distributed nature of the blockchain system itself, all information shared has an auditable trail—a traceable, reliable digital “fingerprint”. confidentiality to ensure privacy and protect confidentiality of sensitive information, it is necessary to structure to only allow that authorized parties are granted access to sensitive information stored on the blockchain. confidentiality should not be taken for granted with blockchain and depends on several key design and implementation decisions. what sensitive patient information goes on the blockchain versus what remains off the blockchain is a key decision that influences the magnitude of the risk to confidentiality. data minimization is a key principle in privacy and preserving confidentiality. hence, a minimal, sufficient approach is recommended for decisions on what to include on the blockchain. given a specific use case and the set of data fields required on the blockchain to serve it, we support the notion of storing those data elements on the blockchain but leave the remaining data off the blockchain. the decision to store all available sensitive data on the blockchain and later figure out how to use it is discouraged. nevertheless, zero-knowledge proof whereby a participant in the network can confirm the validity of pii or phi without exposing the pii or phi data itself may create opportunities to accelerate time to market of innovative, earlyadopter class blockchain solutions in healthcare. whether a blockchain is private, permissioned, or public is another key design decision that affects privacy. scope of access to the blockchain should be limited to authorized entities. if a blockchain truly holds only nonsensitive information intended for public use, then a public blockchain is a reasonable approach. however, in most healthcare blockchains, sensitive information will be stored on the blockchain and only authorized entities should be given access to this information, making private and permissioned blockchains more appropriate. the principle of least privilege is well established in cybersecurity and, applied to private blockchains, requires that entities transacting on the blockchain have minimal but sufficient permissions to fulfill their role on the blockchain network. sensitive information stored on the blockchain may also be encrypted as a method to further restrict access to only authorized entities and help protect confidentiality and ensure privacy.34 data integrity vs. the right to be forgotten protecting the integrity of blockchain data involves protecting against unauthorized deletion or altering of data on the blockchain. however, this can present different challenges. for example, in a case where a patient has the right to be forgotten, requiring the deletion of their stored pii from the blockchain clashes with the immutability goal of the blockchain-enabled solution. in these cases, pii can be stored off chain and referenced using an opaque unique identifier for the patient on the blockchain; and if a patient exercises their right to be forgotten, their pii off chain can be deleted, effectively deidentifying and anonymizing any associated data on the blockchain. availability healthcare is inherently a time-sensitive undertaking, hence timely access to data is largely mission-critical. as the industry is anticipated to accelerate experimentation with page 4 of 15 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.24 and adoption of blockchain-enabled solutions, such ought to meet this requirement. yet blockchain’s decentralized ledgers provide no single point of failure.3 moreover, as we have discussed in the preceding sections of this article, that some of the data will be stored onchain and the rest will reside off-chain is a core design assumption. therefore, standard best practices such as redundant blockchain nodes, availability of off-chain systems, load balancing, automatic failover, and redundant network connectivity ought to be implemented to assure high availability. furthermore, the design of blockchain-enabled solutions in healthcare ought to consider the tolerance for latency and success/failure rate of adding blocks to the chain (e.g., if a block fails validation, it will not be added to the chain). performance optimization techniques may include transaction prioritization, queueing and hatching (i.e., including two or more transactions in a single block); and proper detection, correction, and retry logic strategies and means should be part of the solution design phase. as we do not envision a single blockchain design pattern for healthcare’s high availability challenges, such considerations should be addressed on a use-case by use-case basis. adequacy healthcare is unfortunately very familiar with breaches involving business associates or data processors. maintaining the quality and reducing the cost of patient care requires mitigating these risks. this involves a variety of safeguards, including business associate agreements, as well as ensuring the adequacy of security and privacy controls used by the business associates. blockchain is a new type of middleware that can enable completely new levels of business to business (b2b) networks of healthcare organizations to collaborate. these networks can include both covered entities and business associates. potential breaches involving healthcare blockchains could not only impact the quality and cost of patient care but may also impede growth of blockchain in healthcare and the realization of the associated benefits. effective risk mitigation of these types of breaches requires the following considerations: 1. on-chain or off-chain data breach in a single node of a single healthcare organization would impact the entire blockchain-enabled environment (i.e., all nodes on-chain and off-chain data of all participating organizations); 2. security risk assessment, capability gap assessment, and security benchmarking should be performed to detect vulnerabilities of all participating organizations – leading to proactive remediation. 3. holistic security of the blockchain itself, all the nodes running the decentralized ledgers, and all the non-blockchain systems of healthcare organizations that connect to the solution. it entails administrative, physical, and technical safeguards, as well as a multi-layered, defense-in-depth approach at each level. in so doing, the blockchain-enabled healthcare ecosystem participants would establish, share, and maintain the trust that is key to achieve desired return on adoption of this technology.34,35 page 5 of 15 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.24 compliance key factors that determine the scope of compliance requirements for healthcare blockchains include what sensitive data are stored on the blockchain, what are the data usage agreements, and what is the physical location of the blockchain nodes and decentralized ledgers storing this information. for example, where a blockchain stores phi of u.s. citizens, rules outlined in hipaa are relevant. where blockchains store sensitive patient information of european union (eu) citizens, regulations in general data protection regulation (gdpr) are relevant. if blockchains span countries or regions with different regulations and data protection laws, such that blockchain nodes, decentralized ledgers, and copies of sensitive data contained within are physically located across these regulatory zones, then trans-border data flow will occur as new sensitive data are added to the blockchains. in designing blockchains for healthcare, it is very important to understand upfront what compliance requirements are applicable. these can be predicated on data type and sensitivity intended for storage on the blockchain, the deployment architecture of the blockchain network, and where blockchain nodes are physically located.36 it is also important that blockchain networks may start within a single regulatory zone with no trans-border data flow but may grow later to become international and implicitly add trans-border data flow. compliance requirements during design of such blockchains should anticipate if such growth could occur and design accordingly. as discussed previously, requirements such as a patient’s right to opt-out of sharing data and the right to be forgotten can have direct impact on what sensitive data can go on the blockchain and what sensitive data must remain off the blockchain. designing blockchain-enabled solutions that connect healthcare ecosystem participants across multiple geo-political and geo-governance boundaries may explore the use of off-chain encrypted decentralized storage (such as interplanetary file system, ‘ipfs’), and storage zones for meeting compliance across gdpr, hipaa, and other. development of blockchain with other technologies interoperability interoperability should not be taken for granted with blockchain. it depends on how information is stored on the blockchain, as well as any offchain data sources referenced subject, of course, to data ownership and access policy. to achieve the trusted interoperability that would match blockchain’s data integrity premise, the structure, semantic integrity, reference terminologies and code sets employed, and status of data stored on the blockchain should be defined and enforced.37-41 in cases where a blockchain contains pointers to off chain data, metadata associated with such pointers can include information required to support interoperability. this approach enables interoperability not just for the data stored on the blockchain, but also for the data stored off the blockchain. in the former case, interoperability is enforced at the time of adding data to the blockchain, while in the latter case interoperability is required at the time one healthcare organization directly (peer-to-peer) requests a record from another organization in the same blockchain network, based on discovery of the record using metadata stored on the blockchain. extending vertical service-level healthcare interoperability application programming page 6 of 15 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.24 interface (api) patterns (e.g., fast healthcare interoperability resources; ‘fhir’) to call and serve data transactions via emerging blockchain apis (e.g., dapps) and smart contracts may lead to on-chain/off-chain solutions that optimize the industry’s need for mission critical availability and security. deployment architecture blockchains may be deployed either on public platforms such as ethereum, or in private blockchains with nodes either in perimeter networks of participating healthcare organizations or in cloud environments. each option has major implications to privacy, security, compliance, performance, deployment, and ongoing operational and maintenance costs. careful proactive consideration should be given up front to the blockchain deployment option and its ramifications. conclusions blockchain is a disruptive technology. as such, it challenges legacy thinking about business and operational models of the expanded healthcare universe without borders, data ownership, and data use—while offering new opportunities not previously deemed feasible nor practical. blockchain is also currently surrounded by hype and presented as a panacea for various challenges. it is attracting tremendous attention and resources—from entrepreneurs to investors, economic buyers, policy makers, and consumers around the world. the encrypted distributed ledger has potential to improve the quality of patient care, as well as the economics and efficiency of healthcare operations, particularly considering growing data volumes with emerging data sources such as internet of things (iot). other blockchain characteristics, notably near-immutability, smart contracts, and offchain interoperability open up opportunities to tie in applications and services that extend beyond legacy boundaries of healthcare. going forward, we call for rapidly and broadly disseminating ‘low-hanging-fruit’ use-cases such as supply chain, medication epedigree, medical device identity and certification, and claims management. rapid prototyping of ‘how it works for the user’ (i.e., user experience; ‘ux’), proof-of-concept pilots, and sharing of key learnings from early adopters are needed to keep the innovation and discovery momentum going. constructing and evaluating blockchain solutions roadmaps and value proof points would help patients, healthcare consumers, and ecosystem players around the world reach the ultimate goal: return on adoption. acknowledgement invaluable contributions for the development of this manuscript ranging from ideation to critical review were provided by fellow members of the health information and management systems society (himss) blockchain working group. the authors would also like to thank katie crenshaw and mari greenberger for providing the impetus and logistical support for creation of this work. funding statement there was no public or private funding provided in the creation of this work. conflict of interest the authors whose names are listed immediately below report the following details of affiliation or involvement in an organization or entity with a financial or non-financial interest in the subject matter or materials discussed in this manuscript. rr: founder and ceo of r&d ribitzky, a specialty consulting firm serving the healthcare page 7 of 15 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.24 information technology, life sciences informatics, and precision medicine ecosystem worldwide, has financial and other interest in pre-existing and future projects pertaining to the subject matter and materials discussed in this manuscript; serves on the advisory board of arna genomics and bitmed. js: founder, the institute for healthcare financial technology (healthfintech) is a nonprofit organization dedicated to improving the healthcare value chain to reduce costs and streamline access and delivery of healthcare. healthfintech builds on the innovations of financial technology (“fintech”) and healthcare technology, especially such concepts as artificial intelligence (ai), blockchain and distributed ledgers. ctm: founder and ceo of patientory. ba: employed by medicity, an aetna business. no other disclosures hlf: founder and majority owner of obesity ppm, a disease management and population health company founded in 2009 with emerging blockchain-specific service offerings. kac: has served as a consultant for blockchain companies focused on healthcare and healthcare companies exploring blockchain solutions. the authors whose names are listed immediately below certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. dih: no conflict of interest to disclose. mg: no conflict of interest to disclose. ep: no conflict of interest to disclose. contributors to fulfil all of the criteria for authorship, 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[cited 2017 dec 29]. available from: https://www.gartner.com/doc/3610117?ref= analystprofile&srcid=1-4554397745 41. modern healthcare. chief information officers roundtable: the challenges are getting tougher. modern healthcare; 2017 apr 1 [cited 2017 dec 29]. available from: http://www.modernhealthcare.com/article/20 170401/magazine/304019951 1 (page number not for citation purpose) blockchain in healthcare today 2021. © 2021 the authors. this is an open access article distributed under the terms of the creative commons attribution-noncommercial 4.0 international license (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. citation: blockchain in healthcare today 2021, 4: 154 http://dx.doi.org/10.30953/bhty.v4.154 research article leveraging the hyperledger fabric for enhancing the efficacy of clinical decision support systems ramya gangula*, sri varun thalla, ijeoma ikedum, chineze okpala and sweta sneha department of information systems and security, kennesaw state university, kennesaw, georgia abstract adopting and implementing the clinical decision support system (cdss) technology is a critical element in an effort to improve national quality initiatives and evidence-based practice at the point of care. cdss is envisioned to be a potential solution to many current challenges in the healthcare sphere, which includes information overload, practice improvement, eliminating treatment errors, and reducing medical consultation costs. however, the cdss did not manage to achieve these goals to the desired levels and provide contextappropriate alerts, although integrated with the electronic health records (ehrs) (1). clinical decision support alerts can save lives, but frequent ones can cause increased cognitive burden to clinicians, worsen alert fatigue, and increase the duplication of tests. this ultimately increases health care costs without refining patient outcomes. studies show that 49–96% of clinical alerts are ignored, raising questions about the effectiveness of cdss (1). blockchain, a decentralized, distributed digital ledger that contains a plethora of continuously updated, timestamped, and highly encrypted virtual record, can be a key to addressing these challenges (2). the blockchain technology if integrated with the cdss can serve as a potential solution to eliminating current drawbacks with cdss (3). this article addresses the most significant and chronic problems facing the successful implementation of cdss and how leveraging the hyperledger fabric can alleviate the clinical alert fatigue and reduce physician’s burnout using patient-specific information. the proposed architecture framework for this study is designed to equip the cdss with overall patient information at the point of care. this then empowers the physicians with the blockchain-integrated cdss, which holds the potential to reduce clinician’s cognitive burden, medical errors, and costs and ultimately enhance patient outcomes. the research study broadly discusses how the blockchain technology can be a potential solution, reasons for selecting the hyperledger fabric, and elaborates on how the hyperledger fabric can be leveraged to enhance the efficacy of cdss. keywords: cdss; hyperledger fabric; blockchain; interoperability; alert fatigue; patient outcome received: 7 october 2020; revised: 24 january 2021; accepted: 26 january 2021; published: 17 february 2021 clinical decision support systems (cdss) are computer-based applications that analyze and intelligently filter data within electronic health records (ehrs) to assist providers, clinicians, administrative staff, and patients at the point of care. they comprise of several tools that filter through the tons of data and provide suggestions on the next steps in treatment (4). the cdss tools that aid in decisionmaking include ‘computerized alerts and reminders for providers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic support, and contextually relevant reference information, among other tools (5)’. the aims of integrating cdss into the foundation of ehr were to improve the quality of care by avoiding medical errors, such as adverse drug events and incorrect dosage prescriptions, and to lower health care expenses and enhance patient outcomes (4, 7). the integration of cdss enables in combining prescription information with patient information and provides alerts to patients when there are drug-drug interactions, drug-allergy contraindications, and other critical situations. in addition, it assists the providers in ordering medications, lab and imaging tests (8). drawbacks of cdss while cdss alerts are supposed to enhance patient safety by being the solution to adverse drug events and *correspondence: ramya gangula. email: rgangula@students.kennesaw.edu https://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.30953/bhty.v4.154 mailto:rgangula@students.kennesaw.edu citation: blockchain in healthcare today 2021, 4: 154 http://dx.doi.org/10.30953/bhty.v4.1542 (page number not for citation purpose) ramya gangula et al. inappropriate dosage prescription, they are not functionally as effective as they ought to be. also, alert fatigue in clinicians has been consistently on ecri’s (emergency care research institute) top ten health technology hazards (6). the lack of efficacy of cdss is due to alert fatigue, resulting from the repeated incurrence of irrelevant popups or warnings related to the treatment. alert fatigue is because cdss can only access the patient information silos in the current ehr (1, 9). when integrated into the ehr, cdss provides alert based on limited patient information. this results in generating inappropriate and irrelevant alerts that are neither patient specific nor context specific to the situation. the plethora of irrelevant alerts results in a cognitive overload on the providers, leading to desensitization, physician burnout and eventually medical errors (10). furthermore, cdss is aimed to assist providers in arriving at diagnosis by providing suggestions regarding the treatment plan (1, 8). it aids them in making decisions regarding further diagnostic tests and procedures. as cdss integrates medication information with inadequate patient data, it sometimes leads to ordering (11) duplicate diagnostic tests and inappropriate prescriptions, resulting in an enhanced patient risk and health expenditure (12). although the purpose of cdss is to enable better patient outcomes and reduce the burden on providers, this purpose is not being served adequately (1, 9). this article intends to address this issue by leveraging the blockchain technology to improve cdss functionality. blockchain technology blockchain is a decentralized, distributed ledger technology (dlt), with members in the network denoted as nodes. the transactions made among them are recorded by every node in the network into a distributed ledger (11). all nodes validate the information to be appended to the ledger, and a consensus protocol ensures that the nodes agree to a unique order in which entries are appended. once an agreement is reached, data are permanently recorded in sequential, append-only, tamper-evident blocks to the ledger (13). all the confirmed and validated transaction blocks are hash linked from the origin block to the most current block represented in the figure 1, making the ledger a chain of linked blocks (blockchain) and also harder to change or delete data in any block without network consensus. this property is referred to an immutability of ledger (11). the signature or hash consists of a cryptographically generated sequence of letters and numbers of a defined length that uniquely identifies any (defined and acceptable) digital entity (14). each record in the blockchain includes precise information about when it was created (timestamp) and the cryptographic signature of the preceding document in the chain, and the transaction data in this case health record. immutability lends itself to trust system of any blockchain network (15). there are two kinds of blockchain networks, which are public, permission-less blockchains and private, permissioned blockchains. the principal distinction between public and private blockchain platforms is that anyone can join the public platforms, while only authenticated users can join a private, permissioned blockchain framework. the popular examples of permission-less blockchain platforms include bitcoin, ethereum, and litecoin, and those of private, permissioned blockchains include hyperledger, multichain, r3 corda, and so on (11). research objective with the limitations of cdss above elucidated, this study aims to propose a conceptual model to enhance functionality of cdss in order to improve patient outcomes and reduce cognitive burden on providers. the conceptual model aims to be formulated leveraging a private, permissioned blockchain framework, hyperledger fabric, to facilitate interoperability of patient health information and ensure its safety and privacy while being transacted in between healthcare systems. the following sections of the article provide a background on the hyperledger fabric and its components, elucidating the conceptual model, cite the reasons for selecting hyperledger fabric over other private, permissioned blockchain platforms, and demonstrate how the proposed model intends to work in a real-world scenario. background hyperledger fabric hyperledger is an open-source umbrella project, hosting several distributed ledger frameworks, tools, and libraries. it is a business-driven blockchain framework, which is built to help business organizations to transact through a private blockchain network by the linux foundation along with other collaborators (16). hyperledger fabric is one of the hyperledger frameworks, which is a private, permissioned blockchain in which organizations can participate in data sharing (17). fig. 1. blockchain structure (13). http://dx.doi.org/10.30953/bhty.v4.154 citation: blockchain in healthcare today 2021, 4: 154 http://dx.doi.org/10.30953/bhty.v4.154 3 (page number not for citation purpose) hyperledger fabric for efficacy of cdss components of the hyperledger fabric membership service provider membership service provider (msp) serves as a certificate authority, trusted by all the members in the network. this entity validates the identity of an organization and authorizes its participation in the network (17) by issuing it a root certificate to become a member in the hyperledger fabric framework, and hence, permissioned blockchain framework. there can be single or multiple membership service providers in a hyperledger fabric network, and they are pluggable function, which can be plugged-in by the members instead of building their own (18). nodes members in the hyperledger fabric communicate with each other through their nodes. each member has three different kinds of nodes serving different functions. client node. this is the end user or application, who or which needs information and initiates the transaction (19). ordering service node. this pluggable node is responsible for ordering the transactions, packaging them as blocks, and disseminating these blocks to the peers in the network. at least one of the members in the network must be an ordering service node (16). peers. the peer nodes with chain code or smart contract installed on their machines endorse the transaction requests received based on the endorsement policy and validate the transactions before committing to the ledger (20). smart contract the smart contract is the business logic algorithm that is mutually agreed upon by all the organizations in the network. it is installed on all the endorsing peers on the network, and when executed it records and distributes conditional transactions in an immutable manner, instituting trust among the participants of the transaction (11, 15). ledger ledger in the fabric consists of two components: world state and transaction log (17). world state. it describes the current state of the ledger at that point and holds all the latest transactions or assets. transaction log. it holds all the previous transactions, leading to the current transaction in the world state. the data in the ledger are in the form of blocks that are cryptographically linked to each other (19). figure 2 shows three types of nodes, that is, client node, ordering service node, and peer nodes (endorsing peer with the chain code on its machine and committing peer) and a decentralized ledger in a single channel in a hyperledger fabric framework. methods conceptual model – hyperledger fabric framework in this section, we elucidate the overall picture of how health information can be exchanged among healthcare organizations, employing a hyperledger fabric framework using a conceptual model illustrated in fig. 3. let us suppose h1, h2, and h3 are three independent healthcare organizations that are leveraging the hyperledger fabric to exchange information between them. 1. the msp authenticates all the organizations who are willing to transact using the fabric framework. one of the organizations acts a consortium leader and fig. 2. representation of a member with nodes and ledger in a hyperledger fabric. http://dx.doi.org/10.30953/bhty.v4.154 citation: blockchain in healthcare today 2021, 4: 154 http://dx.doi.org/10.30953/bhty.v4.1544 (page number not for citation purpose) ramya gangula et al. creates a channel to add other organizations to the channel. 2. let us assume that organization ‘h3’ requests some information, and then the client node of h3 initiates a transaction invocation request and sends it to all the endorsing peers in the network. 3. the endorsing peers of h1 and h2 will receive the transaction request and simulate a transaction by executing the chain code on their machines. this results in generation of rw (read-write) sets, which includes the information on what could have been read or written onto the ledger had the transaction been executed. the endorsing peers now endorse or reject the transaction invocation based on the endorsement policy. if majority of the endorsement peers approve the transaction, the endorsement decision along with rw sets are sent back to the client. 4. organization h3 through its client application now will check for the endorsement, and if it is approved, it will be forwarded to the ordering service node in the network. 5. the ordering service node verifies the endorsement of transaction, client identity, and orders and packages the information in the form of blocks in the order, which they are to be committed to the ledger. this node now disseminates the blocks of information to all the committing peers in the network. 6. the committing peers validate the transaction and commit the blocks to the ledger in the order in which they are received. discussion why hyperledger fabric? the word ‘data breach’ sends jitters among the individuals or organizations that deal with sensitive data or information. according to the report by ponemon institute and verizon data breach investigations, healthcare industry is more vulnerable to data breaches than any other sector (21), and nearly 60% of the data breach incidents pertaining to health information (phi) involved insiders (22). given the importance of personal health information and the need to comply with regulations [the health insurance portability and accountability act of 1996 (hipaa) and general data protection regulation (gdpr)] in place, privacy, security, and scalability of the health information exchange are of paramount importance. while there are multiple public and private blockchain platforms that provide similar advantages in terms of information exchange and data privacy, health care data transfer requires minimal latency with heightened security. this concoction and the criticality of medical information interchange are the driving factors behind choosing the hyperledger fabric among the other private, permissioned blockchain platforms. also, according to the results published in the research (23), hyperledger fabric demonstrated increased privacy and throughput with minimal latency compared with other private, permissioned platforms, such as ‘quorum’, ‘multichain’, and ‘r3 corda’ (23). a detailed comparison amongst various private blockchain platforms (hyperledger fabric, quorum, multichain, r3 corda) is provided by polge et al. in terms of privacy, scalability, throughput and latency (23). the hyperledger fabric is a platform for distributed ledger solutions underpinned by a modular architecture delivering high degrees of confidentiality, resiliency, flexibility, and scalability. the architecture and design of hyperledger fabric makes it very suitable and effective platform to exchange vulnerable information like phi. the fabric with its malleable design ensures privacy and confidentiality of the transactions (24) among the members fig. 3. proposed model for exchange of information between nodes in a hyperledger network. http://dx.doi.org/10.30953/bhty.v4.154 citation: blockchain in healthcare today 2021, 4: 154 http://dx.doi.org/10.30953/bhty.v4.154 5 (page number not for citation purpose) hyperledger fabric for efficacy of cdss in the channel or between a subset of organizations in a sub-network. that means, in cases where members want to share an information exclusive to a group in the channel, they can do so via forming a separate channel in the framework (additional administrative overhead) or by creating a sub-network (bypasses overhead) in the existing channel. this flexibility in designing of fabric allows creation of private data collections between a group of members where the transaction can be endorsed, validated, and committed to the private state of the ledger by the authorized peers in a subnetwork. the private transactions are broadcasted without ordering service node via a peer-to-peer gossip protocol, thereby ensuring privacy and confidentiality of the exclusive transaction from the members outside the sub-network in the same channel (25). the fabric’s modular architecture and design provide ease of operation with plug-and-play features and furnish high performance, as there are specific nodes for specific purposes ensuring high transaction throughput and scalability (19, 25). moreover, with the fabric being a permissioned platform, only vetted, authenticated organizations can participate and transact in the channel (25). how hyperledger fabric aids cdss? let us consider a scenario, where a patient continues to visit a health care provider for a long time. the entire medical history records are stored in the ehr system (of the provider) and in the event of the patient being referred to a new specialist, the new provider would not have history on the patient’s medical condition in its entirety. in this case, the cdss in the ehr of the specialist provider might recommend context inappropriate alerts due to incomplete patient information. this can potentially lead cdss to fire inappropriate alerts and result in alert fatigue in physicians as well as ordering of duplicate tests and medications for the patients, thereby increasing the treatment costs (1, 8). the problem in the given scenario is not with the core functionality of cdss as the alerts are based on patient information available in the ehr system. if the entire patient information is made available to the ehr of specialist provider, then the cdss can potentially provide context appropriate alerts based on the entire patient history, in turn helping the patient in diagnosis and the treatment of the disease (26). with the hyperledger fabric framework, the patient’s primary care provider and the specialist provider can form a channel with smart contract installed and exchange patient information, which is immutable. as all the ledgers in a given channel are identical, entire patient information is present with both the providers in the same sequential manner and the safety of the information is ensured (23, 24). this can help in enhanced performance of cdss, reducing cognitive burden on providers and most importantly improving patient outcomes. conclusion and future work this article broadly talks about how hyperledger serves as a potential solution to enhance interoperability, which, in turn, addresses issues related to cdss. blockchain has been a proven technology that other dependent lineups can be built on. this being a distributed ledger and an immutable transaction recorder, resistance to changes is natural and any updates can be traced back easily. we chose the hyperledger fabric and proposed a conceptual model on how information can be exchanged among independent hospitals or systems in a private and secured manner. this study starts off by listing out issues pertaining to cdss, which were having an impact on the overall patient outcomes. this work then moves onto isolating the root cause that is, how interoperability amongst participating systems has been the core concern and how hyperledger fabric can be a viable solution in enhancing the fundamental functionality of cdss. while the current article focuses on the conceptual aspects and provides a framework on how hyperledger fabric can aid cdss, additional research work will be required when it comes to practical implementation, to address ground level application issues & deal with real-time scenarios. conflict of interest and funding no authors had any financial and personal relationship with other people or organizations that could inappropriately influence (bias) this research. the healthcare management and informatics department at kennesaw state university supported this research. authors’ contributions conception or design of the work: ramya gangula data collection ramya gangula, sri varun thalla, ijeoma ikedum, chineze okpala data analysis and interpretation ramya gangula, sri varun thalla drafting the article ramya gangula, sri varun thalla, chineze okpala, ijeoma ikedum critical revision of the article: ramya gangula, sri varun thalla, ijeoma ikedum, sweta sneha http://dx.doi.org/10.30953/bhty.v4.154 citation: blockchain in healthcare today 2021, 4: 154 http://dx.doi.org/10.30953/bhty.v4.1546 (page number not for citation purpose) ramya gangula et al. final approval of the version to be published: ramya gangula, sri varun thalla, ijeoma ikedum, chineze okpala, sweta sneha references 1. ancker js, edwards a, nosal s, hauser d, mauer e, rainu k, et al. effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support 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review the advent of open data in health care has increased healthcare innovation, with the publication of complete datasets aggregated by private and public entities that lead to efficiency through crowdsourcing working code, facilitating research into personalized medicine, and publishing reproducible data pipelines for experimental validation. however, there lacks an internationally recognized definition for health data governance at the scope of individual health data and open source big data, which bring about a discussion about the implications of open data on data privacy. first, healthcare data sourced directly from public healthcare systems: by whom and for what purpose is these data used for within the context of healthcare research. second, health data from private research: the regulations needed for mutual disclosure. third, personal user-generated health data: safeguards in a digital era needed to prevent misappropriation and abuse. this paper addresses the opportunities of open data in healthcare research in a digital age without transparent regulation. the consequence of open data on privacy leads to a framework of four safeguards for stakeholders: public education, operational transparency, regulation for accountability, and validation of research ethics. it also pioneers public policy direction for a balanced agenda between privacy and healthcare research. introduction open data are defined by the government of canada as structured data, that is machinereadable, freely shared, used, and built on without restrictions.1 the development of structured crowdsourcing, data donation, and participatory surveillance leverages public sector https://doi.org/10.30953/bhty.v3.144 mailto:dchen362@uwo.ca https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.144&domain=blockchainhealthcaretoday.com&date_stamp=2020-09-18 page 2 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.144 datasets to be used for robust secondary healthcare research at significantly lower costs compared to primary clinical research approaches.2 open data host such as github (known for its open source community projects, and repository such as open government, a data collection aggregated by the government of canada) also contribute to the significant potential in developing knowledge of diseases, improving validity, and utility of medical diagnostics and treatment options. from an economical standpoint, open data stand to create a value proposition upwards of $300 billion across the world.3 the origin of this value stems from its role to enable innovation in disease diagnosis,4 monitoring and treatment,5 maintaining the cost effectiveness of treatment,6 and innovating new healthcare approaches and products to improve quality of care.7 to wholly capture the value of open data in health care, this requires a robust standard for data governance and right to usage that takes into account the need for individual privacy, government regulation, and changes to make the data as versatile and effectively used as possible.8 healthcare data host sensitive information that is protected due to the proprietor’s right to privacy.9 raw data collected from primary studies can be revised with anonymized identifiers in place of sensitive identifiers, so that the participant’s right to privacy is respected.9 there also exists pressure in favor of open data used in healthcare research and to promote transparency in healthcare operations.10 this raises further discussion on the balance needed when individuals have ownership of their health data: the ability to make informed decisions when sharing or keeping these health data confidential and the extent to which this autonomous discretion is defined and used in practice. role of government regulation of publicand private-sourced data government has the legislative power to form policies and set an overall tone by which the private sector and individual users use and share open data. government public health agencies should set defined rules on data governance, release, with a particular focus on privacy, accountability, confidentiality, and proprietary rights that are based on the value proposition of open data rather than how easily shared the data should be.11 leaders in government can direct the responsibility of open data across multiple public healthcare agencies for the purpose of transparency through open data releases on accessible platforms. this direction of responsibility can extend internationally; for example, canada overseas as co-chair of the open government partnership (ogp), a multilateral initiative that aims to promote government transparency and large-scale open government and public data reforms in partnership with the private sector.12 the ogp is the first step toward large-scale open data in an internationally collaborative forum. the united kingdom leads by example by ranking first in international indexes for open data implementation, stemming from its commitment to quarterly updates on progress in selfimplementation and high impact of initiatives at scale.13 this commitment engages subnational governments to work harmoniously with national approaches in piloting a federated search service for open data. government regulation also has the power to directly influence legal and economic to maximize the value proposition of open data usage, while still addressing the legitimate needs of privacy from major stakeholders and rights to proprietorship for individuals and organizations. taking a lesson from estonia, https://doi.org/10.30953/bhty.v3.144 page 3 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.144 who is a leader in digital solutions in public administration ecosystems and electronic banking, these policies should include definitions on access, usage, and protocols needed to notify data proprietors, and an institutional focus on centralized digital architectures for storage and data transfer.14 it is the role of government officials to uphold standards while adapting policies for data accessibility and versatility among theirs use cases. in spite of this heed to caution, the government should also promote private sector companies to address public health data shortages by collecting and releasing data with effective protections. at a municipal level, education in robust analytical skills when manipulating open data provides significant potential for new innovation in a constantly evolving field of healthcare research.15 standard of individual privacy the concept of open data involves a social network of people, policies by which governance is defined, cultural practices and behaviors, and the state of technology infrastructure over time. the dynamics of this open data ecosystem is ever changing as new fields and datasets are introduced or remodeled. this is particularly important when non-healthcare data are integrated with healthcare data, leading to new interdisciplinary consequences such as unethical research and breaches in privacy safeguards.16 it is understood that the nature of health details sensitive and identifying information by which discussion with trained health professionals is kept confidential. the data used in administrative and primary treatment approach at the healthcare facility while attending a patient is expected to be upheld to the highest standard of professional to patient confidentiality.17 further down the line, secondary use of patient data in translational research instigates a new divide between privacy and open data. even with few pseudonymized data points, modern data processing and statistical inferences can predict missing data points and even identify individuals at an unprecedented level from public and private repositories. this poses a paradox in open data policy: the more detailed a dataset, the more valuable it becomes for innovation, and the more likely sensitive personal data can be traced back to individuals through alternative and often unethical means.18 there needs to be a balance between the value and sensitivity of open datasets, erring on the side of caution for privacy until holistic usage policies have been put into place. the landmark canadian case, mcinerney v. macdonald (1992), established that patients have the right to access information of their own records despite physicians owning the physical record.19 the court found that healthcare providers hold patient information in trust on behalf of the patient, who retains their right to access these data. however, ambiguity remains: the supreme court found that right to access can be denied by the provider if there exists a significant likelihood of an adverse effect due to such information on the medical record. the resolution concludes that the owner of the physical record is responsible for controlling access in accordance to privacy law. when frameworks lack clarity between definitions of open, closed, and shared data, this undermines civilian trust. case study: covid-19 crisis research within the context of a pandemic crisis such as the sars-cov-2 virus, the speed of accessing data and conducting research toward a vaccine directly impacts the progress made. due to the fast-paced demand for crisis research, the need for open data in health care becomes crucial in concerted international efforts, where thousands of teams across a multitude of private and public sectors https://doi.org/10.30953/bhty.v3.144 page 4 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.144 collaborate in parallel. new inventions and innovations abound in an environment that rewards speed of development to product timelines. for example, complete genomic sequences sourced from individual patient data are updated to genbank repositories and are made open source in good faith,20 where a range of researchers to high school students at hackathons propose new ways to tackle this pandemic. this pits the value of open data in health care, its open source, inexpensive approach, with its greatest concern: privacy. crisis research questions stakeholders in public and private sectors how we plan on addressing the quick release of healthcare data with a minimum standard in the robustness of privacy protection.21 it also brings the question on what data should be shared publicly; in this case, should the genomic data of individuals affected by sars-cov-2 found in genbank be shared with public or private researchers if it is in the best interest of society at large? more data often lead to refined healthcare models and treatment approaches that account for more unique factors. it remains unclear at what stage the consent is given, as well as if the consent can be retracted, then at what speed will personalized data be removed following the retraction of consent. this means that if consent can be revoked by the civilian, the speed of reaction by genbank to respond remains unclear and allows for anyone to continue to use these data until they are effectively removed. as a safeguard, the rapid use of genomic data for treatment research should yield to patients who withhold these highly sensitive personal data from an open platform. case study: intelligent interfaces in health care deloitte has conducted a 2019 review of technology in health care, where they identified several fields of innovation that will have a significant impact within the next 5 years.22 in particular, the review noted a marked increase in the degree of cooperation between healthcare providers and private sector technology industries for digital experience, cybersecurity, and intelligent interfaces.22 the innovative technologies include ibm watson, an artificial intelligence capable of answering questions posed in natural language,23 and google’s deepmind health, used to serve patients, nurses, and doctors as mobile medical assistants.24 these systems encompass fields of genomics, drug discovery, and patient monitoring. the deepmind health streams application allows healthcare practitioners to be notified of changes to patient’s vital signs and deliver real time information to mobile devices.24 in developing and rural countries, this allows healthcare practitioners to improve their effective standard of care despite barriers in equipment and distance from major centers. the use of personal health data to facilitate patient monitoring and research is mutually contractual, with proprietary technology owned by the private sector and healthcare data collected by public healthcare systems.24 google’s project nightingale achieves this objective: it has partnered with the largest nonprofit healthcare system in the usa, ascension, in a project aimed to predict emergent health data.25 the ethical issue of data governance and usage when the technology and health care integrate into one entity becomes difficult to quantify. secure solutions in cybersecurity and protection of privacy rights when conducting experimental research with patient clinical data are essential in gaining civilian trust in a personalized field of medicine. case study: care.data—undermining trust the sale of personal health data to commercial entities has become a very sensational media https://doi.org/10.30953/bhty.v3.144 page 5 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.144 topic that often reports on misappropriation of data and failure of full disclosure between consenting parties, which lead to ambiguity in data governance. care.data was a public research repository hosted by the health and social care information centre (hscic, now nhs digital) that extracted data from general surgeries into a centralized database.26 english citizens who participated in general practitioner (gp) surgeries reported in this database were informed that these personal health data would be uploaded to hscic unless express objection was obtained by informing their gp.26 data were anonymized to prevent identification, and identifiable data could only be contained through legal due process.26 care.data was reputable as a research resource for exploratory data analysis, monitoring of specific treatment outcomes, and progress in personalized medicine approaches. the controversy arose when the data were also made available to numerous private sector companies such as the pharmaceutical industry and insurance companies, which have vested interests in sensitive information on patients for economic gain. in 2014, as part of an organization audit, it was determined that pseudonymous and identifiable data were sold for financial gain to organizations despite the supposed open data framework that suggested privacy protection.27 following a request for freedom of information, the hscic made a statement that suggested the identity of individuals may be ascertained through care.data in combination with other data sources.28 this case study sheds light on the minimal degree of anonymization of care.data and the limited use case of pseudo anonymity in a modern era of internet of things. algorithmic technologies within the last 10 years have been developed, which can massively harvest and analyze data to predict identifiable information from piecewise data with high accuracy. therefore, the degree of pseudonymity that is effective is inversely proportional to the improvements in classification algorithms to a point where even the most robust efforts to anonymize data artificially while still maintaining theirs usability no longer protects data privacy.29 by this account, access to sensitive public health open data must be monitored on a case-by-case basis, and the implications of emerging technologies should be consulted as new developments arise. the lesson of this issue is not meant to instigate paranoia; it is a heed to caution about sharing potentially sensitive data without mutual disclosure and the threats to security that exist from vested interests in the private sector. limitations of the privacy paradox health information systems face the looming conundrum often coined as the privacy paradox. the paradox is based on the inconsistencies between people’s privacy attitudes and their associated behavior.30 for complex systems to operative effectively in the healthcare space, an equilibrium must be adaptively maintained between the usage of individual’s information and protecting privacy. yet the demand for both quality healthcare services and privacy of personal information can simultaneously be met if appropriately addressed. the architecture of privacy regulation lends itself to regulation at each step of the personal data economy: collection, storage, usage, and information transfer. solove addressed the need for regulation in his suggestion of contractual agreements between parties during data transfer, adding control points throughout the data economy that makes certain transfers bounded by regulation.31 thereby, the privacy paradox implicates that regulation of privacy exceeds self-management at the individual level but requires restructuring https://doi.org/10.30953/bhty.v3.144 page 6 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.144 of governance and revision of contractual agreements between individuals and third parties. the most clear-cut approach to contractual agreements between parties in the data economy involves explicit consent intended to honor the autonomous right for self-governance of personal data.31 however, the synergistic interplays of the data economy do not easily distill into the binary nature of consent, and even if it could, the constant need for approval of consent can inundate individuals with requests beyond practical means. furthermore, explicit consent may not wholly succeed in this endeavor, given that usage and value of individual data are unpredictable. the need to reengineer legacy systems with modern approaches to individual data management is a monumental challenge and will require extensive testing and implementations of novel technologies that translate into actionable and measurable outcomes within the healthcare data privacy space. blockchain: an emergent technology for information systems blockchain is an emergent technology that decentralizes data across multiparty systems that transact and access information simultaneously. distributed applications based on blockchain involve information that interfaces across multiple nodes of the network, conveying a sense of transparency while continuing to regulate data management through smart contracts that can execute automated approval of individual consent.32 stakeholders clearly understand who has access to their data, who has used their data, when they were used, and in what manner, all of which remain a gray area in the current health data economy infrastructure.33 the nature of blockchain as a distributed ledger technology and its inherent immutability ensures the integrity of data and prevents alterations after they have been appended to the network. blockchain also employs cryptographic hashes of appended blocks of data, which encrypts messages during transit to protect sensitive information of patients until they reach the intended target, where the data are decrypted with legitimate permissions. the european general data protection regulation (gdpr) prohibits the usage of sensitive personal data unless express consent is achieved, such as through blockchain-integrated smart contracts. the combination of these intrinsic blockchain features safeguards against data loss compared to conventional systems reliant on singular, centralized authorities and paves the way for a gdpr-compliant healthcare information system. ransomware attacks have also critically revealed the prevailing security flaws of healthcare facilities with maladaptive data practices that lend itself to systemic exploitation.34 investments into more secure systems now can outweigh the initial costs over time. finally, the oversight of pseudonymity of healthcare records in the face of advanced predictive analytics and big data makes it difficult to truly anonymize data intended for research purposes. also, the case of care.data has shown how easily conventional data management practices can be compromised and how quickly public trust in other parties using their personal data can be lost. blockchain technology maximizes security of information storage and mediates accessibility when sharing healthcare data records, which can be useful in applications beyond primary healthcare venues, such as clinical trials and monitoring systems for out-of-hospital care.35 a limitation of the blockchain implementation lies in the need to reengineer legacy systems and its cost per transaction as part of the blockchain system which can total a sizable cost. the https://doi.org/10.30953/bhty.v3.144 page 7 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.144 implementation of a blockchain system would replace entire electronic databases, medical records, and registries, as well as prevent costly data breeches in order to maximize costefficiency over time.33 blockchain poses a novel architecture for modern healthcare data management in its approach to patient-centered care with security first and should be noted as an emergent technology within the digital health space. responsible development of open data engagement between the public and commercial private sector is necessary for delivering effective outcomes in healthcare research to a standard of personal privacy. neither risk-averse nor high-risk authorities should wholly dictate the spectrum of open data policy; instead, civilians should challenge both sides on each side to engage in establishing an agenda that benefits both healthcare research efforts and respects privacy standards. existing platforms in the united states have shown substantial improvement toward the provision of open data for health research. however, a sustained effort is needed to improve associated metadata and hyperlinks, so that researchers will use these data and consider those as a valuable, trustworthy source.36 first, public engagement should include scaled awareness campaigns that focus on the full disclosure: benefits and risks of sharing personal health data should factor in empirical evidence while promoting the potential use cases for innovation. this will produce an ongoing dialogue between policy makers; authorities in the private sector and civilians meant to increase civilian trust in government policy and understanding the use for open data in research initiatives that can lead to public good. the difficulties of public engagement focus on how the media may continue to portray sensational news on the shortcomings of open data in favor of supplementing its constructive dialogue.37 second, transparency in who will be using the personal health data and for what purpose according to the core regulation processes outlined in the open data principles will be required. transparency needs regulation and enforcement of such regulation. full disclosure and a notification platform to inform individuals of their use of healthcare data should be included in the proposed regulations. the practical implementation of these regulations may differ in format. third, a shift in the mass-scale regulation of data usage by commercial industry should be proposed. with big data, users can regain control of their own data from businesses and should be able to make an informed decision on who they decide to share their data with and at what cost to either party. in this model, the default proprietor of healthcare data lies with the individual rather than businesses or the government. one of the main problems lies in the need for existing business models to rapidly adapt, particularly due to the increasing degree of partnership between private sector researchers and governments. fourth, major stakeholders need to be educated with the modern computing skill and research ethics needed to take advantage of open healthcare data. through a deeper understanding of the source of healthcare open data and its implications, can the privacy needs be wholly appreciated? standards must be set by industry authorities, researchers, and medical professionals to communicate the duality of open data within the context of privacy in an increasingly shared online world. furthermore, education can empower civilians at a local scale to advocate for privacy rights to address community needs through grassroots initiatives. https://doi.org/10.30953/bhty.v3.144 page 8 of 10 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.144 conclusion the open data movement has presented potential in fostering innovation and increasing operational transparency. open data have reduced costs of advancing healthcare research and contributed to the improvement of healthcare provision by sharing information in a connected world. the issue of data privacy requires novel approaches to simultaneously meet the research needs while actively engaging public trust through open data integration that preserves individual privacy. policymakers need to establish shared regulatory frameworks among proprietors and regulatoryauthorities, which meet an equilibrium between privacy safeguards, prevent commercial exploitation, and keep consenting parties informed of their personal data. it is the responsibility of research authorities to advocate with key policymakers and guide the process of outlining a revised multi-stakeholder agenda. open data have the potential to save millions of lives when used in research appropriately. however, the most significant advantage of sharing health data still instigates debate. 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nhs big data sweep, due to a lack of confidence in the project. the independent; 2014 [cited 19 april 2020]. available from: https://www.independent. co.uk/life-style/health-and-families/ health-news/40-per-cent-of-gps-plan-toopt-out-of-the-nhs-big-data-sweep-due-to-alack-of-confidence-in-the-9083806.html 28. bhatia n. register of approved data releases—a freedom of information request to nhs digital. whatdotheyknow; 2014 [cited 20 april 2020]. available from: https://www.whatdotheyknow.com/request/ register_of_approved_data_release 29. nagel e, frith j. view of anonymity, pseudonymity, and the agency of online identity: examining the social practices of r/gonewild | first monday. first monday; 2015 [cited 23 april 2020]. available from: https://firstmonday.org/article/ view/5615/4346 30. li x, motiwalla l. unveiling consumers’ privacy paradox behaviour in an economic exchange. int j bus inform syst. 2016;23(3):307. 31. solove d. the myth of the privacy paradox. george washington univ law school. 2020;89(10). 32. dagher g, mohler j, milojkovic m, marella p. ancile: privacy-preserving framework for access control and interoperability of electronic health records using blockchain technology. sustain cities soc. 2018;39:283–297. 33. chen h, jarrell j, carpenter k, cohen d, huang x. blockchain in healthcare: a patient-centered model. biomed j sci tech res. 2019;20(3). 34. slayton t. ransomware: the virus attacking the healthcare industry. j legal med. 2018;38(2):287–311. 35. vazirani a, o’donoghue o, brindley d, meinert e. implementing blockchains for efficient health care: systematic review. j med int res. 2019;21(2):e12439. 36. martin e, law j, ran w, helbig n, birkhead g. evaluating the quality and usability of open data for public health research. j public health manag pract. 2017;23(4):e5–e13. 37. bourgault j. how the global open data movement is transforming journalism. wired; 2020 [cited 24 april 2020]. available from: https://www.wired.com/ insights/2013/05/how-the-global-open-datamovement-is-transforming-journalism/ copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. see http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v3.144 https://www.independent.co.uk/life-style/health-and-families/health-news/40-per-cent-of-gps-plan-to-opt-out-of-the-nhs-big-data-sweep-due-to-a-lack-of-confidence-in-the-9083806.html https://www.independent.co.uk/life-style/health-and-families/health-news/40-per-cent-of-gps-plan-to-opt-out-of-the-nhs-big-data-sweep-due-to-a-lack-of-confidence-in-the-9083806.html 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https://www.wired.com/insights/2013/05/how-the-global-open-data-movement-is-transforming-journalism/ http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 blockchain in healthcare today: 2022 predictions discussion blockchain in healthcare today: 2022 predictions daniel conway1* , mohan venkataraman2, daniel laverick3 , gabriela pelin4 and anton hasselgren5 1associate director, blockchain center of excellence, clinical professor of information systems, university of arkansas, usa; 2chief technology officer, chainyard, a business unit of it people corp, usa; 3vice president of digital & data solutions, mobile, alabama; 4chief innovation officer, avaneer health, usa; 5department of neuromedicine and movement science, faculty of medicine and health sciences, norwegian university of science and technology, trondheim, norway abstract each year, blockchain and healthcare today reaches out to journal board members, annual conv2x symposium speakers, and ecosystem subject matter experts to share their near-term views and perspectives for blockchain technology advances in healthcare. this article presents insights into where authors anticipate market opportunities and where gaps exist that should be addressed for regional and global collaboration, governance, and efficiency for the year 2022. keywords: supply chain; pharmaceutical manufacturers; chain of custody; smart contracts; cryptocurrency; health information exchanges; blockchain in healthcare; decentralized ledgers; fast healthcare interoperability resources (fhir); id keychain; master index   citation: blockchain in healthcare today 2022, 5: 194 http://dx.doi.org/10.30953/bhty.v5.194 copyright: © 2022 daniel conway et al. this is an open access article distributed under the terms of the creative commons attribution-noncommercial 4.0 international license (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. received: 06 december 2021; revised: 21 december 2021; accepted: 21 december 2021; published: 31 january 2022 funding statement: the authors have not received any funding or benefits from industry or elsewhere to conduct this study. financial and non-financial relationship and activities: daniel laverick is a current employee of zuellig pharma holdings pte ltd, which has not sponsored, funded, or endorsed this work. *correspondence: daniel conway. email: dconway@walton.uark.edu   that great philosopher, yogi berra, once said, ‘it’s tough to make predictions, especially about the future’. despite such difficulty and despite plans often referred to as ‘worthless’, planning is everything. predictions are generally done in several ways. the most common approach is to project meaningful patterns of the past into the future – a momentum approach. we look for trends and seasonality over the past several years and project them forward into 2022. a contrarian approach might suggest a regression to the mean, where the mean itself is projected, but that recent deviations from that mean are likely to reverse. it is, after all, the role of management to make sure the future does not look like the past. we also predict by looking for similarity and suggesting that similar events will reoccur. for example, those who liked these three books will also like this fourth book. many technology trends, such as sales of 5g equipment, are predicted by looking at trends of similar products and suggesting a similar adoption maturity. in the case of blockchain, we have a technology that is what we refer to as a foundational technology. blockchain is not an improvement on an existing technology. thus, we are left without a suitable comparison. blockchain is the technology layer of a digital ecosystem, and we have not had that before. it is difficult to predict. but, it is not over. in the short term, we tend to consider other approaches, such as watching patent applications, paying membership trends in industry consortium, transaction volume patterns, other meta-data, and of course – we follow the money. patent activity suggests that blockchain will soon be involved in pos (point of sale) transactions. this is likely to occur when regulators formalize their approach to the new asset class of cryptocurrency. large retailers and supply chains are building the transaction infrastructures to support this eventuality today. consortium membership suggests a strong move away from private permissioned blockchains toward confidentiality over public blockchains and toward confidential swap automated market makers. and the money following? while the transaction volume is increasing dramatically, within our scope, there are several efforts that are attracting attention. one such effort is that of the sovereign identity. identity was not part of the original internet design, and we have struggled with it ever since. there is significant effort at standardizing this layer. a second effort to note is the increased interest in iot (internet of things) expansion, where blockchain serves as the bookends that define where the ecosystem data should reside. blockchains should contain minimal information, and internets of blockchains (such as cosmos) enable a fairly robust separation of roles architectures. another effort involves distributed replicated file systems, where the blockchain serves as an information repository and is resistant to single points of failure. this eliminates the need for business continuity and disaster recovery it issues that most in the medical world hope go away anyway. finally, with the expansion of 5g networks and some standardization of protocols, we see significant resources coming into the augmented reality and metaverse space. this should continue to reduce the location obstacles for providing healthcare services, globally. cars go fast only if they have good braking systems. the blockchain community welcomes a clear regulatory structure, but currently, that effort trails innovation. we would like to predict a change in the regulatory response, but prediction confidence is lacking. in the following, we share insights that can be useful in predicting blockchain in healthcare for 2022. mohan venkataraman, chief technology officer, chainyard, a business unit of it people corp, usa technology plays a critical role in the healthcare industry, including drug discovery, clinical research, pharmaceutical manufacturing and distribution, patient care, and health plan administration. hospitals, laboratories, and clinics use intelligent devices powered by sensors and ai (artificial intelligence) to detect, diagnose, and administer care. the industry is heavily regulated, and compliance requirements are quite complex. the most notable one is hipaa (health insurance portability and accountability act of 1996) whose primary focus is privacy of patient information and individual consent prior to sharing data. taking a drug to market requires pharmaceutical companies to track and trace every step of the process and provide evidence to regulatory agencies until the drug gets final approval. medical facilities must maintain ‘chain of custody’ to prove that samples, specimens, and other physical materials collected have been properly handled from origination to final disposal. patients must trust data that intelligent medical devices collect, and the associated recommendations made by ai and ml (machine learning) algorithms. blockchain will play a critical role in transforming healthcare in conjunction with peer technologies, such as ai, ml, and iot. we are only at the beginning of this new revolution, as the technologies are still maturing. in the future, we see intelligent edge networks communication with the core, and blockchains will evolve into network of networks. based on the inquiries received from healthcare-related companies, chainyard sees significant applications of those technologies in the next 3 years. the blockchain will address the three main areas of concern (i.e. trust, transparency, and privacy). the coronavirus disease 2019 (covid-19) pandemic has accelerated adoption of blockchain and peer technologies. fake ppes (personal protective equipment) and sanitary products have necessitated the need for supplier qualification and supply chain diversity. chainyard’s ‘trust your supplier’ network has been the key player in this space and is expected to see growth. the second use-case stems from the fact that people are very concerned about the ingredients that go into the production of drugs, vitamins, and vaccines. hence, drug approvals, manufacture, distribution of pharmaceuticals, and the origin and sourcing of the ingredients that compose the final product will all see an increase in the adoption of blockchain for ‘track and trace’ and ‘regulatory reporting’. third, the rise of covid passports will evolve into a broader personal health wallet that will enable individuals to carry their records in tamper proof cryptographically secured digital wallets and enable cryptographic consent for sharing personal data. fourth, health insurance companies will form consortiums to improve trust and enable more transparency in benefit administration and claims process – especially in lowering prescription costs. lastly, intelligent devices at the edge that combine sensor technology and ai will have to be auditable, and hence, new ‘oracles’ will emerge to record proofs of data origin and aiml (artificial intelligence markup language) algorithm signatures that processed the data to deliver insights and outcomes. daniel laverick, vice president of digital & data solutions, zuellig pharma, singapore due to the covid-19 pandemic restricting travel and face-to-face contact, the adoption of telehealth programs has risen astronomically out of necessity. mckinsey reports1 telehealth utilization stabilizing at levels 38x higher than before the pandemic, and industry analyst, idc (international data corporation), also predicts that by 2023, nearly two-thirds of patients will have accessed healthcare via a digital front end.2 factors for growth in adoption include favorable consumer perception, improved regulatory environment, and strong investment into this space. what does this mean for the pharma industry? with blockchain maturing even more in the pharma supply chain, the promise of a single system accessible to all parties involved in a supply chain transaction, and by design immutable, makes blockchain supply chain traceability even more important in a virtual transaction from plant to patient. the aims are quite simple – enable greater traceability, transparency, and the automation of commercial processes and maybe even become an acceptable form of payment for supply chain products and services. traceability and transparency reflect the market’s need for a single source of truth in the supply chain – from a raw material’s provenance to a product’s end of life. a recent industry survey concluded that product traceability throughout production and the supply chain is the primary use case for blockchain-adopting industry giants across multiple industries. as for automating commercial processes through blockchain-based smart contracts, by doing this, we can hugely streamline service and payment transactions while reducing errors in the back office. when predetermined service level agreement criteria are met, smart contracts allow procedures to self-execute without relying on human participation. given the abovementioned factors for growth in the telehealth industry, we foresee them having positive effects on the adoption of blockchain for the pharma industry as well, helping to ensure safe and more accessible healthcare, overall. finally, will we see cryptocurrency make its way into the mainstream as an alternate form of payment for telehealth services? while volatility is still a concern, just like the debit, credit, and methods of payment that precede them, cryptocurrencies are a viable payment method for logistics products and services, and supplementary acceptance may strengthen logistics providers’ competitiveness and further their integration in the digital consumer world. gabriela pelin, chief innovation officer, avaneer health, usa over the past few years, an increasing number of healthcare organizations have begun to evaluate and adopt blockchain-based solutions. the technological landscape is changing from technology assisting independently run processes to being capable of running peer-to-peer interactions and joint processes. in 2022, healthcare companies will begin looking beyond ‘better app’ to seek better connectivity and business process scaling to coordinate care delivery and administration that creates direct interactions over digital highways. implementing and using blockchain will require a new strategy for payers and providers – one based on a new mindset of coopetition. while embracing this new mindset may be a big hurdle to overcome, we all agree that the fluidity of data exchange between patients, providers, payers, and vendors is essential for delivering quality care and an optimal patient experience. this is where blockchain shines and will make an impact on the healthcare experience in this decade. decentralized ledgers that hold accurate and immutable information about the information and prove the provenance of data are essential for doing business. in short, blockchain is a catalyst for making true interoperability possible. to bring about blockchain’s full potential for healthcare, however, we need a comprehensive network equipped with fhir (fast healthcare interoperability resources) servers, where all stakeholders can commit data to be discoverable based on the parameters they set. network users will connect to the network via the cloud, where an id keychain and master index locate the information requested and match it to the data available, then deliver it to the requestor. a network intermediary will provide certification, cybersecurity, and compliance. having this central blockchain network eliminates the need for organizations to build and maintain multiple gateways with multiple entities. this significantly reduces it costs and enables more organizations to participate in all the benefits blockchain has to offer. anton hesselgren, department of neuromedicine and movement science, faculty of medicine and health sciences (ntnu), norwegian university of science and technology, trondheim, norway the year 2021 has been exciting for blockchain technology. cryptocurrencies and blockchain decentralized platforms have taken a leap forward, and the use-cases in healthcare have seen an increased focus, particularly due to the covid-19 pandemic. it has become prominent that decentralized technologies that enable trust between entities, where trust is lacking, are needed in the healthcare space. again, the pandemic has shown us that there is a lack of trust in public health and healthcare. for the year 2022, first, i believe we will have more developed and robust vaccination certificate solutions built on blockchains, not just for covid-19 but for all vaccinations. these solutions could scale and also work as means to prove other types of delivered healthcare services. second, i believe that blockchain will be an important component in clinical trial management with transparent and tamper-proof informed consent management, in particular in decentralized clinical trials. these will benefit greatly by a decentralized data structure that also can provide data provenance and tamper-proof data. during the coming year, we will see more solutions in this area ready for implementation. conclusion: daniel conway while we will know more in 2023, a quick look at the predictions above should generate great excitement. these are not fringe nice-to-have innovations being described-, but rather visions that will impact the core value offering of healthcare itself. many will involve further ethical discussion regarding control of information. ultimately, blockchains provide transparency and a substitute form of trust. they enable efficiencies in the health ecosystem, which were never feasible in the past. these are essential characteristics of any healthcare system. blockchain does not have a mechanism that forgets or can be amended. immutability has different characteristics than traditional records systems controlled by individual organizations. this suggests new thinking in organizational controls and collaboration. it will certainly be an interesting year. authors’ contributions all authors contributed substantially to the research, including study design, data management, data analysis, data interpretation, and manuscript preparation. references bestsennyy o, gilbert g, harris a, rost j. telehealth: a quarter-trillion-dollar post-covid-19 reality? [internet]. mckinsey & company; 2021 [updated 2021]. available from: https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality [cited 28 october 2021]. miliard m. what to expect in 2021 and beyond? idc offers 10 healthcare predictions [internet]. healthcare it news; 2020 [updated 2020]. available from: https://www.healthcareitnews.com/news/what-expect-2021-and-beyond-idc-offers-10-healthcare-predictions [cited 28 october 2021]. page 1 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.113 ethical implementation of the learning healthcare system with blockchain technology dr marielle s. gross,1 and robert c. miller2 authors: 1johns hopkins university bloomberg school of public health; berman institute of bioethics; 2consensys health, new york, usa corresponding author: marielle s. gross, 1809 ashland avenue, baltimore, md 21205, usa. email: mgross23@jhmi.edu section: methodology we propose that blockchain technology complemented by secure computation methods can foster implementation of a learning healthcare system (lhcs) by minimizing upfront patient-facing compromises with unsurpassed data security and privacy, and by optimizing the system’s fulfillment of its obligations to respect patients through transparency, engagement, and accountability. we demonstrate how a blockchain-enabled lhcs could foster patient willingness to contribute to learning by providing desired security and control over health data. in addition, secure computation methods could enable meta-analysis without exposing individual-level data, thus allowing the system to protect patients’ privacy while simultaneously learning from their data. the transparency and immutability of blockchain ledgers would also support the public’s trust in the system by allowing patients to audit and oversee which of their data are used, how they are used, and by whom. furthermore, blockchain communities are communitygoverned peer-to-peer networks in which sharing builds mutually beneficial value, offering a model for engaging patients as lhcs stakeholders. smart contracts could be used to ensure accountability of the system by embedding feedback mechanisms by which patients directly and automatically realize benefits of sharing their data. keywords: bioethics, blockchain technology, data security, data sharing, digital privacy, health data, learning healthcare system, secure computation. a learning healthcare system (lhcs)— the new paradigm for healthcare organization, delivery, and continuous, real-time improvement—has yet to be attained.1 optimizing learning requires integration of clinical care and clinical research, and the lhcs’s proposed ethical framework asserts that https://doi.org/10.30953/bhty.v2.113 mailto:mgross23@jhmi.edu https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v2.113&domain=blockchainhealthcaretoday.com&date_stamp=2021-08-18 page 2 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.113 patients are obligated to contribute to learning, primarily by allowing their health data to be used toward that end. at the same time, however, the rights and interests of individual patients must be protected.2 while patients as a class will ultimately benefit from the lhcs, the normative challenge is how to obligate patients to contribute to learning without violating these rights and interests, including patient interests in avoiding nonclinical risks/burdens, such as compromised privacy and security of health data. striking this balance is problematic given increasingly common health data breaches, which undermine public trust in institutional stewardship of health data.3 thus, a critical barrier to implementing the lhcs is that patients be willing to shoulder this obligation ex ante, before the reciprocal benefits of forgoing some of their rights are established. we propose that blockchain technology—a novel decentralized data structure that gives users unique assurances of trust—together with secure computation techniques can empower and accelerate the shift to a lhcs by minimizing upfront patient-facing compromises and optimizing fulfillment of the system’s obligation to respect rights, privacy, and dignity of patients. objective blockchain technology: a potential solution a blockchain is a revolutionary technology that distributes control of a database over a network of computers. blockchains maintain consensus among this network of computers around the “single state of truth” for a given database. blockchain technology has disrupted the financial and technology sectors in recent years by decentralizing, and thus fundamentally reconfiguring the storage, verification, and exchange of data. the first and most widely known use case of a blockchain is bitcoin, a novel peer-to-peer digital currency leveraging a shared public ledger on which all financial transactions are immutably recorded and guarded from tampering by advanced cryptography. the beauty of blockchain technology is that it maximizes both the security and transparency of digital assets while simultaneously empowering users by allowing direct peer-to-peer transactions without intervening governmental or financial institutions. the system is considered “trustless” in that it does not rely on a third party to adjudicate the shared ledger, and thus does not require its participants to trust each other or a common administrator. more recently, blockchain technology garnered attention as a potential solution to the siloed nature of current electronic medical records (emrs) whose failure to interface from one health system to another makes health care significantly more prone to error and less efficient than it could be given the current state of medical innovation. yet, blockchain technology promises more than an evolution in the state-of-art for medical records. blockchain in healthcare today released its first volume in january 2018, with articles discussing how the blockchain can facilitate sharing of health data, streamlined ethics review under the updated common rule, and public health surveillance. however, interest in blockchain’s application to healthcare and academic literature to date has been dominated by private enterprises advancing proprietary blockchain-based solutions to individual and organizational consumers. here, we demonstrate how a blockchain’s ability to protect rights and dignity of patients and minimize imposition of nonclinical risks will promote patient willingness to embrace their https://doi.org/10.30953/bhty.v2.113 page 3 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.113 obligation to contribute to learning healthcare activities. specifically, blockchain technology addresses critical, rate-limiting ethical challenges for advancement of a lhcs related to concerns about the security of sensitive health data, and will promote fulfillment of the system’s obligations for transparency, engagement, and accountability.4,5 findings security and privacy studies show that consumers are concerned about the security and privacy of emrs and desire greater transparency and control over their data.6,7 while the ethical framework for lhcs would require patients to forgo some privacy and control of their data, these concerns are validated by healthcare data breaches, which have continued to increase over the past decade, with theft/exposure estimated to have affected nearly 190 million us healthcare records as of 2018.8 motivated to take power from institutions and return it to individuals, blockchains were developed and offer a powerful combination of strong assurances of trust, distributed data, advanced cryptographic protections, and underlying immutability. the security of the “trustless” computation-dependent system is reinforced by use of decentralized storage systems. in contrast to current centralized emr storage repositories, numerous locations would have to be hacked before data would be compromised. moreover, blockchains are driven by consensus between the nodes of a network, and this makes it exceedingly difficult to retroactively alter a blockchain. as a result of this, an immutable underlying record of the “truth” is generated, which cannot be corrupted or manipulated by independent third parties. a “trustless” system may be essential to assure the public that the use and exchange of personal health data are consistent with respect for patient rights and dignity. furthermore, an equally transformative technology called “zero knowledge proofs” has been implemented alongside blockchains, such as the zcash blockchain, to enable verification of transactional data without compromising either privacy or security. zero knowledge proofs allow computation to be performed on data without exposing the data’s actual content.9 although a fledgling technology, zero knowledge proofs could theoretically allow us to learn from individuals’ health data without requiring any of the data to be shared. this presents significant advantages over the current ethical standard of protecting privacy via deidentifying data, a practice that has been fundamentally undermined in the era of machine learning and data as identity.10,11 for example, the u.s. health insurance portability and accountability act states that protected health information is sufficiently deidentified, and thus can be disclosed or otherwise used, by removing 18 specific identifiers such as name, birthdate, address, medical record number, and so on. the public is aware that such data have been “undeidentified;” also, there is growing awareness that identity can be reconstructed from deidentified data sets when powerful machine learning is applied to the vast data that now exist about any one individual. the ability for these innovative computation systems to derive the product of big data analytics without ever exposing the primary data could circumvent a key ethical dilemma for obligating patients to compromise their privacy in the name of learning and therefore may eliminate central privacyrelated concerns regarding “broad consent” for health data research.7,12 transparency interestingly, the same system that optimizes data security does so by keeping a transparent record https://doi.org/10.30953/bhty.v2.113 page 4 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.113 on a public, permissionless ledger that is continuously updated and can be accessed by anyone, anytime, and from anywhere.1 taken together, the disseminated data network constitutes a “single version of the truth” that permits users to directly audit their data’s immutable transaction history. applied to a lhcs, this would enable patients or patient representatives to supervise all uses (and attempted uses) of their health data, including, but not limited to, clinical and learning activities. the shared ledger could provide an accurate, up-to-date report of the ongoing research using an individual’s data, what knowledge outputs they have contributed, and which practice updates have been made as a result (more on this in following sections). the fundamental transparency of the blockchain would assure patients that they could always know who is using their data, which portions of their data are being accessed, and for what purpose. while a public, permissionless ledger seems antithetical to some desired features of emrs, not all data must be stored on the ledger. sensitive data can be stored “off-chain” and a hash (a sort of digital fingerprint) of that data can be stored “on-chain” to prove that there has been no tampering with the data. moreover, smart contracts could be used to manage various permissions to this off-chain data. this architecture could leverage the desired benefits of a public ledger while keeping sensitive data private. engagement champions of the lhcs note that the cultural changes required for transitioning to a lhcs pose a greater challenge than securing the necessary technical infrastructure. the ethos of 1 the authors note that there are private or “consortia” deployments of blockchains, but here we are talking explicitly about public blockchains. blockchain technology, in addition to its technical features, could promote a lhcs by virtue of its foundation in peer-to-peer engagement and cooperative nature. at their core, blockchains are communities of stakeholders unified by a collaborative approach in which sharing is normative, incentivized, and yields collective benefits.13 they are organized around interoperable, open-source building blocks with shared standards and information, allowing cumulative layers of value to be built over a common, underlying framework. there are many emergent systems for governing blockchains, but they all operate under the same principles of decentralized control and shared decision-making between stakeholders. several blockchains have made explicit the implicit democratic norms of these communities, instituting formal governance systems where stakeholders vote on proposals, shaping the evolution of the community. bitcoin exemplifies the power of the blockchain ethos to promote individuals’ willingness to contribute to a system in which individual and collective incentives are aligned: tens of billion dollars of de novo value have been generated over several years by the globally disseminated efforts of individual community members, all without a central body coordinating development or controlling the bitcoin network. beyond technological pioneers, the implementation of blockchains has spurred a revolution in communal governance in which individual stakeholders are vested with meaningful stakes in the process. thus, a blockchain-enabled lhcs could apply similar principles to engage patients as stakeholders, simultaneously meeting the ethical imperative of involving patients as stewards of the system’s activities and encouraging patient involvement by asserting their value as contributors. for example, a rotating lottery system, https://doi.org/10.30953/bhty.v2.113 page 5 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.113 or other form of representative self-governance, could periodically identify a random subset of patients who would be responsible for reviewing and provide feedback on current and future learning activities. this built-in system of user engagement could dictate learning priorities, informed consent requirements, and operational aspects of participation in learning (e.g., opt-in vs. opt-out). while further details are outside the scope of the present discussion, premising the lhcs on universal access to affordable health care would help secure individuals’ vested interest in the system’s learning; and empowering patients as voting stakeholders would emphasize the imperative of ensuring their ability to make informed decisions in that role. accountability blockchain technology’s interoperable components, “smart contract” architecture, and token-based incentives can facilitate accountability of the lhcs. where legacy electronic health systems optimize data sovereignty, blockchain networks are designed for seamless, multidirectional data transfer and can be used to implement systematic adoption of innovation with appropriate, predetermined checks and balances. keeping patients informed, applying learning to clinical practice, and sharing learning with external entities like public health agencies would be supported by a network in which peer-to-peer communication between various stakeholders proceeds without extraneous intermediaries. using a blockchain, data exchange can be managed by “smart contracts”: transparent and automatically executing code defined a priori by stakeholders. these can also be thought of as trustless because stakeholders do not have to trust any given party to know that the code defined in a smart contract will be executed. the smart contracts at the core of a blockchainempowered lhcs could automatically incorporate learning into practice. for example, a smart contract could specify that certain patient data are analyzed at specified intervals, with results imputed directly into standardized clinical algorithms within the patient-and-provider-facing user interfaces. smart contracts could build accountability into the lhcs by ensuring that learning from patient data is transparent, accessible, and contractually bound to improving clinical care without depending on further human action. thus far, proposed benefits to patients from the lhcs have been chiefly described as indirect benefits of generally improved health care. meanwhile, the sharing economy has accustomed consumers to the benefits of sharing personal information (e.g., quickly find a taxi based on one’s location)—benefits that increase with the total number of participants. this may have primed individuals to willingly share health information in exchange for real-time, direct benefits. blockchain technology could accelerate realization of direct benefits to patients if smart contracts automatically notify patients and their clinicians when learning activities they contribute to result in new knowledge that may be clinically relevant to their own care. also, blockchains are often accompanied with their own cryptocurrency (tokens) to incentivize disparate parties to organize around a common purpose. similarly, tokens could help incentivize patients to contribute to learning by providing immediately valuable feedback on an individual’s health data in tandem with an asset that promises to accrue in value over time as value of the system they have contributed to grow. finally, blockchain technology could enhance lhcs accountability via improved quality, https://doi.org/10.30953/bhty.v2.113 page 6 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.113 validity, and efficiency of health research. a transparent, immutable data trail would foster replicability and auditing of research findings prior to widespread implementation. by standardizing record forms and building interoperability across organizations and platforms, a blockchain-enabled lhcs could yield enhanced quality and exponentially increased quantity of data for meta-analyses. this proficiency could extend to institutional review boards (irbs), and may support streamlined approval for multisite pragmatic and traditional clinical trials.14 furthermore, blockchains could democratize peer review: requiring consensus among an extended network of pseudonymous experts and direct peer-to-peer research review could help eliminate bottlenecks of human bias and untenable timedelays in scientific literature. blockchain-based “peer-to-peer-review” could also be “tokenized,” a term that refers to developing an incentive structure using a token that helps achieve a stated social goal, and that may yield more diverse, efficient, and judicious review of research, and drive fulfillment of clinician/researcher obligations to be continuously engaged in learning. discussion limitations several issues must be addressed for blockchain technology to help operationalize an ethically sound lhcs. for example, widespread patient buy-in will rely on effectively communicating sophisticated details about how blockchain technology will protect patients differently than existing electronic health systems. the extent of automation and transparency of clinical decisionmaking suggested by this system may threaten respect for clinician’s judgment,2 and could yield undesirable moral or practical outcomes, including resistance from healthcare providers or 2 respect for clinician judgment is also an obligation under the ethical framework for lhcs. overzealous implementation of conclusions from meta-analyses of heterogenous data, which have been notoriously difficult to adequately depict in health records. how to appropriately tokenize the lhcs to align incentives and promote patient and other stakeholder engagement in mutually beneficial learning is yet to be determined. while universal access to affordable health care is likely part of the answer from a patient perspective, significant political and financial hurdles remain. existing puzzles regarding how to balance health outcomes and cost-effectiveness considerations in public health policy and practices may be amplified if smart contract algorithms make determinations that have morally unacceptable consequences, such as allocating scarce treatments to people with higher incomes because they are more likely to do well. while blockchain technology and zero knowledge proofs may prevent identity-based discrimination by protecting security and privacy of individuals’ health data, this will not be sufficient to prevent group-harms that may occur or be facilitated by more powerful and potentially more accurate stereotypes developed by machine learning, particularly because underlying data capture existing biases. novel strategies for moral oversight will be required to prevent smart contracts and machine learning from unintentionally embedding health disparities into the lhcs architecture. inclusion of vulnerable groups who may not have access to or facility with modern technology must be prioritized, and likewise, equity may demand universal access to technology. there are technical challenges inherent to using blockchain technology. determining who has access to which health data will be essential for a truly interconnected system (e.g., whether physicians or insurance companies should have access to user data from durable medical equipment or fitness trackers). this will also https://doi.org/10.30953/bhty.v2.113 page 7 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.113 require schemes for confirming user identities and preventing health information from becoming unrecoverable if users lose the keys to access their data. in addition, further advancements in computer science and engineering are prerequisites for environmentally responsible scaling of blockchain technology to a point where it could support a lhcs; and significant time, human capital, and resource investments will be required to prepare blockchain infrastructure for its most complex use case to date. finally, as a very new technology, there are anticipated threats, such as the development of quantum computing, which could compromise encryption—an especially worrisome threat given the harm that could be done if the entirety of an interconnected system could be exposed. in addition, we should anticipate yet unknown vulnerabilities. conclusion blockchain technology’s ability to deliver unsurpassed data security and privacy, especially in tandem with zero knowledge proofs or other forms of secure computation, and to simultaneously optimize fulfillment of obligations for transparency, engagement, and accountability represents an opportunity to overcome existing ethical and technical barriers to lhcs implementation. next steps will include addressing issues enumerated above, with support from interdisciplinary teams of patient representatives, health professionals, healthcare organizational stakeholders, public and private enterprise, and diverse experts from the fields of public health, economics, health policy, computer science, and systems engineering. to successfully build a blockchain-enabled lhcs, we must first learn how to communicate the ethical and technological advantages of this approach to a broad audience. greater awareness of these potential benefits by patients and other stakeholders will empower a collective exploration of how to optimally leverage blockchain and affiliated 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https://blockchainhealthcaretoday.com/index.php/journal/article/view/18 https://blockchainhealthcaretoday.com/index.php/journal/article/view/18 page 9 of 9 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.113 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. see: http://creativecommons.org/ licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v2.113 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 page 1 of 2 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.119 voice biometrics and blockchain: secure interoperable data exchange for healthcare benjamin chevallereau, phd,1 gracie carter, csm,1,2 sweta sneha, phd2 affiliations: 1synchrogenix, a certara company; 2kennesaw state university corresponding author: gracie carter, gracie.carter@synchrogenix.com section: technical briefs & reports purpose the healthcare system in the united states is unique. from payor to provider, patients have many choices but they lack in the ability to manage or share their health information. this complicated care paradigm places patients at a distinct disadvantage. legislation clearly defines government expectations of data availability but not how to achieve exchange. because methods of sharing are left to the discretion of care providers and software vendors, noninteroperability is the standard. methods the openpharma blockchain on fast healthcare interoperability resources (fhir) (obf) solution is interoperable by design. obf empowers patients with data access through biometric identity authentication, blockchain, and machine-–to-machine secure data access. obf provides authenticated users read-only, real-time access to patient records using the healthcare interoperability standard hl7 fhir. obf is built around a modern, browser-based user interface, blockchain technologies (leveraging either ethereum or the hedera protocols) and modular, modern software exposed as application programming interfaces (apis). this allows obf to meet the office of national coordinator for health information (onc) metrics, which include sending, receiving, and finding information from outside sources and using that information to make informed clinical decisions without additional burden on clinicians or patients. results building on the hl7 fhir application community practices, obf is a smart-on-fhir plug-in for electronic medical record (emr) systems. using obf, patients can identify themselves and gain access to their medical records using their voice. this unique feature is accomplished through the saavha voice print biometrics technology. saavha returns a unique member id that is passed directly to the obf https://doi.org/10.30953/bhty.v2.119 mailto:gracie.carter@synchrogenix.com https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v2.119&domain=blockchainhealthcaretoday.com&date_stamp=2019-11-19 page 2 of 2 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v2.119 blockchain smart contract for storage and interoperable patient record access (the id does not contain public health information [phi]). to ensure complete privacy, all information is passed through multiple layers of encryption where no keys are stored locally. additionally, no phi is shared to the blockchain. to ensure privacy, obf creates a new encrypted address for the fhir patient record object, using the saavha generated member id as the unique identifier. this encrypted address is then published on chain, making it available to participating providers. providers must register their relationships to patients before obf will permit online viewing of patient records. patient record access is accomplished through voice verification and real-time surfacing of encrypted patient data through the obf fhir viewer. conclusions obf is a lightweight, flexible, secure, and stable interoperable solution that places data stewardship with patients. using industry-wide data standards, biometrics, smart contracts, ethereum, and openpharma’s data viewer for the first-time patients can authorize read-only record exchange using their voice. copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work noncommercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/licenses/bync/4.0. https://doi.org/10.30953/bhty.v2.119 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) blockchain in healthcare today 2022. © 2022 the authors. this is an open access article distributed under the terms of the creative commons attribution-noncommercial 4.0 international license (https:// creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. citation: blockchain in healthcare today 2022, 5: 191 http://dx.doi.org/10.30953/bhty.v5.191 applicability of blockchain-based implementation for risk management in healthcare projects in bae chung, ms1* and carlos caldas, phd2 1graduate research assistant, dept. of civil, architectural & environmental engineering, university of texas at austin, texas, usa.; 2professor, dept. of civil, architectural & environmental engineering, university of texas at austin, texas, usa. abstract hospitals provide diverse tasks essential for the delivery of patient care and are comprised of many functional units. this makes healthcare projects in construction highly complex among other types of building projects due to the specific regulations, multiple functions it must provide, complicated mechanical and electrical systems, and so on. this complexity embodies potential risk events during its construction, which not only influences the completion of the project but can impact the patients’ safety and health conditions even after the project is finished. to prevent such outcomes, risk management is a crucial process that can identify, evaluate, and properly mitigate risks along the project lifecycle. a key aspect of risk management is that it requires the interaction and contribution from multiple stakeholders of the project. various frameworks and tools that enable collaborative management of risks among multiple stakeholders have been developed in the past. however, the developed frameworks are not suitable in the sense that it does not protect the confidentiality of individual inputs from the stakeholders. moreover, these frameworks are centralized systems, which can bring issues related to the security and transparency of the information that is being stored. blockchain technology is an emergent distributed ledger technology (dlt) that can provide solutions to the listed problems found in centralized systems. it is a novel system that records information on a decentralized, distributed ledger, where transactions are constantly duplicated and updated. this study explores the applicability of blockchain technology for healthcare risk management. the key functional elements of blockchain that can resolve the challenges faced by prior risk management frameworks have been identified and discussed. based on the discussions, a conceptual information management model for managing healthcare project risks on a blockchain has been conceived. the development of the initial prototype has been explained. the research study illustrates the process, benefits, and limitations of adopting the blockchain technology for collaborative risk management in healthcare projects. keywords: blockchain technology; distributed ledger technology (dlt); healthcare construction projects; risk management; smart contracts section: original research received: 30 november 2021; revised: 5 january 2022; accepted: 20 january 2022; published: 8 february 2022 introduction healthcare projects in construction refer to projects that design, build, or renovate healthcare facilities where medical services are provided, such as hospitals, clinics, and long-term care facilities. healthcare projects are generally very complex compared with other types of construction projects for a number of reasons. these facilities offer a wide range of spaces that are necessary for the diagnosis and treatment of the patients. (1) medical spaces can range from surgical rooms and patient rooms to laboratories, and these spaces need unique systems for the clinical care of patients. many facilities, especially hospitals, have continuous operations throughout the year, which requires thorough planning, especially when it is a renovation project. (2, 3) healthcare projects have special codes and regulations to follow in addition to typical building codes. (2) each state normally has its own hospital code and agency that regulate the design and construction of healthcare facilities. other organizations, such as the american society of heating, refrigeration and air conditioning engineers (ashrae) and the national fire protection association (nfpa), provide specific regulations that address healthcare facilities. according to the us census bureau, total construction spending in healthcare projects has been continuously *correspondence: in bae chung, email: inbae908@utexas.edu https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.30953/bhty.v5.191 https://orcid.org/0000-0002-0858-6259 https://orcid.org/0000-0003-1933-1953 mailto:inbae908@utexas.edu citation: blockchain in healthcare today 2022, 5: 191 http://dx.doi.org/10.30953/bhty.v5.1912 (page number not for citation purpose) in bae chung and carlos caldas increasing and was close to 48 billion dollars in 2020. (4) this total cost includes the construction work done on new and existing structures, including the cost of labor, materials, design and engineering work, overhead costs, interest, and taxes paid for the project. more funds are being allocated for healthcare projects in both the public and private sectors. considering the complexity of these projects, rigorous risk management should be made throughout the project lifecycle to better manage the cost of these projects. due to its complexity and unique characteristics, there are very specific risks when it comes to the risk management of healthcare projects. hospitals have to be in a convenient location for the patients, often being located in crowded metropolitan areas. this makes workface planning very difficult, especially the delivery of materials and heavy equipment. also, many risks can arise from the coordination between multiple disciplines, as hospitals have very complex mechanical, electrical, and plumbing systems. it is very common for healthcare facilities to go through expansions, which can cause multiple risks due to the ongoing operation in the existing facility. hospitals may require extensive fire alarm or sprinkler systems, and the procurement and installation of medical equipment can become very troublesome for the project team. all the aforementioned risks, when not managed properly, could result in cost overruns, schedule delays, and quality problems for a project. (5) there have been numerous cases in construction where things got out of hand due to poor risk management. according to a news article, the construction of the veterans affairs’ newest medical center in colorado was initially planned to be completed by 2013, with an estimated project cost of $328 million. (6) however, the final cost turned out to be $1.73 billion, which was more than $1 billion over budget. the project was also completed 5 years behind the schedule. this example shows that risk events in construction should be taken seriously and managed for the project to be completed on time under the estimated cost. risk management in construction risk management is an essential process in complex construction projects, such as healthcare projects. with proper risk management, projects can prevent cost overruns and schedule delays, and improve the overall quality of construction. (5) the importance of risk management has been emphasized over the years through numerous studies. it has been selected as one of the best practices in construction by the construction industry institute (cii) and as one of the knowledge areas developed by the project management institute (pmi). (5, 7) a typical risk management process is done through five steps: identification, assessment, analysis, mitigation, and monitoring. (5) in the first step, project team members would share their ideas on what could be potential risks in the project based on their knowledge and experience. the identified risk events will then be assessed based on how each of them could influence the project. these assessments are usually made in terms of the likelihood of occurrence and its relative impact on project cost, schedule, safety, and quality. the third step is risk analysis, which can be conducted in a variety of methods. a common practice for risk analysis is to compute the mean values (i.e. probability × impact), rank, and visualize the mean values using heat maps.(8) another approach that can better account for the uncertainty of the risks is the probability risk analysis, which solicits three-point estimates (best case, most likely, and worst case) from the stakeholders. using the estimates, the project team can run a monte carlo simulation to capture a probable range of outcomes. (9) knowing the potential outcomes of risks, the project team would implement mitigation strategies to manage, eliminate, or minimize risk impacts to an acceptable level. the last part of the process is monitoring; the status of the risk should be monitored as long as the risk exists, so that right measures can be taken before things go wrong. (5) a crucial part of risk management is the collaboration among the project stakeholders. to reach a common solution for the group of experts, different points of view, backgrounds, and levels of expertise that they have on project risks should be reflected. (10) click or tap here to enter text. thus, there is a need for a platform where stakeholders can have a joint risk management practice. a conventional way of performing this is by holding a risk workshop, where project team members gather to brainstorm and share their thoughts on potential risks in a comprehensive way. (11) based on the risk workshop discussions, project teams often use a risk register, a tool that enables the project risks to be documented and maintained. (12) however, such practices hold challenges for the stakeholders to fully trust and rely on the process. while effective risk management should facilitate open communication to leverage the participants’ experience and knowledge, in an environment such as a risk workshop, there could be undesired influences between them. (13) participants who have different opinions may be pressured to attain a required level of agreement, and personality traits can have a huge impact on the outcome of the discussion. (14) participants may even prefer not revealing their perspectives on project risks, as they are closely aligned with their individual objectives in a project. hence, there is a need to capture the individual viewpoints of the participants but at the same time keep them private. several studies have developed web-based tools that further facilitate the communication between geographically distributed team members and utilize a decision support system for collaborative risk management. (15-17) web-based tools can ease communication http://dx.doi.org/10.30953/bhty.v5.191 citation: blockchain in healthcare today 2022, 5: 191 http://dx.doi.org/10.30953/bhty.v5.191 3 (page number not for citation purpose) applicability of blockchain-based implementation for risk management in healthcare projects and integrate team members’ views through algorithms. however, the web-based tools are centralized systems, which lays several concerns related to data privacy, security, and transparency. according to nair and sebastian, (18) centralized systems have a single point of control and ownership. all transactions in a centralized system can  be accessed and managed by the central authority, which would prevent the stakeholders from  providing  their most candid inputs on project risks. moreover, centralized systems may cause problems related to the security and transparency of data. in cases when a centrally owned ledger is lost or destroyed by a malicious attack, it is entirely up to the central authority to back up and restore the data manually. in a centralized system, there are no restrictions for the ledger owner to change or append data. if the central owner of the ledger decides to manipulate data, it is extremely difficult for the users to make sure that past transactions have been validated properly. considering all the issues above, a novel system should be developed to provide a better setting for multiple stakeholders working in collaborative risk management. blockchain technology and smart contracts blockchain is a distributed ledger technology (dlt) that enables members in the network to digitally record and access transactions. (19) the users interact with each other through a peer-to-peer network, removing the need for a central authority that controls every transaction. participants take part in validating the transactions through various consensus models and make sure that the entries have been appended in the right order. the information submitted through transactions is added to the network in blocks, and these blocks are added in a chronological order. a cryptographic hash function is used to generate a unique output of the previous data. the header of the block contains the hash of the previous block, which makes all the blocks hash linked from the genesis block to the most current block. (20) thus, blockchain is a linked list via hash pointers, which is append-only and tamper resistant, also allowing the users to trace the previous transactions on the network. (21) in addition to blockchains, smart contracts have the potential to make the technology more powerful. by definition, smart contracts are computerized transaction protocols that execute the terms of a contract. (22) these written lines of code can be deployed on the blockchain network using cryptographically signed transactions. (21) just like other transactions on the network, smart contracts cannot be changed once they are endorsed by the users and installed on the blockchain. once the predefined conditions are met, smart contracts are automatically executed to record, execute, and distribute transactions across the blockchain network. (20) not only does this add more transparency to the technology but it has the potential to extend and leverage the blockchain technology, as it creates an environment of trust among the users and removes unnecessary third parties for transactions. (23) although blockchain technology became widely known with the advent of cryptocurrencies like bitcoin, it has a lot more to offer beyond the financial sector. blockchain technology can implement a distributed ledger system, which can solve the issues of centralized systems. if the benefits of this emerging technology can be integrated properly, it has the potential to revolutionize systems in various sectors. research objective the aim of this research study was to develop a system that can enable collaborative risk management among multiple stakeholders. the earlier section of the article identified the problems in a conventional risk management process and the deficiencies of previous web-based tools for risk management. the system should allow the users to preserve the confidentiality of their individual inputs on risks while providing transparent and traceable transactions. hence, a blockchain-based implementation has been conceived as the basis for the development of the prototype of the system. according to chung, (24) the system should adopt a private permissioned blockchain where only permitted users can access and transact on the network. among multiple platforms that provide a private permissioned blockchain, hyperledger fabric has shown the highest privacy and throughput with minimal latency from a comparative study. (25) therefore, hyperledger fabric was selected as the platform where the system prototype has been designed. the workflow of the prototype is described step by step with examples to help readers understand the process. the working prototype under development has been demonstrated to further elaborate how hyperledger fabric and smart contracts have been used. based on the findings, limitations and future work are discussed along with the applicability of the prototype to other problems in the healthcare industry. conceptual model in this study, hyperledger fabric has been adopted to build a private permissioned blockchain for the proposed risk assessment system. there are specific terms that are essential for understanding how a fabric network works. first, a fabric blockchain network is comprised of a set of peer nodes, which are network entities that each host a ledger. (26) the peers also have http://dx.doi.org/10.30953/bhty.v5.191 citation: blockchain in healthcare today 2022, 5: 191 http://dx.doi.org/10.30953/bhty.v5.1914 (page number not for citation purpose) in bae chung and carlos caldas individual containers to run the chaincode, also known as the smart contract. an orderer node is an entity required in hyperledger fabric for validating the blocks created by the peers, and a channel is a private ‘subnet’ of communication between the network members for conducting private and confidential transactions. (26) in our use case, a single channel will be set up for the peers to access and transact on the network. to implement proper risk management among a group of stakeholders, the project creator assigned to the network plays a role in the process as a middleman but does not intervene with any of the inputs provided by the stakeholders (figure 1). figure 2 demonstrates how the permissioned identities are added to the blockchain to be trusted and recognized by the rest of the network. figure 3 illustrates each step of the conceptual model process. step 1 starts with the project creator starting up a project entity on the blockchain along with a brief description of the project. the description should include some basic project information along with the total cost and duration so that the stakeholders have a good understanding of the project. step 2 includes the solicitation of potential risk events from the stakeholders. each user who is a participant of the project should have been added to the fabric channel to perform this function. thus, the project creator will send requests to the stakeholders through the blockchain channel, and the stakeholders should submit a list of risk events, which they think might impact the overall cost of the project. figure 4 shows an example of risk event solicitation from the stakeholders. assuming that there is a project for the construction of a medical office building, user 1, in this case, identified multiple risks relevant to the fig. 1. risk management process. fig. 2. hyperledger fabric network configuration. fig. 3. conceptual model process. mep: mechanical, electrical and plumbing http://dx.doi.org/10.30953/bhty.v5.191 citation: blockchain in healthcare today 2022, 5: 191 http://dx.doi.org/10.30953/bhty.v5.191 5 (page number not for citation purpose) applicability of blockchain-based implementation for risk management in healthcare projects project such as ‘request for additional medical gas outlets’, ‘delays due to installation of fire alarm systems’, and ‘building requirements for safety and infection control’. each user should provide his or her own set of risk events to the project creator. the process will be followed by step 3, where the project creator validates the risk events that have been collected from the stakeholders and compiles a single list of risks as shown in figure 4. this step is very important, as there can be risk events irrelevant to the project, and more than one user can identify the same risk event in the earlier step. the project creator should make sure that all risk events are valid, and there is no overlap between the risk events when organizing this single list. the list should be distributed back to the users through the blockchain. when users receive the completed list, they should recognize the influence of each risk event to make a proper risk assessment in the next step. in step 4, the users assess risk events by providing their likelihood of occurrence and the relative impact of the risk on project cost. in this model, the likelihood of occurrence represents a percentage of certain risk events happening. the relative impact is also given by a percentage of how much cost would increase compared with the initial cost of the project without any risk event happening. the impact will be evaluated by a three-point estimate, which represents the minimum, most likely, and maximum impact that the risk event could have on the project cost. table 1 provides an example of such risk assessment input. while the model in this article focused on the impact on project cost, it can also be applicable for finding the impact on project schedule, safety, or quality. it all depends on what is the main focus and interest of the project team. an interesting feature of hyperledger fabric that plays a huge role in step 4 is the private data collection. this feature enables the organizations to store private information away from other organizations on the channel. private data collection can be executed through a chaincode definition and removes the need for creating separate channels for keeping private data. (27) figure 5 shows an example of how a private data collection structure can be used in the risk management process. the stakeholders will share the public collection where general information about the project, list of risk events, and risk analysis output is stored. however, any sensitive information related to risk assessment will be kept confidential as it is saved in each peer’s private collection. the feature only allows the organization that actually owns the data to access the information, which fits the research objective of preserving confidentiality on risk assessment inputs. risk analysis takes place in step 5 of the model process. while the risk assessment inputs are kept at a private state, smart contracts can utilize aggregation methods to generate combined numbers and publish these in the public collection of the shared ledger. the aggregation fig. 4. an example of user input and compiled list of risks. http://dx.doi.org/10.30953/bhty.v5.191 citation: blockchain in healthcare today 2022, 5: 191 http://dx.doi.org/10.30953/bhty.v5.1916 (page number not for citation purpose) in bae chung and carlos caldas methods can be as simple as averaging the risk assessment inputs, to using various opinion pooling methods. the combined numbers will not reveal the individual inputs, which eventually protects the confidentiality of the data. using the combined numbers, project teams can run risk analysis off-chain using a method of their choice such as monte carlo simulation. the outputs from the risk analysis can be posted on the blockchain to be shared among the stakeholders and help them collaborate on making a project decision. the last step of the process is the monitoring and tracking of risk events which are further empowered using the blockchain technology. risk management is not a one-time process, as it is iterated multiple times over the project life cycle. (5) new risk events can be identified in later phases of the project, and risk assessments are bound to change as the project proceeds. in a blockchain network, blocks are added in a chronological order. each block references the previous block and the transaction data that have been stored. (23) hence, all transaction history is kept on the network, which makes the information to be transparent and traceable. as a result, users can easily track and monitor how risk events and assessments have changed over time through the transaction history kept on the blockchain network. prototype development this section elaborates on the initial prototype that is being developed. as proposed in the conceptual model section, hyperledger fabric was selected for the prototype and has been set up on the local machine. the network has been configured as in figure 2, with each of the four peers having access to the network through the gateway on the local machine. client user interface (ui) has been developed to enable user interaction to be as simple and efficient as possible, and an application programming interface (api) layer has been added, which works as a bridge between the fabric network and the ui. smart contract algorithms have been programmed in go language (version 1.15.4), which is an open-source programming language used for distributed systems. the functions of the smart contract have been aligned with the conceptual model process presented in figure 3. the smart contract functions can be categorized into two types, which are ‘query’ and ‘invoke’ functions. ‘query’ function will retrieve information from the ledger like getting the list of identified risk events before risk assessment. the ‘invoke’ function creates new blocks and update the ledger. an example of this would be the stakeholder entering a potential risk event or providing a risk assessment on the ledger. the developed smart contract will be packaged and deployed to the channel, allowing each of the channel member table 1. an example of risk assessment input risk id risk description likelihood (%) impact on cost minimum (%) most likely (%) maximum (%) re 001 request for additional medical gas outlets from the owner 5.0 3 5 7 re 002 delays due to installation of extensive fire alarm systems 8.0 5 7 10 re 003 building not meeting the specific requirements for safety and infection control 5.0 2 5 10 re 004 delay in the delivery and installation of medical equipment 10.0 8 12 15 re 005 change in design to meet special code requirements for mep systems 8.0 5 8 12 mep: mechanical, electrical and plumbing. fig. 5. public and private data collection in ledgers. http://dx.doi.org/10.30953/bhty.v5.191 citation: blockchain in healthcare today 2022, 5: 191 http://dx.doi.org/10.30953/bhty.v5.191 7 (page number not for citation purpose) applicability of blockchain-based implementation for risk management in healthcare projects organizations to install the smart contract on their peer node. screenshots of the uis are shown in figures 6–8. in figure 6, the user can select the project and add any risk event that has the potential to impact the project. the name and description of the risk events must be entered, and the risk event id will be autogenerated. when the list of risk events is compiled and validated, the users will be able to enter their assessment as shown in figure 7. the likelihood of occurrence and the three-point estimates of the impact on cost are provided by each user, which will be aggregated. figure 8 shows an example of an aggregated output that has been posted on the prototype. this output can be retrieved by the users so that they can eventually run a risk analysis of their choice. all of the transactions will be marked on the chain while preserving the confidentiality of each user’s input. the prototype has been demonstrated to subject matter experts and is being tested in a local environment. after fig. 6. user interface (adding risk events). fig. 7. user interface (entering risk assessments). http://dx.doi.org/10.30953/bhty.v5.191 citation: blockchain in healthcare today 2022, 5: 191 http://dx.doi.org/10.30953/bhty.v5.1918 (page number not for citation purpose) in bae chung and carlos caldas going through changes, the prototype will be deployed on a cloud service so that it can be pilot tested in an actual project. discussion while the prototype presented in this article holds promises for risk management in healthcare projects, it has limitations that should be considered for future work. first, most functions in the prototype focus on risk identification and risk assessment at the beginning of a project. however, the blockchain-based system could impact the project throughout its lifecycle if more focus is placed on risk mitigation and monitoring. the initial prototype can be extended so that risk mitigation strategies are recorded on the blockchain as well. this way, the effectiveness of risk mitigation strategies can be monitored afterwards. also, the prototype should enable periodic reassessment of risk events, as risks are bound to change during a project. such features will allow the risks to be thoroughly monitored and support the delivery of a successful project. another limitation was that the prototype was based on the assumption that all risk events are independent, and stakeholders have equal weights on evaluating the risks. however, this is not always the case. the prototype will incorporate these features later for the project team to elicit better risk decisions. moreover, the prototype still relies on an intermediary, which is the project creator, for certain functions in the framework. although the project creator has limited roles only in the earlier phases, one of the biggest purposes of blockchain technology is to remove the redundancies created by the trusted third party. (20) further work on smart contracts should be conducted to automate the solicitation of risk events and enable risk event validation among stakeholders. along with addressing the mentioned limitations, future work should be focused on enhancing the working prototype which is in the early phases of development. the prototype blockchain should be tested and validated to see if all functions work properly as stated in the conceptual prototype process. once validated, the working prototype should be deployed on a cloud service and pilot tested on an actual healthcare project to see its effectiveness in handling risks. although the focus of this study was on improving the risk management process of healthcare construction projects, the authors believe that the solution can be applicable to any other problems that involve preserving data confidentiality. especially in health care, protecting the sensitive health information of the patients is essential due to ethical reasons. (28) the concept of blockchain technology and its cryptographic algorithms can play a huge role in meeting the security requirements for hospital privacy. (29) similar to the prototype proposed in this article, studies have addressed how the technology can help clinicians make informed decisions while keeping personal health data confidential. (30) for example, gangula et al. (31) proposed a conceptual model to facilitate the interoperability of patient health information between healthcare fig. 8. user interface (risk assessment output). http://dx.doi.org/10.30953/bhty.v5.191 citation: blockchain in healthcare today 2022, 5: 191 http://dx.doi.org/10.30953/bhty.v5.191 9 (page number not for citation purpose) applicability of blockchain-based implementation for risk management in healthcare projects systems using hyperledger fabric. more research should be conducted in this area so that blockchain technology can maximize the safety and privacy of patient information while mediating accessibility. conclusions this research study explored how blockchain technology can solve concurrent problems in the risk management of healthcare projects. healthcare projects have unique characteristics and can be extremely complex compared with other types of construction projects. different methods have evolved over time to support collaborative risk management; however, there still lies several issues related to security, trust, and reliability when information is shared in a centralized manner. also, preserving the confidentiality of risk information becomes a bigger problem as stakeholders want to keep the sensitive information to themselves. the way to tackle these problems was to develop a blockchain-based implementation and utilize smart contracts to keep the risk inputs private, while using them at an aggregate level for risk management. therefore, a conceptual model process has been conceived to enable collaborative risk management. a working prototype using hyperledger fabric has been demonstrated based on this conceptual process. this prototype has the potential to allow the utilization of private information for risk management and track risk events over time. the solution proposed in this article is just one of the examples of implementing blockchain for preserving confidentiality. the prototype can be altered to solve problems related to patient records and clinical decision support systems. (31, 32) more work will follow to develop a hyperledger fabric network based on the conceptual model. overall, conventional risk management in healthcare construction has a huge room for improvement and blockchain-based implementation seems highly applicable. acknowledgments the authors thank all researchers who are part of the operating system 2.0 industrial affiliates program (os2 iap) for their contributions to this work. funding statement this research work is financially supported by the os2 iap at the construction industry institute (cii). conflicts of interest none of the authors declare any conflicts of interest. contributors this study was designed and conducted by in bae chung under the conceptual and technical guidance of dr carlos h. caldas. references 1. choi j, leite f, de oliveira dp. bim-based benchmarking for healthcare construction projects. autom constr. 2020;119(june):103347. https://doi.org/10.1016/j.autcon.2020.103347 2. gokhale s, gormley tc. construction management of healthcare projects. 1st ed. new york: mcgraw-hill education; 2014. 3. enache-pommer e, horman mj, messner ji, riley d. a unified process approach to healthcare project delivery: synergies between greening strategies, lean principles and bim. banff, ab: construction research congress, 2010; pp. 1376–85. 4. us census bureau. value of construction put in place at a glance [internet]. 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https://hyperledger-fabric.readthedocs.io/en/release-2.2/private-data/private-data.html https://hyperledger-fabric.readthedocs.io/en/release-2.2/private-data/private-data.html https://hyperledger-fabric.readthedocs.io/en/release-2.2/private-data/private-data.html https://doi.org/10.17226/12458 https://doi.org/10.17226/12458 https://doi.org/10.30953/bhty.v1.13 https://doi.org/10.30953/bhty.v3.144 https://doi.org/10.30953/bhty.v3.144 https://doi.org/10.30953/bhty.v4.154 https://doi.org/10.30953/bhty.v4.154 https://doi.org/10.30953/bhty.v3.120 http://creativecommons.org/licenses/by-nc/4.0. page 1 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v2.106 top 10 blockchain predictions for the (near) future of healthcare john d. halamka,1 gil alterovitz,2 william j. buchanan,3 tory cenaj,4 kevin a. clauson,5 vikram dhillon,6 florence d. hudson,7 manouchehr (mitch) mokhtari,8 dennis a. porto,9 ana santos rutschman,10 anh l. ngo11 affiliations: : 1chief information officer, beth israel deaconess system, chairman, new england healthcare exchange network (nehen), international healthcare innovation professor, harvard medical school, and practicing emergency physician; 2faculty, harvard medical school/boston children's hospital, massachusetts institute of technology; 3professor, school of computing, edinburgh napier university; fellow, bcs and iet; 4founder of partners in digital health, and publisher of the open access peer review journals blockchain in healthcare today, and telehealth and medicine today; 5associate professor, lipscomb university college of pharmacy & health sciences; 6research fellow, institute of simulation and training at university of central florida; 7special advisor, trustedci, the nsf cybersecurity center of excellence at indiana university; special advisor for next generation internet, northeast big data innovation hub at columbia university, co-founder blockchain in healthcare global at ieee-isto; 8associate professor, school of public health, university of maryland, college park; 9lewis wendell hackett award recipient at harvard and contributor to the blockchain and bitcoin curriculum at harvard business school; 10assistant professor of law, center for health law studies, saint louis university school of law; 11faculty, harvard medical school, beth israel deaconess medical center, department of anesthesia, critical care, and pain medicine; key words: blockchain, consent, genome, healthcare, prediction, medical records, providers, infrastructure, monetization, remittance, startups, supply chain corresponding author: tory cenaj, teecellc@gmail.com section: opinions, perspectives, and commentary on a current trend or issue impacting the sector to review blockchain lessons learned in 2018 and near-future predictions for blockchain in healthcare, blockchain in healthcare today (bhty) asked the world's blockchain in healthcare experts to share their insights. here, our internationally-renowned bhty peer review board discusses their major predictions. based on their responses, presented in detail below, ten major themes (table ) for the future of blockchain in healthcare will emerge over the 12 months. mailto:teecellc@gmail.com page 2 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v2.106 lockchain will become an essential part of consent management in healthcare first, consent is now stored in individual provider electronic health records, hospital medical record departments, and in stacks of paper on fax machines. consent is gathered for every procedure or at every visit. consent is local to each provider, not at a patient level for all sites of care across a patient lifetime. many startups are working on a radical revision of the consent process—storing patient consent for data exchange/privacy preferences and treatments on the blockchain, such that all stakeholders can access consents from one place and respect patient wishes. this approach will reduce administrative burden and enhance the patient care experience. micropayments will increasingly migrate to blockchain third, in a world of value-based purchasing, healthcare providers are reimbursed for wellness, not the quantity of care they deliver. many startups are working on wellness applications that provide incentives to ‘do the right thing.’ an easy-to-use universal payment interface for providing micropayments to patients when they achieve goals/outcomes would accelerate innovation. similarly, tracking co-pays and the litany of employee share of medical expenses could be simplified with a blockchain-based universal payment interface table . ten major themes for the near-term future of blockchain in healthcare predictions #1. blockchain will become an essential part of consent management in healthcare #2. remittance and micropayments will increasingly migrate to blockchain #3. non-cash assets including outcomes will be tokenized #4. providers will be credentialed on chain #5. improvements to blockchain infrastructure will reduce electricity requirements and enhance speed/scalability #6. supply chain integrity will be tracked on blockchain #7. education of stakeholders will refine use cases for blockchain and accelerate adoption #8. opportunities for monetization of data, including the genome, will be enhanced by blockchain #9. integrity of medical records will be an essential use case for blockchain #10. existing blockchain in healthcare startups will be acquired and we will see substantial consolidation of blockchain in healthcare offerings non-cash assets including healthcare outcomes will be tokenized second, although blockchain has roots in cryptocurrency, a token can really represent anything—real estate, a physical object, or even an outcome. tokens facilitate managing for results or outcomes in healthcare. a new startup, proof of impact, has started to measure patientoutcomes and offer non-governmental organizations the opportunity to ‘buy’ outcomes. for example, there could be a marketplace for patients successfully treated for a disease. instead of just providing grants, foundations could pay for outcomes documented in the blockchain. providers will be credentialed on chain fourth, with over 1000 insurance companies in b page 3 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v2.106 the country, filling out paperwork to document provider training and licensure is a nightmare. the synaptic health alliance aims to simplify this process by putting all credentialing information on a distributed public ledger for all stakeholders to access.1 improvements to blockchain infrastructure will reduce electricity requirements and enhance speed/scalability fifth, proof-of-work approaches consume the yearly electrical output of ireland. building trust through other means will preserve trust but radically reduce the computing footprint. similarly, transactional speeds will increase and blockchain as a service will enhance ease of use (by hiding the complexity of blockchain behind a set of cloud-hosted functions, with simple to use application programming interfaces). supply chain integrity will be tracked on chain sixth, recently i was traveling in india with a few clinicians who purchased medications at a local pharmacy to treat respiratory ailments. there was no way to track the validity of the lot numbers nor the purity of the compounds. a blockchain approach to pharmaceutical supply chain would ensure the integrity of the products. education of stakeholders will refine the use cases for blockchain and accelerate adoption seventh, every day i do an interview with some publication to explain what blockchain doesn’t do (i.e., it’s not a high-performance database, an analytical tool, or the solution to managing person identity). we have no idea who actually posts transactions to the blockchain. there are many reasons to use the blockchain as described above—consent, credentialing, data integrity guarantees, supply chain management, and micropayments. as more stakeholders are educated about the pros and cons of blockchain, high value use cases will be implemented more widely. opportunities for monetization of data including the genome will be enhanced by blockchain eight, many startups recognize that data are the new ‘oil.’ or, for those who remember watching the graduate, in 1967, dustin hoffman would have been told to monetize data instead of plastics. however, resale of data without full disclosure and consent has proven problematic—just look at facebook’s challenges.2 what if a marketplace for data enabled people who want to contribute data—for some public good such as clinical trials/clinical research/population health—to be paid each time their data were used? patients could participate in this marketplace with eyes wide open and be a part of sharing any gains made from their anonymized contributed data. nebula genomics, for example, is exploring this idea to accumulate genomic data for the development of precision medicine tools more rapidly.3 integrity of medical records will be an essential use case for blockchain ninth, malpractice assertions happen. health outcomes are not always good and inevitably someone is blamed. when plaintiff attorneys request historical medical records, they often claim the records are faked/altered to remove any evidence of medical mistakes. we provide audit trails to validate that records have not been changed. the attorneys then claim the audit trails were altered (which is impossible). if a distributed ledger technology with unassailable trust were used to guarantee the integrity of the medical record, the burden on providers, it departments, and attorneys would be reduced. the records themselves would not be stored on chain, just a hash (a one-way mathematical transformation that is unique for every document). if a record is produced today and its hash matches the hash stored on the blockchain page 4 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v2.106 when the record was created, all stakeholders can be assured the record was never changed. existing blockchain in healthcare startups will be acquired and we’ll see substantial consolidation of blockchain in healthcare offerings finally, in my work as a blockchain expert, i advise many startups and hear 50 pitches for everyone i chose to advise. the marketplace of companies offering blockchain is large and filled with more powerpoint presentations than products. many of these startups will fade away for lack of business model. otherwise, the promising startups will be acquired and consolidated, such as change healthcare did with pokitdok (and it’s dokchain), those are our forecasts. below are the original comments from experts that led to this top 10 list: gil alterovitz: practical implementations of blockchain in healthcare will come, but they will not be specific to healthcare. for example, developments in blockchain for supply chain are emerging where healthcare is one of many sectors that will be affected. second, blockchain-based currency has started to be used for remittances. such applications could begin to affect healthcare funding patterns in developing countries. william h. buchanan: this year will be the year of commitment, tokenization, identity, anonymity, regulation, and the start of public key encryption finally replacing ‘wet’ signatures in our healthcare systems. we are now at the point with blockchain that we understand what should (and should not) be added to the ledger, and with regulations such as general data protection regulation (gdpr)4, we must ensure respect for citizens’ rights of privacy and consent. our new health care world must be built on a solid digital foundation of cryptography and trust. increasingly, we must anonymize onto the ledger, but for us still to create a consensus for the current state of an infrastructure. our blockchain world will thus become increasingly anonymized, in order to ensure privacy protection at its lowest layers, and with each transaction blinded in some way. some cryptocurrencies such as monero and zcash, provide enhanced privacy and are guiding lights in hiding transaction details, while still being able to integrate high levels of trust. the owner of the data will be able to hold these blinding factors and reveal them as required. in this way, citizens will show commitments to things and then reveal the details of their commitment to those whom they trust. this will help anonymize our ledgers, while building infrastructures for regulation. in addition, we are entering a year will be the year that our world becomes truly tokenized, and where we move our focus from cryptocurrencies to crypto assets. unfortunately, our legal infrastructures do not quite match to a distributed ledger world; so, we will see some nations of the world move towards developing a legal definition of this world. liechtenstein is a guiding light in this with its blockchain act. other nations are sure to follow, especially to support innovation, while building up trust in transforming our public sector. a strong trust infrastructure and regulatory framework must be core part of the implement of blockchain methods within healthcare. if, in the near-term, we started to use cryptography properly within our healthcare systems, we will have at least started to build our healthcare systems on a trusted foundation. page 5 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v2.106 tory cenaj: we will see a groundswell of education. clinicians and health consumers will begin to understand the technology—what it does and how it applies to the health sector. we will begin discourse not with the history of…, but with “blockchain technology is…” and “blockchain technology does...” there will be an increase in drug discovery business models that use intersecting technologies to lower the cost of developing drugs, such as combining blockchain tech and telehealth and medicine for clinical trials. we will see more bitcoin atms, retailers accepting bitcoin, and mobile tech use of payments (with digital wallets) and scanning codes at counters. this will spur curiosity, education, and help expand initiatives in healthcare. there will be ‘lift-off’ for a token economy that offers direct buying and selling power to healthcare consumers. for example, if a women ‘donates’ her ovum for $10k (through an intermediary),5 how much will personal health data and genome be bought and sold for (without the intermediary)? there will be “lift off” for a regulated global token exchange that is linked on a parallel track with all exchanges around the globe. governments and governing bodies will convene to respond to this urgency, and present one unified solution. i wonder about our beloved elders. it will be challenging to train them on tech at this stage. devices and sensors will expand a new home healthtech work force. blockchain tech security and immutability will enhance solutions for new “smart city infrastructure,” including lowering costs for ecosystems and environments built for the elderly. kevin clauson: from the beginning, healthcare has actively tried to dissociate blockchain and bitcoin. “blockchain, not bitcoin” was the oftrepeated mantra at healthcare conferences, press releases, and on conference calls. while the desire to do so is understandable because of the murky history of bitcoin and cryptocurrencies in the public consciousness and the disruption-resistant, risk-averse nature of the healthcare industry, it is still a mistake to do so. it is a mistake if the intent is to understand and evaluate blockchain in the broader healthcare industry. the cryptocurrency market is still a substantial driver for companies building at the intersection of blockchain and healthcare— particularly for startups. after a meteoric rise in the cryptocurrency market and of cryptocurrency valuations in 2017, 2018 was characterized by plummeting prices of a ‘crypto winter.” prediction: we are entering a time of consolidation and acquisition in healthcare-focused blockchain companies, as foreshadowed by change healthcare acquiring pokitdok. this trend in will lead to healthcare companies demonstrating better resourced efforts in leveraging blockchain and distributed ledger technology (dlt) as well as superior organization, integration, and (hopefully) impact. vikram dhillon: fitness wearables are producing an enormous amount of data that can be captured in actionable formats.6 connected devices can easily provide data points such as blood pressure, weight, pulse, and sleep patterns. scheduling timely exports of such data allows better understanding of how patients implement lifestyle changes for chronic diseases such as diabetes. committing snapshots of these data to the blockchain can incentivize and reward lifestyle changes. page 6 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v2.106 with the microtransaction payment structure built into the blockchain, rewards programs can be turned into smart contracts that dispatch reward tokens to wallets on blockchain. as more healthcare providers and partners join such a network, the tokens will have more value and be redeemable for discounts such as co-pay for clinic visits. florence hudson: blockchain technology is definitely on the ‘hype cycle.’ now is the time for technology and healthcare leaders around the world to work together to bring blockchain into the reality cycle. blockchain will be deployed across the healthcare industry to improve secure healthcare data sharing toward the goal of precision medicine, accelerating research, and improving healthcare outcomes. this will include use cases in personal health records, wearables, lab sample data management, supply chains, and research. as i presented at the 2018 society of women engineers conference in minneapolis, "blockchain: from hype to real value," to a standing room only audience, the data sharing imperative to improve healthcare outcomes will be well served by blockchain and other technologies working together to deliver trust, identity, privacy, protection, safety, and security (tippss) for humans, devices and data. in september 2018, the institute of electrical and electronic engineers (ieee) published that it is sponsoring initiatives with technology and healthcare leaders around the world, including device manufacturers, pharmaceutical companies, providers, and regulators, to enable standards for clinical internet of things device and data interoperability with blockchain, as well as lab sample data tracking and management with blockchain. we are working together to define new standards, create real demonstration pilots, and deliver the value blockchain has to offer. manouchehr (mitch) mokhtari: we will witness an explosion of ‘permission-ed’ or ‘club blockchain’ in the healthcare and other sectors. this will be analogous to the proliferation of the intranets or personal computers, as opposed to the mainframe or blockchain as a computer for the world. the club blockchain will exclude non-members and ensure non-rivalrous benefits to the members. anh l. ngo: my initial response could have been, "total world domination," but that would be an overstatement and only used to catch the reader's attention. more to the point, blockchain will likely be intertwined with other aspects of technological advances such as artificial intelligence (ai), internet-of-things, and mobile applications for ease of adoption and use. i believe the blockchain will permeate several aspects of healthcare, with first adoptions occurring in areas involving financial transactions. those include micropayments (deductibles, prescriptions, traditional cash pay), insurance processing and claims, and payments and quality assurance involving medical supply chain management. these are areas where high friction, massive inefficiencies, and costs can be reduced with a new way of managing trust in a transaction. other areas of healthcare that can involve adoption of blockchain include physician credentialing, patient consent management, and, of course, medical records. of note, medical records have a long way to go since medical data are very resource-intensive and will require creative measures to limit the impact of large page 7 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v2.106 data on stakeholders on the blockchain platforms. dennis a. porto: in the coming year, the distinction between enterprise blockchains and public blockchains will begin to blur. these large public blockchains, especially bitcoin, have best captured what makes blockchains valuable: trust minimization and tamper resistance approaching the notional ideals of trustlessness and immutability. enterprise blockchains will exist perhaps as sidechains to or layers above bitcoin, where they can periodically settle on the main bitcoin blockchain without being encumbered by scaling issues characteristic of a public cryptocurrency. we will see serious efforts to create and capture value in healthcare blockchain: pharmaceutical supply chain management, opioid and cannabis diversion control, physician credentialing, as well as clinical trial and patient data management. we will also see a number of projects fail despite their good intentions. successful projects will be those that have a narrow use of blockchain to solve a problem that is impossible with a traditional database. finally, innovation will continue to occur at the protocol level. i anticipate that proof of work will continue to dominate, and that the most impactful technical innovations will focus on optimizing this particular consensus mechanism. ana santos rutschman: we will see more companies piloting blockchain projects involving collection of genomic data, and we will see more opportunities for monetization of genomic data. at the same time, we will see growing awareness of privacy issues in this field. we will also see more public funding directed at blockchain-based genomic data projects. final thoughts william h. buchanan: let this next year be a time for building a solid foundation for future of healthcare and put digital trust and the rights of the citizen at its core. wishing you success! contributors: each author contributed their experience and insight into the future predictions for blockchain. conflicts of interests: none references 1. synaptic health alliance. joining forces to improve healthcare. 2019. baus a. available at url: https://www.synaptichealthalliance.com/ about-us 2. gaus a. 4 key challenges facing facebook in 2019. thestreet. 2018. available at url: https://www.thestreet.com/technology/4 -key-challenges-facing-facebook-in2019-14815098 3. nebula genomics. the future of your health is in your dna. 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dictionaries. 2019. available at url: https://en.oxforddictionaries.com/definit ion/blockchain 10. cryptocurrency. wikipedia. 2019. available at url: https://en.wikipedia.org/wiki/cryptocurr ency 11. thake m. what’s the difference between blockchain and dlt? nakomoto. 2018. available at url: https://medium.com/nakamo-to/whatsthe-difference-between-blockchain-anddlt-e4b9312c75dd 12. woods j. enterprise blockchain has arrived. criptooracle. 2018. available at url: https://medium.com/cryptooracle/enterprise-blockchain-hasarrived-2d2e4d8ec0d 13. it glossary. internet of things. 2019. available at url: https://www.gartner.com/itglossary/internet-of-things/ 14. monero (cryptocurrency). wikipedia. 2019. available at url: https://en.wikipedia.org/wiki/monero_(c ryptocurrency) 15. immutable object. wikipedia. 2018. avaialble at url: https://en.wikipedia.org/wiki/immutable _object 16. viewpoints. change healthcare acquires pokitdok assets. change healthcare. 2019. available at url: https://www.changehealthcare.com/blog /change-healthcare-acquires-pokitdokassets/ 17. proof-of-work system. wikipedia. 2019. available at url: https://en.wikipedia.org/wiki/proof-ofwork_system 18. massessi d. public vs private blockchain in a nutshell. medium. 2018. available at url: https://medium.com/coinmonks/publicvs-private-blockchain-in-a-nutshellc9fe284fa39f 19. zcash. wikipedia. 2019. available at url: https://en.wikipedia.org/wiki/zcash glossary of terms artificial intelligence (ai): an area of computer science that emphasizes creation of intelligent machines that work and react like humans. some of the activities computers with artificial intelligence are designed for include speech recognition. 7 https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8481273 https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8481273 https://www.techopedia.com/definition/190/artificial-intelligence-ai https://www.techopedia.com/definition/190/artificial-intelligence-ai https://en.wikipedia.org/wiki/bitcoin https://en.oxforddictionaries.com/definition/blockchain https://en.oxforddictionaries.com/definition/blockchain https://en.wikipedia.org/wiki/cryptocurrency https://en.wikipedia.org/wiki/cryptocurrency https://medium.com/nakamo-to/whats-the-difference-between-blockchain-and-dlt-e4b9312c75dd https://medium.com/nakamo-to/whats-the-difference-between-blockchain-and-dlt-e4b9312c75dd https://medium.com/nakamo-to/whats-the-difference-between-blockchain-and-dlt-e4b9312c75dd https://www.gartner.com/it-glossary/internet-of-things/ https://www.gartner.com/it-glossary/internet-of-things/ https://en.wikipedia.org/wiki/monero_(cryptocurrency) https://en.wikipedia.org/wiki/monero_(cryptocurrency) https://en.wikipedia.org/wiki/immutable_object https://en.wikipedia.org/wiki/immutable_object https://www.changehealthcare.com/blog/change-healthcare-acquires-pokitdok-assets/ https://www.changehealthcare.com/blog/change-healthcare-acquires-pokitdok-assets/ https://www.changehealthcare.com/blog/change-healthcare-acquires-pokitdok-assets/ https://en.wikipedia.org/wiki/proof-of-work_system https://en.wikipedia.org/wiki/proof-of-work_system page 9 of 9 blockchain in healthcare today™ issn 2573-8240 https://doi.org/10.30953/bhty.v2.106 bitcoin: a cryptocurrency (i.e., a form of electronic cash) is a decentralized digital currency without a central bank or single administrator that is sent from user-to-user on the peer-to-peer bitcoin network without intermediaries.8 blockchain: a system in which a record of transactions made in bitcoin or another cryptocurrency are maintained across several computers linked in a peer-to-peer network.9 club blockchain: a "permission-ed" blockchain. cryptocurrency (or crypto currency): a digital asset designed to work as a medium of exchange that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets.10 crypto winter: a period where the value of cryptocurrencies decline. distributed ledger technology: an umbrella term used to describe technologies that distribute records or information (e.g., accounting ledgers) among those using it, either privately or publicly.11 enterprise blockchain: the use of blockchain within corporations12 internet-of-things: the interconnection via the internet of computing devices embedded in everyday objects, enabling them to send and receive data.13 monero: an open-source cryptocurrency created in april 2014 that focuses on fungibility, privacy and decentralization. monero uses an obfuscated public ledger, meaning anybody can broadcast or send transactions, but no outside observer can tell the source, amount or destination.14 immutable: an object whose state cannot be modified after it is created.15 pokitdok, inc.: a platform-as-a-service company for healthcare, which was acquired by change healthcare in 2018.16 proof-of-work (pow): a system (protocol or function) that is an economic measure to deter denial of service attacks and other service abuses such as spam on a network, by requiring some work from the service requester, usually meaning processing time by a computer.17 public blockchain: a permissionless blockchain. anyone can join the blockchain network, meaning that they can read, write, or participate. public blockchains are decentralized. no one controls the network, and they are secure in that the data cannot be changed once validated on the blockchain.18 private blockchain: a permissioned blockchain (i.e., places restrictions on who is allowed to participate in the network and in what transactions).18 zcash: a cryptocurrency aimed at using cryptography to provide enhanced privacy for its users compared to other cryptocurrencies such as bitcoin. like bitcoin, zcash has a fixed total supply of 21 million units.19 page 1 of 7 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.126 predictions for blockchain in 2020 george t. mathew,1 dennis a. porto,2 ron ribitzky,3 susan ramonat,4 uli c. broedl,5 kevin a. clauson,6 frank ricotta,7 tory cenaj,8 anh l. ngo9 1chief medical officer, americas dxc, north america; 2breakthrough.capital, skincare.md, new york city, and bhty; 3r&d ribitzky, newton, massachusetts, usa; 4spiritus partners, edinburgh, scotland; 5vp medical & regulatory affairs, boehringer ingelheim canada; 6lipscomb university college of pharmacy & health sciences, nashville, tennessee; 7burstiq, denver, colorado; 8blockchain in healthcare today, stamford, connecticut; 9pain specialty group, committee on admissions, newington, new hampshire corresponding author: tory cenaj, t.cenaj@partnersindigitalhealth.com keywords: artificial intelligence, blockchain, converge-2-xcelerate, e-identity solutions, healthcare, maturation, minimum viable traction, predictions, privacy, production, return-on-adoption, return-on-investment, scale section: discussion during our 2019 converge2xcelerate (conv2x) conference in boston, we focused on the theme “proving market value with pragmatic innovation in healthcare” (see https://conv2x-2019. eventcreate.com/). this year, along with blockchain in healthcare today (bhty) editorial board members, conference speakers were invited to join with tory cenaj, publisher of bhty, to contribute their expertise and share insights for the near-term landscape of blockchain in healthcare. george t. mathew two predictions are worth highlighting. first, there will be more specific use cases that can significantly be better handled by blockchain. these include interoperability, data monetization/ownership/consent. second, eventually these will overlap with the existing tech use cases, including provider credentialing and verification, and claims processing. financial institutions that are not already in healthcare will go here first. dennis a. porto the largest public protocols will become increasingly dominant in the blockchain space. similar to how the internet developed, private (enterprise-focused) blockchains will go the way of closed intranets—perhaps useful in limited scenarios but overshadowed by the open public protocols. lightweight alternatives to blockchains (e.g., timestamps) will gain traction in the large majority of instances where censorship resistance of a blockchain is not helpful. across the health sector generally, sensors of all kinds will help curate our health data. these sensors will bring to light trends and detect (or prevent) disease. proliferation of health sensors combined with artificial intelligence https://doi.org/10.30953/bhty.v3.126 mailto:t.cenaj@partnersindigitalhealth.com https://conv2x-2019.eventcreate.com/ https://conv2x-2019.eventcreate.com/ https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.126&domain=blockchainhealthcaretoday.com&date_stamp=2020-01-27 page 2 of 7 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.126 (ai) will prompt new discoveries that will make us healthier. however, they will pose threats to privacy. and some of the technology behind blockchains (e.g., encryption, time stamps, peer to-peer networks) will be unbundled to manage this large data set and protect patients. ron ribitzky the year 2020 will be marked as the year of minimum viable traction of blockchain in health care (figure 1). historically, in 2019, we emphasized viable, which was marked by proving market value, albeit anecdotal and sporadic. this followed the equivalent of entrepreneur dragracing fanfare as blockchain in healthcare initial coin offerings (icos) came and went in 2018.1–9 going forward, successful and pragmatic newera practitioners will apply their passion toward transforming academic grand vision, privatesector marketing campaigns, and continuously evolving technology capabilities to minimal viable traction (mvt) of blockchain-enabled solutions in healthcare.1,2,4–9 primarily due to the fundamental decentralized “democratization” characteristic of blockchain, achieving mvt is not just about a technology engineering marvel. rather, it is about harmonization of the new information democracy. we call it “the blockchain triad.”1,4,5,8,9 1. disruptive disintermediated business model “owned” by executive leadership in private and public sectors alike. 2. disruptive decentralized operating model required to make the new business model work—engaging ecosystem like never before. 3. new technology architecture required to make the two work by harmonizing on-chain, off-chain, interoperability, with legacy integration put in place. in order to help drive the cross-functional teams working in tandem to make the grand vision of blockchain in healthcare happen, new-era practitioners will up-level the value of measure key performance indicators (kpis) from return-on-investment to return-on-adoption (figure 2).1,2,4–9 second, blockchain will emerge as the desired foundation for software-as-a-medical-device (samd) followed by service-as-a-medical device (semd). expanding its play in internetof-things (iot) in general, blockchain will emerge as the desired foundation for the new model of trust required for fda-regulated samd and semd to gain real-world market traction.4,9–12 the new blockchain-enabled model of trust of samd and semd will help drive return-onadoption in that it will equip consumers with methods and tools to govern access to and use of the data they produce.4,8,9,13 blockchainenabled samd and semd will be key to consumers reaping the economic and figure 1—the market traction gap. source: https://medium.com/wildcat-venture-partners/the-final-hurdle-reaching-minimum-viable-traction-and-preparing-to-scale86b77cf0e52d https://doi.org/10.30953/bhty.v3.126 https://medium.com/wildcat-venture-partners/the-final-hurdle-reaching-minimum-viable-traction-and-preparing-to-scale-86b77cf0e52d https://medium.com/wildcat-venture-partners/the-final-hurdle-reaching-minimum-viable-traction-and-preparing-to-scale-86b77cf0e52d page 3 of 7 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.126 economic-equivalent benefits monetizing their data.4,8,9,13,14 susan ramonat i expect pharmaceutical supply chain consortia to move from projects to limited-scale production environments. uli c. broedl while the value proposition of blockchain technology in healthcare, including trust; transparency; auditability; stakeholder empowerment; cost reduction via automation of processes in a trusted environment; and health outcome improvement seems compelling, blockchain technology in healthcare will continue to be in experimentation mode during 2020. indeed, 2020 will see technological progress with advances in blockchain scalability, interoperability, security, and privacy. there will be more use cases that combine blockchain technology with other innovative solutions, in particular ai. we can also expect an increase in presentations of successfully completed pilots (i.e., the field still needs to develop a “failure culture” to openly discuss and learn from unsuccessful projects) driving broader awareness of, interest in, and experimentation with blockchain technology in healthcare. the speed of technological progress, however, is not matched by the development of key nontechnological aspects of blockchain technology and its application, including, but not limited to, best governance practices, regulatory standards, and legal frameworks that address liability and intellectual property rules in a decentralized ecosystem. we can only expect broad adoption of blockchain technology in healthcare if both technological and non-technological challenges in a decentralized ecosystem are addressed in a manner that allows figure 2—reliable indicators of minimum variable traction. adapted from: https://medium.com/wildcat-venture-partners/the-final-hurdle-reaching-minimum-viable-traction-and-preparingto-scale-86b77cf0e52d https://doi.org/10.30953/bhty.v3.126 https://medium.com/wildcat-venture-partners/the-final-hurdle-reaching-minimum-viable-traction-and-preparing-to-scale-86b77cf0e52d https://medium.com/wildcat-venture-partners/the-final-hurdle-reaching-minimum-viable-traction-and-preparing-to-scale-86b77cf0e52d page 4 of 7 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.126 win–win situations for all stakeholders while mitigating risk. while the 2019 gartner prediction for blockchain technology in healthcare may be somewhat pessimistic (mainstream adoption is expected to take more than 10 years from now15), it is fair to conclude that we, presently, are still at the stage of experimentation with a novel technology that, however, has the potential to dramatically change and improve our approach to healthcare. anh l. ngo in the year 2020, we start seeing maturity in business models for healthcare startups that integrate blockchain technology in their platforms. companies that do not have sustainable business models in healthcare based on the use of blockchain will likely experience liquidity challenges, especially if there is a financial recession in the world’s major economies. these are especially true for companies that received financing or capital from traditional investment firms. as mentioned in 2019, use of the blockchain in healthcare and medicine will likely be intertwined with other aspects of technological advances such as ai, iot, and mobile applications for ease of adoption and use. in the near term, i still believe the most viable blockchain deployments in healthcare are projects involving financial transactions and where there is a direct measurable creation in monetary value and hence a revenue model for the blockchain-related company. those segments include micropayments (e.g., deductibles, prescriptions, traditional cash pay), insurance processing and claims submissions, and payments and quality assurance involving medical supply chain management. these are areas where high friction, massive inefficiencies, and costs can be reduced with a new way of managing trust in a transaction. areas of healthcare with blockchain utilization that may gain some adoption include physician credentialing, patient consent management, and medical records. extracting a revenue model to create self-sufficiency for these projects, however, may be difficult in 2020. kevin a. clauson in 2020, there will be increased adoption of distributed ledger technology and blockchain technology in the healthcare arena, in spite of a substantially smaller spotlight being shown on blockchain itself. this longitudinal shift from focusing on the technology to what the technology enables patients, clinicians, and healthcare systems to accomplish will represent a major milestone in the journey of blockchain toward true integration and maturity. frank ricotta i envision eight advances in blockchain pertaining to healthcare in 2020. these include e-identity, consent, integration and data collection, initiatives by non-governmental organizations (ngo), consolidation, clinical research, supply chain, and fraud. we will see early adoption of e-identity solutions that will become the basis of health profiles. adoption of blockchain-based identity solutions will be driven by regulatory pushes for individuals to have access to their health information. the identity solutions will evolve to health profiles that push beyond health records to include additional information such as genomic data and data on health and wellness. health profiles will drive creation of new access models and speed up the adoption of personalized medicine, telehealth, and ai. blockchain solutions will drive consent, especially in use cases requiring consent to flow between providers and individuals. this solution https://doi.org/10.30953/bhty.v3.126 page 5 of 7 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.126 will support granular consent as mandated by consumer protection laws such as general data protection regulation (gdpr) in europe and those beginning to take effect in the united states. specifically, in the clinical research area, a blockchain solution will emerge as the preferred integration and data coordination platform for advanced digital and clinical therapeutics. the ngo initiatives will begin to rely on blockchain solutions for supply chain, aide distribution, and funds management to combat fraud and provide transparency for their initiatives across the health spectrum (e.g., access to care, food, water, medicine). we will see a consolidation of blockchain solutions, and larger integrators will begin to incorporate, as a core technology, enablement capability within their solution stacks. new market leaders will emerge outside of the cryptocurrency technology stacks that are better suited to deal with complex data structures and smart contracts as related to data ownership and monetization. contract research organization (cro) solutions will begin to deploy blockchain solutions to support advanced clinical research to include improved person and provider engagement and access. we will see the pharmaceutical sector driving the adoption of blockchain within the supply chain ecosystem. finally, regulators will embrace blockchain capabilities to deal with fraud, especially as it relates to the opioid epidemic. tory cenaj i expect 2020 to unveil blockchain developments with a keener eye toward pursuing best practice, standards, guidelines, and regulation. the market clamors for use cases and standards on a continuous basis. the need to advocate and respond clearly originates from both practical and regulatory trenches. as a result, we expect bhty will present an overall influx of use cases through 2020. education remains a critical component for success, as early adopters and innovators pass the baton to the marketplace for evaluation of research and efforts. as in any analysis, the financial impact should be included to gauge success and impact on democratic change in our health system. too many remain disenfranchised and disquieted. conclusions among all the specific advances predicted by our panel for blockchain in healthcare during the next 12 months, one encompassing theme becomes apparent. a maturation process for blockchain in healthcare is occurring. entrepreneurs, innovators, and executives in the public and private sectors will continue their focus on proving market value, while successful early adopters will go ahead toward closing the market traction gap (figure 1). each of our experts touched on it. anh ngo and tory cenaj tell us straightforwardly that maturity in business models is occurring. to this end, ron ribitzky calls for a laser-sharp focus on user experience throughout the blockchain triad to produce scalable return-on-adoption (figure 2). more specifically, george mathew predicts interoperability, data monetization/ownership/ consent, credentialing and verification, and claims processing. dennis porto envisions the largest public protocols becoming dominant in the blockchain space, while susan ramonat foresees the pharmaceutical supply chain consortia moving from projects to limited-scale https://doi.org/10.30953/bhty.v3.126 page 6 of 7 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.126 production. uli c. broedl envisions technological progress with advances in blockchain scalability, interoperability, security, and privacy. kevin clauson tells us that the focus is now on accomplishment—what the technology enables patients, clinicians, and healthcare systems to do. and frank ricotta lists solutions for e-identity, consent, integration and data collection, ngo initiatives, consolidation, clinical research, supply chain, and fraud. final thoughts thanks to our intrepid authors for sharing their unique perspectives. we look forward to the market using the bhty evidence-based platform to share more knowledge, and both positive and negative research, with the sector. in closing, over the last few years, we have marveled at the increasing number of hospitals, startup innovation labs, incubators, investors, and universities seeking capital to attract invention and attention. we invite and encourage bhty manuscript submissions to include the financial impact of outcomes to evaluate overall market cost reduction and, where possible, demonstrate the impact and/or direct benefit(s) to patients. let us begin this collective effort today. funding statement: no outside funding was provided for this article. contributors: all authors contributed to the content of the article. conflicts of interest: see bhty editorial team for information about authors: https:// blockchainhealthcaretoday.com/index.php/ 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predictions for health care in 2021 prasad kothari1*, melanie nuce2, ingrid vasiliu-feltes3, dominique hurley4, mercury fox5, sweta sneha6,7, wendy charles8, jim nasr9 and radhika iyengar10 1director, axtria, vice president – the smart cube, usa; 2svp corporate development, gs1 usa; 3chief quality & innovation officer, mednax, usa; 4vp, strategy & innovation, healthverity, usa; 5executive director, codata at ua, university of arizona, usa; 6executive director and professor, healthcare management and informatics, coles college of business, kennesaw state university, kennesaw, georgia; 7executive director of healthcare management informatics, professor of information systems, coles college of business, kennesaw university, usa; 8chief scientific officer, burstiq, usa; 9ceo and founder, acoer, usa; 10founding partner, starchain ventures, usa abstract with coronavirus (covid) spreading across the world and the health care system being pushed toward more digitization and technology, last year was a unique year of human tragedy. there is a silver lining to this tragedy, that is, providers, payers, and pharma companies have shifted quickly toward better technologies, including artificial intelligence (ai) blockchain, and so on. keywords: covid-19; public–private partnerships; blockchain; digital; health care; pandemic prasad kothari most of the companies have quickly adopted better technologies during the pandemic; that is, from amazon securing a blockchain patent for optimum supply chain, health and human services (hhs) promoting the idea of interoperability of electronic heath records or electronic medical records (ehr/emr) systems to leveraging more artificial intelligence (ai), and centers for medicare & medicaid services (cms) bringing payment parity between in-person physician visits and telemedicine visits. in addition, the south korean government is using the blockchain technology to fight diabetes through sendsquare; payers like anthem are leveraging blockchain for member data; providers like mayo clinic are partnering with google cloud and blockchain organization, such as medicalchain, and ibm has announced that the blockchain technology can play a key role in the distribution of coronavirus (covid) vaccines. in 2020, there was a great push toward building and adopting a digital, decentralized blockchain technology with a layer of cognitive ai. in 2021, we hope to see the affordable care act’s vision of providing a high quality of care delivered at lower costs while maintaining focus on population health, personalized health, and preventative health leveraging technologies, such as ai, data science, and blockchain with patient-centric applications. in 2021, technology will combine to focus on the ‘care’ in health care. drug discovery will be accelerated using drug discovery optimization methods, such as generative adversarial networks (gans). clinical trials will be redesigned using real-world data (rwd) for efficiency, and empowered by data sharing through the blockchain technology. bias in the ai algorithms and data can be treated better with the creative use of technologies like blockchain, and so on. it will hopefully be the year of managing the supply chains related to chronic illnesses and disease management through use of effective therapies like cell and gene therapies leveraging blockchain and ai. it will be the year of medical education leveraging blockchain and augmented reality/virtual reality (ar/ vr). blockchain can also lead to an acceleration of all pharmaceutical research and developmental activities, a complete change in the paradigm for safety testing for medical devices, while reducing adverse events, as well as leading to a new biotech era. the year 2021 will be the era of collaboration through efficient technologies with open science ecosystem and build on collaborative theories of knowledge production. more importantly, the year 2021 will be the year of technology taking the center stage in health care systems * correspondence: prasad kothari. email: prasadkothari74@gmail.com https://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.30953/bhty.v4.162 mailto:prasadkothari74@gmail.com citation: blockchain in healthcare today 2021, 4: 162 http://dx.doi.org/10.30953/bhty.v4.1622 (page number not for citation purpose) prasad kothari et al. with policymakers creating new frameworks so that payers, providers, and pharma companies can leverage these technologies to deliver results while building new public–private partnerships (ppp). the research authors have enumerated below more details and facets of the technology’s impact and their expectations for 2021. melanie nuce the covid pandemic will have a lasting effect on technology budgets in 2021, resulting in the exploration of blockchain, ai, and other solutions to be more strictly centered on the timeliest use cases in health care. this includes vaccine traceability, pharmaceutical drug authentication, and managing the supply chains related to chronic illnesses and disease management with therapies, such as cell and gene therapies. the identification of products and assets in the supply chain will need to be consistent between the item’s physical and digital representation in order for this technology to support effectively these important use cases. we look for prioritization of data quality, as well as more businesses deepening their use of global data standards as a foundation for systems interoperability and data-sharing efficiency. ingrid vasiliu-feltes for the year 2021, i predict we will witness increased deployment of blockchain technology for digital health twins within the life sciences and health care industry. within the health care industry, blockchain powered digital twins can be used in optimizing medical education and medical training, as well as further enhancing patient safety and patient privacy, thereby contributing to the development of a patient-centric care delivery model. combining digital health passports with digital health towns can also address one of the major pain points – patient health records. the powerful trisect of digital health twins, digital health passports, and creation of digital health identities can reshape the global health care ecosystem by offering new solutions for data access and ownership. for the life sciences industry, digital twins build on blockchain technology can expedite development of precision medicine solutions, as well as a large-scale adoption of virtual clinical trials. they can also lead to an acceleration of all pharmaceutical research and development activities: a complete change in the paradigm for safety testing for medical devices, as well as a new biotech era. using the blockchain technology for digital health twins, we can leverage its audibility, scalability, and avoidance of systemic waste or redundancy, thus, decreasing costs and increasing efficiency. by allowing proof of ownership and restricted data sharing, digital health twins can become the conduit for a truly patient-centric health ecosystem. the major barriers we have to overcome for this prediction to become a reality are regulatory and socio-economic. dominique hurley i believe 2021 will bring about a greater focus on data collaboration and increased acceptance of blockchain as a good choice to create and share trusted data across organizations. in 2020, as we tried to trace and track the spread of covid-19, we learned the value of centralized, current, patient healthcare data. in 2021, as we rapidly move to vaccinate our citizens and expand on remote care solutions such as telemedicine and consumer wearables, the need to share data between emerging and traditional healthcare companies will be more pressing than ever. i think we will see consumer device data welcomed by practice management systems either directly or through third party technologies adept at healthcare transaction management. the winners will make it easy for legacy ehrs to absorb remote patient vitals and other biometrics. we will also see data-savvy wearable and other patient care applications deliver practice information back to the patient via their device. and, as pharmacies take on vaccination delivery, the long-desired link between practice and pharmacy will finally become a priority for technology teams on both sides. all parties will want immediacy of information to coordinate across an increasingly complex care ecosystem; however, unlike 2020 where hipaa requirements were loosened to ensure rapid access to needed information, in 2021, the data collaboration will come with the necessary requirement of privacy protection. collaborating companies will need a trusted, current, and shared perspective on patient authorization; and home grown identity and consent systems will not scale to meet this demand. blockchain-enabled solutions that can provide identity, consent, and transaction authorization services will become invaluable outsourcing choices to private and public entities looking to continue the pace of cross-system patient care. mercury fox in the 21st century, there will be a demand for interdisciplinary expertise on a global scale in scientific research and discovery. despite deep investments in interdisciplinary research, the covid pandemic has thrown into sharp relief the gap between the technologies, venues, and policies that facilitate collaborative research, and the norms and practices that drive collaboration. open science blockchain platforms will support the “collaboratory cultures” framework, which defines, measures, tests, and produces evidence-based best practices in cross-domain research. specifically, these distributed http://dx.doi.org/10.30953/bhty.v4.162 citation: blockchain in healthcare today 2021, 4: 162 http://dx.doi.org/10.30953/bhty.v4.162 3 (page number not for citation purpose) blockchain predictions ledger technology (dlt) platforms will facilitate a transformative approach to scientific collaboration by leveraging big data assets and analytics to open existing research pipelines; advancing findability, accessibility, interoperability and reusability (fair) principles by making research artifacts findable: accessible, and re-usable in real time, thus, protecting intellectual property via immutable records and providing the reflexive framework to actively engage ethical and responsible research. this use case for blockchain research platforms as an enabling technology will advance the effectiveness of international research collaboration in the open science ecosystem and build on collaboratory theories of knowledge production, as well as advance open science best practices, which continue to privilege existing research infrastructures over the under-represented research communities they were meant to benefit. the blockchain technology will help researchers in future to realize the open science and data-sharing goals that we have set forth today. sweta sneha the health care space in the united states is humungous, critical in nature, and one of the avenues that invites massive investment toward the associated technology and learning. while there was incredulity initially around the proven results of piloting blockchain in health care, investing in this technology seems to be the popular choice. many prominent organizations are investing in the blockchain technology with an intent to solve technology, interoperability and data-related issues. it is the day and age where patients realize the importance of having control over their own health care data. in an environment where such sensitive data are of paramount importance, there is a definite need for appropriate checks, enhanced security, and augmented audit capabilities. there is a continued increase in the need for such a technology in health care facilities, given the focus on interoperability and sharing of medical records or information. from the cryptographic hashtags to the inherent layered security, the blockchain technology could potentially be a solution to the problems that have been lingering around the health care space for the last two decades. in the coming few years, it might be a solution that a complete generation believes in and could continue to be the choice for future generations. automating many administrative tasks and speeding up the care process has been one of the pivotal objectives of health care internet technology. the distributed ledger and smart contract abilities, if implemented with utmost care, can quicken the care process and improve the overall health outcomes. this can further be linked to track pharmacy and medical supply chains from the origin to destination with zero data compromise. while the possibilities can be endless, the real-life impact hinges on a thorough impact analysis and prudent implementation. wendy charles in the next year, i predict there will be a greater emphasis on virtual health care and research, which requires non-traditional methods for health care organizations to access and share their health information. i also predict there will be more use and acceptance of consumer-grade sensors and wearable data in home-based health care monitoring and health-related research. as virtual health care and research involves web-based interactions, this has forced a progressive narrowing of the ‘digital divide’. smart phones and internet-connected e-readers are less expensive, while cellular reach and broadband are more widely available. to achieve the necessary infrastructure to integrate and secure these diverse sources of electronic health information, i am encouraged to see that health care organizations are increasingly receptive to blockchain. blockchain-based dynamic consent could be an integral part of managing this access and sharing. jim nasr i have three predictions for 2021. prediction 1: innovative health care developers will bundle in skinnier blockchain functionality as a underlayer of their applications. a likely example would be ‘proof of action’ functionality such as proof-of-data exchange or proof-of-data authenticity – the use of blockchain primarily as an immutable ledger to prove and report on things happening as expected and compliantly. prediction 2: health care will demand objective return on investment (roi) from blockchain investments, leading to rethinking of how the blockchain technology can be adopted more practically and in smaller, more incremental parts. smart developers will use this as an opportunity to implement ‘blockchain as an abstraction’, where some blockchain benefits can be passed on to clients in a low friction ‘abstracted’ way with a simple adoption model that does not necessitate users or organizations to change their existing usability or application architecture. prediction 3: consumers will be much more aware of #fakenews in the context of health care and will demand for greater transparency of health data, starting with the distribution and availability of covid-19 vaccines. blockchain can be the backbone of trusted data flow among the many players involved in the covid-19 vaccination data flow, and it can be used to offer a public, frequently updated, and computationally trusted mechanism to show the supply and demand flow for covid-19 vaccination. http://dx.doi.org/10.30953/bhty.v4.162 citation: blockchain in healthcare today 2021, 4: 162 http://dx.doi.org/10.30953/bhty.v4.1624 (page number not for citation purpose) prasad kothari et al. radhika iyengar • ai-based diagnostics, nanotechnology, and three-dimensional (3d)-printed prescription medicines • continuous remote patient monitoring, digital therapeutics, and robotic surgery • star trekkian medicine of the future, with dramatically improved health outcomes. are we there yet? health care and medicine are becoming increasingly digital. sophisticated digital enablement tools and internet of medical things (iomt) are helping us realize highly innovative clinical care – one that we dreamed of but seemed elusive. the great inflection point of 2020 has been covid19, which is driving a new acceptance of technology in the health care sector. for example, the decades-old telehealth movement has finally achieved product-market fit to serve the urgent need for mass remote health care. nevertheless, the pandemic has also underscored the failings of our health care systems and exposed our inability to equitably and better serve the health needs of a global population. one of the biggest failures is around health data. with digital enablement, digital identity, and a data-driven medicine, there is an explosion of data that is left insecure and exposed. furthermore, these disparate data are still siloed, and true interoperability has not yet been achieved. what we expect to see in 2021 and beyond is the ability to secure the whole data story. blockchain-based systems provide the necessary infrastructure to secure edge device data. these systems can also drive better data shareability and interoperability – potentially leading to more diverse and inclusive ai applications in medicine. in future, health care and medicine will deliver better health outcomes powered by the convergence of multiple advanced technologies – iomt, ai, blockchain, 5g connectivity and more. we are finally getting there. conflict of interest and funding there are no conflicts of interest. the authors received no funding for this project. authors’ contributions each author contributed their section. http://dx.doi.org/10.30953/bhty.v4.162 1 (page number not for citation purpose) original research improving transitions of care: designing a blockchain application for patient identity management mustafa abdul-moheeth, md 1; muhammad usman, ms 2; daniel toshio harrell, phd 1; and anjum khurshid, md, phd 1 1dell medical school, university of texas at austin, usa; 2dept of electrical and computer engineering, university of texas at austin, usa corresponding author: anjum khurshid, email: anjum.khurshid@austin.utexas.edu keywords: blockchain technology, decentralized identifiers, interoperability, transition of care abstract background: the current healthcare ecosystem in the united states is plagued by inefficiencies in transitions of patient care between healthcare providers due in large part to a lack of interoperability among the many electronic medical record (emr) systems that exist today. both providers and patients experience significant frustration due to the negative effects of increased costs, unnecessary administrative burden, and duplication of services that occur because of data fragmentation in the system. blockchain technology provides a potential solution to mitigate or eliminate these gaps by allowing for exchange of healthcare information that is distributed, auditable, immutable, and respectful of patient autonomy. our multidisciplinary team identified key tasks required for a transition of care to design and develop a blockchain application, medilinker, which served as a patient-centric identity management system to address issues of data fragmentation ultimately aiding in the delivery of high-value care services. methods: the medilinker application was evaluated for its ability to accomplish various key tasks needed for a successful transition of patient care in an outpatient setting. our team created 20 unique patient use cases covering a diversity of medical needs and social circumstances that were played out by participants who were asked to perform various tasks as they received case across a simulated healthcare ecosystem composed of four clinics, a research institution, and other ancillary public services. tasks included, but were not limited to, clinic enrollment, verification of identity, medication reconciliation, sharing insurance and billing information, and updating demographic information. with this iteration of medilinker, we specifically focused on the functionality of digital guardianship and patient revocation of healthcare information. in addition, throughout the simulation, we surveyed participant perceptions regarding the use of medilinker and blockchain technology to better ascertain comfortability and usability of the application. results: quantitative evaluation of simulation results revealed that medilinker was able to successfully accomplish all seven clinical scenarios tested across the 20 patient use cases. medilinker successfully achieved its goal of patient-centered interoperability as participants transitioned their simulated healthcare data, including covid19 vaccination status and current medications, across the four clinic sites and research institution. in addition to completing all key tasks designated, all eligible participants were able to enroll with and subsequently revoke data access with our simulated research site. medilinker had a low data-entry error rate, with most errors occurring due to work-flow vulnerabilities. our qualitative analysis of user perceptions indicated that comfortability and trust with blockchain technology, such as medilinker, grew with increased education and exposure to such technology. conclusions: the ubiquitous problem of data fragmentation in our current healthcare ecosystem has placed considerable strain on providers and patients alike. blockchain applications for health identity management, such as medilinker, provide a viable solution to stem the inefficiencies that exist today. the interoperability that medilinker provided across our simulated healthcare system has the potential to improve transitions of care by sharing key aspects of healthcare information in a timely, secure, and patent-centric fashion allowing for the delivery of consistent and personalized high value care. blockchain technologies appear to face similar challenges to widespread adoption as other novel interventions, namely recognition, trust, and usability. further development and scaling are required for such technology to realize its full potential in the real world and transform the practice of modern health care. received: december 15, 2021; revised: december 21, 2021; accepted: january 31, 2021; published: march 14, 2022 https://orcid.org/0000-0002-0230-0950 https://orcid.org/0000-0001-5788-5784 https://orcid.org/0000-0002-8946-0622 https://orcid.org/0000-0002-8946-0622 mailto:anjum.khurshid@austin.utexas.edu citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.2002 (page number not for citation purpose) mustafa abdul-moheeth et al. introduction transitions of patient care between healthcare providers in the united states have long been hindered by a lack of interoperability among the many different electronic medical record (emr) systems that are in use today. this lack of interoperability creates data silos that inhibit the efficient transfer of patient’s health information, leading to increased costs, unnecessary administrative burden, duplication of services, and increased frustration among patients and healthcare providers.1 in fact, this siloing of healthcare information likely contributes to inaccurate or incomplete transitions of care, which ultimately may result in adverse patient outcomes, especially in the setting of hospital discharges where accepting providers may be seeing complex patients for the first time.2 although the 21st century cures act (cures act) that was signed into law in 2016 made it mandatory for the federal government to aid in making the transfer of patient data faster and more efficient, it lacked provisions to mandate interoperability, leading to the current manifestation of emrs that lack interoperability, often by design.3 in the complex healthcare ecosystem that exists today, transitions of patient care are a critical leverage point from which larger changes can be enacted if the structure of information flow is fundamentally changed from the status quo. significant effort has been made to tackle issues of healthcare fragmentation over the past half-decade, with many endeavors focusing on the flow of healthcare information. effective communication between providers who are taking care of a mutual patient is key in ensuring that the patient’s care is transitioned comprehensively across the healthcare landscape. it has been shown that poor quality communication during the discharge period was identified as a major barrier to safe and effective transitions.4 on the contrary, the implementation of interprofessional transitions of care programs has been shown to significantly reduce hospital readmission rates.5 safe and effective transitions of care are critical in not only avoiding adverse patient outcomes but also ensuring the cost-effectiveness of healthcare delivery. with the aging population of the united states who utilize increasing amount of healthcare resources, the lack of interoperability among healthcare providers is becoming increasingly apparent.6 combined with the advent of online patient portals and easier access to healthcare information, there has been a growing demand for individual patients to be in control of their own data.7 however, to date, there have been no widespread digital options for such a tool, and in fact, many patients carry hard copies of their healthcare information across providers to manage their own transitions of care. these workarounds provide an opportunity to improve value in the delivery of healthcare services. a widely accepted framework for improving value in health care is encompassed by the institute for healthcare improvement’s triple aim, which is comprised of better patient outcomes, improved patient satisfaction, and lower costs.8 over the past decade, there has been an increased push to pivot toward high-value health care models and to disseminate teaching strategies aimed at increasing high-value practices, but widespread adoption is lacking in the united states.9–11 blockchain technology has the potential to deliver highvalue healthcare by providing patients with control over their healthcare data while improving existing problems with interoperability. blockchain is uniquely positioned to operate in the healthcare data environment due to its decentralized, auditable, and immutable nature that not only protects patient’s confidentiality but also enhances the security of data across transitions of care. using decentralized identification (did), healthcare providers and patients can directly interact with one another in a secure fashion, allowing for the development of meaningful patient–provider relationships.12 once sufficiently developed, blockchain applications may be able to integrate with the existing healthcare infrastructure to bridge the interoperability gap.12,13 our interdisciplinary team has previously described the development and testing of a prototype patient-centric blockchain identity management system, called medilinker, to better understand the utility and viability of blockchain technology for healthcare applications. in our prior work, we created 15 use case scenarios and applied the theoretical framework developed by bouras et al. to test the effectiveness of medilinker in fulfilling the criteria of identity management.14,15 we were able to demonstrate proof of identity and consent for sharing of personal health data during testing, although we did not evaluate user perceptions at that time. in this article, we describe the testing of the second iteration of medilinker,16 which was redeveloped as a robust custom-built ios application to further expand on use case testing of interoperability, patient guardianship, as well as further development of patient data consenting, sharing, and revocation. methods this study examined the effectiveness of medilinker in accomplishing various key tasks needed for a successful transition of care that generates value in an outpatient setting. twenty study participants, each using unique simulated patient data, were asked to perform various tasks as they received simulated care across a simulated healthcare ecosystem consisting of four different clinics and a research enrollment center. tasks included, but were not limited to, clinic enrollment, verification of identity, medication reconciliation, sharing insurance and billing information, and updating demographic information. in addition, we examined how the unique patient-centric http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.200 3 (page number not for citation purpose) improving transitions of care: designing a blockchain application for patient identity management nature of blockchain-based technology would interact with the current healthcare ecosystem; specifically, how the patient’s ability to revoke or partially share individual aspects of his or her healthcare data would affect delivery and transitions of care. medilinker system medilinker is a blockchain-based decentralized identity management solution,14 which provides patients autonomy in managing their self-sovereign identity and medical information. it is designed by keeping seven scenarios in mind. patients can securely log into the web and/or mobile application, and can enroll at a participating clinic. patients will show their physical id card to the receptionist who will then enroll the patient and issue a digital identity on the blockchain. patients can then use this digital identity (without the need for the physical id card) to verify their identity at other participating clinics. medilinker acts as a federated system of connectivity (medical data still reside in the databases of the clinics) between multiple providers. medilinker facilitates interoperability by allowing patients to share their medical data across multiple providers. patients can also modify their information and consent to participate in research projects. patients also have the option to revoke already shared information and/or consent. revocation is different from deletion in that it does not delete the data from an institution but the desire of the patient to deny future usage of shared data is recorded on the blockchain. the shared data are then no longer verifiable. medilinker also gives the option to have a medical power of attorney (mpoa) by which a guardian can be appointed for a patient. the guardian can then act on behalf of the guarded patient and perform the required sharing of credentials. to summarize, medilinker enables the following seven scenarios. 1. initial enrollment at first clinic and creating validated credentials, 2. enrollment at second clinic with only credentials, 3. presenting/consenting personal/medical data with clinics, 4. patient changing personal information on blockchain wallet and validating the modification, 5. patient consent to participate in research projects, 6. patient removing full or partial consent with clinics with credential revocation, 7. mpoa/digital guardianship for geriatric and pediatric patients. medilinker is a digital wallet that can handle six different types of credentials, that is, health id, insurance, medication, credit card, research consent, and mpoa. health id credential contains profile information about the patient. insurance credential includes insurance information of the patient. medication credential includes information about covid-19 vaccination status, medications, and their corresponding dosages of the patient. credit card credential includes information about the patient’s payment method. research consent credential is the consent issued by the patient for participation in a research project. mpoa credential is a mpoa that can be issued to a guardian to act on behalf of the guarded patient. we established an ecosystem of trust that consists of four virtual clinics (‘community clinic’, ‘acute care clinic’, ‘psychology clinic’, and ‘rehabilitation clinic’), bank, insurance company, and a research institution (‘austin community health research center’). within this network, clinics issued health id and medication credentials based on persona’s physical driver’s license and medication prescriptions. an insurance company issued insurance cards, and credit cards are issued by the bank. research institutions received consent credential from participants (figure 1a). mpoa credentials are issued by a notary (figure 1b). these institutions formed a trust network in which participants as holders could share their synthetic data. twenty study clinical use cases this study expanded upon previous work with medilinker14 to better simulate healthcare delivery using 20 unique use case avatars that represented a wide range of patient demographics. we expanded our testing with patient avatars focusing on guardianship and the unique challenges faced by a covid-19 diagnosis. this was in addition to our previous work that captured the experience of individuals on housing insecurity, substance use, and undocumented status. as shown in table 1, the 20 use cases were composed of 9 male and 11 female cases. due to technical limitations with the current iteration of medilinker, additional gen der identification options were not available. five of these cases focused on guardianship and involved the testing of a mpoa function in the setting of both geriatric and pediatric patients. two cases were covid-19 positive patients, and the other two cases focused on patients with mental health or substance use co-morbidities. using the medilinker ap plication and the patient avatars, we tested seven clinical sce narios as detailed above which involved both medical and non-medical facilities. all use cases tested fundamental tasks involving verification of identity, sharing health data, and revoking health data access. in addition, every use case in corporated the testing of research consent and enrollment at least once throughout the study period. simulation design participants interacted with the medilinker system at virtual clinics and shared their persona’s data. the simulated http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.2004 (page number not for citation purpose) mustafa abdul-moheeth et al. information, including health identity, insurance, medication, and credit card, were provided in a pre-study packet. participants, who were assigned as guardians, received their family member’s information and medical scenarios in their pre-study packets. the participants were instructed not to input any personal information into the system. each persona was assigned randomly and had a unique role that tested a specific part within the medilinker system, testing functionality and interoperability. the study consisted of a single simulation over 4 weeks totaling eight interactions with 20 participants. each transaction within the trust network was conducted over zoom due to the covid-19 pandemic. each week, the research participants followed detailed instructions. these instructions included persona demographics or clinical updates, information sharing within and between the four fictional clinics, revoking information, and consenting to research studies. during the first 2 weeks, the participants enrolled at multiple clinics and allowed sharing of their data. in the third and fourth weeks, patients responded to simulated data breaches at clinics and unwanted data requests from institutions to test the medilinker system. for example, participants were presented the following scenario and instruction: ‘you were alerted by email today that the clinic had a breach and your information may be exposed. you may revoke your information from this clinic’. during the last week, the research institutions sought to make a research cohort based upon persona’s demographics and medical history, while participants were able to consent or revoke consent. research team members operated at each institution and interacted with the participants remotely over zoom. participant recruitment and cohort this study recruited 20 ut students to act as personas and simulate our healthcare identification management platform. no health insurance portability and accountability act (hippa) or family educational rights and privacy act (ferpa) data were used during this study. these students were recruited via listserv emails to undergraduate and graduate students from the university of texas at austin’ dell medical school, health leadership apprenticeship program, college of engineering, college of natural science, and school of information. students received compensation of $120.00 each. in a pre-study survey, our participant cohort of students expressed familiarity with technology such as smartphones and had experience using one portal, mobile application, or website to share medical information. eight of 16 respondents reported a nervous feeling about fig. 1. medilinker network of trust and credentials. (a) medilinker framework creates a network of trust in which verifiable credentials [health id, medication list, insurance card, credit card, and medical power of attorney (mpoa)] are issued by participating institutions, including clinics, insurance, and bank. once a credential is issued, users can share these verifiable credentials within the medilinker application to other participating institutions. research institutions received consent credential from participants. (b) mpoa credentials issued by a notary organization. table 1. use case composition and characteristics use case number of cases geriatric patients with a medical power of attorney 1 pediatric patients with a medical power of attorney 4 undocumented immigrant 1 patients with sensitive health information including mental health 2 patients experiencing homelessness 2 patients quarantining due to covid19 2 patients without complicating factors 8 http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.200 5 (page number not for citation purpose) improving transitions of care: designing a blockchain application for patient identity management security of their medical data online. furthermore, 8 of 16 respondents had personally or their relative/friend experienced a type of data breach. (appendix a: weekly medilinker usability survey questions) assessing feasibility (scenario handled, accuracy, interoperability, survey results) we assessed medilinker’s feasibility as an electronic verifiable healthcare identification management system using both quantitative and qualitative measures. the feasibility was evaluated quantitatively with the following measures: (i) to what extent are participants able to setup a validated profile and determine the accuracy of data entered and (ii) to what extent are participants able to share their profile with multiple healthcare entities. in addition, we evaluated the percentage of patients who entered data correctly compared with expected values as specified in their instructions. interoperability was assessed by recording the distribution of each participant’s data across the five institutions. to evaluate the system’s usability qualitatively, mini-weekly surveys were designed to provide insights with likert scores (“very easy,” “easy,” “neutral,” “difficult,” “very difficult”). (appendix a: weekly medilinker usability survey questions). survey questions were distributed weekly before the start of the study and at the conclusion of each week’s activities. the survey results mentioned in this article are part of a larger study, including other survey questions and log data. ethical considerations all medical data entered into medilinker in this study were from simulated patient identities, which minimized the risk of revealing the participants’ private information. participants’ real private information was not used in the analysis; hence, the institutional review board determined this study to be nonhuman subjects. results we quantitatively evaluated application functionality and participant ability to complete these tasks successfully and examined associated errors that occurred during the study period. once completed, we qualitatively assessed participant views regarding medilinker usability, trust, and perceived effectiveness. we found that all 20 participants were able to complete the key tasks designated and successfully transition medical care across the four clinic groups involved. in addition, all eligible participants were able to enroll in and subsequently revoke data access with our simulated research enrollment center. similarly, key healthcare information such as covid-19 vaccination status and current medications was successfully shared across clinics. most errors were due to inaccurate data entry because of work-flow vulnerabilities and were corrected upon subsequent review. medilinker accomplished seven real-world clinical scenarios and provided patient-centered interoperability between virtual clinics throughout the simulation, all 20 participants were able to accomplish all seven scenarios as described above using the medilinker application. throughout the study period, 20 participants interacted with four clinical sites, as well as a research enrollment center. as detailed above, each clinical scenario was successfully completed, indicating that the participants were able to share and modify their healthcare information via medilinker across all the sites tested, which demonstrated full interoperability within the virtual healthcare ecosystem that was created for this simulation. in addition to healthcare data being shared among the participating clinical sites, all participants successfully enrolled in research studies and were able to transfer their information seamlessly. similarly, all participants successfully demonstrated revocation of data at least once throughout the study period demonstrated by the fact that revoked data were not propagated nor shared with new organizations. accuracy of data entry in medilinker the number of data-entry errors made by patients is shown in medilinker, as listed in table 1. the credential type column lists the type of credential. the number of patients column lists the total number of patients with the respective credential and the number of attributes column lists the number of attributes for each credential. number of data entries shows the total entries made across all patients, and the number of incorrect data entries column lists the total number of mistakes made by patients for each credential. the error rate column shows the error rate for each credential, and the accuracy column shows the accuracy with respect to each credential: number of data entries = number of patients × number of attributes 100error rate number of incorrect data entries number of data entries = × 100.accuracy number of correct data entries number of data entries = × all 20 enrolled participants completed the study. we calculated data-entry errors made by participants in completing the tasks assigned to them using medilinker. health id credential had 12 attributes, and a total of 240 individual entries were checked for accuracy. we found that there were seven incorrect entries, with a 97.08% accuracy for health id credential. insurance credential had nine attributes, and a total of 180 individual entries were checked for accuracy. we found that there were four http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.2006 (page number not for citation purpose) mustafa abdul-moheeth et al. incorrect entries, with a 97.78% accuracy for insurance credential. medication’s credential had 10 attributes and a total of 200 individual entries were checked for accuracy. we found that there were five incorrect entries, with a 97.50% accuracy for the medication’s credential. credit card credential had four attributes and a total of 80 individual entries were checked for accuracy. we found that there were two incorrect entries, with a 97.50% accuracy for credit card credential. although we did not get 100% accuracy for any credential, these results are much better than that in phase 1 of the simulation study.14 all errors occurred during data entry and verification, which were categorized as workflow vulnerability rather than an error with medilinker. table 2 presents details of all data-entry errors made by patients. the credential type column lists the credential name, and the attributes column lists the corresponding attributes for each credential. the expected column shows the correct information, and the actual column shows the information entered by the patient. health id credential had the most errors, which may be because it had the most attributes. five patients made one or more mistakes in entering their profile information. one patient entered an incorrect city, while another patient made a mistake in entering both his/her phone number and date of birth. one patient made a spelling mistake in his/her last name, while another incorrectly entered his/her zip code. another patient entered his/her gender incorrectly and made a mistake in entering his/her zip code. four patients made mistakes in entering their insurance information. one patient entered an incorrect expiration date for their insurance card. one patient made a spelling mistake in the insurance provider’s name. one patient made a mistake in entering his/her copay information, while another made a mistake in entering his/ her deductible information. four patients made mistakes in entering their medication information. the medication table 3. data-entry errors found in medilinker credential type attributes expected actual health id credential city san antonio austin phone 5123569231 5123599231 dob 04-10-1976 04-13-2021 zip code 78710 78720 last name antonov autonov gender female male zip code 78751 78717 insurance credential provider bcbs bcs copay 100 75 expiry date 4-21-21 4-23-21 deductible 200 250 medication’s credential dosage apply twice daily apply once daily dosage 10 units 5 units dosage once as needed dosage 16 8 dosage apply topically as needed credit card credential last name choudhury choudhurry number 5085580575754848 5058580575754840 table 2. accuracy of data entry in medilinker credential type number of patients number of attributes number of data entries number of incorrect data entries error rate accuracy (%) health id credential 20 12 240 7 7 240 100 2.92%∗ = 97.08 insurance credential 20 9 180 4 4 180 100 2.22%∗ = 97.78 medication’s credential 20 10 200 5 5 200 100 2.50%∗ = 97.50 credit card credential 20 4 80 2 2 80 100 2.50%∗ = 97.50 http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.200 7 (page number not for citation purpose) improving transitions of care: designing a blockchain application for patient identity management names were always entered correctly. three patients entered one (of the five) medication dosages incorrectly, while another patient entered two (of the five) medication dosages incorrectly. two patients made mistakes in entering their credit card information. one patient made a spelling mistake in his/her last name, while another patient entered an incorrect credit card number. participants reported positive perception of medilinker’s usability our study results have shown that our 20 participants using the medilinker ios application were able to successfully complete the seven clinical use cases and managed their medical data. in addition, throughout the study, the participants reported in weekly surveys (appendix a: weekly medilinker usability survey questions) an overall positive user experience with the majority selecting ‘very easy’ or ‘easy’ for each task (table 3). of the seven use cases, the initial setup of medilinker accounts and credentials including connecting with clinic, creating credentials, and sharing data with first clinic showed most difficulty, with 29.41, 35.29, and 23.53% (n = 17), respectively, reporting a neutral or negative experience. one of the participants reported the need for biometrics on their ios device during medilinker setup caused delays and user hesitancy. after the initial setup, participants reported a ‘very easy’ or ‘easy’ user experience ranging from 85.72 to 100% for each activity. discussion in this second iteration of medilinker, we further expand on previous work demonstrating the utility of blockchain-based patient-centric identity management applications in a healthcare environment.14 our use cases simulated a wide range of patient demographics to emulate the interactions that occur in a real-world healthcare ecosystem. we demonstrated again the ability of medilinker to share dynamically updated healthcare data elements across multiple providers. the results of this study further support the potential use of blockchain applications in improving interoperability once integrated with the existing healthcare infrastructure while maintaining patient-centric data control. improving interoperability aids in the delivery of high-value care blockchain applications for health identity management, as observed with medilinker, provide a viable solution to the data siloing that exists due to our fragmented healthcare ecosystem. both patients and providers have long been frustrated by the inefficiencies that stem from operating in such an environment. without the ability to control their own data, patients must rely on healthcare administrational staff to share relevant information with providers who do not have access to specific emrs. this process of sharing information is usually not optimized, and many times results in provider offices printing out substantial portions of the emr to send over to the requesting party, diluting relevant information, and further scattering the patient’s pertinent healthcare data.17 patients have derived their own workarounds to mitigate this problem by carrying around their own records in paper or digital formats, which are also likely not compatible with the provider’s emr. although this methodology may provide more relevant information, this leads to table 4. participant perception of the medilinker system medilinker use case medilinker task very easy n (%) easy n (%) neutral n (%) difficult n (%) very difficult n (%) setup medilinker account at first clinic (n = 17) connecting with clinic 5 (29.41) 7 (41.18) 5 (29.41) 0 (0.00) 0 (0.00) creating credentials 4 (23.53) 7 (41.18) 5 (29.41) 1 (5.88) 0 (0.00) sharing data with first clinic 5 (29.41) 8 (47.06) 3 (17.65) 1 (5.88) 0 (0.00) sharing data with second clinic (n = 16) sharing health id 8 (50.00) 8 (50.00) 0 (0.00) 0 (0.00) 0 (0.00) sharing medication list 9 (56.25) 7 (43.75) 0 (0.00) 0 (0.00) 0 (0.00) sharing insurance card 9 (56.25) 7 (43.75) 0 (0.00) 0 (0.00) 0 (0.00) sharing credit card 8 (50.00) 8 (50.00) 0 (0.00) 0 (0.00) 0 (0.00) updating credentials (n = 16) updating health id 6 (40.00) 8 (53.33) 1 (6.67) 0 (0.00) 0 (0.00) updating medication list 9 (56.25) 7 (43.75) 0 (0.00) 0 (0.00) 0 (0.00) updating insurance card 8 (50.00) 8 (50.00) 0 (0.00) 0 (0.00) 0 (0.00) updating credit card 8 (53.33) 7 (46.67) 0 (0.00) 0 (0.00) 0 (0.00) creating and managing research consent (n = 7) creating research consent credential 3 (42.86) 4 (57.10) 0 (0.00) 0 (0.00) 0 (0.00) sharing medical data with research institution 3 (42.86) 4 (57.10) 0 (0.00) 0 (0.00) 0 (0.00) revoking credentials (n = 7) patient removing full or partial consent with clinics with credential revocation 3 (42.86) 3 (42.86) 0 (0.00) 1 (14.29) 0 (0.00) mpoa/digital guardianship (n = 2) switching between guardian and dependent wallets 0 (0.00) 2 (100.00) 0 (0.00) 0 (0.00) 0 (0.00) http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.2008 (page number not for citation purpose) mustafa abdul-moheeth et al. additional administrative burden as these outside records require sorting and then eventual incorporation into the native emr, usually in the form of scanned documents that are not easily searchable. while improving interoperability addresses some of the systemic faults of the modern-day healthcare ecosystem, understanding the social dynamics of health care is also critical in the pursuit of delivering high-value care services. patients who rely on a caregiver, such as pediatric and geriatric patients, depend on providers who give timely access to their records so that the caregiver can stay up to date on clinical instructions, medication regimens, and follow-up appointments. traditionally, caregivers usually accompany patients to their provider visits and are authorized to manage the patient’s care, either verbally or with formal documentation. however, this relationship is not necessarily shared across providers, which further impedes a proper transition of care. blockchain technology can be used to securely communicate these relationships across a network of providers to ensure smooth transitions of care. the current inefficiencies of data propagation during transfers of care likely contribute to a poor patient experience, especially in these vulnerable populations like the elderly who expect ongoing and tailored communication with providers regarding their care.18 providing patients the ability to share their own data with multiple providers may significantly improve the lack of care coordination that has developed due to siloed emr systems.19 an additional benefit of providing patients with easier access to their healthcare data is that they may feel more invested in their health, with studies showing that patient participation may depend on being invited to plan the care transition.20 taken together, blockchain technologies can aid a patient or a trusted caregiver to access and share pertinent medical information confidentially, while providing avenues to assist with transitions of care in an otherwise fragmented healthcare ecosystem for the delivery of consistent high-value care. in addition to providing a platform for enhanced patient-centric care, blockchain technology has the potential to create increased value and interoperability in the processes of many adjacent healthcare-related fields. other healthcare use cases that have been studied include management of healthcare provider accreditation,21 clinical trials,22 and supply chains.23 when examining management of patient health information specifically, many proposed methods are still in their infancy when compared with the existing healthcare infrastructure.24 compared with medilinker, many blockchain-based patient identity or data managing systems are undergoing prototyping to develop the fundamental infrastructure needed to integrate information across a wide range of stakeholders in health care. while the specific approaches may differ, comparable work in developing patient-centric blockchain-based healthcare information systems are guided by the same principles of interoperability, trust, and patient autonomy.25 existing issues that are currently being addressed by many blockchain solutions include, but are not limited to, challenges with integration, mainstream adoption, economic factors, ethical regulations, and scalability.12 improving interoperability remains a central focus for many blockchain solutions as it may generate value by avoiding the same limitations noted in our current healthcare ecosystem. blockchain technology allows for personalized healthcare delivery in an increasingly data-driven society, there is more demand for the delivery of personalized healthcare services that integrate information from all aspects of a patient’s life. many patients now expect their providers to comment on information that is obtained outside of the clinic from their smart devices, such as vital signs, sleeping patterns, electrocardiography tracings and other detailed medical data. this only exacerbates the current problems with interoperability as each of these devices has their own, usually inadequately secured, method of data storage and sharing. because of this, many providers opt not to incorporate such data into their clinical practice, which limits the potential information that could contribute to the overall health of the patient. blockchain technology has been proposed to manage protected health information (phi) generated by such smart devices.26 from the results of this study, it would be conceptually possible to integrate this phi into the wider healthcare ecosystem, thus bridging the interoperability gap for information that may come from nontraditional sources. user perceptions of blockchain technology although the widespread use of blockchain technologies would be profound, any new technology will have to overcome the issues of user adoption and ultimately develop trust among the user base to be successful. blockchain, in a broader sense, has had a mixed public reaction with many being introduced to the term in the context of a hyped news cycle without understanding the significance of the underlying technology, leading to potential disillusionment.27 we surveyed perceptions about blockchain and online medical data security and examined how they evolved with our study participant group. it was not surprising that less than a third of our participants initially felt comfortable with the security of medical data online, as about half of them indicated that they or someone they knew had experienced a data breach. however, after completion of our study and interim education about blockchain technology with exposure to the medilinker platform, none of the participants indicated that they felt nervous about the security of medical data online. in fact, a large majority of the participants felt more in control http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.200 9 (page number not for citation purpose) improving transitions of care: designing a blockchain application for patient identity management of their medical data using medilinker, indicating that perhaps increased exposure to blockchain technologies and education about their functions may improve public perceptions and acceptance of such novel technology. data entry changes lead to accuracy improvements this second iteration of medilinker showed an increased accuracy from prior versions due to additional workflow changes and data field verification measures. in our prior iteration, the participants who performed the use cases were tasked with completing data entry and subsequent updates, which resulted in degradation of data accuracy over the study period.14 with this latest iteration, we changed the process, such that only trained staff would input the pertinent data, with patient verification once the data were entered. in addition, our data-entry fields were updated to have preset options as well as limiting inappropriate inputs, such as allowing only 16 numerical digits in credit card fields and preventing digits to be typed into name fields, respectively. after these changes, our accuracy throughout the study period remained greater than 97%. our study results showed that trained staff inputting data may lead to higher accuracy when compared with having patient data entry alone. additional work remains to improve the efficiency of this process by mitigating sources of error, which in our case were due to work-flow vulnerabilities. future work in this area could examine the use of image recognition to pull data directly from hard copy or scanned documents, as well as integrating outside information sources with patient authorization. additional errors could be avoided by having multiple steps of verification across the data-entry process, although this may impede efficiency depending on how it is implemented. altogether, our work provides some fundamental and practical approaches to improving data accuracy for patient identity management. study limitations one of the major limitations of this study was regarding the education level of the individuals who participated. all participants had at least an undergraduate level of education and were familiar with the use of mobile applications, which may have allowed for easier use of the medilinker platform. in addition, it was difficult to emulate all aspects of the patient use cases, including all the challenges associated with unstable housing, given the demographics of our participants. we had limited resources regarding the number of simulations that could be run simultaneously, thus restricting our ability to evaluate scalability. similarly, synthetic medical data were used as opposed to real patient data, which did not encompass all data elements of what would be contained in a patient’s medical record. examples of data elements that were not tested in the use cases include radiographical findings, provider documentation, and vital signs. although we expanded on the types of uses cases tested, additional patient scenarios still exist, which we did not test. future research this second iteration of medilinker once again demonstrates the utility of blockchain technologies for patient identity management and provides further evidence on how such tools can be used to improve transitions of care and interoperability in an otherwise fragmented healthcare ecosystem. additional study of usability, accuracy improvement, data management, and security are needed to fully characterize the utility of blockchain technologies in a live setting. similarly, testing a more detailed healthcare dataset is needed to examine how such data elements interact in the blockchain environment. these elements include data such as radiographic images and other large files that are currently stored and managed by the respective institutions who obtained or interpreted them. in addition to examining different data elements, it will be critical to examine potential methodologies and barriers for integration of blockchain technologies with existing emrs, as well as evaluate the various regulatory and legal circumstances surrounding the use of blockchain technology for healthcare data. assessing the scalability of this technology will be needed to create a foundation for the practical implementation of such tools. as mentioned earlier, understanding the social dynamics and user perceptions of blockchain technologies will be crucial for understanding how best to implement such novel tools in an industry that is usually reluctant to change. there appears to be significant differences in patients’ perceptions of healthcare information exchange mechanisms based on blockchain.28 additional detailed research studies surrounding patient trust and comfortability in all demographic groups are also needed to establish if such technology is ready for mainstream adoption. similarly, provider perspectives will be crucial in understanding how such technology will be used in both the inpatient hospitalized setting and the outpatient clinic setting. conclusions transitions of health care are challenging times for both patients and providers partly due to the lack of interoperability that exists in our current fragmented healthcare ecosystem. blockchain applications for health identity management, as observed with medilinker, provide a viable solution to the data siloing that is the root cause of such problems. by placing control of healthcare data in the hands of the patients in a secure and auditable manner, medilinker and similar tools have the potential to improve interoperability by allowing for the timely delivery of accurate healthcare information across a trusted healthcare network. in a healthcare system designed in part without the ability to communicate across providers, http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.20010 (page number not for citation purpose) mustafa abdul-moheeth et al. patient-mediated interoperability and data control may be the best solution for the delivery of consistent high value care. although the integration of blockchain applications with the existing healthcare infrastructure has immense potential, further development, research, and scaling of their use are needed for their benefits to be achieved in the real world. competing interests the authors declare no potential conflicts of interest. funding the authors thank the university of texas-blockchain initiative for providing partial funding for this work. contributors mustafa abdul-moheeth, muhammad usman, and daniel toshio harrell designed the feasibility study and clinical scenarios investigated. mustafa abdul-moheeth, muhammad usman, and daniel toshio harrell drafted the manuscript with revisions from anjum khurshid. acknowledgements the authors acknowledge ladd hanson, eliel de oliveira, john robert bautista and eric t meyer of the university of texas at austin for their guidance and support on the design of medilinker system and the feasibility study. the authors acknowledge ishav 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mirzaei t. the potential of blockchain technology for health information exchange: experimental study from patients’ perspectives. j med internet res. 2019;21(6):e14184. https://doi.org/10.2196/14184 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/ licenses/by-nc/4.0. http://dx.doi.org/10.30953/bhty.v5.200 https://doi.org/10.2196/18619 https://doi.org/10.2196/13598 https://doi.org/10.1007/s10916-018-0982-x https://doi.org/10.1007/s10916-018-0982-x https://doi.org/10.1016/j.elerap.2020.100935 https://doi.org/10.1016/j.elerap.2020.100935 https://doi.org/10.2196/14184 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.20012 (page number not for citation purpose) mustafa abdul-moheeth et al. appendix a: weekly medilinker usability survey questions pre-study survey please answer all of the following questions about the study you have agreed to participate in regards to health identity management. these questions relate to your experience of using the medilinker system. your responses will remain confidential. 1. how comfortable are you in using mobile phones to accomplish various tasks? 1 2 3 4 5 (very uncomfortable) (uncomfortable) (neutral) (comfortable) (very comfortable) 2. have you ever used any online portal, mobile application, or a website for sharing medical information? (yes) (no) (unsure) 3. how do you feel about the security of your medical data online? (choose one) 1 2 3 4 5 (very uncomfortable) (uncomfortable) (neutral) (comfortable) (very comfortable) 4. have you or someone from your relatives/friends ever had any type of data breach (e.g., hacking or unauthorized access? (yes) (no) (unsure) week 1: setting up medilinker accounts these questions relate to your experience using the medilinker system. your responses will remain confidential. 1. i found the process of connecting with the clinic to be ___________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 2. i found the process of getting credentials to be ___________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 3. i found the process of sharing data to be ___________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) week 2: sharing with another clinic and updating information these questions relate to your experience using the medilinker system. your responses will remain confidential. 1. i found the process of sharing health id with the second clinic to be ______________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 2. i found the process of sharing credit card with the second clinic to be ______________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 3. i found the process of sharing insurance card with the second clinic to be ______________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 4. i found the process of sharing medication list with the second clinic to be ______________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) http://dx.doi.org/10.30953/bhty.v5.200 citation: blockchain in healthcare today 2022, 5: 200 http://dx.doi.org/10.30953/bhty.v5.200 13 (page number not for citation purpose) improving transitions of care: designing a blockchain application for patient identity management 5. i found the process of updating health id to be ______________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 6. i found the process of updating medication list to be ______________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 7. i found the process of updating insurance card to be ______________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 8. i found the process of updating credit card to be ______________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) week 3: revoking information with another clinic these questions relate to your experience using the medilinker system. your responses will remain confidential. 1. i found the process of revoking access to my medilinker and medical data with another clinic to be __________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) week 4: managing consent for research and medical power of attorney (mpoa) these questions relate to your experience accepting and revoking consent for research studies with your medilinker system. your responses will remain confidential. 1. i found the process of creating the research consent credential to be __________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 2. i found the process of sharing the medical data with the research institution to be __________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 3. if applicable, i found the process of revoking the research consent credential to be __________. (choose one) 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) 4. for those who acted with a medical power of attorney (mpoa), how did you find switching b e t w e e n wallets? 1 2 3 4 5 (very difficult) (difficult) (neutral) (easy) (very easy) http://dx.doi.org/10.30953/bhty.v5.200 page 1 of 3 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v4.192 accelerating digital health trends and transformation through scientific communications tory cenaj authors: tory cenaj, owner and publisher of partners in digital health (pdh) corresponding author: tory cenaj, t.cenaj@partnersindigitalhealth.com section: editorial focus accelerating healthcare modernization, efficiency, and interoperability does not come without sacrifice. in this postcovid-19 era, we have experienced an aversion to change and the necessity to reach across cultural, geographic, and economic divides. in early september, president xi jinping called for china to achieve ‘common prosperity’ – a vision that seeks to narrow the wealth gap in urban and rural communities (1). we can apply this concept to the telehealth global divide across communities and geographies. ‘common prosperity’ is a concept that places china alongside nations where equity has become an international thematic and economic byproduct of covid-19. i propose to include health equity in this pursuit to minimize inequality between urban and rural areas, while strengthening social stability and making quality healthcare safely accessible. success in this regard will serve as evidence of ‘solid progress’ toward a global prosperity. this endeavor requires policy makers to drive policy changes for the benefit of citizens. examples include tax regulation, tech sector ethics, healthcare fraud, counterfeit drugs, price transparency, minimum standards for connectivity, cybersecurity, low-cost drugs and services, and increased digital literacy and education reform to improve a public service social safety net. it is clear, visionary leadership can design policies that create opportunities for equity, innovation, and scientific truth. these elements will bridge divides and provide the best care through common priorities that cross cultural and geographic barriers. sometimes answers can be found by sharing published, failed or negative results. yet, in some cultures, this a not acceptable. the scientific community tends to reward success. this cycle can and must be broken, as the current reward system in academia hampers progress, clogs innovation pipelines, and stymies innovation by early-stage career researchers. https://doi.org/10.30953/bhty.v4.192 mailto:t.cenaj@partnersindigitalhealth.com https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v4.192&domain=blockchainhealthcaretoday.com&date_stamp=2021-12-21 page 2 of 3 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v4.192 many groups argue that focusing on journal ‘brands’ intensifies competition between researchers and journals in ways that distort behavior and undermine a healthy and productive scholarly enterprise (2). as we know, a journal impact factor is a poor predictor of the number of citations a paper receives. our own bhty journal is a perfect example of this. blockchain in healthcare today has no journal impact factor rating (yet), but it does have an inordinate number of citations! shifting the emphasis from journals to articles is highly desirable and precisely how we at bhty approach the content (i.e. amplified on its own merit). i am certain our rigorous peer-review process has strengthened these highly cited articles, making them valuable contributions to the scientific community. the partners in digital health (pdh) portfolio is one that sets trends, is forward reaching, and followed by many. the role and impact of social media in scientific communications have been part of our strategy from the outset, knowing we would add a greater value to authors and their work while waiting on index application reviews that are often touted ‘black holes’, with wait times and resubmissions taking anywhere from 1 to 3 years. we must minimize gaps in scientific dissemination. this includes the volume of published research that stymies the pace and quality of published research. as stated by dr. johnan chu, northwestern university kellogg school of management, and james evans, professor, university of chicago, ‘the size of scientific fields may impede the rise of new ideas. when the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers, and newly published papers become unlikely to disrupt existing work (3).’ this suggests that progress of large scientific fields may be slowed. as a result, we should expect policy measures that shift how scientific work is produced, disseminated, consumed and rewarded to push fields into new areas of study. this is exactly what we experienced when we launched bhty. we created the niche, and provided the credibility and validation for it to grow and flourish as a new field in science and technology in health care. we must come to terms with ‘true innovation and creativity’ versus publishing in ‘top tier’ journals for more profit and production iteration. many novel innovations are going to ‘2nd tier’ and specialized journals (4). we experience this effect with our journals and happily publish landmark research. glenn bagley stated, sloppy science costs the us$28 billion a year in lost opportunity and non-reproducibility. we must give money to innovative scientists to keep the unites states at the forefront of discovery and innovation – a hot topic of late – with all the spigots open and flowing for grant opportunities. there is no room in our market, or nation, for complacency when new frameworks initiatives are advancing a revolution of borderless interoperable frontier tech led by consumers, activists, and pioneers who are ready to challenge market integrity and sensibilities. like our portfolio motto, i encourage us to ‘build trust through truth’. to view the complete conv2x 2021 symposium remarks, including predictions for what to expect from digital health, distributed ledger technology (dlt) and scientific communications markets, visit and follow partners in digital health on youtube here, or click the link https://doi.org/10.30953/bhty.v4.192 https://blockchainhealthcaretoday.com/index.php/journal/index https://blockchainhealthcaretoday.com/index.php/journal/index https://www.youtube.com/channel/uctyrpy0nghj8yqw56w4ywlq page 3 of 3 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v4.192 https://www.youtube.com/channel/ uctyrpy0nghj8yqw56w4ywlq tory cenaj, owner and publisher of partners in digital health (pdh). the views expressed are solely her own and do not reflect those of the editorial board, reviewers, or staff members affiliated with pdh. references 1. yao k. explainer: what is china’s ‘common prosperity’ drive and why does it matter? reuters. september 2, 2021. [cited 2021 nov 29]. available from: https://www. reuters.com/world/china/what-is-chinascommon-prosperity-drive-why-does-itmatter-2021-09-02/ 2. hatch a, patterson m. how journals and publishers can help to reform research assessment. sci ed 2019; 42: 41–3. [cited 2021 nov 29]. available from: https://www.csescienceeditor.org/article/ how-journals-and-publishers-can-help-toreform-research-assessment/ 3. chu jsg, evans ja. slowed canonical progress in large fields of science. pnas 2021; 118(41): e2021636118. https://doi. org/10.1073/pnas.2021636118 4. ozin g. is your work not novel enough? advanced science news. 2020. [cited 2021 nov 29]. available from: https://www. advancedsciencenews.com/is-your-worknot-novel-enough/ copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http:// creativecommons.org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v4.192 https://www.youtube.com/channel/uctyrpy0nghj8yqw56w4ywlq https://www.youtube.com/channel/uctyrpy0nghj8yqw56w4ywlq https://www.reuters.com/world/china/what-is-chinas-common-prosperity-drive-why-does-it-matter-2021-09-02/ https://www.reuters.com/world/china/what-is-chinas-common-prosperity-drive-why-does-it-matter-2021-09-02/ https://www.reuters.com/world/china/what-is-chinas-common-prosperity-drive-why-does-it-matter-2021-09-02/ https://www.reuters.com/world/china/what-is-chinas-common-prosperity-drive-why-does-it-matter-2021-09-02/ https://www.csescienceeditor.org/article/how-journals-and-publishers-can-help-to-reform-research-assessment/ https://www.csescienceeditor.org/article/how-journals-and-publishers-can-help-to-reform-research-assessment/ https://www.csescienceeditor.org/article/how-journals-and-publishers-can-help-to-reform-research-assessment/ https://doi.org/10.1073/pnas.2021636118 https://doi.org/10.1073/pnas.2021636118 https://www.advancedsciencenews.com/is-your-work-not-novel-enough/ https://www.advancedsciencenews.com/is-your-work-not-novel-enough/ https://www.advancedsciencenews.com/is-your-work-not-novel-enough/ http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 sponsored content from ernst & young llp sponsored content from ernst & young llp page | 1 the business case for blockchain in health care — part i evaluating emerging technology: a solution framework and the potential business value when considering the business case for blockchain in health care, history proves to be an accurate guide. the questions raised about the business case there are consistent with those asked of other emerging technologies. interestingly, the questions are not only consistent across industries but also stand the test of time. whether it is the emergence of computerized patient records, as electronic health records were first called, or the new kid on the blockchain, emerging technologies meet with healthy skepticism, if not downright dismissal. ► what are the use cases? ► what are the associated benefits and costs? ► what are the regulatory and compliance issues? ► is it secure? ► will it see widespread adoption? emerging technology innovators, usually through persistence and shared vision, find those early adopters who will test, break and refine the technology until it truly becomes mainstream. no business wants to be in the “late adopter” category at which point they will be at a competitive disadvantage or worse yet, be that company, the one that dismissed the technology that totally disrupted their industry and eventually led to their demise. while the emerging technology itself is questioned, it is about much more than the technology. successful organizations build and leverage successful emerging technology business cases because of strong leadership who know their organization’s mission, business needs, pain points and the market landscape and who have a deep knowledge of their end users. this combination allows organizations to take advantage of emerging technologies at the optimal time for them and flex to the market landscape and demands. leading organizations ask the tough questions of themselves, of their commitment to lead, of the business case and of the technology. in this way they advance their legacy solutions and mission instead of being disrupted by outsiders. great companies have a solution set of people, knowledge and methods coupled with technology assets such as products, data and tools. so, while conversations around the c-suite and board table might favor whether a given technology is worth investing in, time is better spent looking at an organization’s solution set. if organizations do not have, or cannot augment, the people, the knowledge or the methods to support capitalizing on an emerging technology, they must wait. they can only be “early majority” adopters as opposed to innovation leaders. health care is an especially risk-averse environment with constrained labor and capital resources. what must organizations (that are risk-averse, slower to adapt and less agile) do to benefit from the vast promise of emerging technologies? how can they learn from others and, at the same time, meet unique organizational goals, constraints and circumstances? they have the option to wait to see if enough organizations that look like them have realized a positive impact from their solutions. their alignment will be on similar pain points or desired key performance indicators. however, sponsored content from ernst & young llp page | 2 this alignment only goes so far. an organization still needs to emulate those leading organizations’ methods, capitalize on knowledge sharing, and secure implementation and maintenance support. so where can, or should, blockchain fit in a health care organization’s solution set? what are the solution set requirements, knowledge, methods, people, data and tools for determining this? and if a good fit is found, what are some of the associated costs, key success metrics and the ultimate business case? this article, the first in a series, will address the requisite leadership and emerging technology evaluation solution framework along with potential blockchain business values. these all contribute to the ultimate business case. subsequent papers will address the costs of blockchain systems and take the reader through a case study. a prerequisite for emerging technology evaluations is strong leadership aligned with a clearly articulated mission. the leaders must also provide the people and capital support for a thorough analysis of the organization’s unique business needs. this is independent of any technology. organizations are not looking to use technology for technology’s sake. they are looking to solve deep pain points or advance their core business to better serve their end users. they want to advance their mission with technology, not be disrupted by it. they are willing to do the up-front investment to thoroughly complete the business case analysis to assure an emerging technology will indeed advance their mission. with this leadership support, the organization must next evaluate whether it has the full emerging technology solution set of people, knowledge, methods, and complementary technology assets, products and data to fully evaluate and stress test the fit of an emerging technology. while the organization may have strong internal resources, it is essential to stress test viewpoints against external resources such as industry groups, analyst reports, academia and standard development organizations. the match to current or rapidly changing market landscape or the ability for end-user capture and retention are also important for the success of a given technology. after all, the value of a fax machine is dependent on how many other organizations also have them. likewise, with blockchain, the greater the number of participants using a blockchain application, the greater potential for a realized business people: leadership, champions, missionand values-driven, end-user input, human-centered designers, operations, training, support knowledge: industry experience, technology experience, regulations and compliance specific to industry and the emerging technology, market landscape methods: business analysis for process and workflow, costs and potential savings, change management, human-centered design, technical analysis technology assets — products and data: trusted data, currently owned technology, potential new technologies, cybersecurity organizational prerequisites leadership clear mission support: capital investment stakeholder alignment market landscape end-user focus emerging technology evaluation — a solution framework sponsored content from ernst & young llp page | 3 value. however, blockchain ecosystems themselves, if not well-designed, may beholden an organization to specific business process and costs. closed networks (or private blockchains) may require significant numbers of users to buy in to the network or create cumbersome governance structures. both undo one of blockchain’s greatest characteristics, that of decentralization. in contrast, a public blockchain ecosystem can be designed for greater ease of participation, flexibility in process design and lower costs to operate. those emerging technologies aligned with standards development organizations are also more likely to add current and future scalability. an organization can turn to industry groups and academia to assure a holistic market landscape view. whether there is a larger “ecosystem play” is dependent on the specific ecosystem and the mutual benefits of healthy “coopetition.” the larger ecosystem players can also determine whether there are mutual benefits from streamlining complex business processes or improved transparency between organizations. blockchain uniquely establishes participant trust, data integrity, transparency, security and full transaction auditability. this is particularly true for health care supply chains or data exchanges. blockchain allows for targeted transparency in an ecosystem. no one organization must expose its data to every other player in an ecosystem, such as is seen in large health information exchanges or one in which all participants use a common supply chain system. instead, authorization and access rules are embedded into a blockchain protocol with paired public and private key cryptography assuring that only the intended businesses see mutually relevant information. the market landscape and ecosystem view are ultimately about the end users. there may be multiple end users in an ecosystem. too often, an emerging technology gets a slow start, fails or causes unintended burnout with use because the technology architects or implementers failed to appropriately engage with those ultimate end users. additionally, a system may be perfectly designed for one group of end users while creating cumbersome experiences for other groups. for example, electronic health records in the united states are designed to maximize billing requirements rather than drive clinical outcomes. legions of clinical end users of these systems are bemoaning feelings of “burnout.” the technology detracted from those end users’ goals of providing the high-quality care, instead of enabling it. the most successful mp3 player was not the first one in the market. it was the one with the best user interface. ideally, a new technology delights the end user in some fashion. it either introduces a new capability or it significantly decreases the effort to accomplish a current capability. either way, the end user’s capabilities evolve. therefore, human-centered design must not only drive emerging technology architecture and implementation but continue to be at the forefront of ongoing technology evaluations and modifications lest it solve for one capability but detract from another. a wellconsidered human-centered design will also positively impact the implementation, training, support and ongoing maintenance efforts. market landscape industry groups analysts reports academia government regulatory bodies standard development organizations end-user focus interviews user groups human-centric design sponsored content from ernst & young llp page | 4 human enterprise design: always ask, “what are the human implications of this decision?” empathy is the driving principle when designing solutions in a human enterprise, ensuring technology is like well-designed furniture — ubiquitous, essential, unobtrusive, intuitive and supportive. it not only automates mundane, repetitive tasks, but acts as a tool to unlock greater creativity and collaboration. it enables and empowers change and innovation, and is agile.1 business analysis: primary blockchain business case drivers in health care while there are many business case models for health care, most fall into one of three primary buckets: ► operational efficiencies ► rapid growth, scale and new business capabilities ► regulatory compliance these drivers are not mutually exclusive either. they tend to be complementary in their key performance metrics. for each use case consideration, determine the current costs to achieve business aims, desired increased revenues, improved efficiencies or new capabilities as well as the costs not to adapt to changing market landscapes (e.g., loss of revenue, personnel or customer attrition, penalties, fines). those costs may be both quantitative and qualitative. blockchain can deliver network effects to drive mutual value: the more users, the greater the benefits, security and realized operational efficiencies. many health care organizations are ecosystems unto themselves and are rapidly expanding through mergers and acquisitions. an organization may have multiple internal locations with disparate systems, as well as unique or ingrained business processes. yet organizational leaders need to make decisions based on data from these multiplicities. ripping and replacing existing systems or trying to design for a one-size-fits-all system can be costly, time-consuming and fraught with inflexible processes. blockchain allows customizable business processes and data calculations with the ability to pull data from disparate systems and create a “records to record” back to those same systems. introducing blockchain does not require a significant change management component to train and familiarize end users on how the new system interacts with existing and more familiar systems. 1 “embrace the human enterprise,” ey website, www.ey.com/en_us/technology/embrace-the-humanenterprise, 6 december 2019. examples of assets in health care: data access rights, authorization to functions, supplies (biologics, pharmacologics, machines, surgical instruments, etc.), credentials, payments benefit analysis: determine if benefits such as increased visibility and trust, near-real-time insights and an immutable record of transactions alleviate current business challenges or create new business opportunities. thus far in the framework, much of the people, knowledge and methods discussed are agnostic of a specific technology. however, these factors all drive the design and the selection decisions made with respect to emerging technologies. these forerunners position an organization to weigh the additional costs of a variety of technologies against potential benefits. sponsored content from ernst & young llp page | 5 this minimizes one of the biggest barriers in implementing any technology, which is the amount of change required for the intended benefit. instead, blockchain automates much of the export, import and reconciliation work currently done manually and offers cross-ecosystem transparency. blockchain gives leadership trusted, combined data from multiple systems for analysis and decision-making. complementary technologies such as data analytics, machine learning and artificial intelligence require this pure data. having this type of data reduces human biases to inform decisions. an example would be multiple hospitals or clinics within a health system, each needing to order supplies. a blockchain system can assure they are ordering from the correct supplier to maximize savings and in keeping with supplier inventory and pricing incentives. in this way, blockchain enables rapid, trusted incorporation of new entities and systems and can improve operational efficiencies. blockchain allows for agile operating models and systems instead of today’s legacy structure-driven operating systems and models hindered by either over-standardization or conversely, a lack of standardization. the merged entities are then organized around issues, not processes, and value agility over hierarchy. the merger enhances relationships and fluid teams to encourage creative thinking and knowledge sharing. in this approach, blockchain adapts to the needs and enhances the abilities of end users rather than constricting them. blockchain is implemented with a technology at speed ethos. this means that new functionality required by mergers and acquisitions or changing user requirements can be met more promptly than within legacy environments or implementing entirely new systems. health care regulatory compliance is essential, time-consuming and expensive if not properly implemented. the costs of noncompliance are not just financial. it can be damaging to public relations as well. successful health care systems pride themselves on being high-trust, high-touch entities. the trust between the systems and those they serve is invaluable. uncertainty on how to comply with and track certain data sharing regulations is often cited as a barrier to health care data interoperability. for example, the centers for medicare & medicaid services (cms) has pending regulations to draw attention to lack of information sharing otherwise required by regulation with a final ruling expected this year. a health care organization is caught between maintaining data privacy and security and releasing data in a timely manner if appropriately prompted to do so. health care systems will need to prove they have appropriately gathered consent from a positively identified requester and complied with data requests in a timely manner. a component of blockchain platforms called smart contracts can assure participants meet required specified data sharing agreement privacy and security standards and certifications and create an immutable ledger of data consents, requests and authorizations, and data transmissions. it is important to clearly define the business quantitative and qualitative value for all use cases across all primary business drivers. the qualitative values apply to people, data and business-to-business interactions. an organization may draw input from human resources, internal user groups and business development executives. examples are outlined below and will be specific to the use cases contained within the business drivers. this detail will be the basis for potential value against the cost to apply a technology to affect those metrics. sponsored content from ernst & young llp page | 6 quantitative value i. operational efficiencies a. number of assets tracked b. number of processes automated c. hours saved from manual entry and reconciliation d. decreased dispute resolution time e. speed to payment and cash on hand ii. grow and scale a. time to onboarding b. hours saved from manual reporting and reconciliation c. capital optimization — cash on hand iii. regulatory compliance a. number of automated audit trails b. number of regulation requirements automated c. avoidance of fines or public relations issues d. hours saved from manual reporting i. current capabilities ii. allow to operate at the top of their license qualitative value i. people a. well-being of end users (personnel or customers) i. evolved capabilities ii. improved efficiency in increased job satisfaction iii. equalization and for access to knowledge and to spread and encourage ideas iv. change management minimization b. increased end-user and personnel retention i. ability to attract talent ii. customer loyalty and repeat business ii. business a. trusted business-to-business interaction, ethical b. cohesive business processes or interactions within or between organizations c. decreased litigation iii. data a. transparency b. trust c. immutable record and audit trail d. secure sponsored content from ernst & young llp page | 7 summary organizations that have, or can augment, their critical solution set elements are in the best position to evaluate emerging technologies and determine whether they have the surrounding solution set to align and advance mission-critical priorities for their end users. blockchain especially advances operational efficiency, allows for rapid growth and expansion, and assures auditable regulatory compliance. yet, the ability for a business to have new capabilities is the ultimate reason for leading organizations to explore blockchain. subsequent papers in this series will examine what drives the cost to build, optimize and maintain blockchain technologies and provide a case study. for comments or questions please contact: ali loveys, md ey us health care blockchain leader ali.loveys@ey.com chen zur partner/principal us cgp blockchain practice mailto:ali.loveys@ey.com sponsored content from ernst & young llp ey | assurance | tax | transactions | advisory about ey ey is a global leader in assurance, tax, transaction and advisory services. the insights and quality services we deliver help build trust and confidence in the capital markets and in economies the world over. we develop outstanding leaders who team to deliver on our promises to all of our stakeholders. in so doing, we play a critical role in building a better working world for our people, for our clients and for our communities. ey refers to the global organization, and may refer to one or more, of the member firms of ernst & young global limited, each of which is a separate legal entity. ernst & young global limited, a uk company limited by guarantee, does not provide services to clients. information about how ey collects and uses personal data and a description of the rights individuals have under data protection legislation are available via ey.com/privacy. for more information about our organization, please visit ey.com. ernst & young llp is a client-serving member firm of ernst & young global limited operating in the us. © 2020 ernst & young llp. all rights reserved. 2001-3368475 ed none this material has been prepared for general informational purposes only and is not intended to be relied upon as accounting, tax or other professional advice. please refer to your advisors for specific advice. ey.com 1 (page number not for citation purpose) editorial innovative minds shine bright at the conv2x 2023 ignition pitch competition: meet the winners! tory cenaj founder, owner and publisher, partners in digital heath, stamford, connecticut, usa corresponding author: tory cenaj, email: t.cenaj@partnersindigitalhealth.com keywords: artificial intelligence, blockchain technology, clinical trial management, conv2x, digital health, healthcare, innovation blockchain in healthcare today issn 2573-8240 abstract on sept 1, 2023, partners in digital health, publisher of blockchain in healthcare today (bhty,) conducted the 4th annual converge2xcelerate (conv2x) ignition pitch competition. the competition was recorded for journal and on demand broadcast viewing. seven competitors in the digital health and dlt (distributed ledger technology) markets presented to eight decerning judges. entrants were asked to demonstrate how products and services directly impact telehealth and blockchain in healthcare technology fields from around the globe. product solution categories included, but were not limited to: ai & tech in telehealth and medicine • smart home care design, ai, sensors, robotics, chronic care condition(s) using rpm, reducing cost for health systems or patients, enhancing the physician-patient relationship, mixed reality to enhance outcomes in patient care, etc. advancing the business of health with blockchain technology • monetization of data, interoperability, improving population health, precision medicine, public health & equity, digital twins, secure identity, defi, medical metaverse, supply chain, clinical trials, etc. scores were rated from 1-5, with 5 being the highest/best. criteria appear below. scores were tallied, and winners were selected for each category. • impact: ° how likely is the solution to improve outcomes for the problem identified? • innovation: ° does the team provide a convincing rationale for why their solution may work? ° does the solution provide a creative approach and address specific user needs? • scalability: ° how easy would it be to develop and implement this solution? ° could this solution scale across different markets and therapeutic areas? • presentation: ° how effective was the presentation overall? ° was the solution they are proposing convincingly articulated, presented or visualized? submitted: september 28, 2023; accepted: october 3, 2023; published: october 13, 2023 mailto:t.cenaj@partnersindigitalhealth.com 2 (page number not for citation purpose) citation: blockchain in healthcare today 2023, 6: 283 https://doi.org/10.30953/bhty.v6.283 tory cenaj the excitement was palpable as entrepreneurs, innovators, and healthcare leaders gathered for the highly anticipated 4th annual conv2x 2023 ignition pitch competition focusing on artificial intelligence (ai), telehealth, and blockchain technology in healthcare. after rigorous evaluation of exceptional presentations and market initiatives, judges determined the winners – and undoubtedly, revolutionized healthcare. the proud winners of the conv2x pitch competition blockchain technology in healthcare bloqcube inc: clinical trial management & financial system (ctmfs) platform, designed to accelerate decentralized clinical trials (dct), stood out as a remarkable solution. your innovative approach to streamlining clinical trials with blockchain technology is nothing short of inspiring. ai and digital health veta health (prosper): commitment to empowering patients with personalized telehealth solutions is a testament to the transformative potential of ai in healthcare. your dedication to improving patient care is commendable. rama rao, ceo, bloqcube, expressed sentiments of gratitude for organizing the conv2x ignition pitch competition stating “it was an incredible platform for innovators like ourselves to showcase our contributions, and the entire experience has been deeply enriching.” camaraderie and expanding support networks are hallmarks of the conv2x experience. here’s what some of the competitors had to say: seth dobrin, ceo, quantm ai: “wow! what a great group of technologies and use cases. i am humbled by you all, what you have built, and where you are going. it was an honor to be part of this group.” ryan wright, founder, nvelope: “thank you for the opportunity. more than anything, i was delighted to see focused people doing passionate projects that address tangible goals. progress and sagacity are around the corner!” paniz jasbi, founder, theriome: “the depth of innovation and dedication showcased by all teams was truly commendable. alex and i are grateful for the opportunity to have been part of this gathering and look forward to potential collaborations and interactions in the future. thank you, everyone, for sharing all of your amazing projects and companies. it is inspiring to be presenting alongside you amazing innovators! thank you for the opportunity; it was such a pleasure.” first prize winners in each category received remarkable prizes valued at $13,000, along with a myriad of prestigious opportunities to propel their ventures to new heights including a seat in the prestigious october 2023 scholarly cohort at ilt entrepreneur academy, each valued at $3,000 each (details at ilt academy). runner-ups didn’t go empty-handed, and earned a waived journal article apc for an accepted manuscript submission. startups are encouraged to publish research in peer reviewed journals to add credibility and validation to products and services; which also distinguishes ad elevates a company’s reputation in the market and venture capital community. companies that competed for top accolades alongside the winners included: • quantm ai: innovating the future of healthcare safety • nvft bio oracles, nvelope llc: pioneering bioinformatics solutions for enhanced patient care • mygenoverse, genobank.io: unlocking the power of genetic data for personalized healthcare • prims, pragmaclin research inc: advancing clinical research with cutting-edge technology • aristotle, biome, theriome inc: shaping the future of healthcare with data-driven insights the judging panel featured some of the brightest in the industry: • nisa amolis, managing partner, a100x ventures • dr. fernando de la peña llaca, ceo, aexa • ruby gadelrab tudor, ceo, mdsrupt • shwen gwee, former vp and head of global digital strategy, bristol myers squibb • sweta sneha, phd, executive director of healthcare management informatics. professor of information systems, coles college of business, kennesaw university • william taranto, president, merck global health innovation fund • nick tietz, founder & ceo, ilt academy • dr. shayan vyas, former executive physician/medical lead, teladoc, health systems nisa amoils, esq., managing partner, a100x, stated “thank you for organizing the conv2x 2023 ignition pitch event. it is so important that healthcare use cases of blockchain and ai get recognition for their traction, which have been largely ignored by media.” winners are poised to reshape the healthcare landscape with their innovative solutions. as we celebrate their achievements, we also look forward to witnessing the impact they will have on the future of healthcare. for 2024 details and information, please contact tory cenaj, at info@partnersindigitalhealth.com https://doi.org/10.30953/bhty.v6.283 http://genobank.io: mailto:info@partnersindigitalhealth.com page 1 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 critical care equipment management reimagined in an emergency winston yong, bs, phd, mhsa, anya kundakchian, bs, phd, mhsa affiliation: rti international, research triangle park, nc, usa corresponding author: winston yong, 76 upper ground, london se1 9pzgb, united kingdom. winston.yong@ uk.ibm.com keywords: covid19 pandemic, critical care, watson section: proof of concept summary: the covid19 pandemic created a surge in demand for critical care equipment against a backdrop of fast-moving geographic virus hotspots. a team from ibm europe was put together to prove that a devolved healthcare system can be rapidly bridged by a mix of advanced and legacy technologies to provide a federated view of critical care equipment deployment and use during an emergency. this was achieved with the deployment of predictive analytics and blockchain, integrated with conventional hospital management system. the corollary investigation determined the manner in which this system can be harnessed in a postemergency recovery to provide a national supply chain efficiency backbone. method: during a period of 2 weeks, a team of ibm consultants set up a technology sandbox environment to represent a network of an equipment manufacturer, a central national emergency monitoring center, and several hospitals managed by their respective trust organization. within this environment, a hospital asset management system, maximo, was configured to manage and track critical care equipment within a hospital; a blockchain traceability platform, ibm’s blockchain transparency system, was configured to ingest multiple hospital data reports; and a predictive analytic dashboard, watson analytics, would retrieve data from the blockchain platform to supplement other data sources to provide national views and support decision-making for the supply and movement of equipment. three key principles in the design of this environment are speed, reuse, and minimal intrusion. results: the hypothesis was to test whether the chosen technologies can overcome the challenges of misaligned demand and supply of critical care equipment during a national emergency. the execution of the tests led to successful simulation of three scenarios: (1) the tracking of the location and usage history of any single equipment https://doi.org/10.30953/bhty.v3.146� mailto:winston.yong@uk.ibm.com mailto:winston.yong@uk.ibm.com https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.146&domain=blockchainhealthcaretoday.com&date_stamp=2020-12-04 page 2 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 that has been placed into the network; (2) the movement of equipment between independent hospitals is recorded and reported; (3) a realtime interrogation of the current location and status of all registered equipment. conclusions: the successful completion of this proof of concept has demonstrated that emerging technology can be used to overcome poor macro level coordination and planning, which are the drawbacks of a devolved healthcare system. the corollary was that this proof also demonstrated that blockchain technology can be used to prolong the useful life of conventional technology. background introduction the united kingdom operates a hybrid-federated healthcare system for its population. the four countries constituting the united kingdom— england, wales, scotland, and northern ireland (ni)—each have their own publicly funded healthcare system and are accountable to separate governments and parliaments, together with smaller private sector and voluntary provision. as a result of each country having different policies and priorities, a variety of differences now exist between these systems. additionally, within each country and region, hospital trusts have the independence to operate their hospitals—allowing for localized flexibility but detracting from national coordination. this hybrid-federated healthcare system, where healthcare policy and funding are managed centrally but healthcare execution is decentralized, is not unique to the united kingdom. australia, spain, italy, and brazil have a similar healthcare system, and countries like taiwan, united arab emirates, etc. are moving from a centralized health care to a hybridfederated system as in the united kingdom. figure 1 provides an overview of the patient– provider ecosystem: 1. patients are assigned to primary care units, which are the local general practitioner (gp) clinics. 2. the gp will refer the patients for specialized care when required, at the hospitals (except for accident and emergency where patients are seen directly). 3. doctors and medical care workers are certified to work at hospitals and clinics. 4. hospitals are managed by trusts with a trust capable of operating several hospitals. 5. the devolved nature of health care means that the hospitals/trust are governed by four different national health services (england, wales, scotland, and ni). each national health service sets its own key performance metrics, standards, and also sets the levels of care to be provided within its jurisdiction. it also provides funding to hospitals and gp practices. 6. in turn, the national health services respectively receive federal government funds obtained from taxpayers as well as policies and guidelines to improve the quality of care. this pre-covid19 situation of health care in the united kingdom had both its supporters and detractors. nevertheless, it is a working system and shares similarities with healthcare systems in other countries globally. the arrival of the covid19 emergency caused an unusual high surge in demand for respiratory treatment and a corresponding demand for critical care equipment, specifically ventilators. this situation was exacerbated by fluctuation in virus hotspots, leading to fluctuating demand for ventilators, which normally would be positioned as on-site equipment. https://doi.org/10.30953/bhty.v3.146� page 3 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 objectives the overriding medical objective in the covid19 emergency is to continually provide the critical care assets, equipment, and services at the point of need in response to the dynamics of the covid19 situation, and to provide enduring, assured, and optimized operational resilience in the immediate aftermath of the crisis. this provided several challenges: 1. the challenge of matching the demand for critical services with the availability of assets, equipment, and specialist staff. effective management requires • a clear, timely, and accurate picture of the dynamically changing data to indicate what equipment is available, where it is located, and where it is needed for the moment. • tools to model dynamic demand patterns and to link these with supply processes and operational workflow at both local and regional level. 2. the absence of a coherent and unified view of critical assets and equipment presents challenges for care providers and the supply chains that support them—and ultimately acts to impede clinical effectiveness: • a lack of visibility (an aggregated picture) makes it difficult to plan effectively and match the demand and supply dynamics of key assets and equipment. • effective asset and supply chain management will be as important in the post-covid19 nhs (national health service), as it is today. 3. new equipment needs to be assured and managed effectively through life: • there is a need to understand the provenance and to assure of key assets and equipment. • a solution is needed to manage and remedy failure patterns to optimize utilization and availability at local and regional levels. (n.b.: this refers to the optimal use of the commissioned critical care equipment and not to the optimization of the critical care equipment manufacturing process.) thus, the objective of the “ibm critical care equipment tracking” proof of concept is to demonstrate that emerging technology, in the figure 1—healthcare system in the united kingdom. https://doi.org/10.30953/bhty.v3.146� page 4 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 form of predictive analytics and blockchain, can be operated in a nonintrusive manner to overcome the aforementioned operational challenge. scope the proof of concept is based on the uk healthcare system but the situation is deemed similar and applicable to the aforementioned countries that have similar healthcare systems. to define the parameters of the proof of concept, the ecosystem for critical care equipment manufacturers, users, and a national coordination body was modeled and is represented in figure 2. figure 2 outlines the organizational entities in the critical care equipment supply chain. a detailed explanation of the two use cases is described later, based on which the ibm team built the scenarios necessary for us to prove the concept. note that the manufacturing process is excluded from the scope of this effort. use case 1—use and maintain effectively in this use case, the goal is to manage and track critical care equipment via the hospital management system on a microlevel, that is, within an individual hospital. thus, the key participants include a trust to manage equipment orders, hospital to manage operating assets, a field technician responsible for investigating and fixing failures, and a manufacturer to create and maintain the critical care asset. use case 2—monitor and distribute efficiently in the second use case, a macrolevel view is required to provide visibility of the supply and demand for critical care equipment, and to make decisions on distributing the equipment across england. the key participants include the national demand management (in this proof it is assumed to be a central organizational body such as the uk national healthcare service) with a holistic view of stock levels across hospitals, a trust with the view of stock levels in hospitals under their management, a hospital with its local stock levels, and a manufacturer with the equipment stock in its warehouses. method key principles three key architectural principles were mandated in this proof of concept. they are figure 2—participant model. https://doi.org/10.30953/bhty.v3.146� page 5 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 1. speed of deployment. this means that a fit-for-purpose approach is prioritized over a best-of-breed approach for decision-making in this test. 2. minimum intrusion. during a period of emergency, a criterion for success is measured by how little changes have to be made to existing hospital technology and operations. 3. realistic. a blend of conventional technology with emerging technology has to be shown working in alignment, as opposed to a completely new system that disregards prior sunk investment. the aforementioned principles were translated to the system context in figure 3 and the following conditions and assumptions in the proof of concept: 1. speed of deployment is paramount, and new functionalities will be prioritized and staggered. the normal duration for a technology proof of concept project is between 4 and 8 weeks. the goal set for this project at the start of the project was to achieve this in less than 4 weeks. at the conclusion of the project, it was achieved in 2 weeks. the minimum functionality to achieve the “process flow” was prioritized, and the additional functionalities were documented in a “backlog.” 2. front end—desktop html access prioritized over mobile access. 3. integration—csv (comma-separated values) file upload (for excel) in the first instance, followed by apis (application programming interfaces). 4. infrastructure—all technologies to be demonstrated on the cloud. however, a single cloud provider is used. volumetric scaling and hardening of emerging technology are acknowledged challenges when deploying beyond proof of concept into production environments. this will be a subject to be tackled in the next phase of work. in this phase, we are taking the approach of testing with low volumes. 5. security—first iteration with single-factor followed by two-factor in subsequent iteration. design requirements the target architecture for this proof of concept was developed using existing ibm components. the major components are listed in figure 4. figure 3—system context. https://doi.org/10.30953/bhty.v3.146� page 6 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 the main components are: 1. watson analytics. ibm’s watson studio deployed as a software service in the ibm cloud with cognos dashboard service. the focus of this analysis is to proof the accuracy and secure the availability of data for watson analytics’ use. the proof is not to test the predictive modeling engine or criteria that have been configured to produce the dashboard. 2. maximo. critical care asset monitor is a multihospital cloud platform for healthcare providers to manage lifesaving equipment. the focus of this analysis is to proof the accuracy and secure the availability of data from maximo for use by blockchain and watson analytics. the proof is not to test the functionality of maximo. 3. blockchain. ibm’s blockchain transparency system configured to trace the equipment provenance. 4. ibm security. the integrity of the system’s security is assumed and not the purpose of this test. hence, standard ibm cloud security configured as required. in this proof of concept the blockchain protocol used is hyperledger fabric. four nodes were employed, which represent the manufacturer, the central health authority, a hospital running on a maximo hospital management system, and a non-maximo hospital system, respectively. an integration layer is defined in the target architecture, but for this, it was not implemented. instead, data were ingested through xml (extensible markup language) files being uploaded into the system to simulate the results of system interfaces. the design of the integration, though not part of the test, is included in figure 5 for completeness of design. an observation on the role of data standards was made during this project. the availability and adherence to an open data standard would play an important factor in the ease for deployment. however, the team also recognizes the practical hurdles in practice for adoption of such a standard. therefore, in this proof of concept, we have adopted a data translation layer to adapt the flow of data between technology components. figure 4—technology components. https://doi.org/10.30953/bhty.v3.146� page 7 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 process flows in addition to the definition of the participants in the ecosystem for tracking ventilators (see figure 2), it is the usual practice for ibm blockchain consultants to define the process map to model the events and facilities (or locations) that are involved. this, in turn, also provides guidance to the optimization of the blockchain peers and nodes. figure 6 demonstrates the process flow of a critical care equipment asset (a respiratory ventilator in this scenario) as it moves throughout the supply chain from the asset being created by a manufacturer through to being decommissioned. the process flow comprises the following steps: 1. a ventilator is created and commissioned at a manufacturer location. 2. the ventilator is shipped from the manufacturer to the central storage hub, where critical care equipment assets are stored until being distributed to hospital locations. 3. once a strategic decision on the allocation of the ventilator is made, it is dispatched from the central storage hub to a hospital. 4. upon arrival at the hospital, the ventilator is inspected by a hospital technician. if it is in good order, the ventilator is installed in a hospital ward, and a patient is put on it. 5. based on the demand for ventilators across hospitals in england, the ventilator may be requested by another hospital if there is a shortage. in this step, the ventilator is shipped from the first hospital to the new hospital that has requested it. 6. once the ventilator has arrived at the new hospital, it is inspected by its technician and is subsequently put in use if it is in good order. 7. after a certain time, the ventilator may break down or develop a fault, in which case it will need to be repaired, serviced, or recalled from the supply-chain and ultimately decommissioned. in this step, the ventilator is sent from the hospital to the central storage hub. 8. the ventilator is decommissioned at the central storage hub. figure 5—integration and data flow. https://doi.org/10.30953/bhty.v3.146� page 8 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 this process flow represents the high-level functional requirements, against which the multiple technologies were configured to enable the execution of the tests. execution and test cases the availability of test data is always a concern for technology projects. in this case, we were using all ibm technologies and had access to demonstration data, that is, accurate but simulated (or desensitized) data used by maximo presales personnel to demonstrate their maximo’s functionalities. this became the base data that were used in the following test cases. test case 1—asset management track assets (e.g. equipment), their location, movement, and state/condition (table 1). test case 2—workflow management triggering the request for work asset/equipment lifecycle management activities (e.g. maintenance, reallocation, ordering, purchasing, approvals) for regular or ad hoc tasks (table 2). test case 3—analytic models to predict demand and usage of assets/equipment at hospital or other locations. the purpose of the test here is to test the secure availability of the data for use in the predictive analysis, rather than test the accuracy of the criteria within the predictive modeling tool (table 3). results and key learnings the tests described in “execution and test cases” section was executed over 2 days and repeated twice to different colleagues as observers. the successful tests (results of the three cycles of test recorded in “execution and test cases” section) conducted earlier demonstrated the success of meeting the objective of the proof of concept—to demonstrate that emerging technology, in the form of predictive analytics and blockchain, can be operated in a nonintrusive manner to overcome the earlier operational challenge. in the process, the authors were able to elicit three key learnings: data, interoperability, and intrusion. data it is crucial in this proof to have a canonical data structure, through which we are able to exchange data between the three main distinct and separate technology components (table 4). it is recognized that an industry data model is neither a current reality nor would it be realistic to assume that one is available. a critical decision was made to adopt the sitrep report. since october 2017, all nhs hospitals in the united kingdom are required to submit a daily situation report (sitrep) through an automated data collection system. the authors used a sitrep report to identify data that were relevant to the use of ventilators and produced a logical data model, which was further modified to produce a physical data model used in the creation of the xml files used in these tests. the decision to use the sitrep data is based on the fact that it would reflect the real-life situation, that is, sitrep data are already being used for decision-making. additionally, the figure 6—business process map of a ventilator. https://doi.org/10.30953/bhty.v3.146� page 9 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 table 1. asset management use case (ui = user interface design). test script # test script description entry criteria exit criteria results may 12, 2020 1 2 3 1 ventilator asset is commissioned first and essential step to initiate the process asset recorded as being commissioned and shown in the blockchain platform ui ü ü ü 2 ventilator is shipped from manufacturer to the central storage hub asset has been created on the blockchain and appears as commissioned in the blockchain ui event of shipment from manufacturer location and receiving advice at central storage hub were recorded and shown in the blockchain ui ü ü ü 3 ventilator is dispatched from central storage hub to a hospital asset has been created on the blockchain and appears as commissioned in the blockchain ui event of shipment from central storage hub and receiving advice at the hospital were recorded and shown in the blockchain ui ü ü ü 4 ventilator is shipped from hospital a to hospital b asset has been created on the blockchain and appears as commissioned in the blockchain ui event of shipment from location a and receiving advice at location b were recorded and shown in the blockchain ui ü ü ü 5 ventilator status is updated to being inspected by a technician asset has been created on the blockchain and appears as commissioned in the blockchain ui “inspecting” event recorded and shown in the blockchain ui ü ü ü 6 ventilator is recalled from the supply chain asset has been created on the blockchain and appears as commissioned in the blockchain ui asset marked as decommissioned in the blockchain ui ü ü ü quality of the data is deemed of secondary importance to the objective of the proof of concept—which is to proof that emerging technology, in the form of predictive analytics and blockchain, can be operated in a nonintrusive manner with existing conventional technology. interoperability demonstration of interoperability is crucial to meet the objective of this proof. at its basic level, data interoperability level was achieved by this proof of concept. the solution established the interconnectivity requirements needed to exchange data between disparate applications, on https://doi.org/10.30953/bhty.v3.146� page 10 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 table 3. analytic models use case. test script # test script description entry criteria exit criteria results may 13, 2020 1 2 3 1 watson prediction model produces output for demand for ventilators across the united kingdom input of the following demand data: • number of covid19 patients over time • equipment required for treatment categorized by case severity watson analytics displays the predicted demand figure in the “forecast stock demand” dashboard section categorized by asset type ü ü ü table 2. workflow management use case (pr = pull request, uk = united kingdom). test script # test script description entry criteria exit criteria results may 12, 2020 1 2 3 1 asset added to the hospital management system (maximo) first and essential step to initiate the process asset is recorded in the hospital management system (maximo) database ü ü ü 2 notification of a failure of an asset asset exists in maximo database “damaged ventilator” is displayed on the maximo dashboard— the work orders section ü ü ü 3 raise a service request for the damaged asset asset exists in maximo database and is shown as “damaged/not operating” inspection request added to the asset on the maximo dashboard ü ü ü 4 push the forecast for the latest suggested united kingdom demand for ventilators from watson into maximo watson prediction model (described in use case 3) produces output for demand for ventilators across the united kingdom. user confirms and forecast is pushed into maximo pr pull request updates from watson prediction model are displayed in maximo ü ü ü 5 approve the latest suggested demand for ventilators pr updates from watson prediction model are displayed in maximo user approves the prs and updates the forecasts to the relevant hospitals ü ü ü https://doi.org/10.30953/bhty.v3.146� page 11 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 different technologies. the canonical data model referred in the earlier section provided the format, syntax, and organization of data to be exchanged. this is encapsulated in table 4. the team first looked at the data captured by maximo hospital management system and analyzed which data points should be captured by the blockchain system—time of event, asset number, and location data. the configured xml files allowed data obtained from hospital system (maximo) to be incorporated into blockchain with ease and with no intrusion to the maximo system. upon data ingestion into the blockchain, a traceability flow for an asset was produced with all the relevant data carried over with it. minimal intrusion investment in technology is a necessity in all industries in the current digital economy. ironically, the hurdle to adopting emerging technology is prior investment in technology. the sunk capital cost of technology investment needs to be recouped over time through returns via benefits of the technology—constraining new investments until the prior investments are recouped. the success of this proof demonstrated that emerging technology can be adopted with minimal intrusion to existing systems of records—allowing emerging technology and legacy technology to coexist. conclusion implications for a devolved healthcare system the objective of this solution was to prove that, through appropriate integration of advanced technologies with legacy technologies, hurdles relating to coordination and planning caused by the devolved nature of the uk healthcare system can be reduced. that is, the solution outlined in this paper proved that the constraining elements, or the pain points, of a devolved healthcare system can be mitigated by blockchain technology. in the process of proving this, the authors also showed that emerging technology does not have to replace legacy technology. they can work together, thereby prolonging the life of investments on conventional technologies. post emergency recovery implications even in the pre-covid19 emergency period, many organizations had been frustrated with their table 4. data structure for interoperability (api = application programming interface, id = identification, rest = representational state transfer, xml = extensible markup language). data file format communication channel data source data destination data fields output xml rest api calls maximo hospital management system blockchain transparency system • asset number/id • timestamp • event id • source location (e.g. a hospital) • destination location (e.g. a different hospital) • read point a traceable record of an asset traveling from one location to another, tagged with an event id https://doi.org/10.30953/bhty.v3.146� page 12 of 12 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.146 traditional forecasting models, which relied heavily on historical data. many organizations had tried to assemble a supply chain control tower—a cross-functional team reviewing real-time data to make decisions quickly. this had some success in some organizations, but inter-organization data sharing collection was still proving to be a hurdle. the critical care equipment proof outlined earlier demonstrated how a decentralized ledger overcame this hurdle. in the post-covid19 world, the authors see this as an effective way to implement the supply chain control tower as an inter-organization planning and monitoring mechanism, which would replace forecasting methods based on historical data. if it is done correctly, a decentralized ledger control tower will be an effective approach, whether in an emergency or not. the authors also believe that the embedded nature of smart contracts in blockchain technology is an open door for the implementation of the next step of supply chain transformation—autonomous planning. the vision for autonomous planning is one in which blockchain and advanced analytics are used in harmony with legacy systems, in every step of the supply chain planning process, enabling faster and better decision-making with minimal manual intervention. beyond the proof of concept this proof has been conducted within narrow constraints to prove that the emerging technology, particularly predictive analytics and blockchain, can work with legacy healthcare technology to assist with handling the current covid19 emergency. additionally, it has provided insights into how it can be harnessed in a postemergency recovery situation. as a next step, the authors envisage that potential adopter will utilize the knowledge from this proof of concept to develop pilot projects that will see measured steps toward adopting and adapting this to suit localized data requirements and other nonfunctional requirements such as security levels, volumetrics, and latency. acknowledgments the authors acknowledge the following: ibm; jessica douglas—project sponsor; richard bolton—supply chain expert; robert musgrove—cognitive & iot; kevin elliott— maximo solution manager; davorka vunic— maximo managing consultant; mark restall—cognitive business decision support. funding statement internal ibm funding was the source of funding that has supported the work. it is part of the ibm response to contribute to the effort to fight the covid19 emergency. the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. conflicts of interest the authors declare no potential conflicts of interest. contributors both authors contributed to the conception, writing, and revisions to the article. copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. see: http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v3.146� http://creativecommons.org/licenses/by-nc/4.0� http://creativecommons.org/licenses/by-nc/4.0� page 1 of 2 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.152 2020 reflections: how a pandemic will catalyze innovation katie crenshaw, mpa, senior manager informatics, himss keywords: identity, trust, revenue cycle, provenance, quality, blockchain section: discussion the covid-19 pandemic has caused a major disruption to how we move through the world and has upended our daily lives. without a clear end insight, the world has needed to reassess how we move forward, what our immediate needs are, and what systems are necessary to function effectively and securely in the long term. the current pandemic has thrust the door open for emerging solutions and technologies to address the challenges plaguing our world, as well as forcing us to reevaluate the status quo and think about how we can more effectively fulfill our needs and goals. like so many of us, the healthcare information and management systems society (himss) blockchain in healthcare task force continues to monitor developments in the health information and technology arena, where distributed ledger technology has immense opportunities to further enable and deliver efficiencies within the healthcare ecosystem. transforming reimbursement the financial impact that covid-19 has had on the society-at-large, and health care specifically, is tremendous. as healthcare organizations rebound, they have a chance to strategically reconsider their existing revenue cycle management (rcm) systems and work to optimize these ingrained processes. current rcm typically includes a variety of processes involving multiple players from the moment a patient registers to after they have received care. blockchain technology can address various aspects of rcm by providing transparency and access to all players, automating elements of the process using smart contracts, and enabling trust with immutable data and a consistent, automated process. as healthcare organizations and payers add in new forms of reimbursable care, such as telehealth services, there are opportunities to make available key documents for reimbursement via the blockchain and to leverage smart contract automation to streamline steps around prior authorization, provider directory, clinical reconciliation, claims processing, and payment between the provider, payer, and patient. managing and verifying digital identities identity management plays a major role in health care, from the tracking and validation of devices, supply chain processes, software, drugs and materials utilized in care delivery, the verification of credentials for clinicians providing care, and the matching of the correct https://doi.org/10.30953/bhty.v3.152 https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.152&domain=blockchainhealthcaretoday.com&date_stamp=2020-10-28 page 2 of 2 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.152 patient to their related health data. the management of these various identities is complex. while the issue is not new, covid-19 has highlighted opportunities to streamline antiquated identity proofing and authentication processes and expand the use of digital identities as more businesses move online. decentralized identity and verifiable credential solutions can provide an opportunity to establish trust and verification without the traditional intermediary. beyond just health care, collaborative efforts are underway to standardize and align frameworks around self-sovereign digital identities that are built upon frameworks to establish trust and security. determining data quality covid-19 has clearly exposed opportunities to improve the sharing, tracking, and verification of the quality and integrity of data across multiple sources that contribute to care delivery, research efforts, and coordinated pandemic responses. currently, pandemic data are collected from a wide array of sources, from healthcare organizations, local public health departments, and third-party testing sites, from which trust may need to be established before assessing as part of aggregated data for our pandemic response. furthermore, nontraditional care settings have become even more essential during the covid-19 era with their growing capabilities to establish home-based care techniques. tracking information about data collection, such as data source types and the collection methods and conditions, is critical to assess the quality of these data. data provenance provides historical information around the data’s journey and is a type of data, which is suited for blockchain’s transparency and immutability characteristics. blockchain is designed to provide tamper-proof data provenance that can be leveraged to inform the overall data quality. covid-19 has forced many innovative processes to be designed and implemented at a rapid pace. the himss blockchain in healthcare task force continues to explore blockchain proofs of concept and pilots emerging as part of the response efforts. several members are actively contributing to initiatives that aim to collaboratively solve many inefficiencies that currently exist in health care, both before, during and will continue, after this pandemic is over. the industry is in need of creative ways to address deep-rooted challenges, and the opportunity is upon us to apply blockchain technology to several critical use cases that address the immediate ramifications of this pandemic. we must be thoughtful and cautious when applying new solutions to old paradigms that pave the way for sustainable and scalable results addressing the needs in health care. acknowledgments mari greenberger, mppa, senior director informatics, himss; jorge a. ferrer, md, mba, lsa, famia, medical informatician-health system specialist, u.s. department of veterans affairs. conflicts of interest the authors declare no potential conflicts of interest. copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is noncommercial. see: http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v3.152 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) editorial creating a health data marketplace for the digital health era imtiaz khan, phd1* , mohamed maher1,2 , anjum khurshid, phd3,4 1cardiff metropolitan university, cardiff, wales, uk; 2soulindata, cardiff, wales, uk; 3harvard pilgrim health care, boston, massachusetts, usa; 4harvard medical school, boston, massachusetts, usa *corresponding author: imtiaz khan, email: ikhan@cardiffmet.ac.uk doi: https://doi.org/10.30953/bhty.v7.338 keywords: dementia, diabetes, digital health, hypertension, non-communicable chronic diseases submitted: july 17, 2024; accepted: august 13, 2024; published: august 31, 2024 globally, non-communicable chronic diseases (ncds) such as hypertension and diabetes account for 75% of direct mortality.1 concurrently, mental health diseases like dementia have recently become the biggest killer disease in countries like the uk2, with no cure, treatment or even effective intervention.3 to address this pressing issue, healthcare 4.0 introduces a patient-centric paradigm shift, transitioning from traditional reactive medicine to predictive diagnosis and personalized preventive interventions.4 this shift leverages on the large volume and variety of health data generated from the growing use of electronic health records (ehr), the internet of medical things (iomt) and personal wearable devices like the smartwatch, along with the growing capability of predictive and generative algorithms to create value from this health data. however, unlike industry 4.0, which extensively benefited from the internet of things (iot) and sensor-derived data, healthcare 4.0 has yet to fully capitalize even on ehr datasets, let alone iomt and wearables-derived data. ehr data are often siloed in centralized databases of various service providers, such as hospitals and clinics, whereas iomt and wearable-derived data remain in the respective vendor’s cloud, with limited and or complex data access and interoperability procedures.5 this data inaccessibility and incompatibility undermine the predictive and analytical capabilities of machine learning algorithms and data analytics, limiting medical practitioners’ decision-making abilities as well as the 4p vision of healthcare 4.0 —prediction, prevention, personalization, and participation.6 from the patient’s perspective, restricted access to their health data, negatively impacts their perception towards data ownership and stewardship. this limitation can make patients feel coerced into a passive role in decision-making, knowledge, and value-creation processes, often leading to non-participation or even non-adherence to medications and physician instructions.7 to realize the 4p vision of healthcare 4.0 particularly participation of patients, decentralization and democratization of health data is fundamental. however, adverse incentives of healthcare business models that are driven by organizations with interest in complicated and nontransparent financing mechanisms, prevent such decentralization that empowers patients. we posit that the establishment of a blockchain-based health data marketplace where ehr, iomt and wearable-derived data can be monetised by selling it to data consumers like medical professionals, researchers, regulators, thirdparty (e.g. ai service providers) and policymakers can solve this data centralization problem.8 blockchain-like distributed ledger technology (dlt) introduces new opportunities to develop such a health data marketplace. the immutable feature of blockchain enables the attribution of ownership of digital assets (health data in this case) in a highly secure environment, while the smart contract feature provides flexible data stewardship capabilities (i.e. patients can choose which health data attribute to sell to which type of consumer), thus promoting privacy and trust. the smart contract also facilitates efficient and equitable distribution of revenue among the stakeholders (patient, clinics, caregivers etc.), fostering a coopetition-like socioeconomic ecosystem.9 finally, the capability of cross-organization (hospitals, clinics) and technology (iomt, wearables) data interoperability, traceability and integrity by dlt-like technologies10 makes them the most suitable candidate on which to build such a marketplace. blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0001-7624-1319 https://orcid.org/0000-0003-2130-7717 https://orcid.org/0000-0002-8946-0622 mailto:ikhan@cardiffmet.ac.uk https://doi.org/10.30953/bhty.v7.338 citation: blockchain in healthcare today 2024, 7: 338 https://doi.org/10.30953/bhty.v7.3382 (page number not for citation purpose) imtiaz khan et al. in recent years, several blockchain-based health data marketplace have emerged11 with different business models and service choices. patientory12 as a pioneering example facilitates monetization of health data, opportunities to participate in clinical trials, ai and video based heath coaching services etc. here blockchain-like technologies been used to ensure data privacy and security. the platform’s interoperability further enhances its value, facilitating seamless data exchange among patients, healthcare providers, and researchers, thereby improving healthcare delivery efficiency. however, despite these successes, patientory faces challenges in achieving long-term user engagement and satisfaction. this is primarily due to the fact that monetization and generalized static servitization (video coaching service) are not enough for sustainable user engagement. here, using the user’s data the marketplace needs to offer (directly or through third party) a suite of ai or data analytics-based services with personalized and predictive capabilities tailored to adapt with the change of personal lifestyle, health and social conditions. the marketplace also needs to offer a community environment (through metaverse like technologies) through which collective intelligence, community surveillance can be achieved through collaboration and competition. integrating wearable-derived patient-generated data with clinical records will allow healthcare providers to use artificial intelligence and improved computing capabilities to analyze large amounts of data and to develop more accurate evidence-based diagnostic tools and treatment plans tailored to individual patients. health regulators, in particular, stand to benefit significantly from this model. with access to a vast pool of real-world data, regulators can make more informed decisions to improve efficiency, coordination, and accountability that will improve public health. last but not least, the marketplace model encourages a shift from a service-oriented healthcare system to a knowledge-driven one, where patients are no longer passive recipients of care but active participants in their health journey. disclaimer this editorial is based on the podcast discussion and the paper titled “from sharing to selling: challenges and opportunities of establishing digital health data marketplaces using blockchain technologies.” for a more detailed understanding, readers are encouraged to refer to the original paper and listen to the full podcast. financial and non-financial relationships and activities this editorial received no specific funding from any public, commercial, or not-for-profit sectors. conflicts of interest imtiaz khan and anjum khurshid are members of the bhty editorial board. mohamed maher reports no conflict of interest. contributors this editorial is based on the podcast where the first and second authored were questioned by the third author. all authors contributed to drafting and writing the editorial. data availability statement (das), data sharing, reproducibility, and data repositories none listed by author. application of ai-generated text or related technology none listed by author. acknowledgements the authors would like to thank tory cenaj for inviting us to write this editorial. references 1. zimmermann m. diet, nutrition, and the prevention of chronic diseases: by the world health organization, 1991, 203 pages, softcover. who, geneva. am j clin nutr. 1994;60:644–5. https://doi.org/10.1093/ajcn/60.4.644a 2. alzheimer’s research uk. dementia leading cause of death in 2022. [cited 2024 july 07]. available from: https://www. alzheimersresearchuk.org/news/dementia-is-the-uks-biggestkiller-we-need-political-action-to-save-lives/#:~:text=our%20 new%20ana lys i s%20shows%20that , can%20save%20 people%20from%20dementia 3. van der flier wm, de vugt me, smets ema, blom m, teunissen ce. towards a future where alzheimer’s disease pathology is stopped before the onset of dementia. nat aging. 2023;3:494– 505. https://doi.org/10.1038/s43587-023-00404-2 4. dash s, shakyawar sk, sharma m, kaushik s. big data in healthcare: management, analysis and future prospects. j big data. 2019;6:1–25. https://doi.org/10.1186/s40537-019-0217-0 5. wiederrecht g, darwish s. the healthcare data explosion. [cited 2024 july 07]. available from: https://www.rbccm.com/en/gib/ healthcare/episode/the_healthcare_data_explosion 6. li j, carayon p. health care 4.0: a vision for smart and connected health care. iise transac healthc syst eng. 2021;11:171–80. https://doi.org/10.1080/24725579.2021.1884627 7. davis re, jacklin r, sevdalis n, vincent ca. patient involvement in patient safety: what factors influence patient participation and engagement? health expect. 2007;10:259–67. https:// doi.org/10.1111/j.1369-7625.2007.00450.x 8. maher m, khan i, prikshat v. monetisation of digital health data through a gdpr-compliant and blockchain-enabled digital health data marketplace: a proposal to enhance patient’s engagement with health data repositories. int j inf manag data insights. 2023;3:100159. https://doi.org/10.1016/j.jjimei.2023.100159 9. narayan r, tidstrom a. tokenizing coopetition in a blockchain for a transition to circular economy. j cleaner prod. 2020;263:121437. https://doi.org/10.1016/j.jclepro.2020.121437 https://doi.org/10.30953/bhty.v7.338 https://doi.org/10.1093/ajcn/60.4.644a https://www.alzheimersresearchuk.org/news/dementia-is-the-uks-biggest-killer-we-need-political-action-to-save-lives/#:~:text=our%20new%20analysis%20shows%20that,can%20save%20people%20from%20dementia https://www.alzheimersresearchuk.org/news/dementia-is-the-uks-biggest-killer-we-need-political-action-to-save-lives/#:~:text=our%20new%20analysis%20shows%20that,can%20save%20people%20from%20dementia https://www.alzheimersresearchuk.org/news/dementia-is-the-uks-biggest-killer-we-need-political-action-to-save-lives/#:~:text=our%20new%20analysis%20shows%20that,can%20save%20people%20from%20dementia https://doi.org/10.1038/s43587-023-00404-2 https://doi.org/10.1186/s40537-019-0217-0 https://www.rbccm.com/en/gib/healthcare/episode/the_healthcare_data_explosion https://www.rbccm.com/en/gib/healthcare/episode/the_healthcare_data_explosion https://doi.org/10.1080/24725579.2021.1884627 https://doi.org/10.1111/j.1369-7625.2007.00450.x https://doi.org/10.1111/j.1369-7625.2007.00450.x https://doi.org/10.1016/j.jjimei.2023.100159 https://doi.org/10.1016/j.jclepro.2020.121437 citation: blockchain in healthcare today 2024, 7: 338 https://doi.org/10.30953/bhty.v7.338 3 (page number not for citation purpose) creating a health data marketplace for the digital health era 10. shahaab a, khan i, maude r, hewage c, wang y. public service operational efficiency and blockchain—a case study of companies house, uk. gov info quart. 2023;40(1):101759. https://doi.org/10.1016/j.giq.2022.101759 11. built in. blockchain in healthcare: 18 examples to know [internet]. [cited 2024 aug 7]. available from: https://builtin.com/ blockchain/blockchain-healthcare-applications-companies 12. patientory inc. case study: forging the path to consumer directed health through blockchain technology [internet]. 2019 may 20 [cited 2024 aug 7]. available from:  https:// patientory.com/blog/2019/05/20/case-study-forging-thepath-to-consumer-directed-health-through-blockchaintechnology copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see http://creativecommons. org/licenses/ by-nc/4.0. https://doi.org/10.30953/bhty.v7.338 https://doi.org/10.1016/j.giq.2022.101759 https://builtin.com/blockchain/blockchain-healthcare-applications-companies https://builtin.com/blockchain/blockchain-healthcare-applications-companies https://patientory.com/blog/2019/05/20/case-study-forging-the-path-to-consumer-directed-health-through-blockchain-technology https://patientory.com/blog/2019/05/20/case-study-forging-the-path-to-consumer-directed-health-through-blockchain-technology https://patientory.com/blog/2019/05/20/case-study-forging-the-path-to-consumer-directed-health-through-blockchain-technology https://patientory.com/blog/2019/05/20/case-study-forging-the-path-to-consumer-directed-health-through-blockchain-technology http://creativecommons. org/licenses/by-nc/4.0 http://creativecommons. org/licenses/by-nc/4.0 halamka page 1 of 2 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.19 real blockchain use cases for healthcare john d. halamka, md, editor-in-chief, bhty lockchain is at the peak of the hype curve right now, and venture capitalists are eager to fund any company with cyber-currency, blockchain, or bitcoin in the name. as the editor-in-chief of blockchain in healthcare today, my goal is to publish highquality opinion pieces and research papers about use cases that really require blockchain. just using blockchain in healthcare because it's cool does not make sense. in 2017, i worked on several production blockchain applications, so i have a sense what works and what does not. blockchain is not meant for storage of large data sets. blockchain is not an analytics platform. blockchain has very slow transactional performance. however, as a tamperproof public ledger, blockchain is ideal for proof of work/proof of stake. blockchain has the potential to implement smart contracts, making data available to those appropriately authenticated and authorized. blockchain is highly resilient and decentralized, so it works well in infrastructure-challenged locations. in my view, there are 3 general classes of appropriate blockchain use cases in healthcare. 1. provide proof of work every year, clinicians throughout the u.s. experience malpractice assertions—not a judgement of malpractice, just a claim. plaintiff attorneys request medical records and at times contend that medical records have been falsified. as a cio, i have been involved in cases in which i provide electronic medical records and then spend hours extracting audit trails to prove that no alteration was done. what if ehrs (electronic health records) posted a hash of every signed note to a blockchain? a tamperproof ledger of hashes could easily be compared to the original signed note to prove it was not altered. standard database technology cannot provide that level of assurance. it's a perfect application for blockchain. 2. guarantee data integrity the gates foundation has funded an effort in south africa to unify the hiv lab data of the country in support of the 90/90/90 national policy—90% of all hiv-infected patients should know they are positive, 90% of those should be treated with anti-viral medications, and 90% of those should have evidence of viral suppression based on two successive viral load tests, six months apart. b page 2 of 2 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.19 to architect this solution, gates has chosen a combination of biometrics, very basic phone apps, and blockchain. south africa has infrastructure challenges such as unreliable power and expensive bandwidth; so a distributed, decentralized data layer that is not affected by the failure of any local node makes sense. however, it's important to guarantee the integrity of that distributed data. the combination of relational technologies on decentralized servers with a very thin blockchain layer that validates the integrity of the data via a hash of every 10,000 records should work very well. 3. support an economic model in the meaningful use era,1 we did a great job with vocabulary standards (naming labs, medications, and problems), a reasonable job on the payload standard (medical summaries are good but often lack important data or have too much data of limited value), but we did not create a comprehensive transport environment (data governance, national provider directory, universal consent policy). in 2007, i suggested a kind of "smart contract" that i called the consent assertion markup language (caml).2 some blockchain implementations include smart contract capability built in. part of the smart contract idea could include micro-payments for data sharing, which could solve the healthcare information exchange sustainability challenge we continue to face. the idea that one decentralized infrastructure could provide support for various consent models and payment for data flows is appealing. in the mit medrec project we experimented with these concepts.3 startups are leveraging the same blockchain capabilities i.e. 1. the actual healthcare data are not stored in blockchain—it can remain in the underlying ehrs or registry databases operated by healthcare stakeholders 2. the blockchain infrastructure provides three benefits—a ledger of where a patient's records are to be found, smart contracts to determine who can access those records, and key pairs to ensure only authorized parties access the data 3. it also provides a simple micropayment mechanism for funding the ecosystem of data exchange the advice i give to stakeholders, investors, and innovators is to avoid statements like, "we're creating a cloud-hosted machine learning-driven blockchain-based interoperable mobile api [application programming interface]". i've heard over 50 start up pitches in the last 90 days that had that sentence on the first slide. instead, use the blockchain term only when something very unique to blockchain concepts is truly needed— proof of work, data integrity, and economic models. references 1. halamka jd, tripathi m. the hitech era in retrospect. n engl j med. 2017 sep 7;377(10):907-909. 2. halamka jd. the patient as steward of healthcare data: managing consent preferences. 2009. slide presentation. url: http://mycourses.med.harvard.edu/ec_re s/nt/60c424be-0489-432a-9056dcb46e95dd77/caml.ppt 3. ekblaw a, azaria a, halamka jd. lippman a. a case study for blockchain in healthcare: “medrec” prototype for electronic health records and medical research data. white paper. 2016. url: http://dci.mit.edu/assets/papers/eckblaw. pdf 1 (page number not for citation purpose) blockchain in healthcare today 2021. © 2021 the authors. this is an open access article distributed under the terms of the creative commons attribution-noncommercial 4.0 international license (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. citation: blockchain in healthcare today 2021, 4: 176 http://dx.doi.org/10.30953/bhty.v4.176 proof of concept markit: a collaborative artificial intelligence annotation platform leveraging blockchain for medical imaging research jan witowski1#, jongmun choi1#, soomin jeon1, doyun kim1, joowon chung1, john conklin2, maria gabriela figueiro longo2, marc d. succi3 and synho do1* 1laboratory of medical imaging and computation, massachusetts general hospital and harvard medical school, boston, ma, usa; 2division of emergency imaging, department of radiology, massachusetts general hospital and harvard medical school, boston, ma, usa; 3medically engineered solutions in healthcare (mesh) incubator, massachusetts general hospital, boston, ma, usa abstract current research on medical image processing relies heavily on the amount and quality of input data. specifically, supervised machine learning methods require well-annotated datasets. a lack of annotation tools limits the potential to achieve high-volume processing and scaled systems with a proper reward mechanism. we developed markit, a web-based tool, for collaborative annotation of medical imaging data with artificial intelligence and blockchain technologies. our platform handles both digital imaging and communications in medicine (dicom) and non-dicom images, and allows users to annotate them for classification and object detection tasks in an efficient manner. markit can accelerate the annotation process and keep track of user activities to calculate a fair reward. a proof-of-concept experiment was conducted with three fellowship-trained radiologists, each of whom annotated 1,000 chest x-ray studies for multi-label classification. we calculated the inter-rater agreement and estimated the value of the dataset to distribute the reward for annotators using a crypto currency. we hypothesize that markit allows the typically arduous annotation task to become more efficient. in addition, markit can serve as a platform to evaluate the value of data and trade the annotation results in a more scalable manner in the future. the platform is publicly available for testing on https://markit.mgh.harvard.edu. keywords: artificial intelligence; data annotation; learning from crowds; blockchain; rewarding system received: 26 february 2021; accepted: 24 march 2021; revised: 24 march 2021; published: 22 june 2021 as the field of supervised machine learning (ml) and artificial intelligence (ai) becomes more mature, researchers working on medical ml or ai aim to integrate more data to improve their models, as opposed to merely changes in the algorithm architecture (1–3). ensuring high-quality annotations is critically important in medicine: imaging can be non-diagnostic, and intraand inter-observer variability is high. about 25% of radiologists do not agree with other radiologists’ diagnoses and 30% do not agree with their own previous decisions (4). the ultimate ground truth, such as pathology reports, is not always available, and trained models often rely on the ‘soft’ annotated ground truth. biases from poorly annotated datasets can result in critical consequences for ml algorithms in clinical use. crowdsourcing annotations have been investigated for decades, including methods of combating noisy labels (5–9). however, to date, there have been few available collaborative annotation platforms for ml systems capable of handling medical imaging datasets. improving the quality of the database requires the participation of well-trained experts and a thorough curation process, which is based on voluntary commitment. it is important to consider that crowdsourcing data *correspondence: synho do. email: sdo@mgh.harvard.edu #these authors contributed equally to the work. to access the supplementary material, please visit the article landing page https://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.30953/bhty.v4.176 https://orcid.org/0000-0001-6211-7050 https://markit.mgh.harvard.edu mailto:sdo@mgh.harvard.edu http://dx.doi.org/10.30953/bhty.v4.176 citation: blockchain in healthcare today 2021, 4: 176 http://dx.doi.org/10.30953/bhty.v4.1762 (page number not for citation purpose) jan witowski et al. collection methods can be easily contaminated by mislabeling caused by undertrained participants. consider that the value of the data or accuracy of annotation may be easily estimated. in this scenario, it is possible to construct a high-quality dataset with an appropriate proportion of positive features for ai training by exchanging or trading datasets between researchers and vendors. furthermore, this transaction can be fairly evaluated and securely monitored. this research study introduces a web-based, zero-footprint collaborative annotation tool for medical imaging data, markit. the proof-of-concept experiment includes implementing the platform with pre-trained ai models and blockchain features, and using them to create preliminary annotations of a chest x-ray dataset for classification tasks. methods the study was approved by institutional review board of our hospital. the platform is currently available online using a modern web browser without the need for downloading or installing additional software. users are required to have an internet connection and to create an account to access the platform. the platform is implemented, including several modularized functions, as shown in fig. 1. the main module for image annotation consists of a simple digital imaging and communications in medicine (dicom) viewer and labeling tools. the dicom viewer allows annotators to change image brightness and contrast, and to zoom-in to read images in full resolution (fig. 2). for classification, the markit system provides the ability to save boolean annotations and associated confidence levels as six iterative grades (0, 20, 40, 60, 80, and 100%). for object detection problems, rectangular or free-line region-of-interest (roi) annotations are available. users can easily define their subcategories of labels for the specific target projects when they set up a project. this can be easily modified if the user is either an owner or manager of the project (fig. 3). the dicom viewer and annotation tools were implemented utilizing the open-source cornerstone.js javascript library [https://docs.cornerstonejs.org/]. cornerstone.js includes features for image loading, parsing, decoding, and tools commonly encountered in dicom viewers. our platform is capable of fetching images from any vendor-neutral dicom storage. we implemented a connection with both standard picture archiving and communication system (pacs) systems and dicom web-based restful web services and application programming interfaces (apis). in this proof of concept, we fig. 1. high-level data flow of markit. blockchain ledger storage and access are separate from the regular database. artificial intelligence interface allows to train new models based on gathered annotations and make annotation suggestions to speed up the workflow. http://dx.doi.org/10.30953/bhty.v4.176 http://cornerstone.js https://docs.cornerstonejs.org/ http://cornerstone.js citation: blockchain in healthcare today 2021, 4: 176 http://dx.doi.org/10.30953/bhty.v4.176 3 (page number not for citation purpose) markit: a collaborative artificial intelligence annotation platform utilized orthanc [https://www.orthanc-server.com, liege, belgium] and google dicom store (through google healthcare api, ca, usa), where image retrieval can be performed through wado (web access to dicom® persistent objects) protocols (10). connecting to standard pacs systems and fetching images with the c-get protocol were also implemented. additionally, our platform allows users to use non-dicom image files, common in fig. 2. main module for image annotation combining basic dicom viewer features (i.e. change brightness or contrast, zooming, etc.), displaying radiological reports and annotation tools (above the x-ray image). annotators can determine their confidence with regard to each label (blue bars on the right) and preview annotators by other team members (blue and red rectangles). fig. 3. various stakeholders and their roles in managing large projects for scalable medical image datasets. (a) the platform described in this study facilitates workflow for all parties, maximizing their focus on a single part of the process, project managers defining the project and managing access levels, data owners on image upload, and annotators on labeling. (b) project managers can coordinate projects by specifying labels in accordance with planned ai tasks, controlling visibility for all users, as well as granting and revoking permissions for annotators (c) data owners can upload images with additional options for choosing desired data storage systems and file naming conventions. (d) project managers can also export project-related data, including annotations by all team members and information about the time spent on labeling by users. http://dx.doi.org/10.30953/bhty.v4.176 https://www.orthanc-server.com citation: blockchain in healthcare today 2021, 4: 176 http://dx.doi.org/10.30953/bhty.v4.1764 (page number not for citation purpose) jan witowski et al. large-scale non-volumetric medical datasets, for example, national institutes of health (nih) and stanford chest x-ray datasets (11, 12). in markit, all patient information is anonymized. all stored studies are organized privately by users into projects. the platform allows project managers to assign access privileges to the project for other readers and annotators, thereby preventing unwanted access to sensitive medical data. in addition, specific users can have various access levels, limiting some features, such as data export, progress tracking, project statistics, and management (fig. 3). all annotations are saved in the database and include information on the time spent on a single case from the moment the image is completely loaded to the click of the submit button, the time of mouse clicking for labeling, or motionless duration to evaluate each label’s duration of tasks. users are allowed to export annotations in comma-separated values (csv) format for both classification results and roi labels. it is also possible to import radiological reports to the platform, matching them with specific cases. markit is developed to optimize annotation workflow, especially in large-scale datasets with multiple collaborators and stakeholders, and their roles were taken into account when designing the platform, as shown in fig. 3. the markit system is also connected with a locally developed ai inference restful (representational status transfer) service, running on the same device through a docker container [https://www.docker.com, ca, usa]. this service includes four ai classification models for chest x-ray data, predicting view position (i.e. ap: anterior-posterior vs. pa: posterior-anterior), pathological features, gender, and age. view position and gender predictions are framed as binary classification tasks, feature prediction as a multi-label classification task, and age prediction as a regression task. we plan to further expand the collection of available pre-trained models and to improve the performance of current models by changing datasets or model architectures. users of the platform can request model prediction on the loaded image in real-time by passing the input through a gpu-accelerated inference service. images are sent to the service from dicom storage via an api that evaluates sent data and returns predictions, probabilities, and feature activation maps. predictions are returned to users onto a markit user interface. feature activation maps in the form of gradient-weighted class activation mapping (grad-cam) (13) are overlaid over a dicom image as a red-green-bluealpha (rgba) matrix with adjustable opacity. the markit system provides a ‘review mode’ to evaluate discrepancies between annotators. the process of annotating medical imaging data for ml purposes is different from making diagnosis in the actual clinical environment, and annotators may have different standards for determining whether a particular feature exists. therefore, a plan to resolve disagreements is desirable to maintain consistency of the dataset. project managers may save time by running smaller sample projects before the main annotation project to assess the presence of various problems. this function shows the labeling results and reliability of annotators in the form of a heatmap, and allows the second annotators to check whether their results are in agreement with the preceding annotators. with this mode, annotators can develop better annotating strategies and prevent trial and error in main annotation projects. furthermore, we can take advantage of this mode for training or education purposes. before implementing the main project, it has a combined function that reduces unnecessary mistakes and pre-training sessions using the review mode. users can quickly check and resolve their discordance problem in pre-training sessions with markit using a review mode. finally, the presented platform includes an experimental blockchain implementation to partially track user activity, including annotating images, and uploading and exporting data. in the future, blockchain may facilitate better security and traceability of medical imaging datasets, especially when considering global platforms that deal with sensitive data, such as medical imaging. experiments and results three fellowship-trained radiologists classified the chest x-ray images as proof of concept in the private project. in total, 1,000 anonymized pa-view chest x-ray images with dicom format of massachusetts general hospital were uploaded to the markit platform. twenty-five classification labels were determined and assigned to the project (fig. 2, supplementary material 1). we compared these binary classification results with ai-generated prediction results from in-house data, generated the time statistics, and estimated the task difficulty. detailed ai algorithms are not included because they are not related to the scope of this study. you can think that it is not different from the general ml or ai algorithm. to suggest a clear analysis method, we only concentrated on seven critical labels with a clinically high value (i.e. interstitial lung disease, pneumonia, pulmonary edema, pleural effusion, cardiomegaly, pneumothorax, and atelectasis). other features can also be analyzed in the same way and have similar characteristics. the inter-rater agreement between the three annotators was measured (0.90) by matching the results of three annotators, and we also measured fleiss’s kappa value (0.63) to assess the reliability of agreement between three raters when assigning categorical ratings in this case, seven pathological feature annotations. of 7,000 labels that were annotated, in total (7 labels times 1,000 images), http://dx.doi.org/10.30953/bhty.v4.176 https://www.docker.com citation: blockchain in healthcare today 2021, 4: 176 http://dx.doi.org/10.30953/bhty.v4.176 5 (page number not for citation purpose) markit: a collaborative artificial intelligence annotation platform 370 were labeled by all annotators as positive and 5,954 as negative. the remaining 676 labels had differences in assessments between readers. the 95% confidence interval of total mean labeling time was 6.16 ± 0.21 sec, and cardiomegaly cases took the shortest labeling time (4.63 ± 0.54 sec); in contrast, pneumothorax cases required the longest labeling time (13.92 ± 3.93 sec, supplementary material 2). as shown in fig. 4, distribution of the time spent annotating varied depending on both the label type and radiologist. when annotations on a new dataset are received, it is important to understand the following: 1) how much is the data worth? 2) how much is any annotation worth? 3) which annotator contributed and how much? for that, we formulate the value of the data based on the dataset characteristics, time cost of entering the annotation, and its annotation accuracy. the average labeling time was identified as an indicator for estimating the labor in the annotation. to calculate accuracy, we measured agreement between the annotated label and pseudo-ground truth, defined as the majority rule between annotators. to evaluate the annotator’s contribution for cxr pa dataset, we exported the binary classification data and generated the annotation evaluation sheet consisting of true or false. as shown in fig. 5, for the k-th label of j-th image, the i-th annotator’s annotation results (true or false) are recorded as aijk. we consider the seven labels (fig. 4) for 1000 cases annotated by three annotators, so i=3, j=1,000, k=7. using the table, we devise an algorithm 1 to estimate each annotators’ contribution, level of challenge of each image and task, respectively, and to evaluate each annotator’s reward (eq. 1). in algorithm 1, we evaluate the contribution credit of i-th annotator for the k-th label of j-th image rkji, the value of k-th label of j-th image for data dkj, and the task value tk. basically, the lower the correct answer rate, the higher the value of the image and task and the annotators’ contribution. we set the pseudo answer akj for the k-th label of j-th image and consider it as a ground truth for each task (k-th label of j-th image). to count the laboring factor, we put the normalized time mean for k-th label t̄k. here, χ{condition} is a characteristic function having the value 1 if the condition is true, otherwise 0. it is obvious that x ∈ [a,b] can be normalized oppositely (b maps to −1, a maps to 1) as [ ] − + −    ∈ − b a a b x 2 2 1,1 fig. 5. annotation evaluation sheet. fig. 4. time distribution of each label and annotators. annotator c spent the shortest time among the annotators for all labels, except pneumothorax. the pneumothorax labeling requires the longest time, in general, likely due to the use of ancillary tools such as zoom to view the pleural line, compared with the cardiomegaly cases requiring the shortest time. algorithm 1. an algorithm for calculating reward factors. for k = 1, 2, 3, …, k # for each task for j = 1, 2, 3, …, j # for each image = 0, = 0 # number of true (nt) and number of false (nf) for i = 1, 2, 3, …, i # for each annotator = + { = = } # count the number of true = + { = = } # count the number of false end if > ; = 1, = # determine the ground truth else = 0, = for i = 1, 2, …, i (annotator) = { = = }/ ( { = }+ { = }) # add a credit to the correct annotator end = ( { = } + { = })/ ( + ) # compute the image difficulty end = ∑ /= 1 # compute the task value end http://dx.doi.org/10.30953/bhty.v4.176 citation: blockchain in healthcare today 2021, 4: 176 http://dx.doi.org/10.30953/bhty.v4.1766 (page number not for citation purpose) jan witowski et al. applying it on dkj ∈ [0,1] and rkji ∈ [0,0.5], we scale them as follows:  ( ) [ ]= ∗ − ∈ −d d2 0.5 1,1kj kj  ( ) [ ]= ∗ − ∈ −r r4 0.25 1,1 .kji kji each annotator’s reward reward (i) can be formulated as a linear combination of each factor as follows:     ( ) ( ) ( ) ( ) = ∗ σ + σ σ σ + σ = = = = = reward i total budget t t d r t t d r /k k k k j j kj kji i i k k k k j j kj kji 1 1 1 1 1 (eq. 1). this approach considers the information on the dataset and the estimated annotation quality and the time it takes to determine it, and the label-specific accuracy of the annotator. for our experiment, we assumed the value of the entire dataset as 1000t med token (a cryptocurrency used in the current research; any cryptocurrency could potentially be used in the platform) for the seed money of the data trading system, and distributed the markit currency to three annotators (i.e. annotator a: 371t, annotator b: 347t, and annotator c: 282t med token) from (eq. 1, supplementary material 3). equation 1 is the most fundamental formula that can be modified in different ways depending on the situation, for example, the importance of various factors can be considered through the sum of weights. in reality, the value of data will change. initially, we will start with a small amount of data, and the performance of ai developed using these data will also have limitations. as a large amount of data are, however, added gradually and more annotators label the data, the data’s value will increase, and the entire data’s value will increase. in this case, what is calculated by equation 1 is repeated according to the change in the quantity and quality of the data, and the value will change accordingly. to prevent non-expert annotators from exceeding experts in number making wrong ground truth, we introduced the ai as a quality controller. another research group developed this ai in our laboratory for chest x-ray analysis. according to the ai result, we set a temporary ground truth and assumed that it has better performance than a random choice (i.e. coin tossing). we calculated cohen’s kappa values between ai and each annotator. if this value was greater than 0.05, we assumed that this annotator has a better prediction power than random selection. all annotators show better performance than the threshold in each label (supplementary material 4), so we used all labels for reward calculations [https://github.com/ mgh-lmic/annotation_blockchain_share_calculation]. we tested a panacea blockchain in our implementation, which is developed on top of cosmos sdk [https:// cosmos.network/sdk] and tendermint framework [https:// tendermint.com/sdk/]; however, markit can be integrated into any framework that supports blockchain implementations. interacting with the blockchain can be executed through the restful api and the command-line interface (cli) for a go (programming language) application. in our experiments, we were saving user activity as hashed information in separate transactions on the blockchain. using this information, we tried to calculate the dataset’s value and estimated the awards for annotators via a blockchain currency to facilitate accurate annotation. in addition, you will be able to add various applications and services. however, in this proof-of-concept experiment, everything is not completely implemented and is continuously added in the future. discussion crowdsourcing annotations for training ai models have been used extensively and effectively for various computer vision tasks. however, in the medical imaging field, annotation tasks require the expertise of a trained radiologist, and even for simple tasks, crowdsourced annotations can be noisy or inaccurate. a successful crowdsourcing platform include the following benefits: • faster production of high-quality labeled datasets, • more economical cost of obtaining annotations on the large datasets, • accelerated development of ml or ai for multiple medical imaging tasks. the presented platform allows researchers and commercial vendors to accelerate the annotation and development of medical imaging datasets. current tools enable labeling for classification and object detection tasks, and provide various data and project management tools. integration of ai can already assist users by providing activation maps as a suggestion of the area of interest. in the future, connected ml or ai models will further speed up the annotation process by offering users ai-processed labels. this study confirms that quick annotation of largescale images is possible in the above-mentioned platform. the results show high variability of the annotation speed between readers, which may help determine annotator engagement in the process. demirer et al. (14) showed a locally designed graphical user interface, where a single radiologist performed single-label classification and http://dx.doi.org/10.30953/bhty.v4.176 https://github.com/mgh-lmic/annotation_blockchain_share_calculation https://github.com/mgh-lmic/annotation_blockchain_share_calculation https://cosmos.network/sdk https://cosmos.network/sdk https://tendermint.com/sdk/ https://tendermint.com/sdk/ citation: blockchain in healthcare today 2021, 4: 176 http://dx.doi.org/10.30953/bhty.v4.176 7 (page number not for citation purpose) markit: a collaborative artificial intelligence annotation platform object detection of proximal femoral fractures on radiographs. expert annotators spent about 10 sec per study (iqr: interquartile range, 3-21 sec per study), labeling over 1,000 radiographs over 7 days. their study results confirm that a layout familiar to radiologists might speed up the annotation process. however, their solution was limited in terms of scalability and accessibility outside of a single institution. this research study presents the development of a zero-footprint, web-based tool that is easy to implement on both the local and global scale. our approach suggests using a minimal number of features for the user-friendliness of the interface. furthermore, we provide annotators with additional clinical information, including radiological reports or patient history, to improve annotation accuracy as an option. the results of high-reliability annotation can be obtained by providing annotators with information comparable with the real clinical environment. to combat the widely known problem of ‘soft ground truth’, imaging does not always correlate with hidden clinical information. previously, a few other image annotation tools were presented in the literature. rubin et al. presented epad, an online platform for quantitative imaging (15). their solution was focused more on clinical trials and cancer imaging, providing annotation templates for quantitative image features and tools for size measurements and several additional plugins. the proposed platform did not have tools or workflows optimized specifically for tasks typical in computer vision. lesiontracker is a platform with very similar functionalities, which is also dedicated to cancer imaging research (16). other developed tools, such as ril-contour proposed by philbrick et al., are often more task specific (17). the authors presented software focused on volumetric annotation, especially image segmentation. they also included using locally developed ai models and displaying saliency maps to understand model interference better. however, their solution is not web-based and synchronized, which drastically limits the potential for crowdsourcing annotations. in addition, they used the nifti file format instead of the dicom standard. deeplnanno proposed by chen et al., which is another web-based system that implements deep learning models inside the platform for pre-annotation (18). having said that their solution is explicitly dedicated to lung nodule annotation in ct studies. in contrast, markit attempts to overcome previous research limitations, presenting a robust platform dedicated to cnn-based ml or ai research in the medical imaging field. connecting multiple restful services allows this platform to scale and rapidly increase further functionality. the blockchain technology has been widely recognized to deliver decentralization and transparency to solutions in many areas. some attempts have been made in medicine to utilize those benefits, mostly when handling electronic health records, promising better management for data ownership, sharing, or authorization (19). nevertheless, only rarely attempts to utilize blockchain have resulted in developing a tool useful in the clinical setting. our experiments explored how the blockchain technology could encourage transparency and trust when crowdsourcing annotations are practiced by saving their activity in an immutable ledger. the blockchain technology has the advantage of defending against data manipulation without the installation of an additional security system. we could achieve security for image upload or annotation record modulation without compromising user convenience. in the future, blockchain might give incentives for annotators or inspire anonymous data sharing. it is still not fully clear when and where the blockchain technology might serve better than the standard approaches (e.g. traditional databases) in radiological tools, but further research will most likely maximize their benefits. currently, markit still has a few limitations. first, our software does not yet include tools for image segmentation. although cornerstone.js library does offer basic segmentation tools, we believe that other tools, such as 3d slicer, are more appropriate for complex image segmentation, which often requires more complex workflow and multiple manual and/or semi-automatic segmentation processes. second, at present, our platform does not fully support volumetric images and non-image dicom instances, such as dicom-seg and dicom-sr. finally, although markit enables users to upload dicom and non-dicom (e.g. jpg) image files, several other simple imaging formats are commonly used in the radiology research community, for example, nifti (especially in the neuroimaging community) or nrrd (nearly raw raster data). as this study was conducted by three radiologists of similar levels belonging to a hospital, it would be exciting to see how annotators of various hospitals’ at different levels participated. although not yet realized, the blockchain technology will enable us to obtain a free and fair data exchange system among researchers in the near future. currently, most of the data are owned by healthcare providers, major national research institutes, and large research institutes. ultimately, sharing all data without condition will help create a new value. still, it would not be easy to actively share data without compensation for intellectual labor, such as annotation and curation, reflecting the benefits of the institution that owns it. we aim to continue to improve the markit system and leverage the potential of blockchain technology to reflect the value of the data and use it as a currency for data transactions in the near future. the issuance of currencies with a specific purpose for data exchange may also help in data acquisition for ai development while being less likely to cause ethical problems related to data ownership issues. http://dx.doi.org/10.30953/bhty.v4.176 http://cornerstone.js citation: blockchain in healthcare today 2021, 4: 176 http://dx.doi.org/10.30953/bhty.v4.1768 (page number not for citation purpose) jan witowski et al. conclusions the value of data is variable, and therefore, it would be challenging to adjust prices according to supply and demand principles. our mathematical evaluation tool is based on various factors, and it reflects the accuracy of annotators, the balance of labels, and the depth of information. researchers can quickly evaluate the value of a dataset and prevent data contamination caused by the wrong annotation. we used the mean annotation time to measure each label’’s labor intensity, and the ai prediction value was used to estimate the ground truth. owing to the nature of cloudsourcing notations, it is difficult to clearly assess the ability of annotators, leading to problems in estimating the ground truth by majority rule. the participation of many people who do not have prior knowledge of the annotation task can cause serious problems with data reliability. it will be an exciting research topic on how markit and blockchain technologies can induce positive effects on crowdsourcing annotation and data exchanges. we presented a collaborative annotation platform dedicated to medical imaging. the markit system efficiently performs crowdsourcing annotation and provides indicators to evaluate the value of data and efforts of annotators. funding this research was conducted in part with the help of medibloc and google cloud. the sponsors took no role in the study design, data collection, analysis, decision to publish, or manuscript preparation. conflict of interest the authors declare no potential conflicts of interest. acknowledgements we acknowledge the financial and technical support of medibloc and the massachusetts general hospital it team and dr. michael c. muelly (former google cloud) in supporting this study. thank you for the foundational research of the early lab members, sehyo yune, myeongchan kim, and jinserk paik. thank you to the members of dr. joseph schwab's team (orthopaedic spine surgery and spine oncology) who are still actively using markit. references 1. abaho m, bollegala d, williamson p, dodd s. correcting crowdsourced annotations to improve detection of outcome types in evidence based medicine. ceur workshop proc [internet]. 2019 [cited 10 march 2021]. available 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http://proceedings.mlr.press/v9/yan10a.html http://proceedings.mlr.press/v9/yan10a.html https://ieeexplore.ieee.org/abstract/document/8953406 https://ieeexplore.ieee.org/abstract/document/8953406 https://doi.org/10.1007/s10278-018-0082-y https://ojs.aaai.org//index.php/aaai/article/view/3834 https://ojs.aaai.org//index.php/aaai/article/view/3834 https://ieeexplore.ieee.org/document/8099852 https://ieeexplore.ieee.org/document/8099852 https://ieeexplore.ieee.org/document/8237336 https://ieeexplore.ieee.org/document/8237336 https://doi.org/10.1148/ryai.2019180095 https://doi.org/10.18383/j.tom.2018.00055 https://doi.org/10.18383/j.tom.2018.00055 citation: blockchain in healthcare today 2021, 4: 176 http://dx.doi.org/10.30953/bhty.v4.176 9 (page number not for citation purpose) markit: a collaborative artificial intelligence annotation platform trials. cancer res 2017; 77(21): e119–22. doi: 10.1158/00085472.can-17-0334 17. philbrick ka, weston ad, akkus z, kline tl, korfiatis p, sakinis t, et al. ril-contour: a medical imaging dataset annotation tool for and with deep learning. j digit imaging 2019; 32(4): 571–81. doi: 10.1007/s10278-019-00232-0 18. chen s, guo j, wang c, xu x, yi z, li w. deeplnanno: a web-based lung nodules annotating system for ct images. j med syst 2019; 43(7): 197. doi: 10.1007/s10916-019-1258-9 19. abdullah s, rothenberg s, siegel e, kim w. school of block-review of blockchain for the radiologists. acad radiol 2020; 27(1): 47–57. doi: 10.1016/j.acra.2019.06.025 http://dx.doi.org/10.30953/bhty.v4.176 https://doi.org/10.1158/0008-5472.can-17-0334 https://doi.org/10.1158/0008-5472.can-17-0334 https://doi.org/10.1007/s10278-019-00232-0 https://doi.org/10.1007/s10916-019-1258-9 https://doi.org/10.1016/j.acra.2019.06.025 page 1 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 enforcing human subject regulations using blockchain and smart contracts olivia choudhury,1 hillol sarker,2 nolan rudolph,3 morgan foreman,4 nicholas fay,5 murtaza dhuliawala,6 issa sylla,7 noor fairoza,8 amar k. das9 authors 1olivia choudhury, ms, phd, postdoctoral researcher, ibm research, cambridge, massachusetts, usa. 2hillol sarker, ms, phd, postdoctoral researcher at ibm research, cambridge, massachusetts, usa. 3nolan rudolph, bs, research engineer, ibm research, cambridge, massachusetts, usa. 4morgan foreman, bspsy, healthcare data scientist, ibm research, cambridge, massachusetts, usa. 5nicholas fay, mcs, healthcare data scientist, ibm research, cambridge, massachusetts, usa. 6murtaza dhuliawala, mcs, research engineer, ibm research, cambridge, massachusetts, usa. 7issa sylla, ba, research engineer, ibm research, cambridge, massachusetts, usa. 8noor fairoza, ms, dev ops engineer, ibm research, cambridge, massachusetts, usa. 9amar das, md, phd, director of learning health systems, ibm research, cambridge, massachusetts, usa corresponding author olivia choudhury, phd, olivia.choudhury1@ibm.com keywords: blockchain, clinical trial, “common rule”, data security and privacy, distributed ledger, healthcare and medical research, human subject regulations, hyperledger fabric, protection of human subjects, smart contracts section: use cases/pilots/methodologies recent changes to the common rule, which govern institutional review boards (irb), require implementing new policies to strengthen research protocols involving human subjects. a major challenge in implementing such policies is an inability to automatically and consistently meet these ethical rules while securing sensitive information collected during the study. in this paper, we propose a novel framework, based on blockchain technology, to enforce irb regulations on data collection. we demonstrate how to design smart contracts and a ledger to meet the requirements of an irb protocol, including subject recruitment, informed consent management, secondary data sharing, monitoring risks, and generating automated assessments for continuous review. furthermore, we show how we can employ the immutable transaction log in the blockchain to embed security in research activities by detecting malicious activities and robustly tracking subject involvement. we evaluate our approach by assessing its ability to enforce irb guidelines in different types of human subjects studies, including a genomic study, a drug trial, and a wearable sensor monitoring study. https://doi.org/10.30953/bhty.v1.10 mailto:olivia.choudhury1@ibm.com page 2 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 keywords: blockchain, clinical trial, “common rule”, data security and privacy, distributed ledger, healthcare and medical research, human subject regulations, hyperledger fabric, protection of human subjects, smart contracts he federal policy for the protection of human subjects in research (the "common rule") has recently undergone regulation revisions (the "final rule"). the common rule is the subpart a of the department of health and human services (dhhs) regulations, 45 cfr part 46, and outlines the basic provisions for institutional review boards (irbs), informed consent, and assurances of compliance. the final rule revision is expected to be effective july 2018 and has been the source of much ethical debate around consent management. irbs are established under the common rule to review and approve of research that is not directly conducted by a federal department. an irb protects the rights and welfare of human research subjects recruited to participate in a research activity conducted at its affiliated institution. the board has the authority to approve, require modifications to, or disapprove of a research protocol based on the federal regulations and local policies at their institution. an irb must ensure that the research protocol details the implementation of adequate informed consent and study procedures so as not to jeopardize the rights, safety, or wellbeing of the human subjects.1 obtaining informed consent is one of the most sensitive and complex ethical issues in clinical research.2 no entity may involve a person as a subject in research without obtaining the legally effective informed consent of the subject or the subject's legally authorized representative.3 while there are many different types of consent, broad consent has been used by the research community for many years to collect, store, and use subjects' data and samples for unspecified future research. the final rule, designed to address broader types of research, creates new regulations for establishing a framework of broad consent as a substitute for traditional informed consent. prior to the final rule, there were only two alternatives for using identifiable data or biospecimens in a research study for which researchers had not secured study-specific consent: (1) obtaining an irb waiver of consent or (2) removing personal identifiers. the final rule creates new exemption categories for the storage, maintenance, and research of data and biospecimens involving identifiable information under which broad consent is a condition for the exception. exempt research in the new categories is required to undergo limited irb review to ensure adequate privacy safeguards are in place for identifiable private information and identifiable biospecimens. to enforce broad consent, the healthcare institution has to maintain a tracking system of biospecimens approved for future research. the final rule also creates a provision that multi-site research will use a single irb for the part of the research conducted within the united states, effective in 2020. an individual institution from this group, however, may still conduct an additional internal irb review not limited to the standard regulatory guidelines. finally, the final rule removes the requirement for ongoing research studies that received an expedited review to conduct a continuing review. this is also the case for studies that have completed interventions and are solely analyzing data or continuing observational follow up. 4 a major challenge of enforcing the common rule and the final rule regulations is that, once a protocol is approved by the irb, determining whether the protocol procedures are being violated is difficult. institutions can easily become overwhelmed in the pursuit of compliance with the dhhs regulations, often due to manual record keeping.5 although federal regulators expect institutions to adopt better data management strategies, institutions continue to struggle on this front, leading to corrective actions becoming necessary. when subjects t https://doi.org/10.30953/bhty.v1.10 page 3 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 withdraw consent from a study, their data is still allowed to be used by the researcher. for data that can be used for multiple studies, especially biological samples, it is difficult to guarantee that it will not be used for research that conflicts with the subject's values. further, to prepare for continuing or final reviews of the research, investigators spend a significant amount of time compiling the data within a strict deadline. to mitigate these challenges and ensure proper enforcement of the guidelines and regulations set forth by the irb, we propose a novel approach that leverages the distributed, shared ledger technology called blockchain. although blockchain gained prominence in the financial domain through the popular cryptocurrency bitcoin,6 it has been deemed as a promising solution for several applications in healthcare and medical research.7-10 many healthcare applications have proven the benefits of this technology in building a secure platform for managing and analyzing sensitive healthcare data. medrec11,12 is a decentralized record management system to manage electronic medical records using ethereum (an opensource, public, blockchain-based distributed computing platform). the orange consent management service13 offers a consent management system for ehealth based on hyperledger fabric (a permissioned blockchain infrastructure). in this paper, we demonstrate the use of private blockchain in designing a system that enforces the requirements of irb protocols, as defined in the common rule and the final rule. the system incorporates the functionalities and regulatory constraints in the smart contract, stores consent and sensitive information on the ledger, and monitors risks and generates results for continuous review using the immutable transaction log. by being an integral part of how data in a research study are collected, stored, managed, and analyzed, it insures that the regulations are consistently enforced across all the entities involved, including investigators, subjects, and research organizations. human subject regulations a. irb protocol review after submission to an irb a research protocol goes through one of three types of review: (1) exempt, (2) expedited, or (3) full board. the type of review is determined by the level of risk based on certain categories defined in 45 cfr 46.101(b) and 45 cfr 46.110. exempt review can occur for protocols that involve anonymous or publicly-available data, including surveys, retrospective chart reviews, or analysis of specimens without subject identifiers. research that falls under exempt review still requires registration with the irb. an expedited review can be used by an irb when the research protocol involves no more than minimal risk to a human subject. examples of expedited research protocols include studies collecting samples of dna, voice recordings, or specimens with subject identifiers. all other research that does not fall into the categories of exempt and expedited review is subject to a full board review. the irb conducts a continuing review of research protocols that underwent full board reviews at intervals deemed appropriate to the degree of risk, but not less than once per year. the continuing review examines any changes or negative instances that occurred in the research, including withdrawals, adverse events, and unanticipated problems. if the research has been completed, the continuing review will become the final report. if the research needs to extend past the approval period of the irb, the protocol must be resubmitted and approved for renewal. b. irb protocol requirements the criteria for irb approval of research includes the following seven requirements, according to 45 cfr 46.111(a): 1. risks to subjects are minimized by using study procedures which are consistent with sound research design and which do not unnecessarily expose subjects to risk. 2. risks to subjects are reasonable with respect to anticipated benefits, based only on the risks and benefits resulting https://doi.org/10.30953/bhty.v1.10 page 4 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 from the research, not collateral therapies. 3. selection of subjects for the study is equitable, given the purpose of the research and the setting in which it will be conducted. 4. informed consent from the subject, per cfr 46.116(a), must include the purpose of the research, expected duration of the subject's participation, and the details of the procedures to be followed. the basic elements also cover the benefits and foreseeable risks to the subjects, disclosure of appropriate alternative procedures or courses of treatment, and a description on how confidentiality of records identifying the subject will be maintained. cfr 46.116(b) describes additional elements in informed consent such as unforeseeable risks, anticipated circumstances under which the subject's participation may be terminated by the investigator, consequences of a subject's decision to withdraw from the research, procedures for orderly termination of participation by the subject, and approximate number of subjects involved in the study. 5. informed consent must be documented using a written consent form approved by the irb and signed by the subject or the subject's legally authorized representative. under a broad consent model, this consent then carries over to the secondary usage of the data as it is de-identified and requires no further consent for secondary usage. 6. when appropriate, the research plan must make provisions for monitoring the data collected to ensure the safety of subjects. 7. when appropriate, there are adequate provisions to protect the privacy of subjects and to maintain the confidentiality of data. 8. in addition, cfr 46.111(b) mandates safeguards are included for subjects that are likely to be vulnerable to coercion or undue influence, such as children, prisoners, pregnant women, mentally disabled persons, or economically or educationally disadvantaged persons, to protect the rights and welfare of these subjects. c. informed consent the process of informed consent is a dynamic and ongoing process. even though consent for the research is given, subjects are not obligated to continue the research until the completion of the study period or study activities. once consent is withdrawn, researchers are obligated to no longer contact the subject about collecting more data. the researchers are, however, still able to use the data collected up to the point of withdrawal. consent may also be required again if any major changes happen to the subject's ability or willingness to take part in research or if any new major information has become known during the conduct of the study.14 when consent is needed, there are multiple types that can be used for research studies. the most customary form of consent is specific consent, which is only applied to one research study. biobank research may involve subjects participating in multiple research questions or studies. obtaining specific consent from each time new research questions arise can be challenging. as a result, broad and blanket consent are used. blanket consent allows research without any restrictions to data, while broad consent allows for a wide range of future studies, subject to specified restrictions. a major issue with blanket consent is that the subject's data may be used for studies that conflict with the individual's values. meta-consent goes a step further and allows individuals to express preferences for the consent they want to give for certain types of research to ensure the research aligns with their values. dynamic consent facilitates the consent process with two-way ongoing communication with researchers and subjects through on-line platforms.15 blockchain technology a. public versus private blockchain https://doi.org/10.30953/bhty.v1.10 page 5 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 when bitcoin's6 rising popularity showcased there could be trust in a network despite the fact that no particular node could be trusted, it paved the way for many further implementations and uses of open, public blockchains. for any public or permissionless blockchain network to function, some consensus model must be implemented such that potentially malicious or faulty actions of a compromised user will be negated by the participation of the remaining users. bitcoin is successful due to its use of a proof of work (pow) consensus model.16 while pow has been relied upon for creating a trustworthy system to ensure malicious users cannot interfere or tamper with the network for personal gain, it comes at a cost of high energy usage and slow transaction rates. this is due to the fact that “miners” are tasked with solving a computationally intensive puzzle in order to add a block of transactions to the chain, and they are rewarded in cryptographic tokens upon success. other proposed methods of instilling trust in a network include proof of stake (pos)17 and proof of elapsed time (poet)18 proof of stake eliminates the energy usage and transaction rate sacrifices of pow, but still requires the network to incorporate a cryptographic token. participation in such networks comes at a cost, in tokens, to the user. although poet gets away from the energy or token cost of pow and pos, it still cannot achieve immediate finality of transactions, as a fork in the blockchain can temporarily exist before being solved algorithmically. its reliance on the literal passing of time also means it cannot realize the high transaction rate possible with other models. the above models solve trust in a permissionless network. however, private blockchains operate under much different circumstances.19 these are based on permissioned networks, which restrict who can join the network, read the ledger, propose transactions, and participate in consensus. therefore, permission to participate in the network can be limited only to known, trusted entities. if a particular use case is suitable for a closed network rather than a public one, advantageous simplifications can be made. although a cryptographic token can be implemented if such a need exists, it is not required to incentivize mining (pow) or to prove one has a financial stake in the network (pos). transactions in a permissioned network can be considered immediately final, as the possibility of having to resolve a fork is nonexistent. due to immediate finality as well as expedited consensus, their transaction rates are higher than any existing public blockchain model can produce. in addition to increased transaction speed and finality, permissioned networks also benefit from improved privacy. the authors of medrec11,12 pursued a blockchain framework built on top of ethereum,20 a pow-based, permissionless network, to develop a solution for electronic medical record management. while it offered many improvements over traditional systems, an acknowledgement was made that frequency-based analysis of even encrypted data transactions on the public chain could provide insights on network activity to unwanted third parties. this issue is solved by the inherent design of permissioned networks, where only approved nodes could view the underlying activities. b. implementations of private blockchain within the domain of private or permissioned blockchain frameworks, there are several options to consider. many of the first pursuits in this area were created to become solutions in the financial sector. these networks, including quorum,21 ripple,22 and chain.23 are opensource and very promising in their own right, but were not created with healthcare-specific considerations in mind. many of them, such as quorum, fork the popular permissionless blockchain network ethereum or other cryptocurrency-focused designs, and add permissioning and other functionalities as needed. in contrast, hyperledger fabric 24 was designed from the ground up to enable permissioned, secure use of distributed ledger technology.25 it allows for modular inclusion of different consensus models and membership service providers, as well as the creation of private channels within a network that only https://doi.org/10.30953/bhty.v1.10 page 6 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 specified participants operate on. this flexibility in design and privacy makes it a promising solution for applications in medical and healthcare research. in prototyping a system to store patient's consent for sharing sensitive health data with health practitioners, researchers adopted hyperledger fabric to build a platform for secure and efficient management of sensitive data.13 c. hyperledger fabric hyperledger fabric, introduced by the linux foundation, is one of the primary private blockchain frameworks currently available.24 it is based on a permissioned network comprising only interested stakeholders as participants. this restricts anyone from joining the network, updating the ledger, or initiating transactions. a network typically consists of multiple nodes, a smart contract implementing the business logic, and a ledger maintaining transaction log and its state as a key-value store. nodes are logical entities running on a physical server that can be maintained by participants. they can be categorized into client, peer, and orderer nodes. client nodes invoke transactions and are connected to both peers and orderers. peer nodes maintain the ledger and receive state updates in the form of blocks. they can also act as endorsers for verifying and validating a requested transaction. unlike public blockchains, hyperledger fabric employs an endorsement policy that defines the necessary conditions for a valid transaction. a transaction is approved only when it acquires endorsement signatures from designated endorsers, as defined in the policy. orderers support communication between clients and peers. when a client invokes a transaction request, the message is broadcasted to all peers. on receiving signature from all the endorsers, the orderer broadcasts a message to all peers to update their copy of ledger. this is further illustrated in figure 1. hyperledger fabric provides an additional layer of security by creating private channels between members of the network. each channel maintains a ledger that can only be accessed by the members of that channel. since it does not rely on the compute-intensive proof of work protocol to attain consensus, the overhead of transactions is significantly reduced. this helps the system to be scalable when increasing the workload or adding users.26 since the immutable transaction log records all transaction requests, the system can also track unauthorized or malicious transactions initiated by nodes. we further describe the different components of hyperledger fabric figure 1: outline of transaction flow in hyperledger fabric. it depicts a use case where: (1) a client node sends a transaction proposal to the endorsing nodes (for this example, all the peer nodes act as https://doi.org/10.30953/bhty.v1.10 page 7 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 endorsers). (2) endorsers simulate the transaction and generate endorsement signature. (3) client node collects endorsement signatures. (4) client node sends the signatures to the ordering service. (5) ordering service verifies signatures and broadcasts a message to the peer nodes to update their ledger. 1. smart contract. smart contract, referred to as chaincode in hyperledger fabric, is the business logic written in a machine readable and executable language. the features offered by the smart contract are agreed upon by relevant parties and define the functionalities afforded to the members of the network. it enables fine-grained access checks to verify the authenticity of proposed transactions. unlike a regular database, the ledger can only be accessed or updated through the functionalities defined in the smart contract. once the smart contract is installed and instantiated on a network, it can only be updated upon mutual agreement between the parties. 2. transaction. transactions encompass the act of invoking the functionalities outlined in the smart contract. users can initiate transactions to read or write to the ledger with the help of application program interface (api) calls. a request is sent as a transaction proposal to endorsing peers for simulation. the results of the simulation are collected and sent to the orderer node for verification. since all the peers in a network maintain an identical copy of the ledger, simulations should produce the same results. once the results are verified, the orderer broadcasts a message to all peers to update their ledger. transactions are considered successful upon distribution by the orderer, indicating the proposal was successfully simulated and accepted upon validation. although an unsuccessful transaction cannot update the ledger, the request is logged in the history, in support of an auditable system. figure 1 depicts the scenario of transaction flow in hyperledger fabric. 3. endorsement policy. the endorsement policy informs the committing peers on the validity of a proposed transaction. the decision is based on the contents of the collected simulation results, endorsing peer signatures, and authorization certificates. the policy defines a list of endorsing peers and number of endorsements required to validate a proposed transaction. once all the criteria are met, the results of the simulated transaction are propagated to the relevant peers, thus updating their ledgers and data to ensure consistency. hyperledger fabric further allows more granular access control that requires involvement at the level of participant, rather than the high-level organization. based on the use case, the system can be designed to meet both the requirements. system design to address known challenges with irb protocol implementation and ongoing enforcement, we designed a blockchain-based system in which irb protocols are integrated authoritatively as steps in the research process. in our system, all transactions pertaining to consent management, data collection, and data sharing are executed through a smart contract's programming logic. interactions with the smart contract make the system secure, efficient, and auditable, thereby ensuring reliable enforcement of irb guidelines. a. consent management our design includes a simple interface for subjects to interact with the system. this consent may also include granular access rights for data. they can specify the users, duration, and type of data they intend to share. once consent is obtained from the subject, the details are securely stored on the ledger. since consent is typically collected and maintained by the principal investigator or coordinator of the study, they can have direct access to this information on the ledger. all other entities must request access to the data collected during the study through an access-control server, which communicates with a consent server to validate the authenticity of the request. if a subject withdraws from a study or revokes permission to share data, the corresponding information on the https://doi.org/10.30953/bhty.v1.10 page 8 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 ledge is updated through the smart contract. the updated consent information gets reflected upon subsequent verification by the consent server. in figure 2, we demonstrate our design of informed consent management using blockchain technology. b data collection after receiving consent, the principle investigator (pi) or research coordinator collects data from subjects. methods of data collection are tailored to the research objectives of the specific study in question, dictated by the study protocol, and enforced by the smart contract. this may involve a set of multimodal measurements, or, in the case of longitudinal research, repeated measurements. personal information that can potentially identify a subject are retrieved and stored separately. since blockchain provides a secure platform, we use the ledger to store protected health information (phi) attributes and consent information. this ensures that only authorized entities in the network have access to data. due to the distributed nature of the ledger, data is replicated on multiple nodes in a channel. this can cause an overhead when storing large volumes of data on the ledger, often experienced in genomic studies. to address this challenge, we store the high-volume data collected during the study in a database. access to this database is restricted by an access-control server. depending on the type of consent, identifiable information from the data is removed prior to storing it in the database or repository. in the case of verbally administered interviews, the identity of subjects may be revealed by their response to a questionnaire. our proposed system generates a unique key for each subject and stores it on the ledger. before transferring data to the database, each record is purged by replacing the real identity with this key, making the data anonymized for downstream analysis. this is also relevant to clinical trial of drugs, where subjects are typically divided into a control group and a treatment group. once the study coordinator records outcome measures and cases of adverse effects, the identifiable information can be saved on the ledger and the demographics and outcome measures can be stored in the database. focus group is another conventional method of qualitative health research, where the study coordinator records audio or video of subjects. such recordings pose a risk of identifying the individuals from their speech traits. the study coordinator or a speechto-text module should annotate recordings and replace any mention of a specific person's name by his or her unique, anonymous key. in some studies, certain types of data may raise an additional risk of privacy. for instance, in genomic studies, a set of specific genes can be used to uniquely identify a subject. for such cases, we use an intermediate step of deidentification to obfuscate identifiable traits before storing the data. pre-processing may require one-step or two-step de-identification. generalization, suppression, randomization, and sub-sampling are some of the widely-used techniques of de-identification.27 c. data sharing once data are collected and stored in a database, a third-party research organization may request access to these data through the access-control server. for blanket consent, the least restrictive type of consent, the access-control server does not restrict data access. for all other consent types, it conveys the request to the consent server, which verifies the access rights stored on the ledger for authentication. for valid requests, the access-control server queries the database to retrieve data of consented subjects, thereby sharing the data only with intended research organizations while maintaining a strict consent system. if a subject withdraws consent for data sharing, the updated access rights on the ledger will restrict further data access. an auditor requesting statistics of the collected data will follow a similar procedure in order to have the data shared with them. the method of secondary data sharing implemented by our blockchainbased solution is depicted in figure 2. hyperledger fabric offers an additional layer of data security through endorsement policy, which authenticates a transaction request. such a policy https://doi.org/10.30953/bhty.v1.10 page 9 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 defines the set of endorsing nodes that must simulate a requested transaction to return a response and a signature for proof. for transactions that involve sharing data with thirdparties, the nodes corresponding to research subjects and investigators can act as endorsers. even if a third-party acquires necessary access rights for secondary data sharing, any discrepancy in the simulated results will immediately halt data access. the rules can be tuned to conform to the requirements of different types of transactions. the endorsement policy coupled with granular access control empower the data owners to restrict unauthorized data sharing. evaluation in this section, we describe how our proposed blockchain-based system enforces the major requirements of irb protocols. these mechanisms are evaluated for coverage and completeness against the specific irb protocols from three different research studies: a genomic study,28 a drug trial,29 and a wearable sensor monitoring study,30 our scheme is shown to be sufficient for enforcing the irb guidelines of all three studies. figure 2: design of our proposed blockchain-based system for enforcing irb protocol. the workflow involves: (1) subjects consenting to participate in the study. (2) storing access rights for data sharing and phi attributes of subjects on the ledger. (3) storing data collected from subjects during the study in a database. (4) a third-party or research organization requesting to access data through access-control server. (5) access-control server requesting consent server to verify access rights of the organization. (6) consent server using smart contract to retrieve access rights stored on the ledger. (7) consent server responding to access-control server with the access rights. (8) for a valid data request, access-control server fetching data from database. (9) access-control server returning the data to research organization. a. time and place of study protocols often dictate when and where a study can take place. we enforce these requirements through the smart contract. before conducting a study, we programmatically set limits based on the approved time window for different phases of the study. since each subject in the study corresponds with an entry on the network's ledger, the number of subjects is controlled by setting a fixed limit when the study-specific contract is instantiated. likewise, to enforce geographic limits we set additional checks in the smart contract to accept or reject subject enrollment based on their location. for example, a subject or the representative, given that they are providing reliable information, may enter a zip code which is checked against a set of predefined accepted zip code values. this https://doi.org/10.30953/bhty.v1.10 page 10 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 verification will dictate whether enrollment can successfully progress. b. subject selection to satisfy regulations on subject selection, as described in the third requirement of clause cfr 46.111(a), inclusion and exclusion criteria for participation in the study can be checked in the smart contract prior to enrollment. the smart contract verifies specific login information against the ledger to determine if the attributes of that individual satisfy enrollment criteria for a given study. this provides an immutable provenance of the subjects and the data collected for a research study. duration of recruitment is also defined in the smart contract. this given interval is set upon registration and made transparent to give the users a deeper view of what they are involved in. as one of the additional elements of informed consent in the fourth requirement of cfr 46.111(a), the number of subjects is restricted by setting an upper bound within the enrollment functionality of the smart contract. at the time of registration, each subject is presented with the terms and conditions of participation in the study. as required by the fifth requirement of cfr 46.111(a), a copy of this consent is also stored in the blockchain network. this transaction is recorded for future need, with no possibility of being tampered or altered without consent. there is a set of criteria agreed upon and implemented during initial enrollment that specify the ability to withdraw from a given study. for cfr 46.116(b), the withdrawal criteria are also enforced through the smart contract, where a subject is informed of his or her eligibility to withdraw and any criteria that must be met to do so. the transactions can track if certain incentives or benefits offered to a subject were discontinued as a consequence of withdrawal from the study. c. informed consent management as per cfr 46.116(a), the irb protocol requires the investigator to define the duration of a subject's involvement. the smart contract is programmed such that all the functionalities implementing a subject's involvement, such as enrollment and informed consent, are operative within a specified time interval. to ensure the fifth requirement of cfr 46.111(a) are met, the consent granted by subjects to participate in the study and share their data are stored on the ledger. this consent is verified by the consent server for an organization requesting data access. figure 2 illustrates the underlying method of informed consent implemented by our system. the digital record keeping through the smart contract and ledger offers an additional security measure. based on the type of consent defined in the protocol, subjects provide necessary information pertaining to it. for example, if the subjects grant specific consent, their data can only be used for that research study. accessing their data for any other study is restricted by the system. if a research organization not enlisted as an intended user of the data tries to access it, it will be blocked by the access-control server and stored as an unauthorized transaction request in the transaction log. this is further explained in the secondary data sharing part of figure 2. in dynamic consent, subjects may also revoke consent to partially or completely share their data. our proposed system supports this by allowing or limiting data access accordingly. the consent further indicates if the subject has agreed to the investigator contacting them for secondary research or changes in the current study. the access-control server, consent server, and the smart contract embedded in our proposed system enforce the requirements for all consent types. d. secondary data sharing de-identification or removal of personal information is often required prior to collecting, storing, sharing, or analyzing data. as mentioned in the seventh requirement of cfr 46.111(a), investigators must declare the underlying method of de-identification, storing protected health information (phi), and duration of storage. since a private blockchain network restricts unauthorized access to ledger, all the sensitive information is securely saved on the ledger, which can only be accessed by the investigator. the ledger also stores the mapping https://doi.org/10.30953/bhty.v1.10 page 11 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 between code and identifiable information. in addition, subjective assessments, such as hipaa (insurance portability and accountability act of 1996) compliance, taken by investigators, pi, or study coordinators are recorded here. for more restrictive type of consent, the coded, deidentified data collected from subjects can be stored in a database. certain data formats may require intermediate steps to further obfuscate sensitive information before making it available on the database. for example, in genomic studies, a set of genes that may uniquely identify a subject can undergo a two-step deidentification process before sharing it for public usage. in the case of more generic consent types, such as broad or blanket consent, the data may be stored in a repository without deidentification. for the most restrictive consent scenario, when a third-party research organization requests data through the access-control server, they communicate with the consent server to retrieve corresponding access rights previously consented by the subjects. the smart contract verifies if the request was initiated by an intended organization for a valid data type and duration. if all the criteria are met, the consent server responds to the access-control server with the list of consenting subjects and the data type. in addition to this, the endorsers' signatures collected during the phase of transaction endorsement provide a further means of authenticity. an approval from both granular access rights and endorsement policy allows the organization to fetch data from the database. hence, our blockchain-based system guarantees secure storage of sensitive information and authorized access of permitted data, as required by the irb guidelines. e. safety measures collateral benefits are a natural side effect of research studies, which are often beneficial to the recipient. all test results that are generated from a specific study are recorded with provenance and maintained for each subject. if any underlying condition that is found as a side effect of the study or the regiment of the study provides positive outcomes to the patient, it is recorded using the underlying technology. risk assessment and adverse effects are major concerns for ensuring safety during and after a research study as they can often be surprising, and sometimes, detrimental. each transaction is timestamped in the ledger which can record any incident of risk or adverse effect that occur within the timeline of a study, thus addressing the requirements of the sixth requirement of cfr 46.111(a). these data can later be referenced as sparsely labeled, time series data for analysis to be performed on top of the timeline of each trial conducted. this final form of reporting in conjunction with continuing review ensure safety measures are enforced, and any negative effects are handled immediately. any self-reported adverse effects by the subjects can also automatically trigger an alert to the study coordinators to allow for just-in-time intervention and close monitoring. as required by cfr 46.111(b), additional safeguards for vulnerable subjects can be recorded and tracked during the course of the study. such measures may also incorporate a validation or endorsement by the subjects to ensure they received the desired treatment or safeguard measures. f. continuing review principal investigators of on-exempt studies, that did not undergo expedited review, must compose and send continuing reviews to the irb board at predefined intervals. we compose functionalities in the smart contract to query and organize data required for the review, as well as generate automated reports. while defining the network's ledger, we include all the pertinent data fields that can be potentially used for later submission. since transactions that alter the ledger are tracked in an immutable and sequential order, our system captures reliable timelines of subjects' detailed involvement that can be submitted for review. for example, the irb must be informed of any subject withdrawal, including the reason.31 when composing the continuing review, the smart contract defines ledger queries which reveal all https://doi.org/10.30953/bhty.v1.10 page 12 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 subjects (ledger entries) who have withdrawn consent and provide details surrounding their withdrawal. as an additional consideration, the institution conducting project overview can be added as a network node so that it can access the shared ledger to directly make queries and propose changes. if the irb chooses to enforce suspension or termination due to conclusions reached from the continuing review, or a missed or improper submission, the smart contract can be written such that the irb can revoke all subjects' participation consent on the ledger. while traditionally, irb intervention is recognized at an administrative level, this proposed method offers an accompanying technical enforcement to provide further trust such that the irb's decision is respected. discussion the recent amendments to the federal policy for the protection of human subjects or the common rule have sparked controversy around the status quo of security and privacy measures. the revisions defined in the final rule necessitate more comprehensible and transparent informed consent management, revised concept of identifiable biospecimens, consent for research involving biospecimens and identifiable data, and considerable measures for privacy safeguards. moreover, it is often difficult to track and ensure that the rules and regulations approved by the irb are imposed throughout the study. to alleviate these challenges, we propose a novel data management framework based on blockchain technology that implements and enforces the requirements of human subject regulations, as outlined in the irb protocol. we provide an overview of blockchain technology, distinguishing between public and private blockchain frameworks to elucidate our adoption of a private blockchain implementation in designing the system. we describe how we leverage smart contracts to enforce the requirements of irb guidelines, the ledger to securely store sensitive data and prevent unauthorized access, and the transaction history to generate statistics and track activities. although blockchain technology is a promising solution for healthcare applications, its adoption in this community is still at a nascent stage. to realize its full potential, we must continue exploring different implementations of this technique and their applicability in the domain of medical and healthcare research. the effectiveness of our blockchain-based solution largely depends on all the entities of a research study adopting and embracing this new technology. hyperledger fabric allows updating the smart contract upon successful endorsement from network participants to accommodate changes required by the irb during a study. however, any inconsistency in the data previously stored on the ledger or database must be resolved. setting up a private blockchain network requires a comprehensive knowledge of the underlying technique, which can be a challenging task. for future work, we intend to assess the scalability of our system when increasing the number of nodes and workload. we would also like to extend the system for active involvement of the regulatory board to automate and log communication with them. although our proposed system currently becomes effective after the approval of irb protocols, it may help to also include the irb review process as a part of the system. finally, if permitted by the protocol, we plan to include additional functionalities in the smart contract that allow sharing study results with subjects and their healthcare providers. acknowledgement we would like to thank daniel gruen at ibm for valuable comments and suggestions. we are also thankful to woong a. yoon, john paul filippone, and farhan arshad at ibm for providing technical assistance for the system described in this paper. funding statement there was no public or private funding provided in the creation of this work. https://doi.org/10.30953/bhty.v1.10 page 13 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 conflict of interest the authors are employees of ibm, an organization with financial interest in the subject matter discussed in the article. no other potential conflicts are reported. contributors to fulfil all of the criteria for authorship, every author of the manuscript has made substantial contributions to all of the work and participated sufficiently in the work to take public responsibility. references 1. hhs.gov. approval of research with conditions: ohrp guidance (2010) [internet]. hhs.gov. 2016. available from: https://www.hhs.gov/ohrp/regulations-andpolicy/guidance/guidance-on-irb-approval-ofresearch-with-conditions2010/index.html#section-a 2. nijhawan lp, janodia md, muddukrishna bs, bhat km, bairy kl, udupa n, et al. informed consent: issues and challenges. j adv pharm technol res. 2013;4(3):134–40. 3. gupta u. informed consent in clinical research: revisiting few concepts and areas. perspect clin res. 2013; 4. 82 fr 7149. department of health and human services. code fed regul. 2018 jan;7149–7274. 5. portier w, dunne c. current challenges and opportunities in clinical research compliance. ochsner j. 2006;6(1):21–4. 6. nakamoto s. bitcoin: a peer-to-peer electronic cash system. 2008;9. 7. mettler m. blockchain technology in healthcare: the revolution starts here. in: 2016 ieee 18th international conference on e-health networking, applications and services (healthcom). ieee; 2016. p. 1–3. 8. yue x, wang h, jin d, li m, jiang w. healthcare data gateways: found healthcare intelligence on blockchain with novel privacy risk control. j med syst. 2016;40(10):218. 9. irving g, holden j. how blockchaintimestamped protocols could improve the trustworthiness of medical science. f1000research. 2016;5. 10. nugent t, upton d, cimpoesu m. improving data transparency in clinical trials using blockchain smart contracts. f1000research. 2016;5. 11. ekblaw a, azaria a, halamka jd, lippman a. a case study for blockchain in healthcare: “medrec” prototype for electronic health records and medical research data. in: proceedings of ieee open & big data conference. 2016. 12. azaria a, ekblaw a, vieira t, lippman a. medrec: using blockchain for medical data access and permission management. in: open and big data (obd), international conference on. 2016. p. 25–30. 13. genestier p, zouarhi s, limeux p, excoffier d, prola a, sandon s, et al. blockchain for consent management in the ehealth environment: a nugget for privacy and security challenges. j int soc telemed ehealth. 2017;5:21–4. 14. title 45 part 46. department of health and human services. code fed regul. 2009 jul; 15. budin-ljøsne i, teare hja, kaye j, beck s, bentzen hb, caenazzo l, et al. dynamic consent: a potential solution to some of the challenges of modern biomedical research. bmc med ethics. 2017;18(1). 16. dwork c, naor m. pricing via processing or combatting junk mail. in: advances in cryptology — crypto’ 92. 1992. p. 139–47. 17. vasin p. blackcoin’s proof-of-stake protocol v2. self-published. 2014; 18. proof of elapsed time (poet) [internet]. 2017. available from: https://intelledger.github.io/ 19. baliga a. compliance oversight procedures for evaluating institutions. 2017. 20. buterin v. a next-generation smart contract and decentralized application platform. etherum. 2014;(january):1–36. 21. quorum [internet]. available from: https://www.jpmorgan.com/global/quorum 22. ripple [internet]. available from: https://ripple.com/ 23. chain [internet]. available from: https://chain.com 24. hyperledger fabric. 2017. 25. vukolić m. rethinking permissioned blockchains. proc acm work blockchain, cryptocurrencies contract bcc ’17. 2017;3–7. https://doi.org/10.30953/bhty.v1.10 page 14 of 14 blockchain in healthcare today™ issn 2573-8240 online https://doi.org/10.30953/bhty.v1.10 26. li w, sforzin a, fedorov s, karame go. towards scalable and private industrial blockchains. in: proceedings of the acm workshop on blockchain, cryptocurrencies and contracts. 2017. p. 9–14. 27. lo b. sharing clinical trial data: maximizing benefits, minimizing risk. jama. 2015;313(8):793–4. 28. dressler lg. disclosure of research results from cancer genomic studies: state of the science. clin cancer res. 2009;15(13):4270–6. 29. chan a-w, tetzlaff jm, altman dg, laupacis a, gøtzsche pc, krleža-jerić k, et al. spirit 2013 statement: defining standard protocol items for clinical trials. ann intern med. 2013;158(3):200–7. 30. silva de lima al, hahn t, de vries nm, cohen e, bataille l, little ma, et al. large-scale wearable sensor deployment in parkinson’s patients: the parkinson@home study protocol. jmir res protoc. 2016;5(3):e172. 31. guidance for irbs, clinical investigators, and sponsors, us department of health and human services and food and drug administration and others. irb contin rev after clin investig approv silver spring, md us food drug adm. 2012 supplementary data: none this is an open access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited as first published in blockchain in healthcare today™, and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v1.10 http://creativecommons.org/licenses/by-nc/4.0 page 1 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 creating a patient-centered, global, decentralized health system: combining new payment and care delivery models with telemedicine, ai, and blockchain technology kenneth antonio colón1 author 1izzy care llc, wyoming, usa keywords: artificial intelligence, behavioral health, blockchain, collaborative care, decentralization, direct primary care, ethereum, integration, payment models, telemedicine, virtual assistants section: methodology over the past decade, there have been many innovations in new payment and care delivery models and technology, from telemedicine to artificial intelligence (ai) to blockchain. these innovations, however, must be used in tandem to drive real change. we review each of these innovations and propose a model for how they can be combined to be greater than the sum of their parts. in doing so, we can create a global, decentralized health system that truly puts patient care at the center, while supporting and further enabling the clinicians who make this care possible, to deliver higher quality care at a fraction of the cost. it is no secret that our healthcare system, in its current form in the united states, is beset by large and fundamental challenges. first and foremost is the rising cost of care. the average healthcare cost per person in organization for economic co-operation and development (oecd) countries exceeds $5,000 per year.1 in the united states, this number exceeded $10,000 per year in 2016 and is expected to hit nearly $15,000 per year by 2023.2 second, there is considerable disparity in access to care. rural and sparsely populated areas in the united states (and across the world) experience a disparity in access to care. as a result, populations suffer more often from chronic conditions3 than their urban counterparts. collaborative care, or the integration of general and behavioral healthcare, has been shown often in studies to be more effective than traditional primary care alone4 in treating these chronic conditions. unfortunately, it is not very common page 2 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 in today’s health ecosystem, where physical health and behavioral health are treated as separate entities. furthermore, the current “cookie cutter” or “one-size-fits-all” approach is pervasive in health care, leaving treatment that is tailored to the individual to be rare, even when it is shown to drastically increase positive outcomes for many patients—perhaps most notably for cancer patients.5 finally, physician burnout remains widespread, with 42% of physicians reporting burnout6 according to medscape’s latest report among us physicians. this high rate of burnout is thought to be due to factors outside direct patient care, such as bureaucratic tasks and dealing with difficult-to-use technology.7 many attempts have been made to introduce incremental improvements to the current system. however, these attempts have proved to be the equivalent of working on an old, rusted, brokendown car with a faulty transmission—eventually, we must face the fact that we would be better off replacing the car. what is needed is a fundamentally different health system. we need a health system that leverages new payment and care delivery models, coupled with innovative technologies, to truly put patients at the center of their care, thus creating an improved healthcare experience and better outcomes for patients at radically lower costs. in the following sections, first, i will outline what i believe are the key solutions needed to effectively reshape health care into a more cost-effective and more patient-centric model. we will begin by exploring two care paradigms, “direct” care (as in direct primary care [dpc]) and collaborative care, their advantages over other models of care delivery, and how they can be effectively linked together. second, we will discuss the key benefits of both telemedicine and artificial intelligence (ai) (in particular, intelligent virtual assistants), and how together they can enhance direct and collaborative care models. third, we will quickly recap the topics we have discussed thus far (direct care, collaborative care, telemedicine, and ai), how they come together, and what their limitations are. following this recap, we will examine how blockchain, or distributed ledger technology, can help us overcome some of the limitations of the models and paradigms discussed, as well as some of the challenges that are associated with blockchain solutions (particularly those involving tokenization). finally, we will combine them all together—direct care, collaborative care, telemedicine, ai, and blockchain—into a cohesive model that can be implemented in today’s healthcare ecosystem. virtual, direct collaborative care as first line of defense before delving into the more technological innovations that can help reshape healthcare, it is important that we examine two key care delivery models: direct care and collaborative care. direct care model a direct care model forgoes third-party insurance by establishing an unimpeded financial relationship between the physician and the patient. one increasingly common implementation of this model is dpc, in which a clinic charges a patient a low cost monthly (e.g., $50–$75) or an annual membership fee for unlimited access to its primary care physicians.8 using this structure, patients can connect with their doctor anytime via text, video, phone, or a same-day page 3 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 clinic appointment. many dpc clinics give their patients, or “members,” access to additional benefits, including discounts on generic medications and laboratory testing, house calls, and even certain procedures at no additional cost. these benefits allow dpc members to rely less on their insurance plan for the majority of their healthcare needs, reserving its use for coverage of large medical expenses, which enable them to save considerably on the cost of their care.8 direct care model also allows patients to spend more time with their doctors. direct primary care physicians typically have a caseload of 500–600 patients, while physicians at insurancebased clinics might see two to three times as many patients.9 because of this higher physician-to-patient ratio, and because physicians save time by not having to request and receive prior treatment authorization for insurance reimbursement, dpc appointments can last for 30–60 minutes, compared to 13–16 minutes per appointment in the average insurance-based setting.10 the benefits of dpc are also significant for physicians. health systems are exploring direct models as a way to combat physician burnout, as these models enable a focus on patient care rather than dealing with third-party insurers and complicated electronic health record (ehr) workflows. in fact, ehrs are cited as occupying over 50% of a physician’s work hours.11 direct models position the physician–patient relationship at the center of the healthcare model and have enabled primary care physicians to earn more than their counterparts in insurance-based clinics (up to 1.5 times as much in annual salary),12 while seeing one-half to one-third the number of patients. direct primary care also represents a path forward for eliminating fee-for-service models and moving to truly value-based care. in order for value-based care to work, however, we must reshape our definition of “primary care.” we must look for ways to treat patients holistically, with the goal of personalization and prevention. one of the most promising approaches for doing just this is known as collaborative care. collaborative care collaborative care, as defined here, refers to a model consisting of equal parts of primary care, mental/behavioral health, and personalized guidance in nutrition and fitness. research reveals that improved positive outcomes can be achieved through this integration.4 despite their importance to overall health, diet and exercise coaching are, in some conceptions of collaborative care, touched on only briefly or merely included under the scope of either primary care or mental/behavioral health. however, the effects of diet and exercise on other aspects of physical and mental health are documented,13,14 and addressing these areas must be integrated into a patient’s overall treatment plan in order for him or her to thrive physically and emotionally in the long term. in this conception of collaborative care, a patient’s care team consists of his or her family medicine physician or internist, plus a mental/ behavioral health specialist (i.e., a licensed psychotherapist or psychologist), and a nutrition and wellness coach (i.e., a registered dietitian or certified nutrition specialist). the nutrition and wellness coach possesses a working knowledge of basic exercise programming, which is applied to improve patient health by, for example, prescribing daily walks of increasing duration or basic strength training using bodyweight or resistance bands. page 4 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 a large body of research would suggest that by breaking down walls between general medical needs, mental health needs, and nutrition, we can effectively reduce untreated mental illnesses, curb the worsening of chronic conditions, identify issues in food security and help patients identify healthy and cost-effective alternatives, help patients meet their personal health and wellness goals, and perhaps even prevent unnecessary procedures and hospitalizations15–23. clearly, collaborative care, especially when combined with elements of direct care models, establishes an ideal system for delivering highly personalized and prevention-focused medicine. direct models nurture the development of long-term relationships between the patient and the doctor, allowing for a personalized approach to care delivery, while collaborative care enables the treatment of the whole patient, with the promise of more effective management and prevention of chronic diseases. this notion of preventive and personalized medicine can be taken one step further with the utilization of full genomic sequencing for patients, included in the cost of their care. the insights gleaned from this testing can then be integrated into the patient’s treatment plan. for example, for patients identified at highly increased risk for a certain heart condition, steps can be taken by the care team to encourage lifestyle modifications to prevent development of the condition before any signs or symptoms appear. virtual care telemedicine and artificial intelligence with that foundation, it is possible to integrate telemedicine and ai into the healthcare model. telemedicine the benefits of telemedicine (using secure live video or messaging) are well documented24–26. these benefits include enabling patient access in areas of low clinician density, connecting patients to clinicians who speak their native language, and reducing missed appointments by meeting the patients where they are—on their devices. this is especially beneficial for the most vulnerable populations, including seniors and those with disabilities and extensive comorbidities. furthermore, by using telemedicine as the first line of defense, significant reductions in overhead costs can be achieved by eliminating the need for a physical office location and expensive hardware/machinery25,26. through a direct collaborative care model, these savings can be passed directly to patients and thus can foster the development of a long-term relationship between physician and patient. this model stands in stark contrast to many of the larger telemedicine platforms on the market today, platforms in which appointments are one-off and transactional in nature. this transient nature of care hinders the formation of long-term relationships between a patient and a particular doctor. experts posit that this lack of a substantial physician–patient relationship in turn results in low rates of telemedicine utilization.27 under the proposed system, patients interact with the same core care team every time, which, in addition to enabling long-term relationships, allows for personalized treatment and improved outcomes through precision medicine. artificial intelligence: chatbots using natural language processing virtual care can be further enhanced by the introduction of automation via digital assistants (or chatbots) using natural language processing (nlp). digital assistants can automate clinician workflows, enabling clinicians to focus their time on patient care, not repetitive tasks, as well as page 5 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 enabling a deeper level of personalization in treatment. a few key uses for digital assistants are presented in table 1. application to practice patients, or “members,” would be charged a recurring monthly or annual subscription fee for unlimited access to their personal care teams. as stated earlier, core care teams would consist of a primary care physician, a mental/behavioral health specialist, and a nutrition and wellness coach; and a digital assistant would be integrated to support patients and clinicians. from a single mobile application, members could connect to their care teams via encrypted messaging and live video to interact with the digital assistant for symptom triage, follow-ups, assessments, for 24/7 response and connection to appropriate parties, etc., and view or manage permissions to their records. the clinician version of this application would allow members of the care team to coordinate care, connect with their patients, review and add to records, and assign tasks to the digital assistant. care teams could refer patients for necessary in-person care at an urgent care clinic, specialist visits, or laboratory testing, allowing members to save on their healthcare costs by reserving the use of their insurance plans for these instances. table 1. application of digital assistants to improve patient care application action symptom triaging ·   connect with patients and guide them through a series of questions regarding their histories and current symptoms ·   deliver a report of presented symptoms and possible conditions to the patient’s physician, which the physician can consult during a follow-up appointment with the patient reminders ·   specific use cases, including to take their prescribed medications, reminders about upcoming appointments, etc. automated follow-up ·   review status of symptoms presented during a previous consultation with the physician (e.g., if a physician marks that a patient presented symptoms of cough or sore throat and was prescribed an appropriate treatment regimen, a digital assistant automatically follows up to inquire about the success of the regimen [e.g., “how has your cough been since beginning your medication?”]) assessments and questionnaires ·   automate common assessments, for example, a phq-9 for depression to gain information for intake forms and other documents in a more user-friendly manner 24-hour hotlines or emergency services ·   direct patients during crises or times when care teams are unavailable ·   instantly notify care teams and/or a patient’s dedicated emergency contact as needed automate preventive health measures ·   if data points deviate significantly from the patient’s average (e.g., a decrease in step count, limited movement, and reported symptoms of sluggishness), message the patient in real time, suggesting consultation with his/her care team. source: phq: patient health questionnaire. page 6 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 what is missing? the system described thus far does not actively incentivize patients to engage in their care. it neither rewards patients nor gives them an opportunity to save on the cost of their care as an incentive for taking an active role in the management of their health. neither does it enable true patient ownership of data, nor provides the opportunity for patients to safely and securely monetize their data. if patients do not own their data or do not have sole discretion over how their data are used, the health system could theoretically sell access to patient data without restriction or use the said data for personal gain. patients must be the sole proprietors of their data, with full discretion over how that data are used; and they should stand to benefit financially should they choose to share that data with medical researchers (e.g., payors, pharmacies, and government). moreover, the system should be open-source, allowing for third-party auditors to investigate the security of the platform, developers to contribute to the technology stack, and entrepreneurs to launch their own services and applications to further serve members of the community. what about members of the community who might be unable to afford the full cost of care? aside from the obvious social good of better serving these patients, there are also cost savings to be had by redirecting healthcare spending from reactive care (which includes immensely costly hospitalizations and procedures) to proactive or preventive care. furthermore, the system described relies on a centralized authority for administration of care. at any time, the centralized authority could, in theory (and oftentimes in practice), raise the prices of basic care needs, cut back on the needs/services covered/provided, or stop honoring the rewards or discounts they might promise consumers. all parties involved in the ecosystem, including patients and clinicians, should have the right to guide how the system evolves and ensure that their best interests are preserved. blockchain for tokenization, patient data ownership, and decentralization by implementing blockchain technology in the described system, we can enable patients to benefit financially from investing in their personal health and wellness and save on the cost of their care, enable innovators from across the globe to contribute to the growth and improvement of the platform, enable patients to own and even monetize their personal health data (including medical record and genomic data), and enable patients and clinicians to guide decision-making in healthcare administration. tokenizing wellness with smart contracts healthy, engaged patients create immense cost savings for private insurers and governments. but should patients not also benefit financially from investing in their own health and wellness? this is done already, to a certain degree, with the rewards programs used by many large private insurers. this model, however, requires the patient to trust that the insurer will (1) properly track the patient’s progress, (2) issue appropriate awards that are truly indicative of the value created for the insurer, and (3) honor those rewards. furthermore, these programs often match patients with goals predetermined by the insurer, such as weight loss or hitting a certain step count, rather than with the patient’s own goals. if patients’ personal health goal is to maintain weight (for patients already at their ideal body page 7 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 mass index [bmi]), or perhaps even gain weight (for underweight patients), should they not also be entitled to the same rewards? likewise, one’s insurer may offer a program for hitting 10,000 steps daily, but what if one’s mobility is limited due to age, injury, or disability? should the person not be able to still participate with a set of goals tailored to you? what if your personal goals are entirely unrelated to the programs offered by your insurer—such as spending less time browsing on the web or using apps that might have a negative impact on those in vulnerable emotional states (e.g., facebook or instagram)? should you not be rewarded for your progress in these areas as well? finally, patients enrolled in these programs can only use these rewards towards the cost of their care. on the contrary, these rewards can and should be an asset that patients earned and are therefore free to utilize as they see fit. you should be able to exchange these rewards for other assets or currencies, which can be spent as patients please or put toward other expenses (e.g., housing or education). one solution is the creation of a tokenized patient incentive program that leverages internet-ofthings (iot) data and blockchain technology and is designed to increase patient engagement and positive outcomes. this program would allow patients to opt in to share data from the mobile applications and wearable devices they select to track and measure progress toward their personal health and wellness goals. using these data in conjunction with smart contracts would allow for tokenized rewards issued to patients in an automated, trustless manner. tokens could then be redeemed by patients toward the cost of their care or be exchanged for other assets or currencies at each patient’s discretion. in this way, patients would have true, full ownership of the tokens they earn. discounts on the cost of care might be offered for payment using tokens to provide yet another incentive for achieving wellness goals and encourage liquidity of the token supply. a similar strategy is common with cryptocurrency exchanges, using their own native token for discounts and other benefits. for example, binance (a cryptocurrency exchange) allows users to pay trading fees in binance coins (bnb) with their erc-20 token (designed and used solely on the ethereum decentralized network platform) for discounts of up to 50%28. healthy and engaged patients enable greater cost savings on the payor side. this type of incentives program combined with the model of care described thus far could assist in mitigating the cost (fiscally and physiologically) of chronic conditions by increasing patient engagement and personal progress toward their individual goals. this aspect of tokenization allows patients to share in this cost saving and truly own the rewards (or assets) they earn. true patient data ownership, monetization and precision medicine one of the biggest promises of blockchain technology in the healthcare sector lies in enabling true patient ownership of data. these data include patients’ lifetime medical record, genomic sequencing data, and other patientgenerated health and wellness data. implementing blockchain technology enables patients to control access to these data and create the opportunity for patients to monetize their data at their discretion. this stands in contrast to models in which centralized parties simply collect patient data and sell it to third parties for their own financial gain. in a blockchain-based system, patients could opt in to share their data with medical researchers (e.g., government, payors, and pharmacies) in a secure, anonymous page 8 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 manner. crucial to this system would be separate permissions for each research opportunity— accompanied by an in-depth explanation of exactly how the data will be used, how long it will be kept, and whether and how it will be deleted—rather than one single agreement to grant access for every study. rewards can be paid in a trustless fashion through tokens on the chain, which as previously discussed can be put toward cost of care, or traded out for other currencies, allowing patients to benefit financially from the power of their data. in this way, the financial gains from patient ownership and monetization of data could decrease or even eliminate patient out-of-pocket spending. furthermore, patients could decide to share their data with the platform itself in the same secure, anonymous fashion in order to drive improvement of the patient experience and increased positive outcomes. this improvement would be achieved by applying advanced machine learning techniques to these data, thus gleaning population health insights that would more effectively guide precision medicine efforts. tokenization for care subsidies many governments and payors see the bulk of their healthcare spending go toward large medical expenses from a small fraction of the total population, a fraction in which comorbidities of chronic conditions are plentiful. in many cases, this subset of the population consists mostly of those who cannot afford high-quality care and therefore avoid treatment until emergent situations occur, resulting in costly hospitalization and intensive procedures with the taxpayer ultimately footing the bill. take, for example, a patient on a governmentsponsored plan being rushed to the emergency department (ed) for an attempted suicide, resulting in a hospitalization costing $20,000. this type of incident, which the author experienced, is unfortunately much more common than anyone would like to think. aside from saving this patient (or family) and the pain and suffering of struggling with untreated mental health issues, it might ultimately be more cost-effective to direct this spending toward preventive care. an effective preventive care program, much like the direct collaborative care model described, would cost as much as $200 per month*. hypothetically, if the government (and ultimately the taxpayer) was to pay for the cost of this care, this is just 12% of the total cost of the hospitalization previously described. by directing spending toward preventive care, avoidable ed visits, procedures, and hospitalizations can be eliminated, decreasing the funding needed (or enabling more efficient use of current funding) for governmentsponsored public health programs. through an allotted portion of the total token supply, or as a built-in mechanism of the previously described rewards and monetization programs, tokens can be used to subsidize the cost of care for individuals and families of low income. for example, 10% of the token rewards issued might be tokens designed and used solely on the ethereum platform toward subsidies, with the remaining 90% going to the patient who earned (or in a blockchain context, “mined”) those rewards. * $200/month/patient achieves significant profit margins while covering the cost of technology and care team salaries (pcp, psychotherapist, rd). exact breakdown of unit economics, care team assignments, and patient populations is to be covered elsewhere. page 9 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 subsidies need not cover the full cost of care. for example, their use can be pegged to a percentage of annual income—for example, if membership fees exceed 10% of annual household income, fees will be subsidized via the token supply to cap costs at 10%. in this example, a family earning $50,000 annually will have their annual healthcare spend capped at $5,000, with token subsidies covering the remaining expenses and no disruption to services provided. in this subsidized system, all members contribute what they can, but no individual or family needs to pay 50% of their income toward healthcare at the expense of affordable housing, education, job training, childcare, or proper nutrition. granting individuals and families with low annual incomes access to the same collaborative preventive care through the model described via subsidies can create immense long-term savings for governments and payors. through such a system, we can effectively curb untreated mental illnesses and chronic conditions, increase productivity and earning potential, and ultimately increase the quality of life. an open-source platform the codebase for the system would be made open-source, allowing developers and technologists from across the globe to audit and contribute to the codebase in exchange for token rewards. furthermore, this open-sourcing would allow entrepreneurs and innovators to create and launch their own applications and services atop the same platform. this allows opportunities for an ecosystem of applications to serve the community and its members, as opposed to just a single service. for example, a group of developers could create an application atop this platform to aid individuals in the management of a particular chronic condition (e.g., diabetes). the developers benefit from, among other areas, ease of distribution to a wide number of patients, with full integration into the patients’ existing healthcare experience. patients in turn benefit from access to specialized tools to help them manage their conditions without disrupting, and perhaps even enhancing, how they already receive their care. decentralized decision-making and leadership there are immense challenges and problems with centralized systems, especially on the administrative side of health care. many of us have witnessed in our own personal lives, have heard from friends and family, or have read in the news how central authorities reduce coverage, restrict networks, and exclude some patients altogether. to combat this, all members of the ecosystem— healthcare professionals, patients, and developers—should together guide the decisionmaking process for the future of the system, instead of decisions leaving at the sole discretion of corporate executives and upper management. although a patient-centered health system as described here would likely need to begin as centralized, it should, over time, fully decentralize the decision-making process, leaving it in the hands of the healthcare professionals and patients who interact with it every day. this can be achieved through staking and voting mechanisms similar to those being explored on ethereum and eos,29,30 with built-in safeguards to avoid loss of funds and immense confusion as experienced in the infamous dao incident,31 and to ensure that basic levels of care will always be provided. page 10 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 bringing it all together table 2 lists advantages associated with utilization of the virtual, direct collaborative model described above, with some modifications to leverage all the benefits of blockchain technology. figure 1 illustrates the flow of data and crypto assets. impact on payors this system offers immense cost-savings for government and opportunities and challenges for private insurers. for governments, this system could lead to huge economic benefits by mitigating untreated mental table 2. advantages associated with utilization of the virtual, direct collaborative model stakeholder/device event patients or members ·   charged a recurring monthly/annual subscription fee for unlimited access to a personal care team ·   connect to care teams via encrypted messaging and live video, interact with the digital assistant (for symptom triage, follow-ups, assessments, 24/7 response and connection to appropriate parties, etc.), and view or manage permissions to their records from a single mobile application ·   view ongoing studies and research opportunities, with details describing how their data will be used and token bounties to which they are entitled for participation. using distributed ledger technology, these data can be shared with permission of parties while preserving security and anonymity. ·   might opt to share data with the system itself to improve the patient experience and increase positive outcomes, with the same level of detail on how data will be used. core care teams (primary care physician, mental/ behavioral health specialist, and nutrition and wellness coach) ·   coordinate care, connect with patients, review and add to records, and assign tasks to the digital assistant ·   make referrals to necessary in-person care at an urgent care clinic, specialist visits, or laboratory testing, allowing member saving on healthcare costs by reserving their insurance plans for these instances ·   paid by default (for ease of covering personal expenses), but could be paid in tokens or a combination of their choice. digital assistant, mobile application ·   integrated to support patients and clinicians ·   members set personal health and wellness goals and connect data from the iot devices to track progress, with token bounties for achieving goals machine learning ·   applied to this data to glean insights for population health and precision medicine tokens and token staking and voting mechanisms ·   stored in the member’s virtual wallet, where they can be put toward cost of membership or can be sent to a third-party exchange to trade for other assets or currencies ·   allow patients, clinicians, and developers to control decision-making and advance the future of the system indigent patients ·   a portion of the total token supply (or alternatively, a small percentage [<10%] from “mining” rewards) would contribute subsidies for those unable to afford full cost of membership. iot: internet-of-things. page 11 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 illnesses and chronic conditions, thus increasing productivity and earning potential, and ultimately increasing the quality of life. this enables government-sponsored/public health programs to make more efficient use of funding. the system would also allow for private insurers to save significantly on spending by providing a redesigned first line of defense directly to consumers, specifically focusing on personalization and prevention, thereby mitigating the amount of large medical expenses that need coverage. there is, however, a distinct possibility to disrupt existing private insurers. if enough members of the system are amassed, there becomes an option for the system itself to offer a supplemental insurance plan to fill the gaps of what is not covered by the virtual, direct collaborative model. for example, the system could give members the option to pay a small extra monthly or annual cost for full insurance coverage—no deductibles, no co-pays, just the flat fee. when this amount is pooled across all members, it can effectively fund the full scope of care at a fraction of current individual healthcare spend, which, as mentioned previously, exceeds $5,000 annually in oecd) countries and $10,000 annually in the united states. members would still receive the majority of care through their core care team, but would now be able to rely on the supplemental plan for major medical expenses. the supplement would provide universal coverage, meaning no restriction to certain networks or hospitals, which would in turn enable more consumer choice, with care teams able to recommend best course of action. for example, let’s say this supplement for existing members is priced at just $25/month per adult†. with 1 million adult members, this creates a monthly pool of $25 million to cover large medical expenses (or $300 million annually), which again in theory should be curtailed in the first place through the use of preventive care teams. by providing this supplement, the system—thus its members, as the system is decentralized— captures the cost savings provided by focusing on personalized, preventive care. furthermore, † by no means is this number prescriptive. historic healthcare spending trends would indicate that this amount need be much higher. it is merely meant to serve as an example to illustrate the power of a large and sufficiently diverse patient population. figure 1—flow of data and crypto assets. patients control if/how their data are shared, including medical record and patient-generated health data from wearable fitness trackers, health applications, etc. they may share their data with medical researchers and/or track progress toward individual health and wellness goals. through both methods, patients can “mine” the erc-20 (technical standard used for smart contracts) token of the network, which can be used to pay the cost of their health care at discounted rates or can be traded out to fiat currencies or other crypto assets on third-party exchanges. patient health data sources patient's token earnings (erc-20) pay healthcare costs (discount) trade on exchanges opt-in data sharing with researchers opt-in wellness tracking & rewards page 12 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 as the decision-making process is owned by members of the system, they can ensure that the best interests of all members are preserved into the future as the system grows and develops. legal frameworks and challenges challenges related to legal frameworks include those related to tokenized assets, the need for clarity in federal regulation, greater clarity in federal regulations, issues related to liquidity and volatility, and legislative actions by states. securities versus utility tokens one of the most common concerns in dealing with tokenized assets is their legal classification: will the token be considered a security or utility token? while a complete analysis of the properties and legal repercussions of securities versus utility tokens is outside the scope of this paper, it is, however, useful to introduce the basics and impacts concerning what was discussed thus far. in us jurisdictions, whether or not an asset (or the transaction of assets) can be considered a security follows what is known as the “howey test”. as explained in cnbc’s interview with securities and exchanges commission (sec) chairman jay clayton, “the ruling comes from a 1946 us supreme court case that classifies a security as an investment of money in a common enterprise, in which the investor expects profits primarily from others’ efforts.”32 basically, if the token is purchased from a company, with the promise of financial returns from the company’s profits, the token is most likely a security‡. think of this as purchasing ‡ i am not a legal or financial expert. nothing in this manuscript should be taken as legal counsel or financial advice. shares of a public company like starbucks— owning said shares entitles the owner to a proportionate share of the starbucks’ profits. utility tokens, on the other hand, are redeemable for products or services. as a hypothetical example, assume that starbucks was to tokenize its rewards program. as a member of this program, people owning utility tokens could receive rewards in the form of starbucks’ new (again, hypothetical) erc-20 token which we will call starbuckscoin (sbc), for regular purchases at starbucks—in the author’s case, this would be a vanilla bean frappuccino. in this hypothetical example, it is possible to track and spend sbc from the digital wallet in the starbucks app. as an added bonus, there is a 20% discount on all purchases made using sbc. leftover sbc can be sent to the digital wallet of a friend or family member, or can be sold on an exchange for other digital assets like bitcoin or ethereum (which can then be exchanged for us dollars). clarity in federal regulation this type of utility token framework is ideal for building the decentralized system. patients would be able to freely earn and spend their token rewards, and these rewards need not be restricted to “accredited investors” only—or those having a net worth of at least $1,000,000 or an income of at least $200,000 annually for the last two years33—less being subject to securities regulation. at the federal level, however, the united states has yet to pass legislation or provide a legal definition of utility tokens. there is simply the existing securities legislation; if the howey test concludes that it is a security, then it must be regulated as a security and subject to oversight by the sec. with the howey test at the federal page 13 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 level essentially serving as the arbitor of what is considered a security, the interpretation can be vague, as courts have used different interpretations of the sec vs. howey ruling. furthermore, basing decisions on a technology that arose in 200934 from a court case in the 1940s, three decades before the advent of the personal computer35, can be problematic, as new technologies have drastically transformed our landscapes and worldviews in the past few years alone. imagine using the same regulation for horse-drawn carriages as for self-driving cars. the lack of clarity in existing legislation has resulted in the country’s top legal and technical experts differing as to how to classify certain tokens or coins. this becomes even less clear when discussing initial coin offerings (icos), when a coin or token is first made available to the public. to return to the previous hypothetical example, starbucks could hold an ico in which sbc are available for purchase. starbucks aficionados could stock up on sbc for the discounts they afford, and starbucks as a company can use this new wave of capital to invest in marketing, recruiting corporate talent, opening new locations, etc. would this hypothetical token, or its purchase, fall under federal securities regulation? with existing federal regulations, it is difficult to say. current sec chairman jay clayton stated that all icos, in his eyes, are in fact securities.36 then in june, the sec declared that ethereum, which ran its ico in 2015, is not in fact a security. clayton has gone on to say that tokens “can evolve toward or away securities.”37 furthermore, according to coindesk,38 “clayton illustrates an example using bitcoin as one end of a spectrum, and stocks stored on a blockchain as the other: ‘the question is, where does our jurisdiction begin?’” admittedly, these comments sound promising, but upon reflection, they offer no concrete answers. when does a security become not a security, or vice versa? who determines this, and what factors are these determinations based on? if the sec says the range of tokens lie on a continuum, then how is the howey test sufficient for determining how these tokens should be regulated? liquidity and volatility how much is an individual token worth? one factor in determining the value of an individual sbc is the total supply of sbc. if the total supply of sbc is close to 100 billion units, such as in the case of ripple (xrp)39, then the value of each individual token will be drastically different than if the total supply of sbc is closer to 21 million units, such as in the case of bitcoin (btc)40 or zcash (zec)41, due to the higher relative scarcity of tokens with a lower total supply. another factor is its liquidity: is there a large enough (and balanced) group of buyers and sellers active in the market? if there are only a handful of buyers and sellers of sbc, or if the sellers drastically outnumber the buyers, one would say the market for an asset is “illiquid.” illiquidity often leads to immense volatility, where the price can change by a factor of 100 overnight. for example, 10 sbc to purchase a latte on monday need 1,000 sbc to purchase the same latte on wednesday. these illiquid markets are especially vulnerable to price manipulation by “whales,” wealthy individuals or corporations owning large amounts of a certain digital asset, who may intentionally manipulate the supply (and thus price) for their own personal gain. you will notice the “top” digital assets (or largest by market cap) have a high amount of liquidity, often shown by the 24-hour trading volume. page 14 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 this prevents, to a certain degree, wild fluctuations caused by imbalances of buyers and sellers that affect many smaller cap coins. in liquid markets, the going price of an asset would naturally fluctuate with rise and fall in demand in relation to the supply. in digital asset markets especially, this demand can be heavily influenced by consumer sentiment toward news (or in some cases, just “noise”) such as corporate hirings/firings, new updates and product releases, partnership announcements, etc. for example, if starbucks introduces a new frappuccino flavor (and for the sake of this discussion, let’s assume that the market for sbc is sufficiently liquid), then the demand for, and thus the price of, sbc may increase significantly for the week after the announcement. then the price would likely regress toward average prices. by correctly timing the buying and selling of sbc, one could make a decent profit that is not linked to starbucks’ profits or ownership of starbucks stock. does and should this profit potential affect the classification of sbc as a security, or not? some may follow this line of logic, arguing that purchasers or holders of the token are investors seeking profit from the increase in value of sbc over time, much like a venture capitalist purchasing shares in an early-stage company. others might argue the opposite, stating that the inherent utility of the token (being redeemable for starbucks products) and the fact that it is not at all tied to ownership, equity, or profits in or from starbucks mean that it does not fall under sec jurisdiction. again, it is unclear. the author leans toward the latter, but regardless of what side an individual may fall in this debate, many would agree that new legislation should be brought in to support existing legislation and provide additional clarity. pioneering legislation at the state level while significant change at the federal level may take time, there have been considerable efforts at the state level to provide a clear legal framework for utility tokens, resulting in new legislation being passed in us states like wyoming. in march 2018, wyoming became the first to define utility tokens as a new asset class. as defined in the state of wyoming, a token is a utility so long as the following conditions are met: (1) the token has not been marketed by the developer or seller as an investment, (2) the token is exchangeable for goods or services, and (3) the developer or seller of the token has not entered into a repurchase agreement of any kind or entered into an agreement to locate a buyer for the token.42 there are further nuances to the bill, of course, and legislation passed at the state level does not necessarily impact policy at the federal level. it does, however, show that governments can successfully pass new legislation to support existing legislations, while at the same time providing additional clarity in an ever-changing technology landscape. as evidenced by the huge wave of blockchain companies relocating to wyoming43, these types of legislative breakthroughs can help spur immense innovation and economic growth. summary and closing remarks in this article, we have explored how new payment and care delivery models can be combined with telemedicine, ai, and blockchain technology to create a truly patient-centered, global, decentralized health system. to truly put patients at the center of care, we need to fundamentally redesign the healthcare experience and bring together the best of all the innovations we have at our fingertips. page 15 of 18 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.30 most importantly, we must continually redesign the experience as new technologies and new models are created. the system must continue evolving to ensure that the highest quality care is provided. this is not only the “right” thing to do in terms of social good but also drives more positive outcomes and is more cost-effective in the long term. and isn’t just that—better outcomes at lower costs—what all of us, as citizens and as patients, want for our health care? contributors: to fulfill all of the criteria for authorship, every author of the manuscript has made substantial contributions to all of the work and participated sufficiently in the work to take public responsibility. funding statement: the author has received no specific funding for this work. conflict of interest: the author reports the following details of affiliation or involvement in an organization or entity with a financial or non-financial interest in the subject matter discussed in this article. kenneth colón is the ceo and cofounder of izzy care, llc—a healthcare technology start-up incorporated in wyoming, usa. izzy care offers a direct-to-consumer platform utilizing telemedicine, machine learning, and blockchain technology. copyright ownership: kenneth antonio colón references 1. bloomberg.com/news/articles/2018-05-15/ wyoming-aims-to-be-america-s-cry sawyer b, cox c. how does health spending in the u.s. compare to other countries? petersonkaiser health system tracker. kaiser family foundation; 2018 [cited 2018 aug 26]. url: https://www.healthsystemtracker. org/chart-collection/health-spending-u-scompare-countries/ 2. bloom e. here’s how much the average american spends on health care cnbc. cnbc; 2017 [cited 2018 aug 26]. url: https://www.cnbc.com/2017/06/23/hereshow-much-the-average-american-spendson-health-care.html 3. geographic access barriers u.s. health policy gateway. 2018 [cited 2018 aug 26]. url: http://ushealthpolicygateway.com/ vi-key-health-policy-issues-financing-anddelivery/k-barriers-to-access/geographicaccess-barriers/ 4. thielke s, corson k, 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[cited 2018 aug 26]. url: https://z.cash/support/faq.html 42. lindholm b, clem h, larsen m, et al. house bill hb0070 state of wyoming 64th legislature. 2018 [cited 2018 aug 26]. url: http://www.wyoleg.gov/2018/ introduced/hb0070.pdf 43. bain b. wyoming aims to be america’s cryptocurrency capital bloomberg.com. bloomberg; 2018 [cited 2018 aug 26]. url: https://www.bptocurrency-capital this is an open access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work noncommercially, and license their derivative works on different terms, provided the original work is properly cited as first published in blockchain in healthcare today™, and the use is noncommercial. 1 (page number not for citation purpose) editorial problems with medical claims that artificial intelligence (ai) and blockchain can fix joe hawayek, mba1, osama abouelkhir, md2 1board member, tachyhealth, dubai, united arab emirates; 2board member, tachyhealth, dubai, united arab emirates corresponding author: joe hawayek, email: joe@tachyhealth.com keywords: aging population, artificial intelligence, blockchain, fraudulent claims, healthcare utilization, medical claims, middle east submitted: 28 may 2023; accepted: 15 june 2023; published: 25 july 2023 the challenges in medical claims in the middle east are significant. the region is witnessing rapid growth in healthcare utilization and expenditures, making it crucial to find effective ways to manage and control medical claims costs. factors such as an aging population, rising chronic disease burden, and increased demand for healthcare services put pressure on healthcare systems and insurance providers. in addition, the complexity of healthcare delivery systems, reimbursement models, and varying regulatory environments in the middle east poses unique challenges for medical claims management. there is a need for streamlined processes, standardized practices, and effective utilization management to ensure accurate and timely claims processing while preventing fraud and abuse. here, i share my experience in managing medical claims, implementing digital health solutions, and understanding of the healthcare landscape in the middle east. insights and recommendations come from extensive discussions and debates with ecosystem partners to address these challenges. the objective here is to raise awareness of the challenges in medical claims in the middle east, share best practices, and propose innovative strategies designed to improve the efficiency, accuracy, and cost-effectiveness of medical claims processing in the region. issues related to payment of fraudulent claims and non-payment of valid claims are presented as follows. problem 1. unpaid valid claims hospitals want to address (table 1) • delayed or denied treatment: valid claims not paid in a timely manner can result in delays or denials of necessary medical treatment. this can adversely affect the health and well-being of the insured individuals. • the financial burden on patients: when the insurer does not pay valid claims, individuals may be forced to bear the financial burden of medical expenses, which can be significant and cause financial hardship. • loss of trust: unpaid valid claims can erode trust between insured individuals and their insurance provider. this can lead to dissatisfaction and frustration, damaging the insurer’s reputation. problem 2. paid fraudulent or inappropriate claims insurers want to address (table 2) • increased costs: paying fraudulent or inappropriate claims can result in increased healthcare costs for the insurer and the insured population as a whole. this can lead to higher premiums for policyholders and strain the sustainability of the insurance system. blockchain in healthcare today issn 2573-8240 table 1. defining the distinction between valid and fraudulent claims in medical insurance valid and legitimate medical claim fraudulent or inappropriate claims typically involve services and treatments necessary for diagnosis, treatment, or prevention of a medical condition. • doctor visits • hospital stays • surgeries • prescription medications • medically necessary tests or procedures insurance companies are obligated to cover these types of claims as per the terms of the policy. may involve: • intentional misrepresentation of information • billing for services not provided • seeking reimbursement for unnecessary or excessive treatments such claims may be made with the intention of obtaining financial gain improperly or abusing the insurance system. mailto:joe@tachyhealth.com citation: blockchain in healthcare today 2023, 6: 273 https://doi.org/10.30953/bhty.v6.273 2 (page number not for citation purpose) joe hawayek and osama abouelkhir • diversion of resources: when funds are allocated to fraudulent or inappropriate claims, it diverts resources away from legitimate healthcare needs. this can impact the availability and affordability of healthcare services for those who genuinely require them. • undermining the integrity of the system: paying fraudulent claims undermines the integrity of the insurance system and creates an environment that encourages further fraudulent activities. it can also lead to higher levels of waste, fraud, and abuse in the healthcare industry. how ai and blockchain technologies contribute to resolving issues in medical insurance claims artificial intelligence. • fraud detection: ai-powered algorithms can analyze vast amounts of data, including medical records, billing patterns, and historical claim data, to identify patterns indicative of fraudulent or inappropriate claims. machine learning models can be trained to continuously learn and adapt to evolving fraud tactics, improving detection accuracy over time. • claims review and processing: ai can automate and streamline the claims review and processing workflows. natural language processing (nlp) techniques can be employed to extract relevant information from medical records and verify the completeness and accuracy of claims. this helps reduce manual errors and accelerates the overall process. • predictive analytics: ai algorithms can analyze historical claim data and patient information to identify trends and predict the likelihood of certain claims being valid or fraudulent. these insights can assist insurers in making informed decisions, prioritizing claim reviews, and allocating resources effectively. blockchain • immutable and transparent records: blockchain technology enables the creation of a decentralized and secure ledger where medical insurance claims and related data can be stored. the immutability and transparency of the blockchain can help prevent tampering with claims data and enhance trust among stakeholders. • smart contracts: blockchain-based smart contracts can automate claim settlement processes. these selfexecuting contracts can automatically validate the eligibility criteria and conditions of claims, triggering payment or denial accordingly. smart contracts can reduce administrative costs, minimize delays, and enhance efficiency. • data privacy and security: blockchain networks can provide enhanced security for sensitive medical data. by utilizing cryptography and distributed consensus mechanisms, patient data can be stored securely and accessed only by authorized parties, thereby protecting privacy and preventing unauthorized modifications. combining ai and blockchain technologies can bring additional benefits, such as using ai algorithms to analyze data stored on the blockchain for fraud detection or leveraging blockchain’s transparency to improve the accuracy of ai models by providing access to a larger dataset. potential commercial approaches for a services company offering recovery or rectification services to an insurance company regarding mistakenly paid fraudulent or inappropriate claims? • technology solutions: the services company can develop or provide technological solutions tailored to fraud detection and recovery needs. this may involve implementing advanced analytics platforms, ai-powered fraud detection systems, or blockchain-based solutions for secure data sharing and auditing. by leveraging technology, they can help the insurance company automate processes, improve efficiency, and strengthen its fraud prevention efforts. • claims auditing and review: the services company can conduct thorough audits and reviews of the insurance company’s claims data to identify any fraudulent or inappropriate claims that were mistakenly paid. they can analyze patterns, review documentation, and assess billing practices to pinpoint discrepancies. based on their findings, they can provide recommendations for recovery actions. • investigation and fraud detection: the services company can specialize in investigating fraudulent claims and detecting fraudulent activities. they can employ advanced analytics and ai algorithms to analyze claims data, identify red flags, and investigate suspicious cases. by leveraging their expertise, they can help the insurance company uncover fraud, gather evidence, and build a strong case for recovery. table 2. problems associated with unpaid valid claims and paid fraudulent or inappropriate claims in medical insurance claim paid not paid valid valid and legitimate claims that are appropriately paid by the insurer. problem 1. • valid and legitimate claims that are unjustifiably not paid by the insurer. fraudulent or inappropriate problem 2. • fraudulent or inappropriate claims that are mistakenly paid by the insurer. fraudulent or inappropriate claims that are correctly not paid by the insurer. https://doi.org/10.30953/bhty.v6.273 citation: blockchain in healthcare today 2023, 6: 273 https://doi.org/10.30953/bhty.v6.273 3 (page number not for citation purpose) artificial intelligence (ai) and blockchain • recovery process management: once fraudulent or inappropriate claims are identified, the services company can assist the insurance company in managing the recovery process. they can handle the necessary legal and administrative procedures, communicate with relevant parties, and negotiate settlements on behalf of the insurer. their experience in recovery strategies and processes can streamline the overall effort and maximize the chances of successful recovery. • training and education: the services company can offer training and education programs to the insurance company’s staff to enhance their knowledge and skills in detecting and preventing fraudulent claims. this may include workshops, seminars, or online courses that cover topics such as recognizing red flags, improving claim review processes, and staying updated on emerging fraud schemes. in terms of commercial arrangements, the services company can structure its engagement through various models, such as project-based contracts, retainer agreements, or revenue-sharing arrangements based on the successful recovery of funds. the specific details of the commercial approach will depend on factors such as the scope of services, duration of engagement, and mutually agreed-upon terms between the services company and the insurance company. commercial approaches for a services company offering payment recovery or claims rectification/resubmission services to a healthcare provider company regarding unjustifiably unpaid valid and legitimate claims by the insurer. • technology solutions: the services company can provide technology solutions that streamline the claims submission and reconciliation process for the healthcare provider. this may involve implementing billing and coding software, electronic health record systems, or claims management platforms. by leveraging technology, they can optimize the provider’s revenue cycle management and enhance the accuracy and efficiency of claims submissions. • claims review and appeals: the services company can conduct a thorough review of the provider’s unpaid claims, analyzing the denial reasons provided by the insurer. they can identify any errors or discrepancies in the claims submission, documentation, or coding that may have led to the denials. based on their findings, they can assist the provider in preparing and submitting appeals to the insurer, providing supporting documentation and evidence to justify the validity of the claims. • denial management and resolution: the services company can specialize in denial management and resolution, helping the provider navigate the complex process of addressing claim denials. they can work closely with the provider’s billing and coding teams to understand the specific denial reasons and develop strategies to rectify the issues. this may involve reformatting claims, correcting coding errors, providing additional documentation, or engaging in direct communication with the insurer to resolve disputes. • negotiation and settlement: in cases where the provider’s claims have been unjustifiably denied, the services company can assist in negotiating with the insurer to reach a fair settlement. they can leverage their knowledge of industry standards, reimbursement guidelines, and contractual agreements to advocate for the provider’s rights. this may involve engaging in discussions, presenting supporting evidence, and seeking a mutually agreeable resolution for both parties. • coding and documentation improvement: the services company can offer coding and documentation improvement services to the healthcare provider to ensure that claims are submitted accurately and with complete supporting documentation. they can assess the provider’s coding practices, documentation standards, and compliance with billing regulations. by identifying areas for improvement and offering training or guidance, they can help the provider enhance their claims submission process, reducing the risk of unjustified claim denials. in terms of commercial arrangements, the services company can structure their engagement through various models, such as fee-based contracts, contingency-based agreements where they receive a percentage of recovered funds, or a combination of both. the specific details of the commercial approach will depend on factors such as the volume of claims, the complexity of denials, the duration of engagement, and mutually agreed-upon terms between the services company and the healthcare provider. conflicts of interest joe hawayek is a bhty regional mena editor, and currently serves as board member at tachyhealth, an ai-powered platform for addressing payor–provider interactions. dr. osama abouelkhir, is ceo of tachyhealth. https://doi.org/10.30953/bhty.v6.273 1 (page number not for citation purpose) blockchain in healthcare today 2022. © 2022 the authors. this is an open access article distributed under the terms of the creative commons attribution-noncommercial 4.0 international license (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. citation: blockchain in healthcare today 2022, 5: 195 http://dx.doi.org/10.30953/bhty.v5.195 original research decentralized identity management for e-health applications: state-of-the-art and guidance for future work abylay satybaldy1 msc , anton hasselgren2* msc and mariusz nowostawski1 phd 1computer science department, norwegian university of science and technology, norway; 2department of neuromedicine and movement science, norwegian university of science and technology, norway abstract background: the increasing use of various online services requires an efficient digital identity management (dim) approach. unfortunately, the original internet protocols were not designed with built-in identity management, which creates challenges related to privacy, security, and usability. there is an increasing societal concern regarding the management of these sensitive data, access to it, and where it is stored. blockchain technology can potentially offer a secure solution to address these issues in a decentralized manner without centralized authority. this is important for e-health services where the patient and the healthcare provider often are required to prove their identity. blockchain technology can be utilized for creating digital identities and making its management easier, thus giving a higher degree of control to the user than what current solutions offer. it can be used to create a digital identity on the blockchain, making it easier for individuals and entities to manage, giving them greater control over who has their personal information and how they handle it. in addition, it might be utilized to create a higher degree of trust and security for e-health applications. objective: the aim of this research work was to review the state-of-theart regarding blockchain-based decentralized identity management for healthcare applications. based on this summary, we provide a viewpoint on how blockchain-based decentralized identity frameworks might be utilized for virtualized healthcare applications. method: this research study applied a scoping, semi-systematic review approach to summarize the state-ofthe-art. included identity management systems were evaluated based on seven criteria: autonomy, authority, availability, approval, confidentiality, tenacity, and interoperability. results: seven blockchain-based identity management systems were included and evaluated in this work: these include solutions built with ethereum, hyperledger indy, hyperledger fabric, hedera, and sovrin blockchains. conclusion: dim is crucial for virtual health care. decentralized identity management for healthcare purposes is currently being explored in both academia and the private sector. more work is needed with the aim of improving the efficiency of current dim solutions and to fully understand what technical frameworks are best suited for e-health applications. keywords: blockchain; decentralized identity; virtual healthcare; identity management received: 9 december 2021; revised: 12 december 2021; accepted: 21 december 2021; published: 31 january 2022 blockchain and decentralized technologies have seen increased applications in the healthcare sector. many of the inherited properties of blockchain technology have the potential to mitigate some of the current issues with health information systems (1, 2) and, equip a new, digital focused health care system, as an example. healthcare 4.0 (3). the identity management is a crucial part in most healthcare applications; patients need to prove that they are who they say they are and the same for medical professionals. to take a full advantage of the decentralized technologies, a digital identity management (dim) approach should also be considered to move from current centralized solutions to decentralized or self-sovereign systems. many decentralized healthcare applications lose its core value proposition when *correspondence: anton hasselgren, email: anton.hasselgren@ntnu.no https://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.30953/bhty.v5.195 https://orcid.org/0000-0002-7735-4902 https://orcid.org/0000-0002-6245-6041 https://orcid.org/0000-0002-2809-8615 mailto:anton.hasselgren@ntnu.no citation: blockchain in healthcare today 2022, 5: 195 http://dx.doi.org/10.30953/bhty.v5.1952 (page number not for citation purpose) abylay satybaldy et al. it needs to be tied to a centralized identity system, physical or digital. therefore, there is a need to further explore how a decentralized identity management system could work and benefit the healthcare sector in a digital transformed system. the aim of this research work was to summarize the state-of-the-art regarding blockchain-based decentralized identity management systems for healthcare applications. based on this summary, we provide a viewpoint on how blockchain-based decentralized identity frameworks could be utilized for healthcare applications and guide developers and researchers within this area. method this research study has applied a scoping, semi-systematic review approach to summarize the state-of-the-art. the search was carried out mainly using the snowball method (4) with initial search in medline, scopus and google scholar where the free text search terms: ‘healthcare’ or ‘e-health’, were combined with the terms ‘decentralized identity’ or ‘blockchain’ or ‘self-sovereign identity’ using the boolean operator and. inclusion of systems were based on how well the authors and developers had described their solutions and, to get a wide input, some diversification between the different systems was preferred. the review and the search were not meant to be comprehensive but will nevertheless provide an important summary and evaluation. as shown in table 1, the criteria proposed by boaras et al. (5) were adopted to evaluate the different decentralized identity management systems presented in the academic and gray literature. background blockchain public blockchain technology is a recent breakthrough of trusted computing without centralized authority in an open-networked system. blockchain is a broad term for a collection of technologies that offer immutable ledgers that are replicated and synchronized over a large number of nodes. the data consistency is achieved by a special mechanism that makes the additions to the ledger agreed upon by consensus in the network. the main advantage or innovation of blockchains is the ability to achieve consensus and data consistency in the presence of certain number of malicious nodes, thus, enabling the system to operate in semi-trusted environments while maintaining security and trust of the ledger data. the public permissionless blockchains are also characterized by the absence of central administrators, eliminating the need for a trusted third party. originally invented as the underlying infrastructure of bitcoin (6), blockchain’s potential application has reached far beyond cryptocurrency and financial assets. as the technology gained wider recognition in recent years, there have been a flurry of advancements, new use cases, and applications, including in health care (7). blockchain technology solves the decentralized governance over the data, and the elimination of trusted third party in maintaining consistency of data. because of that, blockchain technology has the potential to transform and disrupt the digital society by enabling disintermediated digital platforms that have not been seen before. since the dawn of the internet, we have faced identity management challenges related to privacy, security, and usability. the increasing everyday usage of various online services requires an efficient dim approach. blockchain ledgers offer the ability to publish certain proofs or data snapshots needed for verification of digital identity attributes and credentials. identity this study uses (8) the definition of identity as ‘the set of known values or attributes that characterizes, identifies, or describes an entity’. a person has several attributes, such as a name, birth date, and citizenship, which establish his or her identity in the real world. we may just use our first name or nickname among a group of friends, whereas table 1. criteria for identity management in healthcare criteria abstract autonomy is the identifier independent from identity vendors? identifiers must be independent of any identity provider or a central governor. authority can the individual have full control of his identity? individuals must have complete power to manage their identities. availability can an individual have full access to his or her own data? individuals must have full permission to gain access to their own data anytime anywhere. approval can the individual voluntarily approve the use of his identity? individuals must voluntarily agree and approve requests before using their identity. confidentiality can the individual provide his identity with minimal disclosure of data? individuals should share only the needed credentials with a minimal disclosure. tenacity can the identity live with the individual as long as possible? individuals’ identities must be persistent as long as possible. interoperability can the individual identity exchange data with any system or service globally? individual identities should be universal and widely used by any entities. source: adopted from bouras et al. (5). http://dx.doi.org/10.30953/bhty.v5.195 citation: blockchain in healthcare today 2022, 5: 195 http://dx.doi.org/10.30953/bhty.v5.195 3 (page number not for citation purpose) decentralized identity management for e-health applications within the work environment it might be necessary to use our full name. identity also includes a set of identifiers, such as national identification number and driver’s license number, which are linked to corresponding credentials. these identifiers are usually unique and issued by governmental institutions. digital identity is a relevant concept in the digital world. a digital identity is a set of verified identifiers, digital attributes, and credentials for the digital world, similar to a person’s identity in the real world (9). identity management identity management is issuance, maintenance, and revocation of digital identifiers and credentials in applications, systems, and networks. identity management system is a set of technologies and processes that can be used for identity management (9). traditional identity management the internet was not initially made with identity and digital credentials in mind. these aspects are something that has come after the internet has evolved to become a medium for finance, commerce, e-health, and other broad range of services in the digital world. the traditional solutions, often based on a siloed, walled garden approach, result in privacy, security, and interoperability issues. the most conventional method for user management in online services today continues to be the use of traditional localized register of users. the end user signs up directly on the website, and the service provider creates a unique identifier and credential that is bundled together. the user can ‘claim’ this through identifier/password pair, provided as a form of identification. in this model, both the user and the service provider face several challenges. with the large number of online services an average person uses today, users must manage an increasingly large number of passwords, which is a daunting task. due to this complexity, some users re-use the same password on multiple services, which leads to increased security risks. a leak of password from one of the users’ online accounts results in a substantial risk of compromise of all accounts. the second most used method is federated identity management. most well-known federated identity schemes rely on a third-party identity provider (idp) to broker identification using protocols, such as saml (10), openid connect (11), and oauth (12). the user can then access multiple services using the single account. however, these centralized aggregators represent technologically a single point of control and a single point of failure. typically, most of the service providers rely on centralized databases, which are in charge of storing the large amount of user data that could be potentially hacked by a malicious third party. the other risk is that the credential issued by the identity provider, for example, the facebook account, can be locked by facebook at any time, and the ability for the user to claim the credential is compromised. the credential is not stored or managed by the user, but rather, entirely controlled by the identity provider. moreover, this model facilitates the tracking as the identity provider has access to information on what services the user is accessing and when. the results of surveys (13–15) revealed that the federated identity solution users are feeling ‘lack of control’ over their data and would like to control personal data themselves. decentralized identity management decentralized identity management is an alternative way of thinking and structuring the entire identity management workflows, which offers an improved mechanisms for verifying and authenticating users. it is a new architecture for privacy preserving and user-centric identity management. in this model, the user controls their identity data and interacts directly with the service providers – without relying on a trusted third party. the blockchain serves as a global registry for the decentralized public key infrastructure (dpki) that provides the mapping of keys to the decentralized identifiers (dids). self-sovereign identity (ssi) is often used in synonym with decentralized identity management approaches. by combining the martin h. weik’s definition of identity (8) and peter de marneffe’s principles for self-sovereignty (16), we can describe ssi in its simplest form as a digital representation of the individuals’ characteristics, description, and identifiers where no government, or organization, can violate our right to choose our level of privacy or celebrity with our identity attributes. subsequently, christopher allen has defined the 10 principles of the ssi model (17). the ssi model aims to avoid a single point of dependency and allows individuals to take ownership of their digital identities. in ssi, there is no central authority, so users hold their own digital keys and have full control over their personal information. this information is typically carried around by the user in the digital identity wallet on his or her mobile device. the concept of ssi is becoming the next stage in the evolution of identity management systems. various ssi systems exist, which provide solutions using a distributed ledger technology. evernym (18), sertoid (19), and ion (20) are some examples of identity projects that are working on decentralized identity platforms. however, these sophisticated solutions are still at an early stage, where more discussions, validations, and investigations are needed (21). ssi standards ssi is work in progress, which includes the work on standards for ssi. there are different groups and standardization agencies working to develop new standards, frameworks, and protocols, which could be the base of the ssi architecture. these efforts come from organizations like the decentralized identity foundation (dif), the european blockchain services infrastructure, the internet engineering task force, ieee, nist, iso, sovrin http://dx.doi.org/10.30953/bhty.v5.195 citation: blockchain in healthcare today 2022, 5: 195 http://dx.doi.org/10.30953/bhty.v5.1954 (page number not for citation purpose) abylay satybaldy et al. foundation, oasis, and the world wide web consortium (w3c) (22). until now, the two fundamental base standards for ssi are as follows: dids (23) and verifiable credentials (vcs) (24). did is a new type of digital identifier that should be globally unique, persistent, and not requiring centralized registration authority. its core architecture, data model, and syntax are standardized and developed by w3c did working group. vc is a secure, tamper-evident digital credential that can be cryptographically verified. the w3c credentials community group defines the issuance, storage, presentation, and verification of digital vcs. moreover, dif has several working groups that focus on authentication, secure data storage, and peer-to-peer communication in the context of ssi (25). privacy properties to protect their privacy, individuals must be empowered to control their own digital identities and personal data. blockchains provide a promising operational environment for the trend of ssi, characterized by transformation from a non-user controlled centralized model to a fully user-controlled decentralized model. unlinkability. before decentralized identity management, privacy preserving was incomplete due to the existence of centralized identity authorities. service providers and users need to grant full trust to their identity providers. in other words, centralized identity providers could see activities between users and ser-vice providers, which compromises the identity information privacy. decentralized data storage. a decentralized identity model empowers users to ‘bring their own storage’ and give them control of their own information. this approach provides a privacy-respecting mechanism for storing, indexing, and retrieving data with a storage provider. removing the need for dealing with storage infrastructure (instead leaving it to a specialist service provider that is chosen by the user) allows developers to focus on the functionality of their application. it reduces the compliance burden of managing customers’ personal data in services. user control and consent. in this new digital identity ecosystem, individuals (or entities) have full control over their digital credentials and attributes. users can add, remove, and share digital credentials at their own discretion. moreover, users can have one or more identifiers and can present credentials relating to those identifiers without having to go through an intermediary. credentials made about a user can be self-asserted or asserted by a third-party whose authenticity can be independently verified by a relying party. all the credentials and personal identity information can be easily retrieved by the user when needed (21). security properties no single point of failure. the aggregation of personal data in one centrally controlled data storage brings an enormous risk of data breaches. the latest evidence of such data breaches are seen in twitter (26), equifax (27), cambridge analytica (28), and first american financial (29) cases where the identity information of millions of individuals was exposed. some of the data leaks are through hacks, but some are through design flaws of the data flows in the systems. under the ssi model, identities must not be held by a single third-party entity. encrypted data vaults. as it is declared in the specification by dif, the priority is to ensure the privacy of an entity’s data so that it cannot be accessed by unauthorized parties, including the storage provider. the storage provider can not view, aggregate, analyze, or resell the data. to achieve this, the data must be encrypted while it is both at rest (on a storage system) and in transit (being sent over a network). this method also ensures that application data are portable and protected from storage provider data breaches (25). decentralized identity management for digital healthcare applications healthcare information systems (his) contain medical-related data of patients that should be considered highly private. when health care is delivered both physically and digitally, it is equally important to protect the digital identity as the physical for patients in a his. several blockchain-based applications and concepts for health care have been proposed (7). the digital transformation of the healthcare sector can be considered to decentralize the industry by its core; patients are generating more and more data from different personal devices, have the opportunity to receive healthcare services from a growing set of virtual healthcare providers, and medicines can now often be ordered from a variety of online pharmacies. this creates the need for patients to have the ability to digitally identify themselves more often and with more stakeholders, also outside their regular jurisdiction area (30). in this review, we evaluate seven representative proposals for healthcare and their approach to deal with identity management. we selected these systems because they provide technical documentation, reports, and proof of concepts with the most technical details of their designs. this sample is meant to serve as an example and is not intended to be a comprehensive review of all published research in the area. by choosing seven examples with different approaches, we illustrate how identity management is tackled in a decentralized healthcare environment in the forefront of academic research and in the private sector. it is crucial to be able to verify a patient’s identity and a healthcare providers’ identity to deliver safe health services. this is as important in a digital environment as it is in a physical environment. related work to our knowledge, there are no more than two published papers that present a review of decentralized identity in http://dx.doi.org/10.30953/bhty.v5.195 citation: blockchain in healthcare today 2022, 5: 195 http://dx.doi.org/10.30953/bhty.v5.195 5 (page number not for citation purpose) decentralized identity management for e-health applications health care with blockchain or distributed ledgers technology (dtl) (5, 31). these two papers highlight the need for decentralized identity management for several of use cases in the healthcare sector, in particular in e-health. although these two publications provide insights into the topic, there is a need for further exploring how blockchain-based identity management fit in a healthcare context, especially in a virtual healthcare context. the contribution of this work is to analyze the current stateof-the-art of decentralized identity management concepts and frameworks presented in academic literature and from the industry. furthermore, the paper summarizes how identity is managed in blockchain-based healthcare concepts in the literature. finally, our work provides guidance for future decentralized identity systems for healthcareapplications and explores the fit of the state-of-art concepts for this purpose. state-of-the-art in peer-reviewed literature medilinker (32) proposes a system built with hyperledger indy for the identity management, and hyperledger aeris was used as an application programming interface (api) to connect indy’s identity management features to personalized encrypted digital wallets of the users. the digital wallets hold by the patients contain private keys that control consent and personal data, including identity verifications. no personal data were stored on the blockchain. with given consent from the patients, through their digital wallets, data can be shared on chain. the consent is given using the private key, which is stored in the digital wallet. hyperledger indy was used with the motivation that it supports world wide web consortium (w3c) standards, dids that provides full autonomy to users over their data, and has an active and supportive developer community. mikula and jacobsen (33) presents a system for identity and access management using blockchain technology to support authentication and authorization of entities in a ehr system. the proof of concept was implemented using hyperledger fabric, and basic authentication and authorization operations such as registration, login, grant/revoke permissions, and update of the system were implemented. the system uses traditional front-end and back-end technologies for most parts of the system, the authentications and authorizations are vali dated through the blockchain ledger. the authors concluded that their system, implemented on the consortium blockchain hyperledger fabric, could scale and handle data from all the physician in denmark, assuming that hyperledger fabric reaches its performance goal of 100.000 transactions per second in 15 nodes consortium. in the article by sharm et al. (34), the authors propose a novel healthcare framework using inter planetary file system and smart contracts for storage and access control of ehrs and other medical documents in the context of india’s national health scheme. they utilized zero-knowledge proofs (zkp) as an authentication mechanism in the proposed system to enable access to ehr in a privacy-preserving manner with the objective of increasing interoperability within the healthcare system. in the concept, the citizens of india who are entitled health instance coverage under the prime minister’s, people’s health scheme (pm-jay) scheme (around 500 million individuals) can get access to a unique health id controlled by a set of private/public keys. biometrics is utilized together with national id proofs to get access to the health id at a service desk in any hospital. the individual is checked for eligibility in the pm-jay scheme database through the service desk. if eligible, an e-card with the private key is issued to the individual. together with a sixdigit password this serves as a proof of eligibility of health services that can be used at any health providers in india. the health-id solution presented by javed et al. (35) runs on a consortium network based on the ethereum blockchain. a consortium of healthcare regulators will manage the blockchain. an authority node is responsible for validating new blocks on the chain. the majority of the consortium decides the authority node. their id management systems have both patients and healthcare providers as users. the solutions require the patients to initially verify their identity using passports, national identity cards, or driving licenses. the healthcare providers are requested to use their practice license for the same, initial verification process. the identity owner can choose what kind of storage system to use for his or her identity attributes, and these can be either centralized systems or decentralized systems. the system tokenizes the identities of the participants, and the tokens are signed by healthcare regulators to verify the authenticity. the attributes of the identities are indexed on the blockchain. state-of-the-art developments in practice the sovrin foundation aims to standardize and build an infrastructure for ssi using blockchain as storage for decentralized identities. the sovrin network is a public-permissioned blockchain that has been designed specifically for identity. sovrin was one of the first projects that integrated and provided full support for vcs and dids. moreover, a user can generate pairwise-pseudonymous dids (36) and public keys for every relationship, which makes each identifier unlinkable and protects his or her privacy. the credential exchange mechanism supports the selective disclosure based on an advanced privacy-enhancing technique known as a zkp (37). to avoid security and privacy concerns, no private data, even in encrypted form, are stored on the sovrin ledger. the project was launched in 2017, and the open source code base was transferred to the linux foundation to become the hyperledger indy (38). the net-work relies on nodes called stewards to achieve global consensus. the stewards are approved by the non-profit sovrin foundation with a board http://dx.doi.org/10.30953/bhty.v5.195 citation: blockchain in healthcare today 2022, 5: 195 http://dx.doi.org/10.30953/bhty.v5.1956 (page number not for citation purpose) abylay satybaldy et al. of 12 trustees. truu (39) is one of the sovrin stewards that utilizes the sovrin technology to create a portable, trusted digital id for healthcare professionals in the uk. the company is collaborating with national health service (nhs) to transform the way healthcare organizations in the uk verify staff identities, qualifications, and certifications. medibloc is developing a blockchain-based health information platform that provides patient-centric and reliable health information. panacea is a public blockchain optimized for health data, developed by medibloc to provide a tamper-proof, high-performance data ecosystem. panacea blockchain relies on delegated proof of stake consensus mechanism with the practical byzantine fault tolerance algorithm, which enables block validators, that are decided by votes of network participants, to create new blocks at a high speed. the panacea governance council is responsible for making major technology and business decisions on the panacea project (40). in the medibloc platform, health data providers can issue vcs with did to patients. the integrity of the credential can be verified by anyone with the did document on panacea. the health data are managed only by the patient, and storage of the data is with the same user. the health data are stored in the form of merkle tree, and the root hash of merkle tree is recorded on the panacea blockchain. the main benefit of merkle tree method is that the users can share parts of the data while guaranteeing its integrity (41). it enables the selective disclosure of personal data. other services that utilize a different data format can still be integrated into medibloc through ‘merklizing’ the data, and medibloc provides software tools and guides. the platform has been used by several partner hospitals in south korea, including good moonhwa hospital, yongin severance hospital, and eunseong medical foundation (42, 43). hedera is a public distributed ledger for building and deploying decentralized applications and microservices. the network is made up of permissioned nodes run by the hedera governing council, which consists of various organizations and enterprises representing industry, academia, and non-profits globally. the major software changes and business decisions are governed by council members. the hashgraph consensus algorithm enables distributed consensus on the public hedera ledger. the hashgraph technology relies on the ‘gossip about gossip’ protocol where all nodes on the network ‘gossip’ about transactions to construct directed acyclic graphs (dags). unlike a blockchain, dags time-sequence transactions without bundling them into blocks. ‘gossip’ messages contain transactions, a timestamp, cryptographic hashes of two previous events and a digital signature. this enables hashgraph to form an asynchronous byzantine fault-tolerant (abft) consensus algorithm (44). hedera provides developers with the tools to issue, verify, and revoke identity credentials for subjects and devices in a standards-based and privacy-respecting manner. hedera’s credentials follow the dids and vc standards, which are under development at the w3c. credentials and related sensitive metadata are not stored on the hedera main net. currently, hedera is being used for patient record management and health status verifications by safe health systems and nhs (45). discussion the digital transformation has reached the healthcare sector, and more and more health services are delivered virtually, across jurisdictions and by an increasing amount of providers. to verify one’s identity as a patient (and healthcare professional) has always been important and is perhaps even more important when care is given virtually. although many countries have implemented systems for national digital ids, there are practical and theoretical challenges with such systems. the identity of an individual belongs to that person, and to be able to verify that such be considered a right, and not a privilege. blockchain technology has provided us with tools to decentralize applications that previously required a trusted third party. these new solutions and technologies present an opportunity to rethink how we manage identities and personal information digitally. ssi solutions provide the identity owner with full control over their identity and the ability to selectively disclose parts of that data, while keeping other parts hidden. dim solutions are currently under development and are evolving at a high pace. more research is needed to provide a deeper understanding of their functionalities and their role in e-health. the evaluation in ‘decentralized identity management for digital healthcare applications’ section and its accompanying tables 2 and 3 provide an overview of the current state of the decentralized identity landscape in healthcare sector. this review shows that different technical solutions are used, with different approaches to the utilization of blockchains. all the compared decentralized identity systems in ‘decentralized identity management for digital healthcare applications’ section aim to give more control to patients over their identity data, and they also embrace the need for transparency and trust by providing the source code available for review. the study also reveals some unsolved challenges in developing decentralized identity solutions for healthcare applications. key management control over user’s cryptographic keys, as well as the rest of the contents of digital wallet such as credentials, is probably the single most critical element of the dim architecture. while traditional identity management models provide a key management protocol that rely on a trusted third party, in the context of the dim model, the responsibility of key management is assigned to the identity owners themselves. as the users are notorious for losing passwords and mobile http://dx.doi.org/10.30953/bhty.v5.195 citation: blockchain in healthcare today 2022, 5: 195 http://dx.doi.org/10.30953/bhty.v5.195 7 (page number not for citation purpose) decentralized identity management for e-health applications devices, the dependency on non-technical users to keep credentials safe comes with an undeniable risk. creating a cost-efficient, usable, and secure management of identities is not an easy task. dim requires effective innovative and well-analyzed solutions to support it. usability decentralized identity systems for healthcare applications should be designed to solve the challenges faced by the end users. we can see that the existing implementations primarily focus on the underlying technology and do not pay enough attention to the user interaction. privacy implications for users and usable interface are crucial things when building new user-centric identity systems and need to be addressed by developers. the future decentralized identity schemes with an innovative technological underpinningbut developed with impractical end-user interaction are unlikely to create widespread uptake. interoperability the results of this study show that there is still lack of a standardized implementation method for decentralized identity systems. the existing solutions have applied various methods of storage, authentication algorithms, encryption, and consent mechanisms. due to the lack of a standardized implementation method, the evaluation and comparison of the existing solutions become challenging. however, as shown in table 2, most of the reviewed solutions are based on w3c dids and vc specifications. these standards for ssi are under development and seek table 2. summary of dim solutions dim solutions distributed ledger technology privacy & data minimisation identifiers & authentication mechanism data management o pen source scalable d ata m arket m obile friendly proof of concept sovrin foundation public-permissioned sovrin blockchain pairwise identifiers; selective disclosure using vcs; no private information on sovrin. based on dids and their associated verification keys. cloud + local storage + + + + hedera public distributed ledger hedera hashgraph (dag) credentials or any related metadata is not stored on the nodes of the hedera mainnet. based on dids and their associated verification keys. cloud + local storage + + + medibloc public panacea blockchain (dpos) data minisation is implemented by merkle proof and root hash. based on dids and their associated verification keys in did document onpanacea. merkle tree root as key. key-value database (mobile device) + + + + + mikula & jacobsen consortium hyperledger fabric blockchain users can grant permissions and revoke when needed. but medical identity and pin of a patient is stored in blockchain. the authserver authenticates and authorizes the user by querying the blockchain network. blockchain + sql database + + + health-id consortium ethereum blockchain hash of the identity attributes are stored on the blockchain. identifiers and attributes stored in a json web token. smart contracts are used to authenticate users. blockchain + cloud storage (dropbox, ipfs) + + + medilinker hyperledger indy and hyperleder aries, public permissioned blockchain users give consent for data sharing through their digital wallets. the use of dids and digital wallets containing users private keys. blockchain + digital wallet + + + sharma et al. public-permissionless ethereum blockchain (pow) privacy preserving identity verification using zksnarks. all the medical records are encrypted. authenticate users with existing national ids using zero-knowledge proofs and ecards. ipfs and smart contracts + + http://dx.doi.org/10.30953/bhty.v5.195 citation: blockchain in healthcare today 2022, 5: 195 http://dx.doi.org/10.30953/bhty.v5.1958 (page number not for citation purpose) abylay satybaldy et al. to achieve a unified society with methods that allow communication across systems. scalability scalability challenges due to transaction throughput and latency of blockchain systems have for long time been known and recognized. in particular, the public permissionless blockchains, such as bitcoin and ethereum, face scalability restrictions due to high transactions costs and low throughput. there are various solutions offered for scalability of public permissionless blockchains, including layer 1 solutions (new networks such as hedera or solana) and layer 2 solutions that operate on top of the existing blockchains (such as polygon and arbitrum). consortium or private blockchain usage can also address the scalability issues. all but one of the solutions summarized in this article use a consortium and/or private blockchain, which should enhance the scalability. although you then need to accept a trade-off with decentralization since private and consortium blockchains are more centralized. scalability is important to address for future work of dim in health care. the evaluation framework used in this article is recommended to be used by developers to ensure that their solution has a high degree of autonomy, authority, availability, approval, confidentiality, tenacity, and interoperability. the compliance to the listed criteria in table 3 are not completely binary but rather a scale to which degree the criteria are met, the degree of compliance with a number from 0 to 3 where 0 is not at all complaint and 3 is highly compliant. based on the outcomes and learnings from this study, the authors will develop a dim system, tailored for a virtual healthcare environment, with the objective of improving what has been done previously. conclusion in this article, we have reviewed the current state-of-theart and presented the existing dim solutions for healthcare applications. based on the proposed criteria, we evaluated different dim systems presented in the academic and gray literature. despite a wide variety of proposals for novel dim for health care, current solutions are still limited. there exist open challenges related to privacy, usability, interoperability, and scalability that proposed systems are to address. dim is crucial for virtual health care as it offers novel features those traditional solutions lack. decentralized identity management for healthcare applications is currently being explored in both the academy and the private sector. more work is needed with the aim to improve the efficiency of current mechanisms and to fully understand what technical frameworks are best suited for this particular use case. conflict of interest no conflict of interest reported. funding this research study has been internally funded by the norwegian university of science and technology. authors’ contributions abylay satybaldy contributed to the 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https://www.bcg.com/publications/2012/digital-economy-consumer-insight-value-of-our-digital-identity https://dx.doi.org/10.1007/s11572-012-9157-x http://www.lifewithalacrity.com/2016/04/the-path-to-self-soverereign-identity.html http://www.lifewithalacrity.com/2016/04/the-path-to-self-soverereign-identity.html https://www.evernym.com/ https://www.serto.id/ https://www.serto.id/ https://identity.foundation/ion/ https://www.w3.org/tr/did-core/ https://www.w3.org/tr/vc-data-model/ https://identity.foundation https://identity.foundation https://edition.cnn.com/2020/07/15/tech/twitter-hack-elon-musk-bill-gates/index.html https://edition.cnn.com/2020/07/15/tech/twitter-hack-elon-musk-bill-gates/index.html https://dx.doi.org/10.1109/mc.2017.4451227 https://dx.doi.org/10.1109/mc.2018.3191268 https://bit.ly/3cmekjj https://dx.doi.org/10.1108/lhs-04-2014-0032 https://dx.doi.org/10.1109/access.2020.2994090 https://dx.doi.org/10.1093/jamiaopen/ooaa073 https://doi.org/10.3390/healthcare9060712 https://doi.org/10.3390/healthcare9060712 https://openssi.github.io/peer-did-method-spec/ https://openssi.github.io/peer-did-method-spec/ https://sovrin.org/wp-content/uploads/sovrin-protocolhttps://www.hyperledger.org/projects/hyperledger-indy https://www.hyperledger.org/projects/hyperledger-indy https://truu.id/ https://medibloc.com/en/ https://github.com/medibloc/whitepaper/blob/master/techinicalwhitepaper_eng.md/ https://github.com/medibloc/whitepaper/blob/master/techinicalwhitepaper_eng.md/ https://medium.com/medibloc/welcome-good-culture-hospital-44fb1cb1a327 https://medium.com/medibloc/welcome-good-culture-hospital-44fb1cb1a327 https://medium.com/medibloc/welcome-good-culture-hospital-44fb1cb1a327 https://medium.com/medibloc/welcome-yongin-severance-hospital-c01ac5d64129 https://medium.com/medibloc/welcome-yongin-severance-hospital-c01ac5d64129 https://medium.com/medibloc/welcome-yongin-severance-hospital-c01ac5d64129 https://docs.hedera.com/guides/core-concepts/hashgraph-consensus-algorithms https://docs.hedera.com/guides/core-concepts/hashgraph-consensus-algorithms https://hedera.com/hh_safe-health-systems-case-study_201130.pdf https://hedera.com/hh_safe-health-systems-case-study_201130.pdf http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 page 1 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 accelerating genomic data generation and facilitating genomic data access using decentralization, privacy-preserving technologies and equitable compensation dennis grishin,1,2,3 kamal obbad,1 preston estep,1,3 kevin quinn,1 sarah wait zaranek,3 alexander wait zaranek,1,3 ward vandewege,1,3 tom clegg,3 nico césar,3 mirza cifric,1,3 george church1,2,3 authors 1nebula genomics, inc., san francisco, usa; 2department of genetics, harvard medical school, boston, usa; 3veritas genetics, inc, danvers, usa. corresponding author dennis grishin, nebula genomics inc., 73 sumner street, #401, san francisco, ca 94103, usa; dgrishin@g. harvard.edu keywords: arvados, blockchain, data privacy, data sharing, dna sequencing, genomics, homomorphic encryption, nebula category: use cases/pilots/methodologies in the years since the first human genome was sequenced at a cost of over $3 billion, technological advancements have driven the price below $1,000, making personal genome sequencing affordable to many people. personal genome sequencing has the potential to enable better disease prevention, more accurate diagnoses, and personalized therapies. furthermore, sharing genomic data with researchers promises identification of the causes of many diseases and the development of new therapies. however, sequencing costs, data privacy concerns, regulatory restrictions, and technical challenges impede the growth of genomic data and hinder data sharing. in this article, we propose that these challenges can be addressed by combining decentralized system design, privacy-preserving technologies, and an equitable compensation model in a platform that vests control over data with individual owners; ensures transparency and privacy; facilitates regulatory compliance; minimizes expensive data transfers; and shifts the sequencing costs from consumers, patients, and biobanks to researchers in industry and https://doi.org/10.30953/bhty.v1.34 mailto:dgrishin@g.harvard.edu mailto:dgrishin@g.harvard.edu https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v1.34&domain=blockchainhealthcaretoday.com&date_stamp=2018-12-19 page 2 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 academia. we exemplify this by describing the implementation of nebula, a distributed genomic data generation, sharing, and analysis platform. the human genome project has sequenced and assembled the first human reference genome at a cost of over $3 billion.1 since then, development of next-generation sequencing technology has resulted in exponentially decreasing sequencing cost (figure 1).2 today, the sequencing of a whole human genome costs less than $1,000. this price is projected to drop to $100 in the next few years.3 the exponentially decreasing dna sequencing costs have made personal genome sequencing affordable to patients as well as healthy individuals. personal genome sequencing is becoming more common as prices decline, but most genetic tests to date have been performed using dna hybridization microarrays. these tests are referred to as genotyping and they assess the presence or absence of genetic variants associated with certain traits. for a cost less than $100, genotyping typically reads out only ~0.02% of the human genome, at predefined positions, often missing health-relevant genetic variants that must be reported. in addition, variant identification at a small number of positions does not allow discovery of novel variants, including those that cause disease; the majority of these variants are distributed throughout the genome and remain undiscovered.4 this limits the usefulness of genotyping data to researchers. opportunities as genomic sequencing becomes more affordable, it opens up opportunities for individuals as well as researchers in academia and industry. figure 1—human genome sequencing cost, 2001–2017. https://doi.org/10.30953/bhty.v1.34 page 3 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 personal genome sequencing can support data-driven decision-making for health-related issues. studies estimated that ~2% of people carry genetic variants that cause or predispose them to a wide variety of diseases at various levels of severity, the majority of which can be preventable or treatable.5 in addition, every parent carries, on average, approximately five genetic variants that might cause diseases in offspring if the other parent carries the same variant.6 the presence of certain genetic variants also has been associated with adverse effects for ~7% of food and drug administration-approved drugs.5 personal genome sequencing can also help healthy individuals make better lifestyle choices. for example, genetic variants have been shown to cause sensitivities to certain nutrients7–9 and to increase risks of sports-related injuries.10–12 in the future, advancement in understanding human genetics will make personal genome sequencing more insightful, while correcting pathological genetic variants will become possible as more and more gene therapies enter clinical trials.13 researchers study genomic data sets to identify genetic variants that cause diseases. this enables the research and development of therapies targeting disease-associated genes with increasing specificity. genomicsguided therapeutic discovery has been applied successfully to many types of cancers, rare genetic diseases, and, increasingly, common complex diseases.14 furthermore, genomicsguided patient cohort recruiting can reduce the failure rate of clinical trials by enriching for likely responders and reducing reducing adverse reactions. this approach to clinical trials promises to reduce surging drug development costs and lead to more drugs reaching the market and benefiting patients.15 these opportunities are recognized by the biopharma industry. for example, the leading personal genomics company 23andme received $60 million from genentech16 and $300 million from glaxosmithkline17 for access to genotyping data collected from its customers. other biopharma companies have launched their own sequencing projects. astrazeneca announced it would sequence 2 million human genomes,18 and regeneron is leading a $100 million consortium to sequence approximately 500,000 samples collected by the uk biobank.19 challenges multiple obstacles hinder the realization of opportunities offered by personal genomics. many people are deterred by the costs of personal genome sequencing, as well as concerns over genomic data privacy. research is hampered by the resultant scarcity of genomic data and is further compounded by difficulties with respect to data access. in 2018, the number of genotyped people surpassed 10 million and is expected to grow to more than 100 million by 2021.20 this growth is driven by a combination of factors, notably consumer interest in ancestry analysis coupled with a decrease in genotyping costs below $100.21 in contrast, consumer interest in whole genome sequencing has grown slowly due to a significantly higher cost. a recent survey revealed that only ~3% of people are willing to pay >$1,000 for whole genome sequencing.22 for the majority of consumers, whose primary interest in the area can best be described as nonmedical “infotainment,” the benefits of sequencing over genotyping do not justify the significantly higher cost. at the same time, the surge in popularity of genetic testing, forensic utilization of genetic databases,23 and the purchase of genetic data by biopharma companies24 have increased consumer and media attention to genetic https://doi.org/10.30953/bhty.v1.34 page 4 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 data privacy. studies show that privacy concerns are legitimate, as data sharing policies of many personal genomics companies do not fulfill transparency guidelines with regard to the confidentiality or sharing of customer genetic data.25 these developments are likely to exacerbate reported privacy concerns over genetic data26,27 and deter personal genomic sequencing. for researchers, low adoption of personal genome sequencing has resulted in low availability of genomic data. according to estimates, only ~500 thousand human genomes had been sequenced by 2017.3 this is detrimental for research because very large genomic data sets are necessary to find links between genetic variants and traits, such as disease predispositions. finding such links is difficult because most traits are the product of complex interactions of many genetic variants, while the effects of individual genetic variants are, on average, very small.28 low diversity of genomic data sets further compounds the search for links between genetics and disease.29 the scarcity of genomic data is exacerbated by difficulty in data access due to fragmentation of genomic data across proprietary data silos.30 data sharing is further hindered by the large size of genomic data, which impedes data transfer over networks.31 in addition to logistic and technical challenges, data access is often complicated by restrictive government regulations that hinder data sharing.32 low availability of genomic data combined with data silos also results in high prices, making it unaffordable to many researchers. previous work solutions to the challenges outlined above have been proposed previously. federated data storage systems have been implemented to facilitate genomic data sharing, privacy-preserving computing has been utilized to protect genomic data privacy, and different compensation models have been explored to incentivize genomic data sharing. genomic data sharing the ga4gh beacon project33 and i2b2 shrine34 are two of the most advanced systems for biomedical data sharing. both are networks that enable participating institutions to connect their genomic (and clinical) databases and process queries about the presence of genetic variants and traits, including medical conditions. this federated model minimizes expensive data transfers and enables institutions to retain control of their data. this addresses privacy, regulatory, and technical challenges that are associated with centralized storage and transfers of genomic data. however, there are limitations. first, functionality is currently limited to simple queries. orchestrated, distributed computations required for data processing and analysis are currently not supported. second, participation is limited to academic research institutions and hospitals. there are no patientor consumerfocused portals that would enable individuals to easily contribute their personal genomic data. third, decentralized governance and compensation mechanisms have not been implemented. genomic data protection distributed genomic data storage and computing can help protect genomic data privacy. however, data owners cannot always maintain in-house servers and therefore they often must outsource data storage and computing to third parties, such as cloud service providers. to protect the privacy of genomic data that are shared with untrusted third parties, encryption-based https://doi.org/10.30953/bhty.v1.34 page 5 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 privacy-preserving techniques have been adopted for genomics. these techniques enable third parties to execute computations and return results without having access to plaintext genomic data. privacy-preserving techniques have been applied previously to distributed medical and genomic databases. for example, medco integrates with the i2b2 shrine framework and uses a homomorphic data encryption scheme to enable outsourcing of genomic data storage and query execution to untrusted third parties.35 another example is the secure multi party query language framework that implements similar functionality and privacy guarantees using secure multiparty computations.36 data can also be protected using trusted hardware. an example is the princess framework that executes computations on genomic data inside protected memory regions of intel microprocessors.37 compensation models over the past few years, personal genomics companies have explored different models to compensate individuals for contributing their personal genomic data to research studies. in 2016, genos offered to help its customers sell their genomic data to researchers.38 a similar model that uses a cryptocurrency instead of fiat money was adapted by encrypgen in 2017.39 most recently, lunadna announced that it would compensate genomic data contributors with company stock.40 these models are similar in that individuals who want to participate must already own their personal genomic data, or choose to purchase genetic testing because of the prospect they will be rewarded later for sharing the data. personal genomics 2.0 the traditional model for genomic data generation and sharing that has been adopted by most personal genomics companies contributes to the challenges described in the previous sections. this model requires consumers to pay for genetic testing and result interpretation, while personal genomics companies often take ownership of the generated genomic data and sell it to biopharma companies (figure 2). this model requires consumers to carry the costs and relinquish ownership and control of their genomic data, which discourages genetic testing. in addition, this model promotes genomic data fragmentation across private data silos, which hampers data access and increases data prices. we propose to combine and extend previous work on genomic data sharing networks, privacypreserving technologies, and compensation models to create a new model for personal genomics that may overcome these challenges (figure 3). first, the functionality of genomic data sharing networks must be extended beyond simple queries. this requires a network that can be integrated with a full-fledged bioinformatics platform that supports genomic data processing and analysis. implementing this functionality would bundle fragmented genomic data and make it available for analysis on a single network, thereby facilitating data access for researchers. second, the data sharing network must expand beyond research institutions and must be accessible to individuals who want to share their personal genomic data. however, the resulting network decentralization will necessitate a more democratic governance model. this potentially can be achieved by integrating blockchain technology, which holds the promise of enabling decentralized, self-governing networks. third, the privacy of genomic data must be protected. data access control on the blockchain https://doi.org/10.30953/bhty.v1.34 page 6 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 can ensure transparent consent management, while privacy-preserving technologies can help protect shared genomic data. together with the distributed computing model that “brings algorithms to the data,” these technologies can enable network participants to retain ownership and control of their genomic data, thereby reducing privacy concerns and incentivizing data sharing. fourth, genome sequencing and data sharing also must be incentivized by implementing subsidy and compensation mechanisms. the decentralized data sharing model can facilitate this, as it enables researchers to connect directly with individuals with traits of interest, subsidize their genome sequencing costs, and compensate them for data sharing. elimination of middlemen also may result in a reduction in genomic data prices and thus empower more researchers to access large genomic data sets. design considerations to implement a system as outlined in the previous section, one must integrate a bioinformatics platform that supports distributed data storage and computing with a suitable blockchain framework, as well as with techniques for privacy-preserving computing. here, we review and evaluate existing options. bioinformatics platforms bioinformatics platforms have been developed to facilitate organization of genomic data; to enable parallelized, high-performance computing with support for complex dependencies; and to allow figure 2. the traditional model for genomic data generation and sharing. https://doi.org/10.30953/bhty.v1.34 page 7 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 a modular pipeline design that is flexible and ensures reproducible results.41 table 1 shows a comparison of popular bioinformatics platforms. the development of bioinformatics platforms has been driven by exponentially growing genomic data and marked by adaption of multiple computing trends. storage and processing of genomic data has moved from local servers to remote clouds. this has enabled scalable data storage and computing and facilitated access sharing to genomic data sets. to scale beyond single clouds, efforts are being made to create federated cloud environments that could enable distributed data storage and computing.48,49 furthermore, the growth of genomic data and development of new bioinformatics tools that must be integrated into workflows are driving the development of standardized workflow description languages, containerization of computing environments, and utilization of standardized application programming interfaces (apis). based on these considerations, arvados and dnastack appear as suitable choices for the proposed genomic data sharing platform. both platforms have an api-focused architecture and data sharing functionality. dnastack integrates with the ga4gh beacon network, while arvados supports platform-agnostic, federated cloud environments and has an open-source codebase. blockchain frameworks blockchain technology has three use cases in the proposed system. first, the need to provide transparent consent management can be addressed by the ability of blockchains to store data access permissions on an figure 3—alternative model for personal genomics that may overcome challenges. https://doi.org/10.30953/bhty.v1.34 page 8 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 immutable public ledger. second, blockchains can enable implementation of decentralized systems governed by network participants. third, an immutable ledger can facilitate verification of the integrity of decentrally stored data. based on these use cases, one can create a set of requirements that a suitable blockchain framework must fulfill. first, consent management requires that the identity of researchers who request to access data are known to data owners. to this end, network access must be limited to data buyers whose identity has been verified. therefore, consent management requires a blockchain that supports permissioned access. second, a large, decentralized data marketplace requires smart contract functionality and high transaction throughput. private blockchains can achieve higher transaction throughputs than public blockchains because the ability to write transactions to the blockchain is limited to a group of permissioned validator nodes. however, this makes private blockchains more centralized and less dependable. based on these requirements, permissioned blockchains frameworks such as exonum and hyperledger fabric appear most suitable (table 2). hyperledger fabric has been more widely adopted, but exonum offers transparency and security that is comparable to public blockchains. first, exonum-based blockchains offer public read access but restrict write access to selected validator nodes. by making read access to the blockchain public, transaction audit does not rely on trusted parties. exonum transactions are verified in real time by all nodes. thus, all network participants are able to audit the blockchain state collectively. second, exonum supports anchoring of transaction logs in the bitcoin blockchain. hashes of the exonum blockchain state are periodically written to the bitcoin blockchain, so even if all permissioned exonum nodes collude, the transaction history cannot be falsified unless the attacker succeeds in compromising the bitcoin blockchain as well. third, exonum uses a byzantine fault-tolerant (bft) consensus algorithm that protects against table 1. comparison of bioinformatics platforms criteria arvados42,43 dnastack44 seven bridges45 dnanexus46 galaxy47 hardware federated clouds and servers google cloud with beacon network integration clouds clouds local servers pipeline design api-based; web gui api-based; web gui web gui web gui web gui containers yes yes yes yes yes workflow language cwl wdl cwl custom custom open source yes no no no yes platform launch year 2013 2014 2012 2010 2005 api: application programming interface; cwl: common workflow language; gui: graphical (rather than textual) user interface; wdl: workflow description language. https://doi.org/10.30953/bhty.v1.34 page 9 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 malicious behavior of permissioned nodes. in contrast, hyperledger and other private blockchains rely on less computationally intensive fault-tolerant (ft) consensus algorithms that protect against node breakdown but not malicious behavior. exonum offers both bft consensus and high transaction throughput because it is written in rust, one of the fastest programming languages. furthermore, rust offers memory safety which eliminates many vulnerabilities that are commonly exploited by hackers. privacy-preserving technologies table 3 shows a comparison of privacypreserving technologies that all have been applied to secure genomic data.53 fully homomorphic encryption and secure multiparty computations enable computations on encrypted data that generate encrypted results. these encrypted results, when decrypted, correspond to the results of the same computation on plaintext data. however, fully homomorphic encryption is very slow and typically suffers from very large ciphertext expansion. the limitation of secure multiparty computation protocols is that they require transfers of very large data amounts during the computation. it is possible, however, to improve the performance of fully homomorphic encryption and secure multiparty computations significantly if they are optimized for specific use cases. practical performance levels have been demonstrated for queries on genomic data54,55 and genome-wide association studies (gwas).56 alternative technologies have drawbacks of their own. intel software guard extensions technology is a hardware-assisted approach that protects data privacy by executing computations inside private memory regions. it offers good performance but has been affected by vulnerabilities that can compromise data privacy.57 differential privacy methods protect data privacy by introducing randomness. however, obfuscation of computation results can complicate interpretation of studies.53 nebula in this section, concepts and design considerations outlined in the previous sections are illustrated by describing the technical implementation of nebula—a decentralized genomic data generation, sharing, and analysis table 2. comparison of blockchain frameworks criteria exonum50 hyperledger fabric51 ethereum52 read access public private public write access private private public consensus byzantine fault-tolerant (bft) fault-tolerant (ft) proof of work (pow) transactions per second (tps) ~3,000 ~3,000 ~15 smart contracts yes (rust, java) yes (go, java) yes (solidity) light clients yes no yes public blockchain anchoring yes no na open source yes yes yes na: not applicable. https://doi.org/10.30953/bhty.v1.34 page 10 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 platform. nebula integrates the arvados42,43 bioinformatics platform (github.com/curoverse/ arvados) with the exonum50 blockchain framework (github.com/exonum) and a fully homomorphic data encryption scheme (figure 4). arvados has two core services: keep and crunch. keep is a distributed content addressable storage system that enables scalable storage of genomic big data, high throughput data access, and efficient data management. crunch is a workflow management engine that enables flexible creation and parallelized execution of data analysis pipelines and generation of reproducible results. arvados implements a distributed data storage and computing model that minimizes required data transfers. this helps address big data challenges, regulatory restrictions, and data privacy risks. utilization of a homomorphic data encryption scheme enables implementation of privacypreserving queries on genomic data. the intention is to preserve data privacy by enabling investigators to query the whole database and discover their data of interest, without compromising the privacy of the queried data. in the future, it should be possible to extend the application of privacy-preserving technologies to gwas and other computations. the nebula blockchain is an exonum-based blockchain through which the nebula network will be governed, consent will be documented, and the data will be secured. exonum-based blockchains have three types of nodes: auditors, light clients, and validators. auditors are full nodes that maintain a copy of the entire blockchain content and can generate transactions. light clients also can generate transactions, but they replicate only information that is relevant to them instead of the whole blockchain content. validators are permissioned nodes that verify transactions received from auditors and light clients and write new blocks to the blockchain. while the current implementation of nebula table 3. comparison of privacy-preserving technologies criteria fully homomorphic encryption secure multiparty computations intel software guard extensions differential privacy principle computations (additions and multiplications) on ciphertexts distributed computations on ciphertexts computations inside private memory regions introduction of randomness to data/results of computations computation time very slow slow fast fast memory usage very high high low very low communication cost high very high low low specific limitations none none vulnerabilities have been discovered; requires intel cpus noise makes interpretation of results more difficult cpu: central processing unit. https://doi.org/10.30953/bhty.v1.34 http://github.com/curoverse/arvados http://github.com/curoverse/arvados http://github.com/exonum page 11 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 uses the exonum framework, other permissioned blockchains, in particular hyperledger fabric, can be used as well. the nebula network has four types of participants: data owners, network maintainers, data buyers, and storage and compute providers. • data owners can be private individuals or institutions. they will store encrypted genomic data in public or private clouds that are part of the keep storage system. they will be able to control access to their data and receive payments to their wallets by operating light clients on the nebula blockchain. • network maintainers are organizations that operate validator nodes on the nebula blockchain. validator nodes will collectively control data access by managing encrypted key shares, verifying transactions, and keeping track of data stored in keep and computations executed by crunch. • data buyers are researchers who wish to obtain access to genomic data. they will be operating auditor nodes to keep a local copy of figure 4—overview of the nebula platform. https://doi.org/10.30953/bhty.v1.34 page 12 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 the metadata, which they will use to locate data stored in keep, verify data integrity, and keep track of access permissions. data buyers will be able to query homomorphically encrypted data, utilize smart contracts to acquire data access permissions from data owners, and use crunch to run analysis pipelines. • storage and compute providers are data owners that operate private clouds, or third parties that offer storage and computing services (e.g., google, amazon, and microsoft). they will form a federated cloud environment that hosts the keep storage system and crunch-managed containers within which computations are executed. the development of nebula is ongoing. some parts of the platform, in particular, arvados, have been fully implemented over the past few years and are already being deployed by various organizations. other parts of nebula, in particular, the homomorphic encryption schemes, are a relatively recent addition and are not yet fully integrated. a report on the progress of our work was published in a white paper.58 here we describe the implementation of nebula in greater detail but also revise some previously made design choices. data generation genomic data personal genome sequencing cost is a significant factor in preventing more widespread consumer adoption. therefore, a key consideration in the design of the nebula platform was to provide a mechanism to shift sequencing costs from data owners (e.g., consumers and biobanks) to data buyers (e.g., pharma and biotech companies). this is being implemented by enabling data buyers to query the nebula database, identify data sets of interest, and pay the sequencing costs to generate and access genomic data (figure 5). to this end, the nebula platform enables a data buyer to create a smart contract that specifies the blockchain addresses of data owners previously identified in a query and send cryptocurrency tokens to that smart contract. the data owners are notified that a buyer has offered to pay their sequencing costs. if a data owner accepts the offer by executing the smart contract, the deposited tokens are sent to a sequencing provider. next, the data owner receives a saliva collection kit and submits a saliva sample to the sequencing facility. the sample is sequenced, and the genomic data are deposited on a keep server specified by the data owner. data hashes, along with blockchain addresses of all data owners and buyers, are written to the blockchain. the data buyer who paid the sequencing costs is permitted to access and analyze the data. the data owner receives interpretations of his genomic data and is able to share data access with additional data buyers. phenotypic data information about medical conditions and other traits is referred to as phenotypic data. these data are generated primarily through survey questions. the platform utilizes a phenotyping toolkit that maps plain-language survey responses to clinical descriptions in human phenotype ontology (hpo)59 format. survey data can be verified using two approaches. first, comparing the incidence of medical conditions in the general population to the incidence observed in the platform’s data set will enable identification of survey results that deviate from the expected results. second, survey data can be verified by referencing electronic health records (ehrs) imported through the fast healthcare interoperability resources (fhir) api. https://doi.org/10.30953/bhty.v1.34 page 13 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 data encryption privacy of genomic and phenotypic data are protected through client-side encryption by data owners and encryption key management by blockchain validator nodes. to enable data buyers to discover data prior to purchasing data access, the platform implements a lattice-based fully homomorphic encryption scheme. to this end, blockchain validator nodes generate public– private key pairs and construct a single collective public key (figure 4). data owners encrypt their survey responses and genetic variant lists with the collective public key and upload them to a keep server. the homomorphic encryption scheme protects data privacy by enabling data buyers to execute structured query language (sql)-like queries on the homomorphically encrypted data. files that contain raw sequencing data and are not used for queries are advanced encryption standard (aes) encrypted. the aes keys are encrypted with validator public keys and bundled with the encrypted data. data storage data genomic data are stored in keep, a distributed content-addressable storage system that retrieves files based on their content. addresses of files are generated through cryptographic digest of their content. keep combines content-addressing with the distributed storage architecture of the google file system.60 keep splits encrypted files into 64-megabyte blocks and stores them in an underlying object store or file system (figure 6). the content addresses of the blocks are stored on the blockchain and are used to find data locations and check data integrity. figure 5—genome sequencing subsidy payment on the blockchain. https://doi.org/10.30953/bhty.v1.34 page 14 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 keep is designed for storing genomic and other types of biomedical big data. first, its contentaddressing offers high-speed storage and retrieval by eliminating an indexing service, a potential bottleneck and point of failure, and enabling direct connections between the storage and compute subsystems. second, content-addressing works well for data written to disk once and read many times, a characteristic of genomic data, as it does not change over time but is accessed frequently. third, fixed-size data blocks allow scalable distributed storage of big data, and content-addressing enables easy file verification, which is particularly important for distributed databases. keep is designed to be a distributed, hybrid storage system. data owners can choose to store their data in clouds such as amazon web services (aws), google cloud platform (gcp), and microsoft azure, or on private bare-metal servers. decentralized file storage solutions such as interplanetary file system (ipfs), sia, and storj can potentially be supported if computing on stored data becomes possible. data owners can register new, personal cloud instances or store their encrypted data in shared clouds. based on phenotypic information, data sets that are likely to be analyzed together are stored in physical proximity, which minimizes slow and expensive data transfers. as sequencing data are processed, different file formats are generated and stored in keep. typically, keep stores fastq files that contain raw sequencing data (~200 gigabytes/genome), binary alignment map files that store aligned sequencing reads (~100 gigabytes/genome), and variant call format files that store genetic variants (~200 megabytes/genome). additionally, nebula uses the compact genome format (cgf) to generate compact genomic data summaries. genomes in the cgf format are represented by pointers referencing sequences in a tile library (figure 7). cgf offers a consistent, standardized representation of genomic data that makes figure 6—data blocks are stored in keep. block hashes are stored on the blockchain. https://doi.org/10.30953/bhty.v1.34 page 15 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 different types of sequencing and genotyping data interoperable. the cgf representation is also very space efficient (~30 megabytes/ genome), which facilitates file transfers, and enables fast queries and efficient analysis.60 tabular phenotypic data generated through surveys and imports of ehrs are stored in physical proximity with associated genomic data. in contrast to static genomic data, phenotypic information is much more dynamic and smaller than genomic data. this makes utilization of the google file system and content addressability unsuitable. therefore, phenotypic data files are stored as not only sql documents. metadata to organize data stored in keep, nebula stores metadata on the blockchain in a key-value store. when new data are added to keep or existing data are modified, blockchain transactions are generated. validator nodes verify these transactions, add new blocks to the blockchain, and update the key-value store. storage of metadata on an immutable ledger helps secure the integrity of the decentralized nebula database. to this end, multiple column families are implemented: • data ownership is registered by assigning each block content address the blockchain address of the data owner who added the block to keep. • data locations are described by assigning each block content address the uniform resource locator (url) of a keep server. • data integrity is verified by re-hashing data blocks and comparing their hashes with content addresses that are stored on the blockchain. • data buyer identities, including names and institutional affiliations, are verified, linked to blockchain addresses, and stored on the blockchain. • access permissions to the nebula platform and data stored in keep also are managed on the blockchain. figure 7—simplified representation of a tile library and a compact genome format (cgf) file. the rectangles represent tile variants at different positions and the dotted line illustrates the tile composition of specific genome. https://doi.org/10.30953/bhty.v1.34 page 16 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 data discovery utilization of fully homomorphic encryption is intended to address the privacy barrier to data sharing. it enables data owners to make their data available for discovery without privacy risks, while at the same time allowing data buyers to explore the database before purchasing data access to perform analyses. to this end, data buyers will construct a sql-like query and encrypt it with the collective public key that has been constructed by validator nodes and used to encrypt phenotypic information and genetic variant lists. the encrypted query is executed on homomorphically encrypted data and an encrypted result is generated. the query result is re-encrypted by the validator nodes under a public key provided by the data buyer and shared with data buyer who can now decrypt it with its private key. a query can return the number of data owners that matched the specified criteria, as well as their blockchain addresses. this enables data buyers to connect with data owners to pay sequencing costs or to purchase access to existing genomic data (figure 8). data analysis the arvados container and pipeline management engine, crunch, executes computations on data stored in keep. crunch implements a distributed computing model whereby workflows, and not the genomic data, are moved between cloud instances whenever possible. highly distributed genomic data processing is possible because many intensive bioinformatics computations, such as alignment and variant calling, are performed on single genomes and are easily parallelizable. to this end, crunch executes tasks inside docker containers that are created physically close to the data locations in keep and distributes computations between many processing units. figure 8—secure data discovery through queries on homomorphically encrypted data. https://doi.org/10.30953/bhty.v1.34 page 17 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 crunch ensures result reproducibility through standardization of computing workflows using common workflow language (cwl),61 which enables connection of open-source and proprietary bioinformatics software into workflow pipelines that are flexible, portable, and scalable. crunch can access cwl pipelines stored in public or private git repositories such as github. cwl can be used to implement end-toend bioinformatics analysis pipelines. typically, cwl pipelines include common, computationally intensive “secondary analysis” tasks, such as alignment of sequencing reads to a reference genome and variant calling. however, “tertiary analysis” tasks, which often involve computing on genomic data sets rather than single genomes and are less standardized, also can be incorporated into cwl pipelines. typical examples are statistical tests that are used in gwas to identify correlations between genetic variants and phenotypes. for such tertiary analysis tasks, nebula uses lightning,62 a system for high-performance, in-memory computations on genomic data in the cgf. lightning integrates into cwl pipelines and enables fast queries and execution of complex machine learning tasks on large genomic data sets. cwl pipelines can also be used to analyze and interpret personal genomic (and phenotypic) data. first, users can build their own custom pipelines to analyze their personal data and also share pipelines among each other using public git repositories. second, developers can build and monetize genomic apps. to this end, cwl pipelines can be stored in private repositories, and access by crunch may require a smart contract-mediated token payment to the pipeline developer. the approach of bringing apps to the data facilitates protection of personal information. security homomorphic encryption can enable privacypreserving queries for data discovery. however, most computations that are necessary for typical genomic data analysis workflows do not achieve practical runtimes when executed on homomorphically encrypted data. therefore, other security mechanisms must be utilized. platform access control to protect data owners and their data, data buyers are required to go through a partially decentralized, three-step permission process. the first step is data buyer authentication. here, a blockchain validator node will verify a data buyer’s personal and institutional identity. blockchain addresses of verified data buyers will be added to the blockchain metadata store. data buyers will then be able to connect to nebula rest api servers and use crunch to execute pipelines on data stored in keep. data buyer authentication will enable data owners to verify data buyer identity before agreeing to share data access. furthermore, immutable storage of data buyer identities on the blockchain enables identification of data buyers who have violated consent agreements or have bypassed pipeline execution control. pipeline execution control to protect data privacy, the platform design incorporates the ability to define approved bioinformatics tools and cwl workflows. the intent is to prevent data buyers from downloading genomic data or executing any computations that attempt to extract a large amount of information about individual data owners. this approach was chosen because it has the ability to provide a sufficient level of data privacy protection without significantly restricting data buyers. https://doi.org/10.30953/bhty.v1.34 page 18 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 data access control the first task in every cwl pipeline is to get access to the input data (figure 9). here, a data buyer executes a smart contract on the blockchain. the inputs are the data buyer’s blockchain address and the content addresses of all data blocks of the input files. the data buyer also deposits tokens inside the smart contract and defines a token payout for data access. when a data owner’s light client synchronizes with the blockchain, the data owner is notified of the data access request. the data owner can decide about data sharing based on offered payment and identity of the data buyer. the data owner grants data access by executing the smart contract. the blockchain validator nodes then verify the integrity of the requested data stored in keep by comparing data hashes with the content addresses stored on the blockchain and collectively re-encrypt the data under the data buyer’s public key. finally, the data buyer’s access permission is registered on the blockchain and tokens are sent from the smart contract to the data owner’s wallet. crunch can now load decrypted data into a docker container and begin pipeline execution. governance the nebula blockchain can be used to enable nebula network participants to collectively govern the network, in particular, to help maintain data protection. to this end, for example, token-curated registries (tcrs)63 can be used to conduct elections that determine validator nodes or whitelist data analysis pipelines. tcrs are lists that are curated decentrally by token holders. importantly, economic incentives drive the token holders to curate the list’s contents judiciously. in brief, figure 9—data access control and data purchases. https://doi.org/10.30953/bhty.v1.34 page 19 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 network participants can cast votes whereby the weights of votes scale with their token holdings. since token holders are invested in the network, they are incentivized to maintain its proper operation that ensures data protection. discussion the obstacles that hinder personal genome sequencing and genomic data sharing have a significant impact on the progress of research into disease prevention, drug development, and other crucial aspects of human health. we described one approach to overcoming these obstacles, using a combination of multiple technologies. a number of challenges remain to be addressed regarding data privacy, data validation, data curation, and data economics. data privacy protection requires decentralization of data generation and further development of privacy-preserving technologies. today, genomic data generation is limited to laboratories that own expensive sequencing machines operated by experienced technicians. centralized genomic data generation leads to data privacy risks that may be averted if sequencing is decentralized. we anticipate that this will become possible soon as new technologies are being developed that would enable compact, affordable, and easy-tooperate sequencing machines.64 data privacy protection is also impaired by current limitations of privacy-preserving technologies that do not allow complex computations on large data sets and require extensive optimization for every use case, which hinders effective data analysis. however, practicality of privacy-preserving technologies has been steadily increased over the past few years, and we anticipate continuing progress in the future. data validation and curation are another area of challenge. validation of genomic data requires assistance of the sequencing facilities that have produced the data. if the source of the genomic data is unknown, or the sequencing facility does not cooperate, genomic data cannot be validated. a possible solution to this problem can be a model that compensates personal genomics companies and other genomic data producers for validating data authenticity. data collected from different sources also must be made interoperable. it is particularly challenging to curate health records and other types of phenotypic data. however, standards such as fhir are being developed very actively and have already enabled applications that can collect ehrs across different health systems.65 the idea of a personal data marketplace is very new and has not yet been implemented at scale. a personal data marketplace is likely to differ from traditional marketplaces in important ways. for example, data supply can be regarded as being unlimited because an individual can share data access with an unlimited number of data buyers. personal data marketplaces also would be asymmetric, since individuals are likely to be unaware of the value of their personal data and are thus at risk of not being compensated fairly. the novelty of personal genomic data further compounds these challenges and makes market dynamics difficult to predict. we anticipate that future research into economics of data marketplaces will help answer these and other open questions. contributions: dennis grishin, kamal obbad, and kevin quinn wrote the article. dennis grishin, kamal obbad, and george church developed the ideas described in the article. dennis grishin, kamal obbad, and kevin quinn are leading the development of the nebula platform. alexander wait zaranek, ward vandewege, tom clegg, nico césar, preston estep, and mirza cifric contributed to the development of arvados. alexander wait zaranek https://doi.org/10.30953/bhty.v1.34 page 20 of 23 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v1.34 and sarah wait zaranek developed the compact genome format and lightning. all authors edited and/or reviewed the final article. acknowledgments the authors would like to thank armon rahim and nathaniel tucker for their help in reviewing this article. conflict of interest all authors are employees, advisors, or collaborators of nebula genomics. funding statement development of the nebula platform is funded by nebula foundation. references 1. international human genome sequencing consortium. finishing the euchromatic sequence of the human genome. nature. 2004 oct 21;431(7011):931–45. 2. wetterstrand ka. dna sequencing costs: data from the nhgri genome 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under a creative commons attribution-noncommercial 4.0 international license. authors retain copyright of their work, with first publication rights granted to blockchain in healthcare today (bhty). https://doi.org/10.30953/bhty.v1.34 http://arxiv.org/abs/1802.09085 http://arxiv.org/abs/1802.09085 https://www.nebula.org/assets/nebula_genomics_whitepaper.pdf https://www.nebula.org/assets/nebula_genomics_whitepaper.pdf https://figshare.com/articles/common_workflow_language_draft_3/3115156/2 https://figshare.com/articles/common_workflow_language_draft_3/3115156/2 https://peerj.com/preprints/1426/ https://peerj.com/preprints/1426/ https://medium.com/@ilovebagels/token-curated-registries-1-0-61a232f8dac7 https://medium.com/@ilovebagels/token-curated-registries-1-0-61a232f8dac7 https://medium.com/@ilovebagels/token-curated-registries-1-0-61a232f8dac7 https://ehrintelligence.com/news/breaking-down-how-the-apple-health-records-ehr-data-viewer-works https://ehrintelligence.com/news/breaking-down-how-the-apple-health-records-ehr-data-viewer-works https://ehrintelligence.com/news/breaking-down-how-the-apple-health-records-ehr-data-viewer-works https://ehrintelligence.com/news/breaking-down-how-the-apple-health-records-ehr-data-viewer-works 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 narrative/systematic reviews/meta-analysis impact of blockchain-digital twin technology on precision health, pharmaceutical industry, and life sciences: conference proceedings, conv2x 2023 ingrid vasiliu-feltes, md, emba1* , michael mylrea, phd1 , christina yan zhang, phd2 , tyler-cohen wood3, brian thornley3 1university of miami, coral gables, florida, usa; 2the metaverse institute, london, uk; 3supply excellence, innovation and digital strategy at msd, kenilworth, new jersey, usa *corresponding author: ingrid vasiliu-feltes. email: ivfeltes@miami.edu keywords: blockchain, clinical trials, cybersecurity, digital twins, ethics, healthcare, outcomes, pharmaceutical industry, supply chain abstract the convergence of digital twin technologies with precision health, the pharmaceutical industry, and life sciences has garnered substantial recent attention. as we advance toward personalized medicine and precision health, the fusion of digital twin and blockchain technologies is poised to enhance healthcare outcomes fundamentally. this conference discussion highlighted pivotal drivers accelerating the adoption of digital twin-enabled blockchain solutions, encompassing the shift to a decentralized world wide web (web 3.0), the establishment of a global interconnected health ecosystem, and the distinct advantages offered by converging frontier technologies in optimizing healthcare, pharmaceutical industry, and life sciences. yet, the effective deployment of blockchain-powered digital twins in precision health necessitates robust cyber safety measures, proactive ethical frameworks, data validation, provenance assurance, streamlined supply chain management, and heightened interoperability. these proceedings underscored blockchain-powered digital twins’ pivotal role in reshaping health data management, security, sharing, ownership, and monetization and in revolutionizing pharmaceutical supply chain management and novel drugs and therapeutics development within the precision health domain. received: 04 september 2023; accepted: 17 october 2023; published: 07 november 2023 the rise of digital twin technologies has revolutionized various industries, offering unprecedented insights and optimization opportunities. digital twins have emerged as a powerful tool across diverse sectors, including healthcare, the pharmaceutical industry, and life sciences. according to the latest research, the worldwide digital twin market is projected to reach an impressive usd 140.76 billion by 2032, growing at a cagr of 27.29% and reflecting the escalating interest and investments in this transformative technology. blockchain capabilities augmented with a full artificial intelligence (ai) portfolio of tools, including digital twin technology deployments in healthcare, the pharmaceutical industry, and life sciences, can significantly improve clinical outcomes and optimize the business of healthcare. conference theme and panel scope the conference’s theme centered on advancing the business of health with blockchain technology. this panel discussion was dedicated to blockchain-powered digital twins, which have emerged as promising technologies with the potential to revolutionize various industries, including life sciences. however, their successful deployment comes with several challenges that must be addressed to unlock their transformative capabilities fully. the discussion covered primary challenges that need to be addressed to successfully implement blockchain-powered digital twin technologies in the healthcare ecosystem, specifically in healthcare, pharmaceutical industry, or precision health. furthermore, the distinct blockchain capabilities that can enhance the impact of digital twin technologies, leading to improved https://orcid.org/0000-0001-7276-354x https://orcid.org/0009-0009-5717-3416 https://orcid.org/0009-0008-3462-3245 mailto:ivfeltes@miami.edu citation: blockchain in healthcare today 2023, 6: 281 https://doi.org/10.30953/bhty.v6.2812 (page number not for citation purpose) ingrid vasiliu-feltes et al. outcomes and return on investment (roi) in the healthcare ecosystem, encompassing healthcare, the pharmaceutical industry, or precision health, were underscored. panel discussion summary the initial discussion revolved around the challenges, capabilities, and opportunities of deploying blockchain-powered digital twin technologies in the healthcare ecosystem, including healthcare and the pharmaceutical industry, particularly for managing the supply chain and laying the foundation for precision health. cybersecurity emerged as a primary concern, with the need to safeguard sensitive patient data and protect against potential cyber threats. ethical considerations and ensuring compliance with data protection regulations were also emphasized. standardization efforts, interoperability protocol, and data governance were seen as essential to enable seamless data exchange and integration. furthermore, the need for educating the workforce and mitigating the environmental impact of large-scale technology deployments was also underscored during the discussion. the panel also addressed the unique blockchain capabilities and the impact on financial and clinical outcomes. the speakers shared their enthusiasm for the numerous opportunities blockchain-powered digital twins offer to further optimize the healthcare ecosystem, specifically enabling a patient-centric approach to healthcare, clinical trials, and the pharmaceutical industry. furthermore, the panelists highlighted the opportunity for seamless data exchange between different stakeholders, accelerating medical research and innovation. despite the ongoing challenges emphasized throughout the discussion, the combined technological capabilities of blockchain and digital twin were highlighted by all speakers, underscoring the foundational role of precision health and the emergent precision pharmaceutical industry. key panel takeaways use case selection rigor in use case selection was emphasized as a significant challenge in deploying blockchain-powered digital twins in healthcare. panelists discussed the importance of identifying suitable real-world problems that can genuinely benefit from these integrated technologies. to address use case selection rigorously, the panel suggested conducting comprehensive feasibility studies and rigorously assessing the potential risks of implementing blockchain-powered digital twins in specific healthcare or pharmaceutical industry scenarios. interoperability interoperability was identified as another critical challenge for data exchange among various healthcare systems and stakeholders. panelists recognized the need to overcome technical and standardization barriers. achieving interoperability requires adopting open standards and developing data exchange protocols that allow different systems to communicate effectively. patient-empowerment the concept of self-sovereignty for personal health data was highlighted as a transformative opportunity. panelists discussed how blockchain can empower individuals to control access to their health information, fostering greater patient engagement and ownership. to achieve self-sovereignty, panelists suggested integrating blockchain with advanced digital identity systems. patient-centricity the panel reiterated the significance of a responsible, patient-centric approach in deploying blockchain-powered digital twins. ensuring that patients’ needs, preferences, and rights are at the core of the technology’s implementation is crucial. ethics ensuring patient data privacy, consent, and secure data handling were recognized as critical challenges. to address these ethical concerns, the panel stressed the implementation of clear governance frameworks and compliance with data protection regulations. cybersecurity zero-trust cybersecurity emerged as a top priority. panelists advocated for advanced encryption methods, multi-factor authentication, and continuous monitoring to enhance the security of blockchain-powered digital twins. net-zero optimization ongoing efforts are required to reduce further the negative environmental impact due to high energy consumption triggered by large-scale dual emerging technology deployments. industry relevance and latest research trends the latest research trends validate the insights from the conference discussion and collectively highlight the transformative potential of blockchain technology in healthcare, the pharmaceutical industry, and life sciences. most recent publications showcase applications ranging from clinical trials and supply chain management to personalized medicine and data security. as these technologies continue to evolve, they are likely to reshape the industry’s landscape and enhance patient care in unprecedented ways. simultaneously, recent research topics in the field of healthcare, the pharmaceutical industry, and life sciences have been greatly influenced by emerging technologies such as the blockchain, digital twins, the internet of https://doi.org/10.30953/bhty.v6.281 citation: blockchain in healthcare today 2023, 6: 281 https://doi.org/10.30953/bhty.v6.281 3 (page number not for citation purpose) blockchain-digital twin technology things (iot), ai, and 5g or 6g networks. these trends are reshaping how healthcare is managed and delivered, holding significant promise for enhancing patient outcomes, supply chain resilience, personalized medicine, and data security. the metaverse’s integration into healthcare and pharmaceuticals has been explored in a comprehensive review by shetty and colleagues,1 highlighting the potential of this immersive virtual environment to transform patient care and medical education. similarly, ullah and colleagues2 delved into the applications, challenges, and future directions of utilizing metaverse technology in healthcare. blockchain technology, known for its security and transparency features, has gained traction in the industry. barenji and hariry3 introduced a blockchain-enabled digital twin for improving the quality of clinical trials, enhancing data integrity and trial efficiency. the integration of blockchain and iot has been studied by chen and associates,4 exploring its role in ensuring pharmaceutical supply chain resilience in the post-pandemic era. blockchain’s potential in healthcare privacy and security was reviewed by gami and collaborators,5 who offered insights into preserving patient data while leveraging ai. digital twins, virtual representations of physical entities, have found their way into healthcare. turab and jamil6 presented a survey highlighting digital twins applications in healthcare within the metaverse context. cellina and coworkers7 also pondered the possibilities of digital twins in personalized medicine, raising questions about their role in this evolving landscape. networking technologies for human digital twins were examined by chen and associates,8 who cast light on how personalized healthcare applications can benefit from advanced connectivity. the convergence of ai and blockchain for intelligent healthcare was explored by gaur and associates,9 who offered insights into privacy-preserving techniques. as evidenced by kharche and kharche,10 who discussed the potential role of 6g in shaping the intelligent healthcare landscape, future technology frameworks are also being considered. kavitha and manicka chezian11 reviewed the impact of smart healthcare in the context of cyber-physical systems. moving toward biospecimen digital twins, nanni and associates12 introduced the concept of transitioning from “high quality” to “fit-for-purpose” biospecimen collection in the era of omics sciences. furthermore, the intersection of blockchain technology and sustainable smart cities is explored by ullah and colleagues,13 who indicated the potential for transforming urban healthcare systems. future directions the speakers underscored the synergistic effects of a responsible dual deployment of these two technologies in the near future. from improving patient outcomes through precise interventions to enhancing drug development, clinical trials, or patient treatment pathways, their contributions revealed how dual deployment promises to revolutionize every aspect of the life-sciences industry. by leveraging the benefits of these two frontier technologies, healthcare, pharmaceutical industry, and life sciences teams could better understand individual health profiles, design personalized treatments, and predict patient responses more accurately. conclusions implementing blockchain-powered digital twins requires addressing challenges. cybersecurity, ethics, interoperability, data validation, provenance, and cost optimization are crucial. by navigating these obstacles, blockchain-powered digital twins can reshape global health, pharmaceuticals, and life sciences. funding statement no funding was received. financial and non-financial relationships and activities no financial or non-financial disclosures. author contributors each author contributed to the content of this article. references 1. shetty a, kulkarni gs, rakesh babu sn, paarakh pm. a review on: metaverse in health care and pharma. j community pharm prac 2023;3(1):1–11. https://doi.org/10.55529/jcpp.31.1.11 2. ullah h, manickam s, obaidat m, laghari sua, uddin m. exploring the potential of metaverse technology in healthcare: applications, challenges, and future directions. ieee access 2023;11:69686–707. https://doi.org/10.1109/access.2023.3286696 3. barenji rv, hariry rw. blockchain-enabled quality improvement digital twin for clinical trials. preprints 2023;2023051693. https://doi.org/10.20944/preprints202305.1693.v1 4. chen x, he c, chen y, xie z. internet of things (iot)— blockchain-enabled pharmaceutical supply chain resilience in the post-pandemic era. front eng manage 2023;10(1):82–95. https://doi.org/10.1007/s42524-022-0233-1 5. gami b, agrawal m, mishra dk, quasim d, mehra ps. artificial intelligence-based blockchain solutions for intelligent healthcare: a comprehensive review on privacy preserving techniques. trans emerg telecommun technol 2023;34(9):e4824. https://doi.org/10.1002/ett.4824 6. turab m, jamil s. a comprehensive survey of digital twins in healthcare in the era of metaverse. biomedinformatics 2023;3(3):563– 584. https://doi.org/10.3390/biomedinformatics3030039 7. cellina m, cè m, alì m, irmici g, ibba s, caloro e, et al. digital twins: the new frontier for personalized medicine? appl sci 2023;13(13):7940. https://doi.org/10.3390/app13137940 8. chen j, yi c, okegbile sd, cai j. networking technologies for enabling human digital twin in personalized healthcare applications: https://doi.org/10.30953/bhty.v6.281 https://doi.org/10.55529/jcpp.31.1.11 https://doi.org/10.1109/access.2023.3286696 https://doi.org/10.20944/preprints202305.1693.v1 https://doi.org/10.1007/s42524-022-0233-1 https://doi.org/10.1002/ett.4824 https://doi.org/10.3390/biomedinformatics3030039 https://doi.org/10.3390/app13137940 citation: blockchain in healthcare today 2023, 6: 281 https://doi.org/10.30953/bhty.v6.2814 (page number not for citation purpose) ingrid vasiliu-feltes et al. a comprehensive survey. eee commun surveys tutorials 2023. https://doi.org/10.1109/comst.2023.3308717 9. gaur l, rana j, jhanjhi nz. digital twin and healthcare research agenda and bibliometric analysis. in: igi global, editor. digital twins and healthcare: trends, techniques, and challenges. 2023. p. 1–19. https://doi.org/10.4018/978-1-6684-5925-6.ch001 10. kharche s, kharche j. 6g intelligent healthcare framework: a review on role of technologies, challenges and future directions. j mobile multimedia 2023;19(3):603–44. https://doi. org/10.13052/jmm1550-4646.1931 11. kavitha a, manicka chezian r. a review on smart healthcare in cyber physical system. eur chem bull 2023; 12 (special issue 8):3646–62. 12. nanni u, ferroni p, riondino s, spila a, valente mg, del monte g, et al. biospecimen digital twins: moving from a “high quality” to a “fit-for-purpose” concept in the era of omics sciences. cancer genom proteom 2023;20(3):211–21. https://doi. org/10.21873/cgp.20376 13. ullah z, naeem m, coronato a, ribino p, de pietro g. blockchain applications in sustainable smart cities. sustain cities soc 2023;97:104697. https://doi.org/10.1016/j.scs.2023.104697 copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see: http://creativecommons.org/licenses/ by-nc/4.0. https://doi.org/10.30953/bhty.v6.281 https://doi.org/10.1109/comst.2023.3308717 https://doi.org/10.4018/978-1-6684-5925-6.ch001 https://doi.org/10.13052/jmm1550-4646.1931 https://doi.org/10.13052/jmm1550-4646.1931 https://doi.org/10.21873/cgp.20376 https://doi.org/10.21873/cgp.20376 https://doi.org/10.1016/j.scs.2023.104697 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 page 1 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 the last mile: dscsa solution through blockchain technology: drug tracking, tracing, and verification at the last mile of the pharmaceutical supply chain with bruinchain william chien,1 josenor de jesus,1 ben taylor2; victor dods,2 leo alekseyev,2 diane shoda,2 perry b. shieh1 affiliations: 1ucla health, los angeles, california, usa; 2ledgerdomain, las vegas, nevada, usa corresponding author: ben taylor, ceo of ledgerdomain, 3722 las vegas blvd., south unit 2111e, las vegas, nv 89158-4323, usa. email: ben.taylor@ledgerdomain.com section: research article: use cases/pilots/methodologies purpose: as part of the fda’s dscsa pilot project program, ucla and its solution partner, ledgerdomain (collectively referred to as the team hereafter), focused on building a complete, working blockchain-based system, bruinchain, which would meet all the key objectives of the drug supply chain security act (dscsa) for a dispenser operating solely on commercial off-the-shelf (cots) technology. methods: the bruinchain system requirements include scanning the drug package for a correctly formatted 2d barcode, flagging expired products, verifying the product with the manufacturer, and quarantining suspect and illegitimate products at the last mile: pharmacist to patient, the most complex area of the drug supply chain. the authors demonstrate a successful implementation where product-tracing notifications are sent automatically to key stakeholders, resulting in enhanced timeliness and reduction in paperwork burden. at the core of this effort was a blockchain-based solution to track and trace changes in custody of drug. as an immutable, time-stamped, near-real-time (50-millisecond latency), auditable record of transactions, bruinchain makes it possible for supply chain communities to arrive at a single version of the truth. bruinchain was tested using real data on real caregivers administering life-saving medications to real patients at one of the busiest pharmacies in the united states. results: in addition to communicating with the manufacturer directly for verification, bruinchain also initiated suspect product notifications. during the study, a 100% success rate was observed for scanning, expiration detection, and counterfeit detection; and paperwork reduction from approximately 1 hour to less than a minute. https://doi.org/10.30953/bhty.v3.134 mailto:ben.taylor@ledgerdomain.com https://crossmark.crossref.org/dialog/?doi=10.30953/bhty.v3.134&domain=blockchainhealthcaretoday.com&date_stamp=2020-03-23 page 2 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 conclusions: by automatically interrogating the manufacturer’s relational database with our blockchain-based system, our results indicate a projected dscsa compliance cost of 17 cents per unit, and potentially much more depending on regulatory interpretation and speed of verification. we project that this cost could be reduced with manufacturers’ adoption of a highly performant, fully automated end-to-end system based on digital ledger technology (dlt). during an examination of the interoperability of such a system, we elaborate on its capacity to enable verification in real time without keeping humans in the loop, the key feature driving lower compliance cost. with 4.2 billion prescriptions being dispensed each year in the united states, dlt would not only reduce the projected per-unit cost to 13 cents per unit (saving $183 million in annual labor costs), but also serve as a major bulwark against bad or fraudulent transactions, reduce the need for safety stock, and enhance the detection and removal of potentially dangerous drugs from the drug supply chain to protect us consumers. keywords: blockchain, dscsa, fda, healthcare information technology, hyperledger fabric, patient care, pharmaceutical supply chain background introduction enacted in 2013, the drug supply chain security act (dscsa) outlines steps to build an electronic, interoperable system to help identify and trace certain prescription drugs as they are distributed in the united states by 2023.1 the dscsa intends to enhance the u.s food & drug administration (fda)’s ability to help protect consumers from exposure to drugs that may be counterfeit, stolen, contaminated, or otherwise harmful. this law sets forth requirements for multiple stakeholders along the pharmaceutical supply chain, from manufacturers, repackagers, and wholesale distributors to dispensers.i by allowing for more rapid tracing and potentially even allowing for real-time tracking, the system will also improve detection and removal measures: “the ability to track and trace finished prescription drugs plays a significant role in providing transparency and accountability in the drug supply chain.”2 to facilitate the development of the system by 2023, the fda established the dscsa pilot project program, which prompted the study outlined in this paper.3 (it must be noted that selection into this program should not be interpreted as fda’s position on an entity’s compliance with regulatory requirements or an endorsement of a particular technology, system, or other approach.) ucla health consists of five distinct facilities and over 200 clinics, with roughly 20,000 employees serving nearly 600,000 unique patients per year comprising over 2.5 million patient visits. the ucla health pharmacy supports all these facilities with three hospital pharmacies— an infusion pharmacy, two research pharmacies, and five retail/specialty pharmacies staffed by over 300 employees.4 as one of the nation’s leading dispensers and a blockchain solutions provider with extensive healthcare experience, the team decided to take on the challenge of applying dscsa requirements to the last mile—a i under dscsa, the term “dispenser” “(a) means a retail pharmacy, hospital pharmacy, a group of chain pharmacies under common ownership and control that do not act as a wholesale distributor, or any other person authorized by law to dispense or administer prescription drugs, and the affiliated warehouses or distribution centers of such entities under common ownership and control that do not act as a wholesale distributor; and (b) does not include a person who dispenses only products to be used in animals in accordance with section 512(a)(5).” see reference 1. https://doi.org/10.30953/bhty.v3.134 page 3 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 pharmacy in a large hospital setting—a highly complex area of the drug supply chain. both ucla and ledgerdomain are members of the linux foundation and the hyperledger project, and are active in advancing the use of blockchain to drive pharmaceutical supply assurance directly to patients. ucla and ledgerdomain are also both charter members of the clinical supply blockchain working group, an initiative formed along with pfizer, biogen, gsk, merck, ups, iqvia, and other healthcare leaders. in 2019, the group published results from project kitchain, a collaborative model for immutable digital recordation and inventory and event tracking in the pharmaceutical clinical supply chain.5 pilot program objectives the objective of the study was to focus on the enhanced requirements for package-level tracing and notification that would go into effect in 2023 to comply with the dscsa. in particular, the team focused on the verification system requirements under section 582 of the fd&c act6 and the existing protocols around form fda 3911, which is used by manufacturers, repackagers, wholesale distributors, and dispensers to notify fda and all appropriate immediate trading partners within 24 hours after determining that a product is illegitimate or has a high risk of illegitimacy.7 as part of this endeavor, the team also aimed to learn about the challenges of implementing an interoperable system on a larger scale, identify projected costs, and articulate potential soft costs and benefits encountered that might be worthy of further study. the logical components of such a system are depicted in figure 1. in supply chain systems, transactions are captured as events in the “transaction plane.” these events are aggregated into trends in the “control plane.” exceptions and problems are then surfaced to the “risk management plane.” in the legacy relational database world, the transaction plane was often termed the online transaction processing (oltp) database,8 while the control plane was termed the online analytical processing (olap) database, or data warehouse.9 with an integrated system, not only figure 1—the logical components of an interoperable supply chain system. https://doi.org/10.30953/bhty.v3.134 page 4 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 can a single expired unit simply be blocked at the transaction plane level, but multiple blocked transactions of this expired drug can rise to the level of a risk-management issue. there are security tradeoffs made with relational database implementations of such multiplanar supply chain systems. in these implementations, it is typical for each transacting party to manage its own database systems: there is no shared global system of record representing the single source of truth describing the flow of items through the supply chain. maintaining a private database allows each party to minimize the security threat to which they are exposed while maximizing internal data consistency, as a global relational database would require each party to trust all other parties in the supply chain with the integrity and security of their data. however, a system in which each supply chain participant maintains its own system of record comes at the expense of the supply chain’s resilience to common attack vectors such as man-in-the-middle or spoofing,10 as each participant must be trusted to securely manage user privileges, authentication, and auditing. perhaps more importantly, each participant must be trusted to not carry out malicious edits of data in their system of record to their own benefit. this makes it difficult for any single source of truth to be synthesized. without global and persistent visibility regarding the true state of the supply chain, duplicate “spoofed” counterfeit drugs cannot be identified; counterfeit or defective lots cannot be tracked, traced, and recovered; and malicious “men in the middle” cannot be identified by the signature transaction patterns they leave across multiple points in the supply chain. distributed ledger technologies such as blockchain represent a potential system of record that would lessen the need to trade data integrity and privacy for global visibility and interpretability,11 contributing to the inspiration for the bruinchain study. in a highly regulated community, regulations need to drive the data standards, which drive the implementation. with that in mind, much of this study also focuses on establishing the “ground truths” of regulatory requirements and data standards, as they are ultimately critical to the implementation as well as the financial analysis. the questions this pilot aims to answer are: • barcode reading error rates. is commercial off-the-shelf (cots) hardware capable of delivering highly reliable scanning of typical drug packaging? can the team shed some light on the number of scans per unit and the time required to scan (including failed scans due to damaged packaging or other reasons)? • interoperability implementation and implications. can the application and backend be configured to identify expired, duplicate, unverifiable, and damaged products, and alert the relevant stakeholders? • user satisfaction ratings by role. can ucla’s pharmacy technicians, pharmacists, and prescribers master this technology and incorporate it into their daily routine? • simulated counterfeit and expired lot metrics. how robust would the system be in checking for counterfeits (e.g. duplicates or faulty barcodes) and expired products? • paperwork reduction. can the team validate the generation of form fda 3911 notifications and evaluate the enhanced timeliness and reduction in site burden achieved as a result? https://doi.org/10.30953/bhty.v3.134 page 5 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 • cost, quality, and speed of product tracing information. how fast and accurate might the solution be within a real-world context? • real and imputed costs to implement. can the team gather enough data to generate an estimate of likely cost that would be helpful to policymakers and stakeholders? pilot scope the last mile in the pharmaceutical supply chain is a complex environment, as drugs are moved from shipping containers and pallets to individual packages and amber bottles with several degrees of separation from their point of origin (see figure 2). the first mile might be compared to counting humpty dumpty; the last mile is where we track and trace the pieces. the keystone of this project is bruinchain, a blockchain-based mobile solution and notification system designed to track and trace changes in custody of drug within a “dispenser” organization using fda-stipulated barcodes.12 changes in custody were translated into the application with real-time reporting of inventory counts and locations within the ucla pharmacy system. the following checkpoints were built into this system to prevent distribution of suspect product to patients: (1) verify that the information on the scanned barcode matches the human readable label; (2) verify that the product is fit for distribution, that is, not past its expiration date; (3) obtain verification from the manufacturer; and (4) visual inspection of the product. this flow is outlined in more detail in the “process flow” section. based on earlier learnings from a pilot application designed for the clinical supply chain,5 system requirements included notifications; that is, (1) automated product tracing notifications to key stakeholders and (2) notifications for suspect product with key information that mapped to the existing form fda 3911. for this pilot project, the team assessed bruinchain system requirements for a successful implementation on a larger scale, including identifying, flagging, and preventing the distribution of suspect and illegitimate products, and evaluating the enhanced timeliness and reduction in paperwork that might be achieved. to fully test the system’s track-and-trace capability, the team decided to select one study drug and have bruinchain track its journey from the point ucla received the drug in its receiving dock to being dispensed to the clinic/ patient. while to some observers this may encompass greater scope than that warranted by the dscsa, a closer examination of ucla’s workflow revealed that different ucla colleagues were performing different dscsamandated checks. figure 2—a simplified model of the pharmaceutical supply chain, with the last mile at ucla outlined. note that this study encompasses the administration of drugs to patients. https://doi.org/10.30953/bhty.v3.134 page 6 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 spinraza®ii (referred to as study drug hereafter), a biogen product, is used for treatment of children and adults with spinal muscular atrophy (sma). infantile-onset sma (type 1) results in early mortality.13 this was chosen as the study drug for its ideal tracking characteristics. it is packaged as single-dose medicine with a gs1 datamatrix barcode on the outer packaging (see image).14 in this particular situation, a single md administers all of the study drug in the ucla system. because the md, not the pharmacy, opens the carton, the team decided to track the drug all the way to the prescriber/clinic in order to incorporate visual inspection of the vial as part of the bruinchain process. most importantly, however, the study drug has a complex workflow at ucla health. after ucla receiving takes custody of the drug, the study drug may be forwarded to a variety of pharmacy locations, including the intravenous additive service pharmacy (ivas), the operating room pharmacy (or), the bowyer infusion pharmacy (bowyer), and outside the pharmacy, the md’s clinic; all depending upon a variety of important ii all product names, trademarks ,and registered trademarks are the property of their respective owners. all company, product, and service names used in this paper are for identification purposes only. use of these names, trademarks, and brands does not imply endorsement. spinraza is a registered trademark of biogen inc. pyxis is a trademark of becton, dickinson and company. ucla, ucla bruins, university of california los angeles and all related trademarks are the property of the regents of the university of california. hyperledger and hyperledger fabric are trademarks of the linux foundation. the docuseal, selvedge, and oraculous software programs and the accompanying procedures, functions, and documentation described herein are sold under license agreement(s) by ledgerdomain inc. their use, duplication, and disclosure are subject to the restrictions stated in the license agreement(s). the quarxantine image is property of ledgerdomain inc. variables. this complex real-world setting, depicted in figure 3, was an ideal use case for the bruinchain application. as part of exploring dscsa requirements for verification of the drug as a legitimate product, the team coordinated with the study drug’s manufacturer on a potential solution. this culminated in the development of an automated verification system (supported by the oraculous notification service, detailed in the “bruinchain implementation, soft start” section), involving queries of serial and lot numbers between the ucla pharmacy and the manufacturer’s serialization team. it is important to note that the implementation of the bruinchain pilot study (see the “bruinchain pilot study” section) was conducted as a system parallel to existing systems of record in an active pharmacy at ucla health. in this way, the team met its goal of getting real data in a real-world setting. methods and findings partners and roles a pharmacy purchasing manager at ucla health pharmacy (hereafter the manager) was the system owner and manager, providing ledgerdomain with workflow requirements to design the system. the manager also recruited and managed technicians and pharmacists to test the solution and provide feedback. ucla health also acted as the prescriber. the md personally retrieved the medicine at the relevant ucla pharmacy or received into his clinic directly from a specialty distributor via pre-dispensed distribution (also known as “white bagging”) and administered to the patient. the manager, prescriber, and other roles are outlined in the “technology” section. https://doi.org/10.30953/bhty.v3.134 page 7 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 ledgerdomain designed, coded, and tested the bruinchain iphone application, and supplied the docuseal framework, the oraculous notification service, and selvedge blockchain application server, as well as hosted the hyperledger fabric backend. design requirements why blockchain? blockchain enables a tamper-proof, timestamped, near-real-time, auditable record of transactions, thereby making it possible to enhance privacy and security across a range of collaborative applications.15 unlike a centralized relational database, no single user or organization can access the full record of transactions within a blockchain (provided a sufficient number of nodes within a diversified trading community). unlike in a relational data model, blockchain communities converge on a single version of the truth through the application of validated transactions. once transaction finality is achieved, that single best version is committed to the blockchain. this consensus is particularly important in the drug supply chain, as this single version of the truth cancels out double counting and reveals possible instances of counterfeiting, diversion, spoofing, or man-in-the-middle attacks. in implementations such as bruinchain, each event within the blockchain occurs when relevant parties agree to cryptographically sign a transaction. this agreement, in turn, adheres to an associated “smart contract.” once this transaction has met those conditions and is committed to the blockchain, it is both binding and irrevocable. after a transaction is struck between two parties, each will have keyed access for later decryption and analysis, as might a regulator or auditor. thus, the team saw blockchain as a potential “honest broker” that would allow hundreds of competing pharmaceutical and biotech enterprises and their vendors to work collaboratively and communicate with hundreds of wholesalers and tens of thousands of dispensers. figure 3—the typical “happy path” (blue arrows) of the study drug as it is distributed through the ucla health pharmacy, from receiving to the clinic. at any point the drug may be quarantined (yellow zones) if it is suspected of being illegitimate. if found to be illegitimate, it is removed from distribution (red zone). once a drug is administered in the clinic, it has fulfilled its journey in the supply chain (green zone). https://doi.org/10.30953/bhty.v3.134 page 8 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 bruinchain was designed and scoped to be a shared-permission blockchain-based system; that is, a system where membership and participation in the network is controlled rather than open to the public.iii linux foundation hyperledger fabric components were chosen as a scaffolding for the pilot, as well as a fabric-based framework that allowed for off-chain private storage combined with blockchain-based authentication.16 commercial pharma supply: simplified stakeholder roles the goal of the dscsa is interoperability among thousands of companies that make up the pharmaceutical supply chain shown in figure 2, including manufacturers, repackagers, wholesale distributors, and dispensers. even within these four categories, one can identify numerous organizational and individual roles; between the distributor and the practitioner at ucla there are shippers, technicians in the pharmacy receiving department, and stations/ carts within the pharmacy itself. a key goal of bruinchain was to provide a real-time system that allows all its internal stakeholders to interact in a manner appropriate to their privileges. under dscsa, dispensers are required to be able to trace and verify drugs. the drug barcode may be used to pull the relevant data fields, but the question in the pre-2023 environment is from which external party might bruinchain electronically poll to trace. this presented an additional challenge: how bruinchain might communicate without compromising its security envelope. to address this challenge, iii the benefits of permissioned versus non-permissioned blockchain systems are a well-established and hotly discussed question within the blockchain community, and outside the scope of this paper. ledgerdomain designed a novel approach that leverages the blockchain concept of an oracle,iv detailed in the “bruinchain implementation, soft start” section. process flow a key goal of the bruinchain application and process is to detect and report problems. in the pilot, the team defined the ideal supply chain workflow as the “happy path,” and possible failure states as “sad paths.” a package that (1) has a valid barcode with a gs1 global trade item number (gtin) that matches the expected value, (2) is unexpired, (3) is verified by the manufacturer, and (4) passes visual inspection has fulfilled the happy path (shown in figure 4). at the last stage of the drug’s route through the pharmacy, the md scanned the package using his iphone (see figure 8) to take custody on behalf of the clinic prior to administration (of the drug) and retirement (of the unique asset id within the blockchain system). however, if it fails any of these tests at any of the pharmacy locations (detailed in the “pilot scope” section), the package has taken one of the sad paths. again, in ucla’s current workflow, the study drug is delivered to the prescriber as a sealed package, and the prescriber performs the unsealing and inspection, thereby fulfilling the final requirement. of the four types of problems that are identified by a dispenser locally, each requires a slightly different approach. if a medicine is expired, the transaction is flagged: there may be no external reporting necessary. iv oracles are “services that send and verify real world occurrences and submit this information to smart contracts, triggering state changes on the blockchain.” see reference 17. https://doi.org/10.30953/bhty.v3.134 page 9 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 in the case of the barcode failing to match the medication, or a visual inspection failure, the dispenser is required to escalate the situation and stand ready to file form fda 3911. the bruinchain application automatically generates the xml-formatted dataset for the filing of a 3911 and asks users to take a photo supporting their issue (see figure 5). both documents are encrypted, timestamped, and stored in a secure archive, mediated by docuseal. at ucla, the policy is for the user to report these issues to the manager. the manager then confirms the issue and submits the email if necessary, in addition to any steps stipulated by ucla health. figure 4—an overview of the happy and sad paths. figure 5—an xml message (left) generated by a simulated issue report, in this case an empty box (right). https://doi.org/10.30953/bhty.v3.134 page 10 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 finally, if there is a problem in verification, the bruinchain system will have already provided the manufacturer with all of the fields necessary for the manufacturer to file a form fda 3911. figure 5 shows a sealed document from the blockchain of a simulated issue, including a photograph that supports the issue and is sealed to the blockchain as well. (note that for security purposes, all study drug serial numbers have been obfuscated except for the last four digits; barcodes and contact information have also been obfuscated.) it should be noted that quarantine is an intermediate step within the bruinchain system; human review is required to determine whether the drug is illegitimate, or whether it has been quarantined due to user error or other factors. in the event of a quarantined drug, a sticker or tape (as shown in figure 6, alongside the quarantine bin) is to be affixed to the package until resolution of its status. if a quarantined medication is deemed illegitimate, it is flagged as “bad” within the bruinchain workflow, potentially resulting in a report to fda via form fda 3911. data standards under the fd&c act, manufacturers are required to “affix or imprint a product identifier to each package and homogenous case of a product intended to be introduced in a transaction into commerce.”18 this must take the form of a two-dimensional data matrix barcode for packages,19 with data containing the product’s national drug code (ndc), unique alphanumeric serial number, lot number, and expiration date. the barcode must be “on a data carrier that conforms to the standards developed by a widely recognized international standards development organization.”20 for the bruinchain pilot, it was determined that this barcode would set the stage for intake into receiving for further evaluation as to expiry, storage condition, tampering, and barcode verification. the barcode on the study drug is based on the gs1 standard. gs1, which develops and maintains global standards for efficient business communication, has published a material identification standard for both commercial and clinical pharmaceutical supplies.21 (in gs1-compliant barcodes, the ndc is concatenated within the gtin.) figure 6—a quarantine bin (left) and quarantine sticker (right). https://doi.org/10.30953/bhty.v3.134 page 11 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 the data standards used in the bruinchain pilot further include: a concept of network time and time-stamping; fda form 3911 specified problem-handling fields; user id, email, and location; quarantine and retired statuses; and verification statuses. technology the bruinchain system has five major components: 1. a frontend mobile application (also called bruinchain), 2. an application framework encompassing smart contracts and application logic (docuseal), 3. a notification and verification service (oraculous), 4. a blockchain application server (selvedge), and 5. a backend blockchain (hyperledger fabric). these components are shown in figures 9 and 10. the first thing presented to the member is a modal login screen (member email and password). once logged in, the full application user interface (ui) is presented (figure 7). account actions. all members can change their first name, last name, and avatar; change location; or logout. admins additionally have the ability to invite a new member (sending an email that grants the recipient access to the application) and purge the blockchain of data to reset for testing. distinct roles. there are four roles: • receiver: has the ability to scan and receive new drugs to the pharmacy • pharmacist: has the ability to scan in order to relocate the drug • manager: has the ability to scan in order to relocate the drug, as well as provision new members • prescriber: has the ability to scan and receive new drugs to the pharmacy, as well as administer and retire drugs. scan. the logical custody is keyed off the scan, which is checked for expiration date and validly formatted drug barcode. in addition, the barcode is checked against the register of verified (but not retired) barcodes: if the barcode is not already verified, the verification procedure is automatically initiated by the figure 7—the bruinchain home screen. https://doi.org/10.30953/bhty.v3.134 page 12 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 oraculous notification service. once scanned, the application displays the elements of the barcode (gtin, serial number, expiration date, and lot number) in a human-readable format. users can then compare the information on the screen against the human-readable information on the package (see figure 8). confirming modals. users have the ability to flag units that do not pass visual inspection and start an exception report. if the application detects a duplicate scan, the users are asked to confirm whether or not they believe they scanned the same box twice (“rescan”). any of these routes can turn from the “happy path” to the “sad path,” which eventually leads to quarantine and potentially to “bad status” and form fda 3911 for resolution. while not exhaustive, the following captures the core components of the bruinchain system on final test: • bruinchain pilot app version 1.0(9) (ledgerdomain, swift 4.2) • instabug bug tracker • branch mobile link service • onesignal push notification service • mailgun email service • docuseal framework (ledgerdomain) • selvedge application server (ledgerdomain) • oraculous notification service (ledgerdomain) • hyperledger fabric 1.2 (linux foundation) • hyperledger private data collections (linux foundation) • leveldb figure 8—scanning a barcode (left), awaiting verification (middle), and error message on rescan (right). https://doi.org/10.30953/bhty.v3.134 page 13 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 • docker • amazon ec2 • amazon web services security, privacy, and data retention for this pilot, the bruinchain application was restrictedly used amongst a few participants at ucla health and handled no patient data. it should be noted that no scanned packages had any personally identifiable information or protected health information (pii/phi), except for the white-bagged units in the “live parallel user-site testing with ucla” section, which were handled exclusively by ucla personnel. the team performed multiple cycles of by-invitation only testing. at the end of each cycle, personal and personally identifiable information such as names, email addresses, and ip addresses were discarded.22 in addition, the application was distributed via the apple testflight sandbox and was thus not exposed to the general public in the app store. development and testing this phase of the pilot project comprised (1) gathering of requirements and confirming target drugs and partners; (2) specifying and building the blockchain application; and (3) testing in controlled environments, referred to as user site and developer site. during this phase, existing workflows were mapped, and possible paths to dscsa compliance were workshopped. these were then built as process flows within the system’s roles, privileges, and smart contracts. this culminated in the happy and sad paths workflow (shown in the “process flow” section). the team’s methodology in testing the counterfeit sad path was to flag everything that was not the study drug and generate a modal warning. user site training and testing with bruinchain training was self-guided with ledgerdomain personnel as coach. trainees from the ucla figure 9—a general overview of the bruinchain application infrastructure. smart contracts are run within a hyperledger fabric infrastructure, denoted in orange. https://doi.org/10.30953/bhty.v3.134 page 14 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 figure 10—an overview of ledgerdomain’s standard system architecture. this diagram includes multiple organizations and multiple frontend clients; as a single-organization pilot with a mobile application, the bruinchain implementation test was purposely simplified. https://doi.org/10.30953/bhty.v3.134 page 15 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 pharmacy staff typically spent 15–20 minutes getting familiar with the bruinchain application, roles, and locations. eighteen minutes was the training time mean. board game test, developer site for this phase, a “board game” simulation mat of the ucla pharmacy workflows was used. training and testing were conducted with naive ledgerdomain colleagues playing different roles (receiving tech, pharmacist, and prescriber). board game test, user site training and testing were conducted with a group of ucla pharmacy staff, first in a conference room setting, and later onsite at the pharmacy figure 11—an overview of the blockchain pilot development process. test round objective units scanned outcomes round 1, user 1 gain insight into barcode reading error rates two (2) non-study drug packages and two (2) study drug packages all barcodes were successfully read and packages were correctly identified as non-study drug vs. study drug round 1, user 2 ten (10) study drug packages and randomly selected non-study drugs round 2 quantify the amount of time to scan barcodes four rolling carts (25–50 scannable units per cart) were randomly selected and their contents were scanned and timed four time-measured flights involved 197 units scanned in 27:50 minutes (11.8 seconds per package); zero failures were encountered during scanning user site testing and training objectives and results. objective units scanned outcomes test inventory counts and location of drug seven (7) discarded study drug packages traveling from receiving to clinic scanning was 100% successful, and inventory tracking was 100% accurate developer site board game testing objective and results. https://doi.org/10.30953/bhty.v3.134 page 16 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 (see figure 12). this session was used to test inventory count, location of drug, and “sad path” of expired drug. training was self-guided with ledgerdomain personnel as coach. trainees typically spent 20 minutes getting up to speed. once trainees pronounced themselves comfortable, the trainees worked together to push the packages through the board game supply chain (see figure 13). training took 18 minutes in total, while provisioning the application on the iphone took an additional 10 minutes and scanning 33 packages across a variety of bins took 4:58 minutes or 9.0 seconds/package. verification of drug with manufacturer as an initial test, the team successfully verified 11 out of 11 study drug barcodes with the manufacturer’s serialization team through a manual process (i.e. emailing parsed barcodes for review). bruinchain pilot study bruinchain implementation, soft start the goal of the soft start was to confirm ucla’s forecasted activity for the week of december 16–20 (“live test”) and to verify all on-hand study drugs 3 days prior through the bruinchain system. this enabled the team to pre-verify the study drug and avoid quarantine scenarios, while also gathering further feedback from the manufacturer. the manager scanned all five units on hand. the bruinchain system automatically generated a notification to the manufacturer’s serialization team through the ledgerdomain oraculous notification service. the process was fully automated in-bound and out-bound for ucla users on bruinchain. the oraculous service allowed for manual verification on the manufacturer side, as shown in figure 14. the first five notifications were successfully verified by the manufacturer. the “not verified” test round objective units scanned outcomes round 1: ucla pharmacy staff test inventory count, location of drug, and “sad path” of expired drug seven (7) discarded study drug packages the total time was 45 minutes to individually train and to work together as a team. bruinchain successfully flagged expired drug barcodes and generated modal warning screens to indicate expiry round 2: prescriber receive, transfer, and simulate administration seven (7) discarded study drug packages ambient lighting was poor and scanning was slow, but accurate round 3: ucla pharmacy staff training and testing in the infusion pharmacyvi ten (10) discarded study drug packages (two expired) user was readily able to receive and transfer the drug on the bruinchain application user-site board game testing objectives and results. v training and testing in the infusion pharmacy, in which the workflow includes affixing patient names and subsequently dispensing either directly to nurses or through a pyxis™ interface. https://doi.org/10.30953/bhty.v3.134 page 17 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 figure 12—round 1 user-site test with the simulation mat. figure 13—scanning packages of study drug. https://doi.org/10.30953/bhty.v3.134 page 18 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 functionality was also tested, although it should be clear that this was strictly a test of the software, not a real event. the following day, ucla scanned a second batch of three additional incoming study drug units; again, all three generated automatic notifications and were subsequently verified as good serial numbers. as such, the live test began with six uncommitted study drug units in aggregate across the pharmacies and two study drug units committed with phi in the clinic. these two had come in directly from a specialty distributor, pre-dispensed (“white bagged”). all units were scanned for expiration date as well as verified by the manufacturer. with only eight units, it is unsurprising that no duplicates were found; nonetheless, the bruinchain system does check for this, blocking retired units and requesting user feedback for units that are duplicates within the system. duplicate scans are normative, as units are tracked with bruinchain through their retirement or quarantine, but users are always asked to confirm the unit is indeed an existing unit, as shown in the “technology” section. live parallel user-site testing with ucla during this phase, the oraculous service was updated to provide the manufacturer with more information to aid in verification and improve record auditability. ucla colleagues commenced the live parallel user-site testing by updating the locations of each of the drug doses. ucla typically uses a weekly paper forecast of study drug doses as a fail-safe to ensure an adequate supply: eight doses were confirmed as being on hand and seven patients were scheduled. following a day of testing, the oraculous service was further updated to tune the notifications to the manufacturer. the manufacturer now received a follow-on email confirming “verified” in addition to those confirming “not verified” (shown in figure 15). as such, the manufacturer would receive a record of data sufficient to initiate a form fda 3911 on both suspect and non-suspect units. the client application continued to perform as expected, as all inventories continued to match up with user commands (see figure 16). following the live test with the manufacturer verifying inventories (see “bruinchain implementation, soft start” section) and through the live parallel user-site testing, the team gathered the metrics outlined below. it is important to note that no suspect or illegitimate drug was identified during the pilot, and any error reports or exclusions were performed as part of testing. application events and metrics • 10 distinct asset ids (boxes of study drug) figure 14—the notification and associated verification flow. https://doi.org/10.30953/bhty.v3.134 page 19 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 • 9 distinct user ids (including ledgerdomain personnel who did not transact) • 428 application programming interface (api) calls from 48 ip addresses ▪ 294 inventory summary changes ▪ 37 custody queries (looking up custody information on a particular asset) ▪ 23 changes of custody for 10 assets ▪ 28 asset verifications ▪ 20 user avatar queries ▪ 15 user profile updates (includes ucla personnel reassigning location) ▪ 6 assets administered ▪ 3 queries of user by email address • 120 notifications sent in summary, during the busy pre-holiday week, ucla started with six doses of the study drug in the pharmacy and two additional white-bagged doses in the clinic; by the week’s end, ucla had three in pharmacy. the system tracked pharmacy doses to user input accurately throughout the week, checking each package for expiry and soliciting verification from the manufacturer. further developer site scanning time trials user feedback suggested that some barcodes are faster to scan than others, with the testers favoring larger barcodes with simpler (black ink on white card stock) color contrast. with practice, 3.16–3.59 seconds were seen per scan for the larger barcodes, which includes: (1) pushing the scanning button, (2) aligning the package in the application reticle, (3) scanning, and (4) accepting by pushing the modal screen. time trials of bigger figure 15—verification email sent by the oraculous service (left) and notification of verification within the bruinchain app (right). https://doi.org/10.30953/bhty.v3.134 page 20 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 versus smaller barcodes were performed to determine the importance of this factor.viv discussion key learnings by running a live study at one of the nation’s busiest pharmacies with real medications being delivered to real patients, the team gained important insights into what a larger implementation of a dscsa verification system would look like. while not every dispenser will share that exact experience, extrapolating this performance and cost of the electronic, interoperable system mandated by the dscsa to the dispenser community may provide some insights for stakeholders. processing barcode reading error rates a scanning success rate of 100% was observed, but initially scanning was difficult and slow. vi the smaller study drug barcodes were approximately 7.5 mm2, while the larger were approximately 10.5 mm2. in study trials, the larger barcodes scanned slightly faster. the smaller barcode times were more variable, but the fastest set required 15% more time to perform the scan compared to the larger barcodes. reprogramming the cameras and re-framing the reticle improved performance. subsequent developer-site practice dropped scanning to as fast as 3.16 seconds per scan. this further validates the use of cots phones as scanning and application endpoints.23,24 it should be noted that one of the motivating factors in selecting cots technology was the need to manage equipment costs at an industry-wide level. moreover, the study supports recent findings by amerisourcebergen, mckesson, and gs1 that there has been significant and growing adoption of the gs1 dscsa-compliant standard. for instance, at amerisourcebergen, 71.9% of all packages in 2019 had a readable 2d gs1 datamatrix barcode with all four dscsa-required data elements, compared to 20.4% in 2018 and 7.2% in 2017. in 2019, they reported improved legibility of barcodes as well, as previously they often “could not scan certain barcodes since they were applied on shiny surfaces or were printed in inappropriate colors.”23,24 interoperability implementation and implications throughout the course of the study, the bruinchain application was capable of asset serial number first event last event touches xxxxxxxx9621 fri dec 13 16:14:16 2019 mon dec 16 18:21:35 2019 57 xxxxxxxx7449 wed dec 11 18:30:38 2019 fri dec 20 19:58:16 2019 143 xxxxxxxx9154 wed dec 18 21:46:48 2019 thu dec 19 17:15:13 2019 64 xxxxxxxx4202 wed dec 11 18:30:19 2019 mon dec 16 18:36:27 2019 57 xxxxxxxx2024 fri dec 13 16:14:00 2019 sat dec 21 00:00:32 2019 99 xxxxxxxx6442 wed dec 11 18:41:20 2019 thu dec 12 10:21:32 2019 46 xxxxxxxx6075 thu dec 19 23:07:35 2019 sat dec 21 01:28:17 2019 65 xxxxxxxx5195 fri dec 13 16:13:46 2019 sat dec 21 00:01:49 2019 99 xxxxxxxx2141 wed dec 11 18:41:08 2019 tue dec 17 23:20:37 2019 59 xxxxxxxx0150 wed dec 11 18:30:03 2019 mon dec 16 18:36:10 2019 59 a breakdown of api touches by asset. (note that all times are in utc. touches refers to api calls related to the specific asset. for logging and analysis we used splunk, software designed to search, monitor, and analyze machine-generated big data.) https://doi.org/10.30953/bhty.v3.134 page 21 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 identifying expired and duplicate products. expired products, duplicates, and failed verifications all resulted in the relevant stakeholders being alerted in real time with modal screens, mobile push notifications, and/or emails. an automated verification system was developed (the oraculous notification service) to serve the role of “blockchain oracle,” allowing manufacturer study participants to verify products without requiring provisioning their membership on the bruinchain blockchain. user satisfaction ratings by role study participants were surveyed following test rounds, with six responses. metric rating (%) login success 100 application look and feel 93 notification success 100 barcode scanning reliability 97 notification format and appearance 97 ease of location change 92 overall impression 90 respondents reported two usability issues: slow scanning times and occasional modal screen freezes after sending notifications. simulated counterfeit and expired lot metrics the team experienced a 100% success rate across scanning, expiration detection, and counterfeit detection. (as mentioned in the “development and testing” section, the study methodology for counterfeits was to flag everything that was not the study drug and generate a modal warning.) further on-site study with a significantly higher number of users and drugs is recommended to explore potential new edge cases. paperwork reduction according to fda, “the burden time for [form fda 3911] is estimated to average 1 hour per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information.”25 by automatically sourcing all of the fields required for a form fda 3911, the solution explored in this study reduces reporting times to the push of a button. financial implications for dispensers projected costs to implement for the current study drug workflow at ucla, a minimum of three scans were found to be required: (1) the first scan in receiving checks that the barcode is indeed a valid barcode, that the unit is not yet expired, and initiates the barcode verification process with the manufacturer; (2) after the notification is received from the manufacturer, a second scan is required to pull that unit out of quarantine, placing it into inventory and if desired, to notify the clinic; and (3) the prescriber, who is delivered a sealed box of the drug, re-scans it and after inspecting the vial for visual issues (such as cracks or discoloration) accepts the unit. one could postulate over time that the number of scans required under an industry-standard interpretation of dscsa might be lowered. for typical products that are visually inspected by the dispenser, only two scans are required, assuming the manufacturer verification is delayed more than a few seconds (i.e. not in real time). according to statista, there are 4.2 billion prescriptions dispensed each year in the united states.26 with a barcode scan time of 3.16 seconds, this implies 1,139 scans per hour. based on two scans per package—one to send the barcode to the manufacturer or repackager https://doi.org/10.30953/bhty.v3.134 page 22 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 in order to trigger the verification request, and one to check the package again after verification has been received—one might anticipate 7.4 million hours of scanning nationwide each year. according to the us bureau of labor statistics, the mean average salary for the 309,550 pharmacists in the united states is $123,670 per year, or $59.45 per hour,27 while the mean average salary for the 417,860 pharmacy technicians in the united states is $34,020 per year, or $16.35 per hour.28 at ucla, the first scan was performed by a pharmacy technician and the second scan was performed by a pharmacist. thus, assuming a roughly equal distribution of engagement with the scanning system, one can assume the value of pharmacy time as $37.90 per hour. this drives a best single-point estimate of the cost for barcode scanning at the dispenser level of $280 million in labor costs. with a 30% benefit component added,29 the fully loaded labor-related cost would be approximately $365 million. on top of this, one can also project additional requirements at the individual employee. based on these figures, a full breakdown is shown below: expenditure unit cost per annual cost scan labor (two scans per barcode) $0.087 two scans $365mm client hardware and training (biennial) $1,000 employee $364mm figure 16—the final scan of the bruinchain pilot. https://doi.org/10.30953/bhty.v3.134 page 23 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 thus one may project that the cost of scanning, hardware, and training will amount to $729 million, or approximately 17 cents per prescription. implications of a dlt solution critically, the above analysis presumes that two scans would be required. in a future state with a real-time blockchain-based connection, a workflow involving a single scan for the happy path would potentially be achievable. the dispenser would need to have a fast internet connection and the persistent digital ledger technology (dlt) would need to reply within “internet time” (~120 milliseconds) for the entire process to be completed in a single scan. if the system performance were seconds or minutes, the receivers would have to momentarily set the box aside, and then rescan later to match the unit to its “verified” notification and move it from the quarantine bin into the regular inventory. this represents a “two-tier warehouse” model at the receiving site, consisting of a “quarantine” tier into which drugs are scanned, and a “verified” tier that drugs are moved to upon receipt of verification. the verification tier can consist of several “sub-warehouses” representing separate stocks for programs such as 340b.viivi any single-scan solution would clearly demand a highly performant, fully automated end-to-end system involving real-time access to a persistent dlt with up-to-date statuses. (it should be noted that bruinchain is already driven remotely by hosting it on the cloud and not locally at ucla health; the only change would be that the bruinchain app would interact with a vii a us federal government program that requires drug manufacturers to provide certain drugs to healthcare organizations and covered entities at significantly reduced prices. consolidated dlt rather than bruinchain.) if it could be achieved within the required response time window, it would represent a $183 million annual savings to dispensers in the united states, as well as a major bulwark against bad or fraudulent transactions. further benefits are recognized through the minimization of safety stock needed in the case of a potential quarantine event. at any given time, the dispenser’s safety stock must accommodate the overall latency of the system to serve as a buffer. the dscsa gives each trading partner 24 hours to respond to a verification request, and the dispenser has no way of knowing how many trading partners who handled the drug prior to receipt at the dispenser. given that tracing is performed by chaining together 24-hour-latency messages, and that the number of trading partners is commonly thought to be up to 10, the dispenser must hold safety stock roughly proportional to the requirements of 10 days. even if the message chain can be collapsed to 24 hours, the dispenser must still hold safety stock in case of potential non-verification events. however, if the verification is performed on real-time dlt, no additional safety stock is required. with the top three wholesalers approaching $500 billion in annual sales within the united states,30 one may estimate based on a 5-day week that an extra 10 days of safety stock would equate to $20 billion in inventory. the carrying cost of this safety stock at 7% cost of capital is $1.4 billion.viiivii viii the specific calculations regarding the exact amount of safety stock to hold are complex and depend on a variety of factors, including expected utilization of a drug, daily variance in utilization, relative stock of sub-warehouses in the verified tier, and desired safety margins. https://doi.org/10.30953/bhty.v3.134 page 24 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 practical policy considerations system cost reductions and sharing the cost and scale of the projected dscsa deployment suggests a need to investigate other solutions that reduce total system cost. stricter guidelines for barcode sizes, color, and placement represent such a potential solution, as even a 5% reduction in average scan time would save u.s. dispensers nearly $20 million annually.23 a “bottle bill” model is worthy of consideration to drive compliance and trading partner equity. even if a highly performant system were to reduce the cost of compliance to 10 cents per unit, this still indicates 10 cents taken away from patient care. focus on tracing places the burden squarely on last-mile participants, both in terms of responsibility and cost. a blockchain system could levy 10-cent unit-level charges on those adding assets to the system and remit 10 cents per unit to those retiring barcodes. this cost-shifting31 might be seen as a source not only of trading partner equity and an assignment to the least cost avoider,32 but perhaps more importantly might enhance compliance and notify those adding assets with definitive assurance that their asset had been immutably retired. furthermore, the future adoption of radiofrequency identification (rfid) tags and readers (either standalone or in combination33 with dscsa-mandated datamatrix barcodes) could allow for more rapid and less expensive scanning, as pharmacists would not need to align the barcode with an optical scanner and could scan an entire “tote” with a wave of their arm. other high-level topics worthy of further study include the application of federated models, potential risk mitigation strategies, and governance models. interoperability and scope the deeper question is whether policymakers and stakeholders rally around a dlt solution which includes manufacturers and wholesalers and federates their data into a single actual or virtual persistent data store. a persistent dlt with blockchain security protocols which covers all units traversing the united states could perhaps allow for dispensers to fulfill their obligations under dscsa with a single scan, without trading off the security or integrity of trading partner data. this also has major implications for the broader security of the supply chain. in the absence of a single persistent data store, dispensers and other stakeholders are left vulnerable to spoofing or man-in-the-middle attacks. moreover, the interception of counterfeits becomes an ongoing challenge that must be identified and resolved repeatedly; whereas known counterfeits can be immediately flagged by a persistent blockchain system. in our implementation, transaction timestamps are centralized, but the supporting documentation is stored in a decentralized private store. pharmacies under common control, manufacturers, and other trading partners could have their own user access administration and each act as an organization on the persistent dlt to better manage privileges and desired segregation. conclusion in conclusion, this study has introduced bruinchain, a successful blockchain-based implementation of a system capable of meeting dscsa standards for a dispenser operating solely on cots technology. bruinchain is capable of real-time performance, multiple roles with differing permissions, in-app member provisioning, inventory tracking at the stockroom level, accurate barcode scanning and parsing, and verification from the manufacturer. through a https://doi.org/10.30953/bhty.v3.134 page 25 of 28 blockchain in healthcare todaytm issn 2573-8240 online https://doi.org/10.30953/bhty.v3.134 series of tests performed onsite at the ucla pharmacy, the study has shown users’ ability to use bruinchain to efficiently scan, track, and verify drugs with minimal training. the study has also discussed the nature of bruinchain’s persistent blockchain database, and contrasted dlts with comparable relational databases to highlight bruinchain’s improved security and potential to drive cost savings. finally, the study discussed the relevant policy considerations that must be weighed to ensure american patients receive the full suite of benefits a globally accessible, dscsa-compliant supply chain could afford them. acknowledgements the authors thank ucla health under the leadership of ceo johnese spisso. no outside money was accepted. the authors also personally thank marlon barrios, veronica burwick (pharmd), cheng cai (pharmd), and jacquilin parker, all of ucla health; and ben nichols of ledgerdomain. they are particularly grateful for the support of biogen (manufacturer of spinraza®), especially from imran shakur, and direct and invaluable assistance from their serialization program with lead bjoern rosner (phd) and the operations team, specifically steve van nuffel, derry manley, lindy blom, and donncha phelan. in addition, thanks to leigh verbois (phd), connie jung (phd) and daniel bellingham, all of fda; bob celeste; jennifer colon (pharmd); and desmond hunt (phd) each of whom provided valuable insights. funding statement: the study was a joint collaboration of ledgerdomain and ucla health, and received no external funding. conflicts of interest: the authors declare no competing interests exist with respect to research, authorship, and/or publication of this article. contributors: william chien was the principal author and conducted research at ucla. perry b. shieh played an advisory role as a key participant in the bruinchain workflow. josenor de jesus played an advisory role with regard to ucla workflows and requirements. ben taylor conducted research. victor dods and leo alekseyev conducted research and development on the software framework. diane shoda played an advisory role. references 1. u.s. department of health and human services food and drug administration, drug supply chain security act (dscsa). 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[internet]. not barcodes or rfid, but both. c2012 [cited 2020 jan 13]. available from: https://www.barcodesinc. com/news/not-barcodes-or-rfid-but-both/ copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http:// creativecommons.org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v3.134 https://www.barcodesinc.com/news/not-barcodes-or-rfid-but-both/ https://www.barcodesinc.com/news/not-barcodes-or-rfid-but-both/ http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1blockchain in healthcare today issn 2573-8240 original research technical design and development of a self-sovereign identity management platform for patient-centric health care using blockchain technology daniel toshio harrell, phd1 ; muhammad usman, ms2 ; ladd hanson, ba3 ; mustafa abdul-moheeth, md1 ; ishav desai, bs1 ; jahnavi shriram, bs, ba1,4 ; eliel de oliveira, ms, mba1 ; john robert bautista, rn, mph, phd5 ; eric t. meyer, phd5 and anjum khurshid, md, phd1 1university of texas at austin-dell medical school, usa; 2university of texas at austin-department of electrical and computer engineering, usa; 3university of texas at austin-information technology services, usa; 4university of arizona college of medicinephoenix, usa; 5university of texas at austin-school of information, usa corresponding author: anjum khurshid, email: anjum.khurshid@austin.utexas.edu keywords: blockchain technology, decentralized identifiers, health information, hyperledger, self-sovereign identity, verifiable credentials abstract objective: clinical data in the united states are highly fragmented, stored in numerous different databases, and are defined by service providers or clinical specialties rather than by individuals or their families. as a result, linking or aggregating a complete record for a patient is a major technological, legal, and operational challenge. one of the factors that has made clinical data integration so difficult to achieve is the lack of a universal id for everyone. this leads to other related problems of having to prove identity at each interaction with the health system and repeatedly providing basic information on demographics, insurance, payment, and medical conditions. traditional solutions that require complex governance, expensive technology, and risks to privacy and security of the data have failed adequately to solve this interoperability problem. we describe the technical design decisions of a patient-centric decentralized health identity management system using the blockchain technology, called medilinker, to address some of these challenges. design: our multidisciplinary research group developed and implemented an identity wallet, which uses the blockchain technology to manage verifiable credentials issued by healthcare clinics, banks, and insurance companies. to manage patient’s self-sovereign identity, we leveraged the hyperledger indy blockchain framework to store patient’s decentralized identifiers (dids) and the schemas or format for each credential type. in contrast, the credentials containing patient data are stored ‘off-ledger’ in each person’s wallet and accessible via a computer or smartphone. we used hyperledger aries as a middleware layer (api: application programming interface) to connect hyperledger indy with the front-end, which was developed using a javascript framework, reactjs (web application) and react native (ios application). results: medilinker allows users to store their personal data on digital wallets, which they control. it uses a decentralized trusted identity using hyperledger indy and hyperledger aries. patients use medilinker to register and share their information securely and in a trusted system with healthcare and other service providers. each medilinker wallet can have six credential types: health id with patient demographics, insurance, medication list including covid-19 vaccination status, credit card, medical power of attorney (mpoa) for guardians of pediatric or geriatric patients, and research consent. the system allows for in-person and remote granting and revoking of such permissions for care, research, or other purposes without repeatedly requiring physical identity documents or enrollment information. conclusion: we successfully developed and tested a blockchain-based technical architecture, described in this article, as an identity management system that may be operationalized and scaled for future implementation to improve patient experience and control over their personal information. received: december 1, 2021; revised: january 6, 2022; accepted: january 6, 2022; published: march 18, 2022 https://orcid.org/0000-0002-5085-1183 https://orcid.org/0000-0001-5788-5784 https://orcid.org/0000-0003-4620-7560 https://orcid.org/0000-0002-0230-0950 https://orcid.org/0000-0002-9034-2586 https://orcid.org/0000-0001-6051-778x https://orcid.org/0000-0001-9704-199x https://orcid.org/0000-0002-4892-9543 https://orcid.org/0000-0002-1998-7162 https://orcid.org/0000-0002-8946-0622 mailto:anjum.khurshid@austin.utexas.edu citation: blockchain in healthcare today 2022, 5: 196 http://dx.doi.org/10.30953/bhty.v5.196 2 daniel toshio harrell et al. the 21st century cures act (cures act), signed into law in 2016, has made it mandatory for the federal government to find ways of accessing patient data faster and more efficient.1 billions of dollars have been spent by the government of united states to promote and improve the electronic medical record (emr) systems in its healthcare system. unfortunately, the fragmented design of the health system and lack of a universal unique identity (id) number for everyone have created a highly siloed data ecosystem for medical records. as a result, a patient must prove his or her identity to each provider individually, and during every clinical visit, he or she must fill out forms and provide additional, usually repetitive, information. without identity verification, providers may refuse services, or be unable to read and write health information within the patient’s data stream. there is no reliable method for patients to give, reference, and revoke consent for providers to access their health information.2 consent to read, write, and share health information is often obtained by the patient signing a paper form, which makes accessing and revoking consent impractical or impossible for most patients. a blockchain is a digital ledger of transactions distributed across a trusted peer-to-peer network of nodes, and was first implemented to allow exchange of cryptocurrencies such as bitcoin and other financial transactions.3 beyond blockchain’s original financial use cases, researchers4–7 have suggested that blockchain technology’s distributive model can bridge the gap between the “data silos” in health care and may improve coordination between healthcare providers and patients.5,8,9 this empowering and potentially disruptive technology can provide a novel data model, which provides patients control over their medical data. besides, blockchain implementations have been proposed for other healthcare use cases, such as improving the management of research consent,10 clinical trials,11,12 supply chains,13 and healthcare provider’s accreditations.14 background currently, there is a limited understanding about the technical design and development of a reliable patient-centric healthcare identity platform using blockchain technology for navigating and resolving a fragmented healthcare data environment. in this article, we describe the technical design and development of our blockchain solution, medilinker, which has been tested in simulated real-world settings.15 medilinker provides patients with a digital healthcare identification method and control over how their medical data are stored, shared, and accessed. methods use-case designs for medilinker our medilinker system design is guided by clinical use cases as specified by medical providers and residents at dell seton medical center in the university of texas (austin, texas). with medilinker, users can complete the following seven use cases: 1. initial enrollment at first clinic and creating validated credentials, 2. enrollment at second clinic with only validated digital credentials, 3. presenting or consenting personal or medical data with clinics, 4. patient changing personal information on blockchain wallet and validating the modification, 5. patient consent to participate in research projects, 6. patient removing full or partial consent with clinics with credential revocation, 7. medical power of attorney (mpoa)/digital guardianship for geriatric and pediatric patients. we initially tested the medilinker system for enrolling patients, presenting demographic or medical information to multiple clinics, editing their demographic information, consenting to participate in research projects, and revoking credentials.15 later, we added the issuance of a mpoa/guardianship for the agents of geriatric and pediatric patients. for mpoa design, we relied on the texas health & human services commission medical power of attorney designation of health care agent form.16 with medilinker, participants can manage and present credentials using their family members’ medilinker accounts from their device. medilinker also enables patients to revoke already credentialed data shared among the clinics and institutions within the network of trust. only the issuer of a credential, such as a government agency, can revoke credentials upon request from the credential holder. while revocation does not delete data from an institution, the patient’s desire to deny future usage is recorded on the blockchain and the data set is no longer verifiable in future shares. blockchain frameworks for our healthcare identity management use cases, we required a blockchain framework that can issue patient-held verifiable credentials and then share them securely with multiple institutions. we conducted a detailed environmental scan of existing and proposed blockchain solutions that could provide the appropriate technical platform for an identity management system. this process involved search in electronic databases, discussion with key informants, and review of news and blogs in blockchain-focused online resources. we selected and reviewed in detail the following two potential blockchain frameworks for the development of our identity management system: http://dx.doi.org/10.30953/bhty.v5.196 citation: blockchain in healthcare today 2022, 5: 196 http://dx.doi.org/10.30953/bhty.v5.196 3 platform for patient-centric health care using blockchain • ethereum: it is an open-source platform that uses a proof-of-work algorithm and ensures immutability. smart contracts allow credible transactions to take place without the presence of third parties. this allows interoperability between physicians, patients, and other third parties. however, the focus of ethereum is on transactions and smart contracts rather than identity management.17 • hyperledger indy: it is founded by sovrin foundation in 2018.18–20 it is a platform specifically designed for identity management with a focus on self-sovereign identity (21). users have full autonomy over their information and decide who gets access to which part of their data. it uses a decentralized ledger with a registry of decentralized identifiers (dids). it is used for retrieving and storing public dids for pairwise communication, which increases the security and privacy of identity.20,22 front-end frameworks, cloud service providers, and authentication there are many popular front-end frameworks that can be used to develop web and mobile applications. while blockchain frameworks provide the required functionality at the back-end, user interaction with the system needs to be facilitated using front-end applications. for web applications, some of the commonly used solutions include vuejs, angularjs, and reactjs. kotlin can be used to develop android applications, and swift can be used to develop ios applications. another option is to use the react native framework to develop both android and ios applications using the same code base. the medilinker application controls software running on a virtual machine (vm) in the cloud called a digital agent. the agent software takes actions on behalf of the patient like accepting connection requests and managing communications with other digital agents. servers are also required to store patient’s and clinic’s agents. cloud services can be used to create vms that can store these agents. the advantage of cloud service is that it is more scalable. there are three main global cloud providers which include google cloud platform, microsoft azure, and amazon web services (aws). for authentication, the patients can use their login credentials (i.e. username and password) to login into the application. two-factor authentication and biometric authentication are also among the possible options. team design to develop our healthcare-related blockchain solution, we formed an interdisciplinary collaboration, including medical and design researchers, blockchain subject-matter experts, developers, software engineers, and social scientists. throughout the development, physicians, medical residents, and students helped in defining the patient journey in the clinical workflow. this team also helped develop patient profiles, the most common clinical scenarios, and information needed for testing the functionality of the final product in a simulated environment. development phases medilinker was developed over the period of 2 years (2019–2021). the timeline was divided into two phases: phase 1 (2019–2020) and phase 2 (2020–2021). during phase 1, we developed a web application with the basic use cases. during the development of phase 1, hyperledger aries did not support the revocation of credentials. therefore, patients could not revoke data from the web application. however, the revocation feature became available from hyperledger aries during phase 2 development. we plan to add the revocation feature in the web application as part of future work. in the second phase, we developed a more robust ios/ android application and expanded our use cases to include legal guardianship of minors and geriatric patients. with the medilinker ios/android application, users can present and revoke patient-controlled data from a patient’s phone, while improving system access with on-device biometric authentication. participants with mpoa can act on behalf of their family members through the application. this allows users to issue and share credentials as their family members’ medilinker accounts from their device. users can also revoke previously issued credentials, by which patients can stop future verifications of their data. results six medilinker verifiable credentials: health id, insurance, medications, credit card, research consent, and mpoa medilinker is a digital wallet with six verifiable credential types: health id, insurance, medications, credit card, research consent, and mpoa (figure 1a). the data fields in medilinker are provided in table 1. these credential types were chosen based on our clinical team’s experience of the minimum requirements of information needed during clinical encounter. the health id credential includes demographic information about the patient based on a government-issued id and their contact information. the insurance credential stores information about a patient’s health insurance as specified on a patient’s insurance card. the medication’s credential includes information about patient’s medication, dosage, and covid-19 vaccination status. the credit card credential stores billing information about a patient. the research consent credential includes information about the research study and the record of the patient’s consent participation in the study. the mpoa credential includes information about the guardians and their dependent. http://dx.doi.org/10.30953/bhty.v5.196 citation: blockchain in healthcare today 2022, 5: 196 http://dx.doi.org/10.30953/bhty.v5.196 4 daniel toshio harrell et al. once these credentials are verified, a patient can share all or a subset of his or her information across multiple, preferred healthcare providers (figure 1b), providing patients autonomy and interoperability between clinics. while the patient information is stored securely within the digital wallet, only the schemas of each credential type and dids of each patient and healthcare institution are available on the blockchain, thus ensuring security and privacy of the data while allowing the transactions to occur seamlessly (figure 1c). patients can always see which data are shared with each institution. medilinker clinical and credentialing workflows medilinker enables the issuing and holding of verifiable credentials in their wallet for each type, which are issued by an “issuer” and held by a “holder.” with exception of the research consent credential, an institution’s representative, such as a clinic receptionist or registration clerk, “issues the medilinker credentials after review by the patient.” in contrast, research consent credentials are issued and reviewed by the research participant, and then held by the institution. the patients hold the issued verified credential in their digital wallet. the credentialing workflow relies on a government issued identity or third-party paper-based credentials to be digitized, stored, and shared securely through the blockchain. using medilinker, patients can establish a connection between them and a trusted institution by scanning the qr code available at the clinic. using the web application, the patient enters his or her medical data and then presents his or her government-issued identity or other physical cards to a receptionist for verification ( figure 2a). for ios application, a receptionist enters the data to create the patient’s verifiable credentials based on physical cards, which are then reviewed and approved by the patient (figure 2b). this change in workflow was implemented to avoid data entry errors by patients,15 and the inclusion of revocation into the hyperledger framework by an issuer. once approved, the receptionist can issue the credential to the patient’s wallet, which is verified and sharable digitally with participating institutions without the need for showing physical documentation (figure 2c). the same workflow is used for creating verifiable credentials for credit card from a bank and insurance cards from an insurance company. the research consent credential is created by the participant, reviewed by research institution, and then issued by the participant. with the participant as the “issuer,” they can revoke the credential without the need for asking the research institution. the mpoa credential and workflow enable geriatric or pediatric patient’s family member to create and edit credentials on their behalf through the application. a geriatric or pediatric patient in their medilinker account asks a notary organization to create and issue a mpoa credential. once shared with the guardian’s medilinker account, the guardian can switch between his or her account and his or her dependent’s accounts, by which the guardians are able to share their family member’s data from their own account. we also developed a notification • name • birthday • sex • address • email • phone • did health id clinic #1 clinic #2insurance credit card medications health id share all insurance credit card medications schema health id schema insurance schema medication schema credit card did alice did clinic #1 did research research research research institution schema research did clinic #2 ba c mpoa mpoa fig. 1. medilinker credentials and blockchain design. a) medilinker account consists of six verifiable credentials: health id, insurance, medications, credit card, research consent, and medical power of attorney (mpoa). b) a patient can share all or a desired subset of verified information to multiple institutions. c) the blockchain includes the schemas of each credential type and decentralized identifiers (dids) of patients and institutions. http://dx.doi.org/10.30953/bhty.v5.196 citation: blockchain in healthcare today 2022, 5: 196 http://dx.doi.org/10.30953/bhty.v5.196 5 platform for patient-centric health care using blockchain system to improve alertness of actions within medilinker. if a participant or institution shared or revoked a credential, the holder of the credential is alerted with a banner notification. medilinker was designed for in-person interactions before the covid-19 pandemic. however, due to covid19 lockdowns in the united states, we were able to test our system in a virtual clinic environment without any additional technical development. after clinical workflow modifications to virtual sessions, participants were able to seamlessly use the system’s features and complete the same clinical scenarios in a virtual clinical setting over zoom (zoom video communications, san jose, ca).15 as discussed below, these seamless practical and workflow adjustments indicated system’s applicability for telemedicine, virtual care, and other home care settings. medilinker technical framework blockchain framework – hyperledger indy based on the methods described above, where we evaluated two platforms and examples of previously developed blockchain-based identity systems, we selected hyperledger indy20,22 to develop medilinker. the decision was influenced by hyperledger indy’s characteristic of a decentralized identity framework, which best aligns with patient’s information protection and full autonomy over his or her data. hyperledger indy was used to store data schemas and credential definitions for provider–patient relationships based on the concept of decentralized trusted identity.12 dids are a world wide web consortium (w3c) specification that allows for a verifiable, decentralized digital identity.20,23 this allows the controller of did (e.g. the patient) to prove his or her identity without a centralized registry or identity provider, and without requiring permission from a third party. hyperledger indy makes the creation and use of dids convenient on its platform. besides adopting hyperledger indy for medilinker, we adopted hyperledger aries to act as a middleware layer (api) to connect hyperledger indy with the front-end. hyperledger aries implements a restful programming interface to handle different workflows and interactions, which helps in creating, transmitting, and storing verifiable digital credentials efficiently. the development of medilinker showed that the design of hyperledger indy and hyperledger aries allows those with no prior experience of blockchain application development to use these platforms. the auto-acceptance of credentials built into hyperledger indy makes it extremely useful to experiment with the frameworks. once the developers get experience in using the framework, automated acceptance and rejection of credentials can be replaced with programming code. front-end framework—reactjs and react native the front-end of web application was developed using a javascript framework, react js (facebook open source, menlo park, ca).24,25 it allows developers to break down the user interface into multiple components making the programming of web applications much simpler. react js also uses a “virtual document object model” that can detect which components have changed so that only the required components are rendered. this results in a faster application with a better user experience (figure 3). we also developed mobile applications for medilinker to make it accessible from mobile devices. we used the table 1. data fields in each medilinker credential: health id, medications, insurance, credit card, research consent, and mpoa credential data fields health id given name surname street address city state zip code country sex gender date of birth email phone number medications medications dosage covid-19 vaccination status insurance card patient’s name plan group provider’s name member id emergency-room charge ($) deductible ($) co-pay ($) expiry date credit card patient’s name credit card number expiry date research consent research study name participant’s consent mpoa guardian’s given name guardian’s surname guardian’s address guardian’s phone number dependent’s given name dependent’s surname dependent’s address dependent’s phone number http://dx.doi.org/10.30953/bhty.v5.196 citation: blockchain in healthcare today 2022, 5: 196 http://dx.doi.org/10.30953/bhty.v5.196 6 daniel toshio harrell et al. 1. alice enters enrolling clinic alice smith 3. alice’s wallet contains sharable credential name : alice smith sex: female blockchain credential validated connection established with clinic 2. alice enters data into and data verified by receptionist using web application alice smith a c 1. alice enters 2nd clinic with verified credential connection established with 2nd clinic 2. alice shares validated profile with new clinic 1. alice enters 2nd clinic with ios application 2. receptionist enter data based on id cards and data is verified by alice using ios application alice smith connection established by scanning qr code b blockchain credential validated dob: 01/01/2000 fig. 2. medilinker clinical workflows and two-way verification process. a) a patient, alice, enrolls at a new clinic, and enters her information into medilinker application with a web application. once submitted, a receptionist verifies her data against information available on a verified card such as a government-issued id. b) with medilinker ios application, the receptionist enters data based on the presented government-issued id, which is reviewed and verified by the patient. once verified by both patients and receptionist, a validated blockchain credential is issued, which is useable in other participating clinics. c) at a second clinic, alice can share her digitized verified healthcare information through medilinker web/mobile application. http://dx.doi.org/10.30953/bhty.v5.196 citation: blockchain in healthcare today 2022, 5: 196 http://dx.doi.org/10.30953/bhty.v5.196 7 platform for patient-centric health care using blockchain react native framework (facebook open source, menlo park, ca)26 to implement native mobile applications (android/ios) using the same code base (figure 4). this allows patients to have a more interactive experience via their mobile phones. we used the material-ui (material-ui [user interface] sas, paris, france) framework for implementing ui components.27 cloud service provider – amazon web services we expect that medilinker would work on any public cloud without much modification; however, we used aws (amazon web services, inc., seattle, wa) to host hyperledger indy servers and hyperledger aries agents. the choice of the cloud service was based on convenience because the university has a campus-wide contract with aws, so it was practical to get an account. aws is a leading public cloud provider and is health insurance portability and accountability act (hipaa) compliant, which is required for future adoption in a clinical setting. a vm is required to host each patient’s agent. aws allows us to create and run vms with a customized schedule of when the vms should be running. this helps save cost during development because the developer can shut down the vms when they are not needed. hosting vms also allows the ability to scale the system to n patients and m clinics. in the experiments, we successfully scaled the system from 1 patient to 20 patients. this keeps each of the patients’ agents separate from each other. if one server or vm is compromised, the rest of the agents are isolated and secured (figure 5). this solution worked for our scenario because it was tested using 20 patients’ agents. this technique may not be scalable in a setting where there are many patients. in such a scenario, docker containers28 hosted on a single vm are recommended. for the web application, no installation at clinic or patient’s side is required. for the mobile application, the application was provided to study participants using the apple testflight application. for adoption in a clinical setting, the application can be distributed via google play store and apple store. daniel harrell 1234 demo daniel@email.com 512-123-4567 austin texas 78735 usa m male 01/01/1980 asian 5’ 8” fig. 3. medilinker web application ui (user interface). medilinker is a web application by which patients and receptionist can interact with their medilinker agent. the web application was developed in react js. http://dx.doi.org/10.30953/bhty.v5.196 citation: blockchain in healthcare today 2022, 5: 196 http://dx.doi.org/10.30953/bhty.v5.196 8 daniel toshio harrell et al. authentication for the web application, we developed medilinker’s authentication system that requires users to enter a username and password to log into their accounts. biometric authentication is not a feasible option for web applications because these applications are mostly accessed via personal computers, and to enable biometric authentication, patients should have a fingerprint scanner or similar devices attached to their computers, which is not common. moreover, one of the goals of developing medilinker was to allow people experiencing homelessness to manage their identity. it is common that many people experiencing homelessness do not have smartphones.29, 30 for the native mobile applications, the patients are provided with the option to login using their phone’s native biometric authentication (fingerprints for android devices and touchid/faceid for ios devices). discussion in this article, we have described the design and development of a blockchain-based, patient-centric healthcare identity and research consent management application, medilinker. the unique features of this system promise to add features that are lacking in current management of personal health information and might take many years to achieve. this includes interoperability among diverse information systems, patients’ control over their own data, and ability to prove identity without having to carry physical evidence at each interaction within the system. some of the following aspects are of particular importance: medilinker healthcare identity use for health information exchange as mentioned earlier, fragmentation of clinical data and lack of interoperability among health information systems create safety concerns for patients and result in inefficiencies in the delivery of care. health information exchanges (hies) are platforms that are developed to connect disparate health information systems and emrs through a central hub. while hies may provide an alternative solution to the interoperability issue in the united states, in which patient emrs are sharable through a centralized data repository,31 the patient data are still controlled by the institutions rather fig. 4. medilinker mobile ios application ui. patients and receptionist can also interact with their mobile devices. the medilinker mobile application was developed in react native, which can be used on ios and android devices. http://dx.doi.org/10.30953/bhty.v5.196 citation: blockchain in healthcare today 2022, 5: 196 http://dx.doi.org/10.30953/bhty.v5.196 9 platform for patient-centric health care using blockchain than the patient.32 a central data repository has its advantages but also provides a single point of breach or failure, thus increasing the threat to patient privacy and data security. blockchain technologies are a decentralized alternative9,11,33,34 that leverage their distributed architecture model towards establishing a patient-centric and patient-controlled sharing of medical records across isolated institutions preserving the continuity of care. as designed, medilinker provides the health id credentialing, verification, and management to bridge a fragmented system with patients as the focus. furthermore, the credentialed digitized medilinker id can provide a common patient identifier by which connections in a trust network between healthcare providers, insurance companies, and patients can be established toward minimizing patient-matching issues. aws cloud region public subnet public subnet hyperledger aries agents patient agents clinic agents clinic agents patient agents hyperledger indy cluster indy node 1 indy node 2 indy node 4indy node 3 medilinker clinic application medilinker patient application fig. 5. medilinker system architecture. front-end web application is developed using reactjs, a javascript framework. frontend mobile application is developed using react native. the back-end layer consists of a cluster of hyperledger indy nodes. the front-end layer is connected with the back-end layer using an intermediate layer, which consists of hyperledger aries agents (patients and clinics agents). a virtual machine (vm) is required to host each patient’s agent. each of the patients’ agents is separate from each other. if one server or vm is compromised, the rest of the agents are isolated and secured. http://dx.doi.org/10.30953/bhty.v5.196 citation: blockchain in healthcare today 2022, 5: 196 http://dx.doi.org/10.30953/bhty.v5.196 10 daniel toshio harrell et al. medilinker usage as identity management for vulnerable populations blockchain technologies can help create a transactional identity for vulnerable populations in need of social and health care, such as persons experiencing homelessness.35,36 often individuals lose or misplace their government-issued identifications and medical paperwork, which are needed to interact with social services and the medical system. beyond medilinker’s initial objective, our users can digitize and share digital versions of patient-held id cards. with this digitization of physical id, medilinker enables patient populations to hold a secure electronic health id that is easily managed and recoverable on smartphones or computers toward improving their healthcare access.15 medilinker adaptation to virtual clinical workflows and vaccination status management during the covid-19 pandemic although designed for in-person clinical visits, medilinker was also tested during covid-19 lockdowns in the united states in 2020. the system worked successfully in virtual clinic visits, allowing patients to use the system to register and provide their information and consent. given the need for social distancing in a covid-19 world, the ability to share verified identifiers and medical records for a contactless and virtual format is a major advantage of using blockchain-distributed ledger systems in future clinical workflows. furthermore, medilinker is designed to track patient’s current medications and dose for sharing with healthcare providers. medilinker application can be used to digitally track individual vaccination doses, as a covid-19 passport, that is verifiable.37 using a zero-knowledge proof, a person’s vaccination record can be shared to clinics and institutions without presenting other personal information. furthermore, medilinker could provide a verified and digital record of the covid-19 vaccination record card and serve as an immunity passport for future travel. limitation of current work and future research the medilinker system was developed to understand how to design and demonstrate the use of dids and verifiable credentials in patient identity management while allowing reasonable room for future improvements. the fields in medilinker were free text to explore all human interactions and errors. in the future, we plan to add validation checks to fields, which will allow patients to enter data only in a specified format. while the current implementation is approaching a minimum viable product (mvp), our team desires to transition medilinker to an operational product in clinical settings. in the next step, we plan to integrate medilinker with emr systems to manage highly sensitive medical records. furthermore, this trust network between participants and institutions must include a means of ensuring patient privacy as well as verification of the patient’s presence during digital encounters, something described as a liveness test. we plan to continue to develop our electronic decentralized identity management system (medilinker) toward becoming operational in a real-world healthcare setting. conclusions medilinker is a blockchain solution for identity management designed to allow patient autonomy and interoperability between clinics using a custom-built web and mobile application. the technical design and development of medilinker show how hyperledger indy combined with hyperledger aries can be used to develop an operational identity management system for patients. it allows patients to securely log in, verify their credentials, and share those credentials from their blockchain wallets to other organizational entities on the blockchain. our technical design allows patients to have control over their identity data by using the dids functionality of the underlying blockchain platform. we have shown that the technical architecture adopted for medilinker demonstrated a proof-of-concept patient-centric identity management system that can be operationalized and scaled for future implementation in healthcare settings and provide patients the privacy and control desired for personalized health data. conflicts of interest the authors declare no conflicts of interest. funding this study was partially funded by the university of texas-blockchain initiative, and the authors acknowledge its support for this work. john robert bautista acknowledges the support of the bullard and boyvey fellowship awarded to him by the school of information, the university of texas at austin. the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. contributors as per the “role of authors and contributors” outlined by icmje, each author participated in the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; and drafting the work or revising it critically for important intellectual content; and final approval of the version to be published; and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. 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2020;8(9):e20477. doi: 10.2196/20477 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/ licenses/by-nc/4.0. http://dx.doi.org/10.30953/bhty.v5.196 https://dx.doi.org/10.1109/msp.2018.3111247 https://dx.doi.org/10.2196/10654 https://dx.doi.org/10.2196/16887 https://dx.doi.org/10.2196/20477 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) editorial/discussion blockchain’s transformative potential in healthcare geetika jain, postdoc, phd1 , nishant kumar, phd2 , and colin rigby, phd3 1digital transformation, keele business school, keele university; 2department of business, christ university, bangalore, india; 3entrprise lead, keele business school, keele university corresponding author: geetika jain, email: g.jain@keele.ac.uk; geetikajain02@gmail.com doi: https://doi.org/10.30953/bhty.v7.336 keywords: blockchain for electronic health records, blockchain implementation, blockchain performance blockchain technology, electronic health records, healthcare data exchange, smart contracts abstract blockchain’s transformative potential, its current applications, and the path forward for its integration into the healthcare ecosystem are all explored in the journal blockchain in healthcare today. the healthcare industry is facing significant challenges and opportunities after covid-19. as we navigate the complexities of increasing healthcare costs and technological updates for better patient outcomes, innovative technologies are emerging as pivotal tools for healthcare transformation. healthcare digital platforms have witnessed revolutionizing the dynamics of healthcare systems using disruptive technologies. however, while these technologies have garnered extensive attention for their transformative potential, there remains a critical gap in our understanding of the impact of digital technology on the healthcare industry. population health management has critical challenges in data protection, sharing, and interoperability, where personalized medicines and wearable devices are highlighted as a concern. patients and medical personnel need a safe and simple way to record, transmit, or access information through networks without concern for their safety. using blockchain technology can help address these problems. blockchain technology enhances medical data security by providing a decentralized, immutable ledger that ensures data integrity, transparency, and privacy. it enables fine-grained access control, improves interoperability, and resists cyber-attacks. streamlining regulatory compliance allows patients and medical personnel to safely record, transmit, and access sensitive information across networks. submitted: july 17, 2024; accepted: august 13, 2024; published: august 31, 2024 among emerging technologies, blockchain technology stands out, offering significant potential to revolutionize various aspects of healthcare. blockchain in healthcare has been classified as patient-based and entity-based applications. the patient-focused approach aims to provide authorized entities access to patient data and manage prescription history while upholding data privacy and security. from an entity’s perspective, accurate data management is crucial for avoiding patient misidentification, preventing duplicate medical records, and ensuring data provenance.1 in addition, traditional healthcare systems have traditionally lagged in terms of innovation and growth potential. blockchain is not limited to financial solutions related to data handling, data exchange, and patient data. here are several future opportunities that are popular and audacious regarding the actual application of blockchain: patient-oriented data management structures, smart contracts that have been integrated with trials, and blockchain for genomic data. in as much as blockchain has been proposed and implemented in various aspects of healthcare delivery, this technology is also associated with many challenges, including scalability issues, regulatory issues, and widespread issuances, which are critical barriers that need to be met. progress requires the collective work of healthcare professionals, technology developers, and agencies regulating the healthcare industry to come up with the formats for implementing blockchain technology. these partnerships can help ensure blockchain technology is effectively integrated into healthcare systems and workflows. furthermore, a collaborative process between industry and regulators is necessary to establish regulatory requirements that are operationally and commercially viable for the use of blockchain technology in healthcare.2 this collaboration can help address any regulatory challenges and blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-2733-3767 https://orcid.org/0000-0003-0124-9337 https://orcid.org/0000-0002-8740-9663 mailto:g.jain@keele.ac.uk mailto:geetikajain02@gmail.com https://doi.org/10.30953/bhty.v7.336 citation: blockchain in healthcare today 2024, 7: 336 https://doi.org/10.30953/bhty.v7.3362 (page number not for citation purpose) g. jain et al. ensure that blockchain technology is implemented in a way that complies with existing regulations. collaborative efforts between healthcare providers, technology developers, and regulatory bodies are crucial for successfully implementing blockchain technology in healthcare. by working together, these stakeholders can overcome data security, privacy, and regulatory compliance challenges and unlock the full potential of blockchain technology to transform the healthcare industry. the complexity of healthcare processes and ongoing concerns about patient records give a clear indication of the lack of standardized processes and also the ability to create a unified approach. the integration of the concepts is expected to improve data security and the organization of healthcare services. thus, to establish full-fledged authorized guidelines for the administration and cyclical prevention of diseases, there is currently a focus on a series of comprehensive measures that need to be adapted and implemented in the global network of healthcare facilities based on modernizing the integrated internet of things (iot) networks by using blockchain technology.3 it is, however, important in this process to standardize these protocols so that there can be development of formats for a consistent approach to the processing of this information and programming of various systems.4 in general, the commented approach to the utilization of blockchain technology in the healthcare industry carries a significant potential to enhance the corresponding processes and provide high protection of data. nonetheless, it is crucial to establish the standard techniques for the development of health information exchange and integration models.5 it may be effective to coordinate with others when it comes to different aspects of the health care procedures, and by using blockchain technology, new standards and processes can be created for developing different solutions better suited to patient centricity. the recent traction of blockchain solutions related to the tokenization of healthcare records has created an economic business model where the incentives mechanism could be placed to make healthcare services more approachable and friendly. despite technological advances, there is a great knowledge gap, and bridging this gap by having the consultation of different stakeholders holistically. while technology can bring solutions to improve the efficiency and effectiveness of each process, regulatory bodies must come forward to develop clear guidelines on technology intervention—a highly crucial aspect. for blockchain to truly thrive in healthcare, it is crucial that healthcare professionals, developers, and regulators come together. healthcare professionals understand the real challenges and privacy concerns, guiding the development of solutions that matter most. developers bring the technical know-how to create secure, efficient systems. regulators ensure these innovations are safe, legal, and trustworthy, safeguarding patient data and public confidence. by collaborating, these groups can build a strong blockchain framework that not only enhances data security and communication but also fosters innovation in healthcare, leading to better outcomes for patients and the industry as a whole. moreover, ongoing research related to ethical aspects is necessary to refine blockchain applications and demonstrate their value at scale. investment in education and technological innovation could bring exponential growth to the healthcare industry. by fostering a collaborative approach, investing in research, and addressing the highlighted challenges could pave the way for futuristic healthcare solutions. while the potential of blockchain technology is promising in healthcare, the integration of blockchain technology in healthcare processes is still being done by understanding every aspect. the healthcare industry is highly patient-centric, where better health outcomes and satisfaction are of utmost priority. funding there is no funding attached to this article. conflicts of interest geetika jain is a member of the bhty editorial board; colin rigby is a bhty peer reviewer. nishant kumar reports no conflict of interest. contributors geetika jain, nishant kumar, and colin rigby participated in all phases of the article’s development and revisions. data availability statement (das), data sharing, reproducibility, and data repositories data are not attached to this submission. application of ai-generated text or related technology there was no use of application of ai-generated text or related technology. acknowledgments we are highly thankful for the bhty editorial team. references 1. treiblmaier h, rejeb a, gault m, et al. harnessing blockchain to transform healthcare data management: a comprehensive research agenda. blockchain healthc today. 2024;7. available from: https://blockchainhealthcaretoday.com/index.php/journal/ article/view/301 2. krishnasamy s, gopalakrishnan bn. moving beyond proof of concept and pilots to mainstream: discovery and lessons https://doi.org/10.30953/bhty.v7.336 https://blockchainhealthcaretoday.com/index.php/journal/article/view/301 https://blockchainhealthcaretoday.com/index.php/journal/article/view/301 citation: blockchain in healthcare today 2024, 7: 336 https://doi.org/10.30953/bhty.v7.336 3 (page number not for citation purpose) blockchain’s transformative potential from a reference framework and implementation. blockchain healthc today. 2023;6. available from: https://blockchainhealthcaretoday.com/index.php/journal/article/view/280 3. kharche a, badholia s, upadhyay rk. implementation of blockchain technology in integrated iot networks for constructing scalable its systems in india. blockchain res appl. 2024 jan 6:100188. available from: https://www.sciencedirect.com/ science/article/pii/s2096720924000010 4. world health organization. global strategy on digital health 2020–2025. 2020 [cited 2022 nov 12]. available from: https://www. who. int/docs/default-source/documents/gs4dhdaa2a9f352b0445bafbc79ca799dce4d. pdf 5. anon. reimagining health information exchange in india using blockchain [internet]. pwc. 2019. available from: https://www. pwc.com/gx/en/healthcare/pdf/reimagining-health-information-exchange-in-india-using-blockchain.pdf copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is noncommercial. see http://creativecommons.org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.336 https://blockchainhealthcaretoday.com/index.php/journal/article/view/280 https://blockchainhealthcaretoday.com/index.php/journal/article/view/280 https://www.sciencedirect.com/science/article/pii/s2096720924000010 https://www.sciencedirect.com/science/article/pii/s2096720924000010 https://www.pwc.com/gx/en/healthcare/pdf/reimagining-health-information-exchange-in-india-using-blockchain.pdf https://www.pwc.com/gx/en/healthcare/pdf/reimagining-health-information-exchange-in-india-using-blockchain.pdf https://www.pwc.com/gx/en/healthcare/pdf/reimagining-health-information-exchange-in-india-using-blockchain.pdf http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) blockchain in healthcare today 2021. © 2021 the authors. this is an open access article distributed under the terms of the creative commons attribution-noncommercial 4.0 international license (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. citation: blockchain in healthcare today 2021, 4: 161 http://dx.doi.org/10.30953/bhty.v4.161 research article: proof of concept the case for establishing a blockchain research and development program at an academic medical center muhammad usman1, verena kallhoff 2 and anjum khurshid2* 1department of electrical and computer engineering, university of texas at austin, austin, tx, usa; 2dell medical school, university of texas at austin, austin, tx, usa abstract objective: to develop a research and development program to study factors that will support research, education and innovation using blockchain technology for health in an effective and sustainable manner. we proposed to conduct qualitative research to generate insights for developing a market strategy to build a research lab for the promotion of blockchain technologies in health in academic environments. the team aimed to identify the key barriers and opportunities for developing a sustainable research lab that generates research, education, and application of blockchain in healthcare at an academic medical institution and test those strategies in a real-world scenario. methods: the research team identified potential customers and stakeholders through interviews and snowball sampling. the team conducted semi-structured interviews with 4 faculty researchers, 10 industry leaders, and 6 students from a variety of disciplines and organizations. the findings of these research activities informed our understanding of the needs of stratified customers and helped identify key assets and activities the lab will have to offer to meet those needs. results: the research insights from data analysis were used to build the business model for establishing a blockchain in health impact lab. this systematic study of areas where blockchain technology can impact health will guide the future development of research agenda for the researchers on campus. conclusion: based on our learnings, we hope to design a blockchain in health impact lab to serve as a platform for students and faculty to come together with industry partners and explore current challenges of blockchain in healthcare. the academic medical center’s partnership with other healthcare providers will help create real-world opportunities to demonstrate and implement new technologies. keywords: blockchain; health; academic medical center; research lab; market strategy received: 9 december 2020; accepted: 19 january 2021; revised: 26 january 2021; published: 9 march 2021 the covid-19 crisis has created new challenges and underscored old ones, including the need for better information management systems. in healthcare, the pandemic has amplified the need for secure, timely, and accessible healthcare data—both for better patient care and effective public health policy. blockchain promises to transform the way people interact with the health system—and their own health data. handled intelligently, blockchain can make healthcare safer, accessible, secure, and inclusive (1–7). the technology is particularly well suited for emergencies like covid-19 because it allows us to share information while preserving individual privacy (8–12). blockchain-based information management offers efficient access to patient information to allow more informed treatment decisions, better collaboration between providers, and accelerated insights into emerging pandemics (13–15). but while we have identified myriad uses for blockchain in healthcare, there is still much to explore surrounding applications, logistics, ethics, and more. fulfilling the highest potential of this transformative technology requires collaborative, impartial leadership—a think tank with comprehensive resources. deciding where such a think tank resides is of utmost importance. as one of the nation’s technology hubs, austin is home to both startups and tech giants working in blockchain (16). it is also home to one of the nation’s great research institutions, the university of texas at austin, and the first new medical school at a top-tier university in half *correspondence: anjum khurshid. email: anjum.khurshid@austin.utexas.edu https://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.30953/bhty.v4.161 mailto:anjum.khurshid@austin.utexas.edu citation: blockchain in healthcare today 2021, 4: 161 http://dx.doi.org/10.30953/bhty.v4.1612 (page number not for citation purpose) muhammad usman et al. a century—dell medical school (dms). along with the school’s commitment to innovation in healthcare and value-based care, this convergence of resources positions dms as an ideal partner in exploring how blockchain technology can revolutionize healthcare. the school has already shown leadership in using blockchain through mypass, a joint initiative with the city of austin and austin-travis county emergency medical services (ems) to give people experiencing homelessness secure, durable access to their health information (17). anecdotally, the team had received insights from leaders in healthcare that the high risks involved in testing new technology, combined with the lack of reviews and case studies from impartial organizations, lead to reluctance of testing blockchain solutions in the clinical setting, especially involving patient data. this prompted the team to set out to determine if and how a collaborative lab located at an academic medical center could serve as a convener and subject matter expert to identify opportunities, conduct research, and test solutions in a collaborative environment. we developed a research and development program to study factors that will support research, education, and innovation using blockchain technology for health in an effective and sustainable manner. the research focused on qualitative methods to collect data that helped identify key barriers and opportunities for developing a sustainable research lab that generates research, education, and application of blockchain in healthcare at an academic medical institution and tests those strategies in a real-world scenario. research on blockchain in health at the university of texas at austin blockchain technology is being used in a variety of industries but its use in healthcare is limited. the covid-19 crisis has made the need for better technology in health evident (15), yet even universities, including academic medical centers, are barely engaged in research on blockchain in health. dms at the university of texas at austin was involved in the launch of the mypass initiative led by the city of austin. as part of this initiative, a digital wallet, containing social security number and other important records, is created for individuals experiencing homelessness. upon validation by a notary, typically a social worker, service providers can access the information required for homelessness services (17). other projects include a demonstration project at dms for patient identity management called medilinker and an analysis of peer-to-peer systems in terms of network scalability done at the university of texas at austin. dms also partnered with the austin blockchain collective to form a health workgroup to promote academia–industry partnership (18). austin blockchain collective is a group of over 100 companies hoping to make austin a global hub for blockchain. the collective seeks to bridge the gap between academia and industry partners with collaborators like the city of austin and the university of texas, as well as provide education on blockchain technology (19). current research on the use of blockchain technology in health at the university mainly relies on individual faculty or student initiatives and remains disjointed. due to the multidisciplinary nature of this research and the potential impact of this technology on the multi-trillion-dollar health industry, it seems imperative that the community of researchers and students interested in this area have a place to coordinate and promote educational, research, and development activities focused on these new technologies in health. this led to the exploration of how best to set up a blockchain in health impact lab (bhil) on campus. methodology our multidisciplinary team the planning and management of this research was a collaboration between the texas health colab and the division of health information and data analytic sciences (hidas) at dms with additional participants from a wide variety of colleges, schools, and departments across dms and ut austin. as a part of this study, we also engaged two ut austin students. these students conducted literature reviews on previous blockchain-related publications at ut austin and researched on blockchain hubs around the globe. customer interviews the research team identified potential customers and stakeholders through interviews and snowball sampling. the team interviewed 4 faculty members, 10 industry leaders, and 6 students from a variety of disciplines and organizations. the findings of these research activities were documented to build our understanding of the needs of stratified customers and to identify key assets and activities a blockchain lab will have to offer to meet those needs. the research insights from data analysis were used to build the business model set forth below. this systematic study of areas where blockchain technology can impact health will guide future development of research agenda for the researchers on campus. in general, we asked five research questions from our customers. • rq1: what are the gaps in the research and development of blockchain technology applications in healthcare? • rq2: which specific problems can blockchain solve in healthcare? • rq3: who are our customers? what are their needs? • rq4: how do we provide value? what products do we provide to satisfy our customers’ needs? • rq5: what will bhil look like? how are we different? http://dx.doi.org/10.30953/bhty.v4.161 citation: blockchain in healthcare today 2021, 4: 161 http://dx.doi.org/10.30953/bhty.v4.161 3 (page number not for citation purpose) blockchain research and development program the team used qualitative research methods to gain a deep understanding of the potential customers and their needs. analysis of the interview responses and the learnings from lectures were used to develop a business model for the lab. the business model addresses the most relevant areas of blockchain research identified by stakeholders, outreach, and engagement strategy for researchers and industry partners in an academic medical center setting. the business model is a generalizable research product which serves as a template for other university-based groups to include a draft charter and marketing materials. at the conclusion of the stakeholder interviews, the team of researchers reviewed the recorded conversations and assembled an in-depth understanding of the responses. using an adapted version of the business model canvas (20), the team built an understanding of potential ‘customers’ and their needs, the value proposition of a blockchain lab at a premier academic medical and research institution, key activities, key resources, key partners, and stakeholders. the team also developed a feasible cost structure for a sustainable resource providing unbiased insights and support for the use of blockchain technology in healthcare. results below we report our findings from semi-structured interviews in the form of opinions expressed by our study participants. we also identify sources where some of these opinions have been expressed by others in published literature. rq1: what are the gaps in the research and development of blockchain technology applications in healthcare? blockchain is a promising technology for healthcare but to contribute effectively to its widespread adoption or meaningful evaluation we started off by identifying the current concerns, doubts, and unknowns about this technology in health research and practice. based on our key informant interviews, this section explores the gaps in the research and development of blockchain technology applications in health. blockchain applications are at an infancy blockchain is a complex technology and most medical professionals lack technical knowledge about blockchain (21). they generally consider blockchain to be related to cryptocurrency. due to this, they do not consider other potential applications of blockchain. there is a need to spread awareness of the ability of blockchain to solve some technological limitations in healthcare’s data and technology. another challenge is that blockchain technology is relatively new and developers need more time developing solutions, while giving less time to user interface/user experience (ui/ux) development for end-user satisfaction. consumers prefer applications with better usability designs, and current ui/ux for blockchain applications is not of a high standard (22). limited blockchain r&d another challenge in the adoption of blockchain applications in health is the type of applications that can be developed using blockchain. certain applications are suitable for blockchain, while for others, blockchain is not necessarily the best option (23). it is important to differentiate between these two sets of applications. however, in its early days, developers and researchers have tried to advertise blockchain technology for every application, even when it was not the most suitable approach. the result is customer distrust. blockchain research is still limited in academia. only a limited number of universities are doing research on blockchain, and even those universities are working on problems which do not directly benefit the industry. big hospitals are buying smaller hospitals, and this is increasing interoperability issues (24). in some cases, one hospital chain can simultaneously have several different electronic medical records (emrs), legacy systems from smaller hospitals that merged, a physician organization, and potentially specialty clinics. the burden caused by the lack of interoperability is great, yet hospital leadership is weary of the uncertainty surrounding the implementation of a novel system and instead prefer to undergo the equally pain-staking process of migrating all systems to one emr, if possible, because of more predictable outcomes. legal and regulatory issues there are a lot of regulatory issues in the health industry. medical data are highly confidential, and legal concerns need to be addressed before progress can be made in blockchain for health (25). because of regulatory constraints, healthcare providers are hesitant to relinquish ownership of health data and insist on centralized authority over patient data rather than try a decentralized solution. blockchain is not meant to store large amounts of data, yet many healthcare data files, such as imaging outputs, are very large. also, lots of blockchain networks are coming into the market, and without industry standardization, it is possible and likely that blockchain interoperability issues will arise in the future. having seen the challenges of fragmentation with the electronic medical record systems, which the industry is still struggling to solve, there is an understandable reluctance to adopt another technology that may have the same issues of proprietary platforms. rq2: which specific problems can blockchain solve in healthcare? blockchain is a suitable tool to address only specific problems, and identifying these problems in healthcare is an important task to accomplish. our interviews with http://dx.doi.org/10.30953/bhty.v4.161 citation: blockchain in healthcare today 2021, 4: 161 http://dx.doi.org/10.30953/bhty.v4.1614 (page number not for citation purpose) muhammad usman et al. experts elucidated some of the problems in health that can be solved using blockchain technology. supply chain management blockchain is commonly used in supply chain logistics outside of healthcare already allowing for an immutable tracking of items along the path from production to end consumer (26–30). likewise, blockchain could keep track of medical samples, vaccinations, and medicines. a key informant, a pathologist, emphasized that a unified way to track tissue samples and associated diagnoses would be beneficial to reference labs. in addition, clinics, transportation companies, and labs could benefit from interoperable software. tracking credentials/licenses credentialing of doctors or nurses currently takes many months and is a highly manual task (31, 32). if the education and practice history of physicians were on the blockchain, providers could verify that a health professional holds valid credentials prior to hiring, eliminating delays and frustration currently experienced. such credentialing is very similar to the use of educational degrees and diplomas to be verified by academic institutions. tracking personal medical data genomic data from the tissue samples are not tracked, and therefore, patients cannot benefit when these data are used by pharmaceutical companies for research. if the record of each sample is stored on the blockchain, then patients will be able to control their data and benefit in monetary terms when a pharmaceutical company wants to use their data for research purposes. similarly, the digital identity of people experiencing homelessness can be implemented using blockchain (33). blockchain can also be used to store links to all the medical records of an individual, even if the records are stored in multiple different locations. the patients can also exercise more control over their medical records and their ability to track their own records (34). interoperability blockchain technology has been used to support internet of things (iots) allowing for identification and coordination among computers and electronic equipment. the internet of medical things can also use the immutability and auditability features of blockchain as one of the possible solutions for interoperability (35). research blockchain can be used to store and catalog medical research so that every research work can be audited to confirm its validity (36–38). research papers can be peer reviewed using blockchain leading to more visibility in research. other health applications blockchain will also be helpful when a patient changes his/her insurance (39). the previous insurance company can share the keys with the new insurance company. this will greatly improve interoperability. blockchain can also be used for payroll management and medical billing (40). prescriptions can be managed using blockchain (41). pharmacies can verify that the prescriptions are valid and issued by an accredited doctor. drug trials can be recorded on the blockchain to speed up the procedure for fda approval (42). health insurance settlements can take weeks or even months to complete with the current technology. however, with blockchain, it can be achieved within a day (39). rq3: who are our customers? what are their needs? based on the interviews conducted between april 2020 and july 2020, it became clear that there are diverse groups who could benefit from engaging with an academic consortium focused on blockchain technology and based at an academic medical center. of course, all interviewees mentioned the complexity of the health industry with its broad range of providers, practitioners, payers, and other stakeholders. these groups are interested in more efficient and secure tools to improve their enterprises, yet they are also wary of novel technologies and the validity of the promises its proponents make. hence, access to unbiased information and education on the realistic goals of blockchain technology in health are of utmost importance.  another group that was regularly mentioned was the technology industry. also consisting of a wide variety of stakeholders, their primary interests are opportunities to test and develop better tools as well as access to key opinion leaders and opportunities to network and learn from leading voices in the industry. networking with each other, research, and opportunities to engage their future workforce are also very high on the list for these stakeholders. as blockchain technology faces many hurdles in acceptance, industry is keen on developing a shared voice to provide unbiased information to potential users.  a third group frequently mentioned were legislators and regulators, as well as public health officials and other governmental bodies. these stakeholders are keenly aware of the challenges of the us health system and have been hearing promises from technologists for many years. before enabling, recommending, or implementing sweeping changes, much work must be done to perform in-depth research to truly understand the diverse implications of enabling a new technology to provide tools with much broader functionality. these stakeholders are in dire need of unbiased information obtained through collaborative http://dx.doi.org/10.30953/bhty.v4.161 citation: blockchain in healthcare today 2021, 4: 161 http://dx.doi.org/10.30953/bhty.v4.161 5 (page number not for citation purpose) blockchain research and development program research, case studies that allow for a broader understanding of implications, and access to key opinion leaders that can provide insights and guidance. this group of clients is also in need of more efficient and secure tools, but they must come with the confidence that accompanies thorough unbiased testing. in addition, this group is an important stakeholder in that it can unlock and enable the use of blockchain technology through some of the required regulatory changes. again, the confidence for regulatory changes will come through unbiased information and education that is best obtained through an academic research enterprise.  another important external client to an academically based blockchain in health impact lab (bhil) comprises the patients, patient advocates, and privacy groups. this group has the potential of gaining substantial independence and improvement in their interaction with the health industry by using blockchain technology. access to personalized medicine, the ability to set permissions for use of their personal health information, promises more coordinated and personalized care while decreasing risks for medical errors and omissions due to incomplete or missing data (34, 43). yet, patients, patient advocates, and privacy groups are also rightfully concerned that sweeping changes in underlying technologies may impact the privacy and security of health data, benefitting only the larger corporations with little recourse and opportunity for them (44). as in prior instances, access to unbiased education and information that feeds into regulation and better-informed public opinion is crucial to gain the trust and support from this group of clients and enable them to reap the benefits of this technology. a crucial internal client for the bhil is the on-campus community comprising faculty and student researchers. eager to understand the strengths and weaknesses of the technology in the health setting more deeply, this group is looking for opportunities to test and develop better technological tools, build case studies, and engage in research around deeper implications of the technology. the ability to engage with other professionals in this field provides opportunities to understand the broader implications of their research work, thus enabling more refined case studies and technologies. in addition, professional events, sponsored research projects, and educational opportunities allow trainees to network with potential future employers as well. some of the above observations and comments from experts are summarized in the table 1. rq4: how do we provide value? what products do we provide to satisfy our customers’ needs? the major needs that we identified from the key informant interviews were applied research, new tools, education, and an unbiased source of information on blockchain technology. research and development different research groups are working on various health blockchain projects. the results are published in various conferences and symposiums (45). however, there is no unified repository which combines all the health blockchain-related research. bhil can help create such a repository and determine what are the lessons learned from each project. there is a need to engage with politicians and decision-makers to shape public policy because new rules need to be made so that pharmacies and healthcare providers can be encouraged to implement well-tested and impactful blockchain solutions. awareness campaign and research projects bhil can start an awareness campaign to educate people and funding organizations about the advantages of blockchain. there is also a misunderstanding that one blockchain framework can be used to support all types of applications (23). the lab can help to educate about different blockchain platforms that may be required to implement different applications. blockchain in health is assumed to solve the interoperability problem. however, a detailed study needs to be done to see if blockchain systems of two different companies can work together. the table 1. stakeholder needs for blockchain in health research customer needs health systems, providers and payers better, more efficient, and secure tools; access to unbiased information and education industry opportunities to test and develop better tools; access to key opinion leaders; access to ideas, collaboration to develop better, more efficient, and secure tools; future workforce legislators, government, regulators, public health officials better, more efficient, and secure tools; access to unbiased information and education patients, patient advocates, privacy groups better, more efficient, and personalized care and access; access to unbiased information and education faculty and student researchers opportunities to research, test, and develop better tools; education; future career opportunities http://dx.doi.org/10.30953/bhty.v4.161 citation: blockchain in healthcare today 2021, 4: 161 http://dx.doi.org/10.30953/bhty.v4.1616 (page number not for citation purpose) muhammad usman et al. academic knowledge needs to be published, and the lab can arrange regular conferences in collaboration with research experts. it can bridge the gap between industry and academia by asking companies for relevant projects which can be completed by students, and students in return can be financially supported. this way, students will work on projects which will be used in real settings. the lab can arrange happy hours between industry and researchers. the lab can also provide opportunities to companies to participate in academic and translational research in blockchain. research conferences and consortiums bhil can invite panels to discuss current market developments in blockchain. bhil can also bridge the gap between stakeholders by including research groups and companies from the austin blockchain collective. bhil can bring technology companies, researchers, students, and healthcare providers to the table and discuss blockchain use cases in health. it can act as a platform for experts interested in blockchain applications and technology.  given the feedback from our interviewees, dms is launching the bhil as a collaboration between the school’s division of hidas and its product innovation initiative, the texas health colab. the bhil will serve as a platform for educational seminars and panel discussions addressing clinical, industry, and community-based stakeholders. it will highlight the power of technology to solve some of our most pressing problems and gather experts for workshops to explore challenges, solutions, ethical concerns, regulations, and policies. bhil will endeavor to become a preeminent resource for unbiased information on the use of blockchain technology in healthcare. further, bhil will bring together students and faculty in a variety of disciplines to come together with industry partners to explore current challenges of blockchain in healthcare, identify use cases, develop, implement, test, validate, and partner to disseminate promising solutions. the school’s partnership with other healthcare providers will help create real-world opportunities to demonstrate and implement new technologies. bhil will become a resource and partner for both public and private organizations as the blockchain community advances this emerging field and develops future leaders in the field of blockchain in health. the lab will provide access to experts in ethics, legal, computer science, clinical care, information sciences, finance, and more from all over campus as well as a platform to build and test new applications in health. bhil is envisioned to be an unbiased and collaborative platform to advance the use of blockchain technology for the public good. it is essential that the core activities of the lab have impartial funding. hence, the leadership team will be working to obtain funds from philanthropic sources or through local and federal grants. while open to collaborating with specific partners on projects in areas such as research, innovation, problem definition, or commercialization of particular interest to the partner, the essence of bhil is to become a preeminent, trusted resource for all things blockchain and healthcare.  the research team analyzed the key resources available to further the mission to become a lab created to bridge the gap between academia and industry, different industry sectors and research groups, relying on ut austin’s immense research faculty with diverse research backgrounds and its talented students. the dms is working with faculty from the college of computer science, the ischool, mccombs business school, the lbj school of public affairs, and many others. promoting and supporting the vision of ut austin: ‘what starts here changes the world’ is embedded in the work of the bhil. table 2 summarizes some of our findings in this section. rq5: what will bhil look like? how are we different? the key activities of bhil fall into three main categories: applied research, applied innovation, and education (fig. 1). each category has distinct and major activities but as part of the collaborative bhil, all integrate and overlap to ensure that projects and ideas can seamlessly move from one into the other. work done during thought leadership conversations may lead to a case study led by the applied research team, and a promising project has the opportunity for a seamless hand-off to the applied innovation team for acceleration into a commercial product. at the same time, a problem identified by a diverse set of stakeholders can move from painstorming to applied research and subsequently be presented and discussed during a seminar. the setup of the bhil as a collaboration between the applied research and the innovation initiatives at dms is chosen deliberately to allow for a smooth transition and truly impactful research and innovation. future work and conclusion bhil strategy for sustainability will start with a draft charter, marketing assets, and a draft pitch. in preparation for a possible stage ii, the team will identify additional meetings or conferences to meet with potential industry partners to the lab and evaluate the interest in affiliating with the lab. potential partners may include local and global companies like ripple, cognitive scale, factom, ibm, dell, google, and others. the bhil will become a resource for both public and private organizations as we advance this emerging field and train tomorrow’s workforce. the lab will provide access to experts in ethics, legal, computer science, clinical care, information sciences, finance, and more from all http://dx.doi.org/10.30953/bhty.v4.161 citation: blockchain in healthcare today 2021, 4: 161 http://dx.doi.org/10.30953/bhty.v4.161 7 (page number not for citation purpose) blockchain research and development program over the campus. it will develop research studies and programs that identify, research, and address opportunities and challenges for the use of blockchain technology in healthcare. research projects will address such topics as pandemic emergency needs, healthcare data flow and security, clinical research, pharmaceutical studies, identity management, and data analysis. the partnership will also develop programs that engage students, faculty, technologists, and medical providers to find challenges in healthcare, create solutions, and share knowledge about the use of blockchain in healthcare. blockchain opens the possibility of a whole new world in medical data management and the ability of individuals to control their own data. decentralized, secure, easily accessible information can empower patients and transform healthcare, and we are just beginning to see blockchain’s vast potential. but there are challenges, and before we solve them, we must identify them. dms seeks to explore how blockchain can—and should—revolutionize healthcare. the time for blockchain is now, and this is the place. conflict of interest and funding the authors have no conflict of interest to declare. we want to thank the university of texas blockchain initiative, and dr. cesare fracassi for their support to this work. we also want to thank student intern, shelby griffin, and the key informants who participated in this study. contributors contributions mu, vk, and ak designed the study, participated in developing the details, contributed to drafting of the manuscript, and finalized and approved the revisions to the manuscript. references 1. justinia t. blockchain technologies: opportunities for solving real-world problems in healthcare and biomedical sciences. acta inform med 2019; 27(4): 284. doi: 10.5455/aim.2019.27.284-291 2. raghavendra m. can blockchain technologies help tackle the opioid epidemic: a narrative review. pain med 2019; 20(10): 1884–9. doi: 10.1093/pm/pny315 3. esmaeilzadeh p, mirzaei t. the potential of blockchain technology for health information exchange: experimental study from patients’ perspectives. j med internet res 2019; 21(6): e14184. doi: 10.2196/14184 4. gordon wj, catalini c. blockchain technology for healthcare: facilitating the transition to patient-driven interoperability. comput structural biotechnol j 2018; 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for clinical trials. in: amia annual symposium proceedings. american medical informatics association; 2018, p. 1167. 43. liang x, zhao j, shetty s, liu j, li d. integrating blockchain for data sharing and collaboration in mobile healthcare applications. in: 2017 ieee 28th annual international symposium on personal, indoor, and mobile radio communications (pimrc). ieee, montreal, qc, canada, 8–13 october 2017; 2017, pp. 1–5. 44. meingast m, roosta t, sastry s. security and privacy issues with health care information technology. in: 2006 international conference of the ieee engineering in medicine and biology society. ieee, new york, ny, usa, 30 august – 3 september 2006; 2006, pp. 5453–8. 45. agbo cc, mahmoud qh, eklund jm. blockchain technology in healthcare: a systematic review. in: healthcare. multidisciplinary digital publishing institute, basel, switzerland; 2019, p. 56. *anjum khurshid department of population health the university of texas at austin 1601 trinity street, austin, texas 78712 united states of america +1 512-495-5225 email: anjum.khurshid@austin.utexas.edu. http://dx.doi.org/10.30953/bhty.v4.161 mailto:anjum.khurshid@austin.utexas.edu 1 (page number not for citation purpose) blockchain in healthcare today 2021. © 2021 the authors. this is an open access article distributed under the terms of the creative commons attribution-noncommercial 4.0 international license (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. citation: blockchain in healthcare today 2021, 4: 168 http://dx.doi.org/10.30953/bhty.v4.168 original clinical research evaluation of decentralized verifiable credentials to authenticate authorized trading partners and verify drug provenance ghada l. ashkar, pharmd1, kalpan s. patel, pharmd, mba1, josenor de jesus, pharmd, mba, fache1, nikkhil vinnakota2, natalie helms2, will jack3, william chien, pharmd, mba3 and ben taylor3* 1ucla health, los angeles, ca, usa; 2amgen, thousand oaks, ca, usa; 3ledgerdomain, las vegas, nv, usa abstract summary: in 2013, the drug supply chain security act (dscsa) was signed into law to address the growing threat of counterfeit drugs and to ensure prescription drugs remain safe and effective for patients. as part of this law, us pharmaceutical supply chain stakeholders are required to confirm the authorized status of trading partners for transactions and information disclosures, even when there is no prior business relationship. while larger authorized trading partners (atps) have connectivity solutions in place, newer and smaller atps have not traditionally participated, including tens of thousands of dispensers. to unlock the full potential of the interoperable system mandated by the dscsa, the authors tested extended atp (xatp), a blockchain-backed framework for atp authentication and enhanced verification in a real-world pharmacy with genuine drug packages. the objective of this research study was to prove that electronic authentication and enhanced verification can be achieved between atps using a mobile-based solution. moreover, we tested accurate reading of drug and associated electronic med guides, flagging of expired and recalled drugs, and correct generation of documentation to support saleable returns. methods: this study involved two dispensers and three participating manufacturers. dispensers were onboarded to a mobile application and used supporting documentation to authenticate their identities, and then scanned 2d drug barcodes to submit drug verification requests to manufacturers (including 11 additional, randomly selected manufacturers). genuine and synthetic drug package barcodes were used to test workflows against genuine and synthetic manufacturer serialization data records. manufacturers authenticated the identity of requesting dispensers with verifiable credentials and responded to verification requests. results: enhanced drug verification was achieved, with 100% of requests successfully delivered to participating manufacturers and 88% of requests being delivered to other manufacturers (based on the pharmacist selection of random packages from the pharmacy). drug verification matching against synthetic serialization data records resulted in 86% accuracy, with the 14% error rate attributed to human factors. all barcodes were successfully scanned and provided package-accurate data, and 97% of randomly selected packages successfully generated drug package inserts. all synthetic recalls and expired drugs were successfully flagged. four of the manufacturers contacted were among the top 15 pharmaceutical manufacturers globally; all four responded. conclusions: the xatp framework provides a secure, reliable, and seamless remote method to conduct enhanced verification as required by law. interoperability between manufacturers and dispensers with no prior business relationship can be achieved on ‘day zero’ using mobile devices that enable digital authentication and rapid barcode scanning. as users retain control of their own private keys, the framework also mitigates the single-point-of-attack risks associated with centrally managed systems. keywords: verifiable credentials; identity; dscsa; pharmaceutical supply chain; drug verification received: 9 february 2021; revised: 17 february 2021; accepted: 17 february 2021; published: 11 march 2021 *correspondence: ben taylor. email: ben.taylor@ledgerdomain.com over the past two decades, globalization and technological innovation have profoundly changed the us pharmaceutical supply chain, and thus, stakeholders face new and emerging requirements under the drug supply chain security act (dscsa) (1). one such requirement is an example of a ‘know your customer’ https://creativecommons.org/licenses/by-nc/4.0/� http://dx.doi.org/10.30953/bhty.v4.168 mailto:ben.taylor@ledgerdomain.com citation: blockchain in healthcare today 2021, 4: 168 http://dx.doi.org/10.30953/bhty.v4.1682 (page number not for citation purpose) ghada l. ashkar et al. (kyc) requirement (2), in which each authorized trading  partner (atp) (3) is required to confirm that their trading partner is also authorized (4). as a result, tens of thousands of atps are responsible for authenticating each other’s identities before they can transact with one another, or even share certain information – even when there is no prior direct business relationship. while verification router services (vrs) (5) have served to handle drug verifications for saleable returns as required under dscsa (6), trading partner identity and status authentication remain a missing piece of the puzzle, especially for the broader community of small trading partners (7). to address this challenge, the authors workshopped and tested a framework for atp authentication, verification routing, and saleable returns documentation (8). previously, a working group with representatives from the manufacturing and dispensing sectors found that this framework was capable of onboarding entities and their representatives, accrediting their licenses, and allowing them to share information with unique verifiable credentials (9). the study outlined in this article took the framework out of the virtual conference room and into a real-world production environment. under this framework, a dispenser with an iphone and acceptable form of id can be remotely authenticated as an atp, can scan the 2d barcode from a serialized drug in their hand (10, 11), and can use an ios app to send a verification request (12). this request, which pulls the drug’s gs1 serialized global trade item number (sgtin) (13, 14) from the scan, is used to identify the appropriate point of contact (poc) for the manufacturer or repackager. an email is sent asking the poc for validating each scanned drug against its master serialization record. the response can be used to generate supporting documentation to another atp for a transaction (such as a saleable return (15)). technical specifications the xatp framework consists of five major components: 1. passwordless frontend mobile phone application (also called xatp), 2. application framework encompassing smart contracts and application logic (docuseal), 3. notification and verification service (oraculous), 4. blockchain application server (selvedge), and 5. backend blockchain (hyperledger fabric). users generate and hold their own private keys, and master national drug code (ndc) data are held locally on the client. leveraging prior work with ucla health and biogen (16), the oraculous interoperability service unlocks interoperability between existing relational database management systems and hosted nodes of the distributed ledger (17). in this way, verification requests can be submitted, routed, and processed without the need for verifying organizations to provision their own nodes. the framework leverages proven third-party services, including splunk (analytics), branch (mobile link service), onesignal (push notifications), and mailgun (email service). cloud hosting and processing were achieved with leveldb, docker, amazon ec2, amazon web services, and minio. the selvedge blockchain server was built with golang on top of open-source hyperledger fabric 1.4 (linux foundation) components (18). sealed documentation, private metadata, and product verification certificates were held in private storage using minio, and public hash records were kept on the blockchain (8). objectives the earlier work of the xatp pilot group was conducted remotely using synthetic data in a closed environment. the objective of this study described in this article was to test the application framework in a real-world setting (the ucla specialty pharmacy) using genuine drug packages and manufacturers’ production serialization data records. specifically, the objectives of this study were to test the following: 1. accurate reading of drug and associated electronic med guides, 2. expired and recalled drug flagging functionality, 3. authentication of a verification request with a verifiable credential, and 4. enhanced verification between dispensers and manufacturers. methods and findings this study included two rounds of testing with three sets of participants: dispensers, participating manufacturers, and other manufacturers based on packages randomly selected from the pharmacy (table 1). table 1. overview of study participant groups group members location dispensers pharmacy workgroup consisting of one pharmacist-in-charge (pic) and two pharmacists (dubbed ‘poas’, as they are designated through power of attorney to act on behalf of the pic for the purposes of day-to-day operations) ucla specialty pharmacy participating manufacturers members of three pharmaceutical manufacturer organizations (among the top 15 pharmaceutical manufacturers globally) who participated in zoom tests remote randomly selected manufacturers members of 11 pharmaceutical manufacturer organizations based on drug packages selected randomly from pharmacy inventory by the pharmacist remote http://dx.doi.org/10.30953/bhty.v4.168 citation: blockchain in healthcare today 2021, 4: 168 http://dx.doi.org/10.30953/bhty.v4.168 3 (page number not for citation purpose) evaluation of decentralized verifiable credentials dispensers fulfilled their role using the xatp application and ios email clients, while manufacturers used platform-agnostic email clients and web interfaces. the dispensers and participating manufacturers used zoom for real-time communication. prior to submitting verification requests, dispensers were onboarded to the xatp application and were required to authenticate their identities with supporting documentation. pic documentation was routed to an external validator, as shown in fig. 1. conversely, poa  documentation was sent to the pic, as only the pic has the authority to authenticate and confer power of attorney (poa) to other pharmacy employees (19). in this way, the pic and poa form a single pharmacy workgroup. during the course of testing, dispensers submitted drug verification requests to participating manufacturers, as well as to randomly selected manufacturers, based on drug packages selected randomly from pharmacy inventory by the pharmacist. manufacturer users were able to respond to verification requests embedded in messages encompassing the verification request and the verifiable credential of the requestor. these messages took the form of emails, which could be independently verified by the responder, as shown in fig. 2. round 1 in the first round of testing, 30 genuine drug packages with barcodes were used to test package and medication guide (20) accuracy, and 39 synthetic barcodes were used in combination with pilot manufacturer emails (sent to non-production email addresses at the manufacturer) to test expiration flagging and routing of verification requests (table 2). prior to the test, participating manufacturers were provided with synthetic serialization data records (collectively totaling 1,008 records), and dispensers were provided with 39 synthetic barcodes. twenty-three of these barcodes corresponded to records in the databases (and could be ‘verified’), while 16 did not (and were notionally ‘counterfeit’). it should be noted that no actual counterfeits were uncovered through the course of testing. fig. 1. an overview of the xatp identity framework and enhanced verification routing. after authenticating his or her identity with an independent external validator, the dispenser can scan 2d barcodes on drug packages using an ios app and submit verification requests as part of the saleable returns process. the package labeler (manufacturer or repackager) receives an email with a verifiable credential and buttons that link to secure oraculous endpoints, allowing him or her to indicate that a drug is verified or unverified. this verification can be used to generate product verification certificates that can be shared with, and independently authenticated by, other atps. http://dx.doi.org/10.30953/bhty.v4.168 citation: blockchain in healthcare today 2021, 4: 168 http://dx.doi.org/10.30953/bhty.v4.1684 (page number not for citation purpose) ghada l. ashkar et al. table 2. round 1 test objectives, methods, and results objective data source method results test package and medication guide accuracy 30 genuine drug packages, selected at random from the pharmacy three dispensers each scanning 10 packages, confirming package and med guide accuracy • 100% (30/30) success rate in accurate drug scanning • 97% (29/30) success rate in looking up electronic drug package medication guides test routing of synthetic verification requests (dispenser side) 39 synthetic drug packages, consisting of two different manufacturers and six different drugs (including 12 expired drugs) two dispensers scanning synthetic drug packages and observing app behavior • 100% (39/39) success rate in accurate drug scanning • 100% (39/39) success rate in submitting drug verification requests • 100% (39/39) success rate in receiving drug verification status updates • 100% (39/39) success rate in identifying expired drugs test routing of synthetic verification requests (manufacturer side) emails generated from scanning of 39 drug packages and synthetic serialization data records two manufacturers receiving and manually reviewing extracted synthetic barcode data against synthetic serialization data records • 100% (39/39) success rate in receiving verification requests • 86% (32/39) accuracy in drug verification matching against synthetic serialization data records • 100% (39/39) success rate in responding to verification requests fig. 2. (left) an enhanced verification request received by a manufacturer, with a verifiable credential link highlighted in red. (right) an identity credential verification hosted at a secure web endpoint. http://dx.doi.org/10.30953/bhty.v4.168 citation: blockchain in healthcare today 2021, 4: 168 http://dx.doi.org/10.30953/bhty.v4.168 5 (page number not for citation purpose) evaluation of decentralized verifiable credentials as shown in the table, the authors observed a 100% success rate in submitting, receiving, and responding to drug verification requests on the part of both dispensers and manufacturers. owing to human factors, 32 of 39 verifications (86%) were successful, as there were four false negatives and three false positives. round 2 the second round of testing focused on testing enhanced verification with genuine drugs (fig. 3), with both participating and randomly selected manufacturers (table 3). table 3. round 2 test objectives, methods, and results objective data source method results test routing of genuine verification requests to participating manufacturers 37 genuine drug packages originating from participating manufacturers, selected from the pharmacy two dispensers scanning synthetic drug packages and observing app behavior • 100% (37/37) success rate in accurate drug scanning emails generated from scanning of 37 drug packages and genuine serialization data records three manufacturers receiving and manually reviewing extracted genuine barcode data against genuine serialization data records • 100% (37/37) success rate in receiving verification requests • 100% (37/37) success rate in responding to verification requests test recall flagging functionality five synthetic drug packages with barcodes corresponding to fda recalls one dispenser scanning synthetic drug packages • 100% (5/5) success rate in identifying the recalled product test routing of genuine verification requests to randomly selected manufacturers 27 genuine drug packages selected at random from the pharmacy (resulting in 11 randomly selected manufacturers) one dispenser scanning genuine drug packages and sending verification requests • 100% (27/27) success rate in accurate drug scanning • 88% (23/27) success rate in submitting drug verification requests test verifiable credential two verifiable credentials included in emails to manufacturers two manufacturers authenticating emailed requests • 100% (2/2) verifiable credentials successfully authenticated during this round, 64 packages were scanned, in total. as three ndcs comprising four products could not be matched to manufacturer pocs, 60 verification requests were submitted and 60 emails were confirmed to have been sent. overall, the authors observed a 94% success rate in submitting drug verification requests, with the 6% attributed to smaller manufacturers outside the global top 1,000 pharmaceutical companies (21). post-round evaluations following the completion of round 2, the authors evaluated the independent verifiability of the drug verification requests, as well as the product verification certificates generated by the pharmacy workgroup. they also received feedback from participating and selected manufacturers. for the drug verification requests, participating manufacturers tested and successfully authenticated the associated identity credential shown in fig. 2. this credential, which is linked in the email and is hosted at a secure web endpoint, enables responders to ensure that an email from a requestor is genuine (and not a counterfeiter attempting to gather sensitive information). one of the manufacturers reported that emails had been routed to the incorrect contact. the dispensers and another manufacturer encountered difficulties with the emails, which was found to be the result of link wrapping services executed by their organizations’ it security policies. this required resubmission of verification requests and pointed to the need for domain whitelisting to ensure secure interoperability. as noted previously, dispensers have the ability to generate product verification certificates that can be shared with, and independently authenticated by, other atps. this provides the name, gtin, ndc, serial number, lot number, expiration date, and verification status for each unit listed. as shown in fig. 4, each certificate also bears a url and access token to a web portal where the user can access its corresponding certificate seal, which can be used to authenticate the certificate. this process can facilitate a verifiable record to show that drugs being received by a third party have been verified and may be sold. the certificate seal includes a list of drugs and their verification statuses, but contains only truncated drug information (22). certificate holders have the ability to access the seal and compare it with the data on the certificate, making it possible to ensure that their certificate is genuine and untampered. the authors employed certificate and seal for analysis of the study results, and found that the former could be successfully authenticated by the latter. after the round, five of the 14 manufacturers contacted sent verifications in a timely manner: the three participating manufacturers, another global top 15 manufacturer, and a specialty manufacturer. the authors were also contacted by two randomly selected manufacturers requesting additional information regarding the verification requests. one was based outside the united states and directed dispensers to its us subsidiary; the other indicated that requests are preferably routed through a proprietary vrs (a functionality common for wholesale distributors http://dx.doi.org/10.30953/bhty.v4.168 citation: blockchain in healthcare today 2021, 4: 168 http://dx.doi.org/10.30953/bhty.v4.1686 (page number not for citation purpose) ghada l. ashkar et al. in verification of saleable returns, but requiring expansion to the broader atp community). discussion and conclusions in this study, the authors tested a framework for atp authentication, enhanced verification, and saleable returns documentation in a real-world setting using both genuine and synthetic drug packages to test positive and negative verification workflows. synthetic recalls and expired drugs were successfully flagged, and 94% of genuine drug verification requests were successfully delivered to participating and randomly selected manufacturers. each manufacturer was only able to access his or her own verification requests. once the identity and atp status of the pic were successfully authenticated by an external validator, the pic, in turn, was able to authenticate poas, forming a pharmacy workgroup. within the workgroup, the pic  and poas shared a common pool of scanned barcodes and product verification certificates, and fig. 3. (left) genuine barcodes being scanned at the pharmacy. (right) the scanned drug in the xatp app. http://dx.doi.org/10.30953/bhty.v4.168 citation: blockchain in healthcare today 2021, 4: 168 http://dx.doi.org/10.30953/bhty.v4.168 7 (page number not for citation purpose) evaluation of decentralized verifiable credentials were able to see verification statuses updated in real time. each dispenser user held his or her own locally encrypted private key, which was generated in concert with signup. outside the pharmacy workgroup, other atps were proven able to interact with the xatp framework using email clients and web browsers, without the need to install new software or create accounts. manufacturers received verification requests signed with verifiable credentials, which could be independently authenticated, and were able to respond with the click of a button. dispensers generated supporting documentation in the form of product verification certificates, which were also independently verifiable. by directly addressing the need for atps under the dscsa to have a secure, reliable, and seamless remote method for digital ids, in combination with commercial off-the-shelf mobile phones (23), the framework outlined in this study allowed for ‘day zero’ interoperability between manufacturers and dispensers with no prior business relationship. all of the major pharmaceutical companies that were contacted sent verifications in a timely manner. while the study framework involved human-in-theloop workflows (fig. 5), the authors anticipate that scaled implementations can be partially or fully automated through existing integration to manufacturer serialization data sources. once provisioned with agents to test verifiable credentials, machine-to-machine connections between the framework and manufacturer relational databases would manage identity credentials and automatically respond to and sign verification requests. interoperability with other frameworks could be achieved with an api that enables third parties to make verification requests. critically, by ensuring that private keys are held by stakeholders as part of a robust identity system, the xatp framework mitigates the single-point-of-attack risks of legacy providers, where the keys to responder databases are often held and pooled. much like a janitor’s keyring, which grants access to every room in a building, a single security breach in such systems might allow attackers to hijack other identities, create false identities, or gain access to confidential data (24, 25, 26, 27). by allowing for passwordless access closely associated with a device, xatp also sidesteps the risks associated with passwords, including sharing, leaks, and sharing passwords across multiple services (28, 29, 30). the xatp framework thus mitigates the  risk for stakeholders to rapidly attain compliance with  dscsa  obligations, such as drug verification, and sets a path for greater interoperability leveraging verifiable credentials (31) in the broader healthcare community. fig. 4. (left) the first page of a product verification certificate generated by a dispenser. (right) the first page of the corresponding certificate seal. note that in both cases the ‘not verified’ statuses refer to synthetic barcodes tested during round 1. http://dx.doi.org/10.30953/bhty.v4.168 citation: blockchain in healthcare today 2021, 4: 168 http://dx.doi.org/10.30953/bhty.v4.1688 (page number not for citation purpose) ghada l. ashkar et al. acknowledgments the authors thank ucla health under the leadership of ceo johnese spisso. special recognition is due to vidya rajaram, mark karhoff, nirmal annamreddy, and kathy daniusis (all of genentech, a member of the roche group) and arthi nagaraj (of sanofi) for their contributions to the study effort as participating manufacturers. their contributions to the journal paper are meant to provide clarification, technical accuracy, and considerations to clearly articulate the learnings from the pilot. anita baijnauth of sladg notarization served as the independent external validator. the authors also thank mike mckinley of ucla health, alina grigorian of amgen, greg plante of iqvia, todd barrett, rph of providence health, and ben nichols of ledgerdomain for their contributions as members of the xatp working group. in addition, the authors thank jose arrieta (formerly us department of health and human services); paul hackett (accenture); diane shoda (greyscaling); alan lodder, rph (formerly intermountain); dr. leo alekseyev, rick burgess, alex colgan, dr. victor dods, and mike lodder ( ledgerdomain); ann mehra (splunk); mike marchant (uc davis); and jennifer colon, pharmd (yale). participation implies no obligation nor endorsement. all intellectual property remains the property of respective owners, and no licenses are implied. conflict of interest and funding the authors declare no potential conflicts of interest. the study was a joint collaboration of ledgerdomain, ucla health, genentech, sanofi, and amgen, and had no external funding. authors’ contributions the ledgerdomain team developed the xatp framework 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https://blockchainhealthcaretoday.com/index.php/journal/article/view/122� 1 (page number not for citation purpose) blockchain in healthcare today 2021. © 2021 the authors. this is an open access article distributed under the terms of the creative commons attribution-noncommercial 4.0 international license (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. citation: blockchain in healthcare today 2021, 4: 166 http://dx.doi.org/10.30953/bhty.v4.166 narrative/systematic reviews/meta-analysis commercially successful blockchain healthcare projects: a scoping review hao sen andrew fang1,2* 1singhealth polyclinics, singhealth, singapore; 2singhealth duke-nus, singapore abstract background: the healthcare industry is the new frontier for blockchain technology. given its properties of immutability and decentralization, blockchain represents an opportunity for unprecedented level of privacy and security for all stakeholders by ensuring data integrity while giving patients control over their own health data. on a backdrop of rising interest in blockchain in general and blockchain healthcare applications in particular, there has been a proliferation of blockchain healthcare projects over the past few years. the aim of this review is to identify and understand real-world blockchain healthcare projects that have attained commercial success in the highly competitive blockchain market. methods and findings: a scoping review was performed in january 2021 on all projects in the coinmarketcap database. following a pre-defined inclusion and exclusion criteria, eligible projects were selected. a single reviewer then reviewed each project’s official website and whitepaper (where available) and performed data abstraction; 10 blockchain healthcare projects fulfilled the selection criteria. the review found that these projects made up 0.24% of the total number of actively tracked projects on coinmarketcap. in terms of market capitalization, the total market capitalization for the projects was us$65,078,849, comprising less than 0.01% of the total market capitalization of all projects. among the projects, the most frequent type was for personal health tracking. conclusions: this review revealed that blockchain health projects currently comprise a small fraction of the overall number of commercially successful blockchain projects. however, because this sub-industry is still in its early stages, there are reasons to be optimistic that many more blockchain health projects will emerge and attain commercial success in future. findings from this review done from an entrepreneurial perspective should help with the identification of future projects most likely to succeed. keywords: blockchain; distributed ledger; healthcare; scoping review; success received: 27 january 2021; accepted: 21 march 2021; revised: 21 march 2021; published: 16 april 2021 the global blockchain technology market size was valued at us$1.5 billion in 2018 and is expected to grow at a compound annual growth rate (cagr) of 69.4% from 2019 to 2025 (1). over the past few years, the rising interest in blockchain has seen a proliferation of promising projects leveraging the technology. current estimates put the number of new projects at about 200 a month (2). according to the data from coinmarketcap (cmc), as of january 26, 2021, an estimated 8,326 blockchain projects have since been launched, with a total market capitalization nearing us$1 trillion (3). since the core idea of bitcoin – the first blockchain project – was to decentralize money, many blockchain projects have focused likewise on solutions for the finance industry (4). more recently, projects have creatively started to apply blockchain across to other industries such as manufacturing, media, retail, and education (5). the healthcare industry, which is rapidly embracing digital technologies, has been touted as the next frontier for blockchain technology (6). for healthcare, blockchain technology represents an opportunity for unprecedented level of privacy and security for all stakeholders by ensuring data integrity while giving patients control over their personal health data. it comes as no surprise then that the blockchain healthcare market is forecasted to grow at an even higher cagr of 72.0% from 2020 to 2027 (7). *correspondence: hao sen andrew fang. email: andrew.fang.h.s@singhealth.com.sg https://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.30953/bhty.v4.166 mailto:andrew.fang.h.s@singhealth.com.sg citation: blockchain in healthcare today 2021, 4: 166 http://dx.doi.org/10.30953/bhty.v4.1662 (page number not for citation purpose) hao sen andrew fang reviews of blockchain applications in healthcare have been conducted to better understand developments in the blockchain healthcare domain. katuwal et al. and drosatos et al. both reviewed the major use cases of blockchain in healthcare and found that most projects were limited as conceptual ideas, while a similar review by agbo et al. also showed that there was a lack of real-world implementations (8–10). as these reviews have mostly been through the research lens, they tend to focus on projects published in research journals and conferences. this inherently excludes other projects that have not been shared in research mediums due to commercial reasons for example. furthermore, because the analysis of projects described in these reviews included the entire spectrum from conceptual ideas to real-world implementations, the analyses of projects that actually delivered value were diluted by those that potentially could. in this review, we aim to identify and understand real-world blockchain healthcare projects that have been commercially successful in the highly competitive blockchain market. by scoping the selection to apex projects in the blockchain field, this review should provide entrepreneurs and funders with a better understanding to judge the likelihood of success, novelty, and potential of future blockchain healthcare projects. to the best of our knowledge, this is the first formal review of blockchain healthcare applications performed through an entrepreneurial lens. methods study design a scoping review was performed as the goal was to provide an overview of the successful blockchain healthcare projects instead of answering focused questions or to judge the quality of various projects. a commercially successful blockchain project was defined as a project which had attained liquidity in public financial markets. the presented review method was carried out by defining the following activities: 1. research questions 2. search strategy 3. project selection 4. data abstraction (1) research questions for this review, there were two broad questions we aimed to address: a. among commercially successful blockchain projects, how do the healthcare-related ones fare? b. what are the examples of commercially successful blockchain healthcare projects? (2) search strategy a list of successful blockchain projects were obtained from the cmc database. the cmc developer application programming interface (api) was used to obtain the list of cryptocurrency listings. cmc is the world’s most-referenced price-tracking website for cryptoassets in the rapidly growing cryptocurrency space. according to its website, its mission is to make cryptocurrency projects discoverable and efficient globally by providing users with unbiased, high-quality, and accurate information for drawing their own informed conclusions. data provided from its platform have been cited by several major media outlets such as forbes, reuters, and the wall street journal (11–13). (3) project selection the following inclusion criteria were applied to select the suitable commercially successful healthcare projects: 1) should be actively tracked by cmc, 2) should have a market capitalization of more than us$0, and 3) should be primarily for use in healthcare industry. market capitalization was derived from the multiplication of the last traded token price and the number of circulating tokens. the market capitalization criteria were used to identify projects that had attracted financial investment, and those with a positive value were deemed commercially successful. projects which did not have a website and those which were listed for less than 2 years were excluded. the 2-year mark was selected as a minimum duration of existence as this is typically the time when most projects face cash concerns, and it has been found that one-third of the new businesses do not make it past the first 2 years (14, 15). the project selection was performed in a stepwise manner. first, the api query was configured to extract only projects that were actively tracked in the cmc database. next, the exported javascript object notation (json) list was converted to comma separated values (csv) format using python. microsoft excel was used to filter projects in the csv file with market capitalization values of more than us$0. finally, a single reviewer manually reviewed each project in the filtered list to identify suitable healthcare projects. (4) data abstraction for data abstraction, a standardized data collection form was developed using microsoft excel. a review of each project’s official website and whitepaper (where available) was performed by a single reviewer knowledgeable in blockchain and the healthcare industry. the world health organization’s ‘classification of digital health interventions’ was used to classify the type of healthcare project (16). http://dx.doi.org/10.30953/bhty.v4.166 citation: blockchain in healthcare today 2021, 4: 166 http://dx.doi.org/10.30953/bhty.v4.166 3 (page number not for citation purpose) commercially successful blockchain healthcare projects in total, 10 data elements were extracted for each project. table 1 provides the complete list of data elements and a description of each element. results overview as of january 23, 2021, there were a total of 8,305 blockchain projects in the cmc database. the results returned from the api query on the same date returned  4,087 actively tracked blockchain projects. from  the project selection process, 10 projects were identified as successful blockchain healthcare projects for review. figure 1 illustrates the selection process and the final list of selected projects for review. a summary of the successful blockchain healthcare projects and  the data abstracted are presented in multimedia appendix 1. table 1. list of data elements extracted and their description. no. data element description 1 project name name of the blockchain project 2 project token symbol symbol used to represent token on coinmarketcap (cmc) 3 project website official website url of the project 4 project rank rank of project on cmc based on market capitalization 5 year started year that the project was started 6 market capitalization market capitalization on cmc 7 market pairs number of markets that the project’s token was traded on 8 project mission overarching mission of the project 9 type of healthcare project area of healthcare industry the project is mainly applied to1 10 blockchain platform blockchain platform used by the project’s solution 1based on a classification of digital health interventions by the world health organization. fig. 1. flowchart to illustrate the screening of projects and the final list of projects for review. http://dx.doi.org/10.30953/bhty.v4.166 citation: blockchain in healthcare today 2021, 4: 166 http://dx.doi.org/10.30953/bhty.v4.1664 (page number not for citation purpose) hao sen andrew fang in terms of the number of projects, healthcare projects made up a fraction (0.24%) of the total number of actively tracked projects on cmc. there were no healthcare projects among the top-100 ranked cryptocurrency projects, while there were one and five in the top-500 and top-1,000, respectively. in terms of market capitalization, the total market capitalization for healthcare projects was us$65,078,849. this made up an even smaller fraction (<0.01%) of the total market capitalization of all projects. among the 10 blockchain healthcare projects, the most frequent type was ‘personal health tracking’ (n = 4), while the others varied from health financing to supply chain management. the projects that fell under ‘personal health tracking’ all featured a personal health records system. figure 2 shows the distribution of the various types of healthcare projects. description of successful blockchain healthcare projects the subsequent section provides a brief description of each of the 10 reviewed projects. (1) solve.care (17) solve.care is a healthcare information technology company that was founded in 2017. it is building a platform designed to use blockchain technology for coordinating care, benefits, and payments between all healthcare stakeholders. this includes patients, doctors, pharmacies, laboratories, employers, and insurers. some use cases include appointment scheduling, specialist referrals, and insurance claims submissions. its blockchain solution adopts a dual token mechanism – the solve token and care.coin – with each token having distinct functionality. the solve token is the native utility token required to participate in and transact on the platform. it can be utilized to pay for network fees, establish user accounts (care.wallets), and purchase apps (care.cards) from a proprietary marketplace (care.marketplace). the solve token supply is fixed and its price is variable, as determined by market supply and demand. solve tokens generate care.coins, a stable payment currency, which can be used for the payment of services. (2) medibloc (18) medibloc was founded by a pair of korean entrepreneurs in 2017. it is developing a decentralized healthcare information ecosystem built on blockchain technology for patients, healthcare providers, and researchers. it allows patients to track and record all of their healthcare-related details, such as doctor visits and health records, on its blockchain platform. apart from aggregating data, it also assigns ownership of the data to patients. this allows patients to decide which medibloc partners they wish to share their information with. by sharing information, patients will be rewarded with the native currency med tokens which can be used to pay for medibloc partner products and services such as pharmaceutical purchases and insurance premiums. (3) dentacoin (19) dentacoin was started by a group of like-minded dentistry and digital transformation experts eager to reshape the dental industry by adopting blockchain technology. the aim of dentacoin is for dental patients to write public reviews of the dentists they visit. the team has built a trusted reviews platform, which allows patients to record their reviews on the blockchain, free of censorship. to ensure authenticity, it plans to implement a verified reviews feature to indicate that the review was written by people who were authorized by their dentist to write a review before their treatment began. the platform will reward patients in the form of dentacoins tokens (dcn) fig. 2. distribution of the types of healthcare projects among those reviewed. http://dx.doi.org/10.30953/bhty.v4.166 citation: blockchain in healthcare today 2021, 4: 166 http://dx.doi.org/10.30953/bhty.v4.166 5 (page number not for citation purpose) commercially successful blockchain healthcare projects for participating.  the program will ultimately help users save money by using dcn to pay for their dental treatment or to purchase dental products. in the long run, it envisions that dentists could potentially consider dcn as a financial investment. for example, they could use it to remunerate employees or to pay suppliers with no middlemen and no high international transaction costs. this direct connection between producers and dentists will ultimately allow dentists to provide lower prices to patients. (4) medishares (20) medishares was created by the founders of zhongtopia, the largest mutual aid platform in china with over 10 million users. medishares aims to combine the traditional mutual insurance model with blockchain and smart contract, which provides a low operation cost and guarantee of compensation for risks. it is developing a blockchain platform that will serve as a decentralized marketplace for insurance. with this, participants can generate insurance smart contract, thus making it possible for anyone to find insurance coverage. on the platform, insurers are expected to profit in the form of medishares tokens (mds). (5) lympo (21) lympo is an estonian blockchain startup with a mission to make the world healthier by incentivizing people to adopt healthier lifestyles. it is developing a blockchain and gamification platform which rewards participants with lym tokens for completing certain activities such as simple challenges. for example, via its app, users are required to join six to eight daily walking and running challenges. once a challenge is accomplished, the user will receive a reward of lym tokens in the in-app wallet. these lym tokens have real value and can be used to purchase quality sporting goods from an online shop – the lympo shop. (6) doc.com (22) doc.com was founded in 2012 in latin america, and it provides telemedicine services to patients throughout the world. in 2018, it launched its blockchain platform. essentially, doc.com’s blockchain-based platform seeks to generate value from the data that patients and healthcare providers share by charging those who want to access that data. as reward to patients and healthcare providers for their contribution, they receive payment in the form of medical token currency (mtc) tokens. patients and healthcare providers can then use mtc in order to pay for services and for access to large volumes of encrypted data and valuable aggregate-level healthcare statistics. (7) tokes (23) as part of the multichain ventures ecosystem that uses the tokes token (tks), the tokes platform was founded to solve the cannabis industry’s banking problem via cryptocurrency payments. in addition to providing a payments gateway, the tokes platform is also building out a blockchain-based ‘track and trace’ platform for supply chain management with the capability to integrate with conventional enterprise software via api. this holistic view of the entire supply chain ensures that goods can be tracked from seed to sale with no loss or fraudulent manipulation of data along the way while complying with global supply chain management standards. (8) ai doctor (24) ai doctor was conceived as a decentralized artificial intelligence (ai) virtual doctor in 2016. the platform aims to leverage ai to analyze patients’ data to provide real-time, personalized health advice. to further incentivize users to contribute their health data, they are rewarded with aidoc tokens on the blockchain platform. the aidoc tokens can in turn be used to enjoy health insurance at preferential rates. these data on the platform can also be used by various organizations such as medical institutions, pharmaceuticals, and ai companies for clinical research, drug development, and ai research, respectively. (9) medicalchain (25) founded in 2017, medicalchain aims to use blockchain technology to securely store health records and maintain a single version of the truth. its blockchain platform will enable users to give conditional data access to different stakeholders such as doctors, hospitals, laboratories, pharmacists, and health insurers. the team is also developing a health data marketplace to allow users to negotiate commercial terms with third parties for alternative uses or applications of their personal health data. for example, putting forward their data to be used in medical research, its platform is powered by the mtn token that can be used to pay for various future services on the medicalchain platform, such as telemedicine consultations. (10) patientory (26) founded in 2015, and based in atlanta, georgia, patientory was one of the earliest healthcare projects in the blockchain space. its key objective is to empower people to take charge of their own health. with a hipaa-compliant platform, it enables patients, clinicians, and healthcare organizations to securely access and transfer sensitive health information while providing actionable insights to improve http://dx.doi.org/10.30953/bhty.v4.166 http://doc.com http://doc.com http://doc.com citation: blockchain in healthcare today 2021, 4: 166 http://dx.doi.org/10.30953/bhty.v4.1666 (page number not for citation purpose) hao sen andrew fang health outcomes. on its platform, its cryptocurrency token, ptoy, is a utility token to rent space to store healthcare data. it ensures data quality, helps to regulate payment transactions, and ensures access to the patientory platform. discussion principal findings in this first ever review of blockchain healthcare projects focusing on those that have attained commercial success, we found that healthcare projects make up a small fraction of the overall number of projects that have managed to penetrate the blockchain market, both in terms of market capitalization and number of projects. this may also be due to the fact that majority of projects continue to follow bitcoin’s lead and target the finance industry. also, ‘defi’ – a portmanteau of ‘decentralized finance’ – has become the new marketing buzzword in blockchain investor circles. as a result, these types of defi projects have recently attracted disproportionately large investor interest. on the contrary, the healthcare industry tends to lag in terms of adopting digital innovations, usually due to stricter regulations on new products and innovations. however on that point, it is worth noting that us national health spending is accelerating and is projected to account for almost 20% of us gross domestic product by 2028 (27). this trend will be similar for other countries. so, if governments decide to push for blockchain in healthcare, like how electronic health records were encouraged as part of the us health information technology for economic and clinical health (hitech) act, we may see healthcare easily leapfrog other industries in the adoption of blockchain (28). the use cases of the successful blockchain healthcare projects reviewed are diverse, ranging from patient empowerment to creating mutual aid marketplaces (20, 21). with data privacy and ownership concerns becoming more prominent, it was not surprising that personal health tracking was the most frequent type of blockchain healthcare project (18, 21, 25, 26). this is a natural fit for blockchain which aims to decentralize data ownership by putting it in the hands of the end-users, instead of centralized within health institution’s databases (29). findings from the aforementioned reviews by katuwal et al., drosatos et al. and agbo et al. had also found that health data management was the most frequent use case among projects described in journals and conference publications (8–10). this indicates a healthy pipeline of projects experimenting in the area, and this may augur more innovations for the use case. this review also came across interesting projects that combined blockchain technology with other new technologies such as ai and telemedicine (22, 24). another new technology that is being studied in combination with blockchain is internet-of-things (iot). outside this review, we researchers are also working on building infrastructure for data sharing on blockchain which can potentially integrate with iot devices in the future (30). this appropriate blending of technologies may open up new use cases and will help blockchain projects to stand out and attract greater investor interest. ethereum was the most frequently used blockchain platform among the blockchain health projects. while ethereum itself is upgrading to a new version (version 2.0) to improve scalability, other blockchain platforms are also rapidly building out new capabilities (31). for example, neo will be adding new capabilities such as native oracles, identity management, and decentralized file storage systems to its blockchain (32). these blockchain platform enhancements will likely see more exciting new blockchain projects in future, especially healthcare-related ones that target patient data management. separately, projects are also beginning to integrate multiple blockchains. patientory, which was initially built on ethereum, later integrated another blockchain platform, dash, for its payments function (33). medicalchain was developed on a dual blockchain structure, using both ethereum and hyperledger fabric (25). in future, we may see more of such projects adopting multiple blockchains. however, more interestingly, we may expect projects to develop cross-chain interoperability where tokens can seamlessly flow from one chain to another using the same interface (34). limitations it is acknowledged that this review has its limitations. first, by only including projects that were linked to cryptocurrencies, it excludes other blockchain healthcare projects that did not use cryptocurrencies such as estonia’s electronic health records based on blockchain technology (35). however, the advantage of this narrower scope allowed both a qualitative and quantitative comparison (based on number of projects and market capitalization) of the various healthcare projects within and also against non-healthcare projects. second, it only searched projects from a single database – cmc. this limitation is mitigated by the fact that cmc is the leading tracking service for cryptocurrency projects, and alternative databases such as coingecko and icobench were comparatively smaller, with 6,185 and 5,728 projects, respectively, at the time of search. furthermore, cmc was transparent with its listings criteria published on its website (36). third, we recognize that there are other definitions of commercial success, such as revenue generated and monthly average users, apart from purely market capitalization. however, these other metrics tend to be less well reported in the public domain and therefore were not included in this review. http://dx.doi.org/10.30953/bhty.v4.166 citation: blockchain in healthcare today 2021, 4: 166 http://dx.doi.org/10.30953/bhty.v4.166 7 (page number not for citation purpose) commercially successful blockchain healthcare projects future work this review focused on the successes. it leaves unanswered the proportion of total investment amount from healthcare industry and number of projects this small group of blockchain health projects comprise. to answer this, a subsequent review may look to include unsuccessful blockchain health projects in the analysis. also, the sub-industry is likely at its first inning of an exciting journey of blockchain adoption in healthcare. it would therefore also be informative to continue trending its developments over the coming years. conclusion this scoping review of the commercially successful blockchain healthcare projects has revealed that such projects make up a small fraction of the overall number of projects that have penetrated the blockchain market. however, we found diverse ideas and use cases among the projects. on the backdrop of rising interest and innovation in this space, there is optimism that many more blockchain health projects will emerge and attain commercial success. individuals with enthusiasm and knowledge in blockchain and healthcare will play an increasingly important role in helping to separate the wheat from the chaff. as the first review done from the entrepreneurial perspective, we expect this review to be a useful reference for those who take up the role and serve as a basis for future reviews to track the progress of this space. supporting information multimedia appendix 1. summary of successful blockchain healthcare project and the data abstracted. conflicts of interest and funding the author declares no conflicts of interests. the author has not received any funding or benefits from industry or elsewhere to conduct this study. contributor’s contributions the author was the sole contributor to this article. he would like to thank his family and work organization, singhealth polyclinics, for their support. references 1. grand view research. global blockchain technology market size report, 2019–2025 [internet]. 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[cited 26 january 2021]. available from: https://www.forbes.com/sites/ jonathanponciano/2021/01/21/bitcoin-losses-near-200-billionas-jpmorgan-warns-its-the-least-reliable-dollar-hedge/ 12. staff r. crypto market cap surges above $1 trillion for first time [internet]. reuters. 2021 jan 7 [cited 26 january 2021]. available from: https://www.reuters.com/article/ crypto-currency-int-iduskbn29c264 13. michaels d. bitmex co-founders charged with u.s. rules violations [internet]. wall street j. 2020 [cited 26 january 2021]. available from: https://www.wsj.com/articles/bitmexfounders-charged-in-indictment-that-says-they-flouted-u-srules-11601579782 14. freshbooks. how long it takes for a small business to be successful: a year-by-year breakdown [internet]. freshbooks. [cited 26 january 2021]. available from: https:// www. freshbooks.com/hub/startup/how-long-does-it-takebusiness-to-be-successful 15. petch n. startup life: six rules for surviving the first two years [internet]. entrepreneur. 2016 [cited 26 january 2021]. available from: https://www.entrepreneur.com/article/285751 16. who | classification of digital health interventions v1.0 [ internet]. world health organization; [cited 28 january 2021]. available from: http://www.who.int/reproductivehealth/ publ i cat ions /mhea l th /c lass i f i cat ion-d ig i ta l -hea l th interventions/en/ 17. solve.care website [internet]. [cited 26 january 2021]. available from: https://solve.care/ 18. medibloc website [internet]. [cited 26 january 2021]. available from: https://medibloc.org/en 19. dentacoin foundation. dentacoin: the blockchain solution for the global dental industry [internet]. 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[cited 26 january 2021]. available from: https://lympo.com/ 22. doc.com. universalizing access to healthcare – doc.com [internet]. [cited 26 january 2021]. available from: https://doc.com/ 23. tokes platform. tokes platform [internet]. [cited 26 january 2021]. available from: https://tokesplatform.org/ 24. aidoc project. aidoc – ai doctor on the blockchain [internet]. [cited 26 january 2021]. available from: http://www.aidoc. me/ 25. medicalchain website [internet]. [cited 26 january 2021]. available from: https://medicalchain.com/en/ 26. patientory [internet]. patientory. [cited 26 january 2021]. available from: https://patientory.com/ 27. keehan sp, cuckler ga, poisal ja, sisko am, smith sd, madison aj, et al. national health expenditure projections, 2019–28: expected rebound in prices drives rising spending growth. health aff (millwood) 2020; 39(4): 704–14. doi: 10.1377/ hlthaff.2020.00094 28. hipaa journal. what is the hitech act [internet]. hipaa j. [cited 27 january 2021]. available from: https://www.hipaajournal.com/what-is-the-hitech-act/ 29. dickson b. how blockchain solves the complicated data-ownership problem [internet]. the next web. 2017 [cited 5 july 2020]. available from: https://thenextweb.com/contributors/2017/08/17/ blockchain-solves-complicated-data-ownership-problem/ 30. nguyen dc, pathirana pn, ding m, seneviratne a. blockchain for secure ehrs sharing of mobile cloud based e-health systems. ieee access 2019; 7: 66792–806. doi: 10.1109/access.2019.2917555 31. the eth2 upgrades [internet]. ethereum.org. [cited 27 january 2021]. available from: https://ethereum.org 32. neo-project. neo smart economy [internet]. [cited 27 january 2021]. available from: https://neo.org/ 33. patientory foundation. patientory to integrate dash payments using blockcypher web services [internet]. patientory. 2017 [cited 27 january 2021]. available from: https://patientory.com/ blog/2017/08/24/patientory-integrate-dash-payments-usingblockcypher-webservices/ 34. pillai b, biswas k, muthukkumarasamy v. cross-chain interoperability among blockchain-based systems using transactions. knowl eng rev [internet]. 2020 ed [cited 27 january 2021];35. available from: https://www.cambridge.org/core/journals/ knowledge-engineering-review/article/abs/crosschain-interoperability-among-blockchainbased-systems-using-transactions/ f411cf8796f08afbea09a3153a5f2183 35. heston t. a case study in blockchain healthcare innovation [internet]. rochester, ny: social science research network; 2017 [cited 21 march 2021]. report no.: id 3077455. available from: https://papers.ssrn.com/abstract=3077455 36. listings criteria [internet]. coinmarketcap. [cited 27 january 2021]. available from: https://support.coinmarketcap.com/hc/ en-us/articles/360043659351-listings-criteria http://dx.doi.org/10.30953/bhty.v4.166 https://lympo.com/ http://doc.com https://doc.com/ https://tokesplatform.org/ http://www.aidoc.me/ http://www.aidoc.me/ https://medicalchain.com/en/ https://patientory.com/ https://dx.doi.org/10.1377/hlthaff.2020.00094 https://dx.doi.org/10.1377/hlthaff.2020.00094 https://www.hipaajournal.com/what-is-the-hitech-act/ https://www.hipaajournal.com/what-is-the-hitech-act/ https://thenextweb.com/contributors/2017/08/17/blockchain-solves-complicated-data-ownership-problem/ https://thenextweb.com/contributors/2017/08/17/blockchain-solves-complicated-data-ownership-problem/ https://dx.doi.org/10.1109/access.2019.2917555 http://ethereum.org https://ethereum.org https://neo.org/ https://patientory.com/blog/2017/08/24/patientory-integrate-dash-payments-using-blockcypher-web-services/ https://patientory.com/blog/2017/08/24/patientory-integrate-dash-payments-using-blockcypher-web-services/ https://patientory.com/blog/2017/08/24/patientory-integrate-dash-payments-using-blockcypher-web-services/ https://www.cambridge.org/core/journals/knowledge-engineering-review/article/abs/crosschain-interoperability-among-blockchainbased-systems-using-transactions/f411cf8796f08afbea09a3153a5f2183 https://www.cambridge.org/core/journals/knowledge-engineering-review/article/abs/crosschain-interoperability-among-blockchainbased-systems-using-transactions/f411cf8796f08afbea09a3153a5f2183 https://www.cambridge.org/core/journals/knowledge-engineering-review/article/abs/crosschain-interoperability-among-blockchainbased-systems-using-transactions/f411cf8796f08afbea09a3153a5f2183 https://www.cambridge.org/core/journals/knowledge-engineering-review/article/abs/crosschain-interoperability-among-blockchainbased-systems-using-transactions/f411cf8796f08afbea09a3153a5f2183 https://papers.ssrn.com/abstract=3077455 https://support.coinmarketcap.com/hc/en-us/articles/360043659351-listings-criteria https://support.coinmarketcap.com/hc/en-us/articles/360043659351-listings-criteria 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 opinion/perspective/commentary leveraging decentralized clinical trial management systems (dctms) to advance science: exploring challenges related to the diffusion of innovation and its execution rama krishna rao, mba co-founder, ceo, bloqcube, piscataway, new jersey, usa corresponding author: rama krishna rao, email: rama@bloqcube.com doi: https://doi.org/10.30953/bhty.v7.305 keywords: blockchain, ctms, decentralized clinical trials, drug development, healthcare, pharmaceutical industry, solutions, decentralized clinical trials management systems, dctms abstract decentralized clinical trials (dcts) recently gained attention in research necessary for drug development. while the covid-19 pandemic proved to be a challenging time in this arena, drug development was a critical area of emphasis in the rapid advancement of vaccines. the dcts were necessary to allow research activities to occur across many locations. the use of dcts can profoundly impact reshaping healthcare by enabling participants to partake in clinical trials remotely; however, implementation challenges must be considered as technology expands. a working group of participants was assembled during an interactive learning exercise at the conv2x conference (2023) to explore challenges related to the diffusion of innovation among key stakeholders. pain points experienced with using and implementing technologies were identified, and an innovative solution using a blockchain-anchored option was presented. participants were divided into three stakeholder groups: patients, payers, and pharmaceutical sponsors. after a time of discussion, the groups reconvened for review. several themes that can be supported by blockchain technology emerged. these include enhanced efficiencies, patient experience, and demographic diversity, as well as data integrity, privacy, security, and cost-effectiveness. future research might focus on strategies to facilitate the adoption of the idea across key stakeholder groups. received: march 10, 2024; accepted: april 4, 2024; published: april 30, 2024 clinical trials and their associated regulatory processes are critical to drug development. the covid-19 pandemic proved to be a challenging time, yet it spawned a host of innovations. drug development was a critical area of emphasis for developing covid-19 vaccines, and decentralized clinical trials (dcts) were necessary to allow research activities to continue.1–3 in a dct, some or all trial activities occur at locations other than a traditional clinical site, including the patient’s home or a local healthcare facility or laboratory.1 in recent years, significant strides in technology have occurred, which make it widely available (e.g., smartphones, artificial intelligence (ai), wearables, etc.). regardless of the stunning advancements and the critical nature of data sharing necessary for the timely approval of new drugs, the use of select emerging technologies in clinical trials lags. the essence of “decentralization” represents a shift in the locus of power or authority. in a fully decentralized model, study activities are executed without in-person contact. this description suggests that dcts would have a structural reordering, yet that has not been the case. the dcts in the current state are a misnomer in that they represent a change in the locus of trial conduct/locations—from sites to alternative locations (e.g., patient homes). currently, dcts exist along a continuum, with some believing that many trials (e.g., oncology-related) may never be fully decentralized due to complexity and sponsor-mandated in-person interactions for safety and regulatory purposes.1,4 mailto:rama@bloqcube.com https://doi.org/10.30953/bhty.v7.305 citation: blockchain in healthcare today 2024, 7: 305 https://doi.org/10.30953/bhty.v7.305 2 (page number not for citation purpose) rama krishna rao aware of dcts’ promise and challenges, the u.s. food and drug administration (fda) is issuing resources and guidelines to address their novel development in healthcare.1 simultaneously, technical hurdles that slow their adoption should also be explored. this exploratory exercise aimed to assess, analyze, and synthesize stakeholder opinions related to the current challenges slowing the diffusion of innovation for dcts and capture considerations among three key groups (i.e., patients, payers, and pharmaceutical sponsors). the diffusion of innovation refers to a five-step process (i.e., knowledge, persuasion, decision, implementation, and confirmation) by which an individual decides to adopt an idea at a certain speed.5 methods conference session / workgroup conv2x is a global conference advancing the business of health with blockchain technology. the 2023 conference highlighted pivotal drivers accelerating the adoption of blockchain solutions.6 during a 60-minute interactive learning exercise, a convenience sample of conference attendees, primarily industry practitioners and experts, participated in a workgroup to explore considerations related to dct uptake and delayed diffusion of innovation across key stakeholders. this activity was completed as part of a conference session and was intended for something other than research. attendees voluntarily and anonymously participated, and no identifying information was linked to responses. furthermore, opinions were presented as group consensus and did not include individual participant comments. prior to the assessment, broad “pain points” in existing technology utilization were presented. these included: 1. the lack of availability of real-time data driven by substantially batch-driven processes; 2. the presence of multiple systems for various parts of the processes exacerbating inefficiencies; 3. the need for manual, risk-based monitoring and source data verification as a governance process; 4. the presence of data integrity issues; and finally, 5. the disconnect between operational activity and delayed payments to study participants. additionally, a representative from bloqcube® demonstrated an innovative approach that uses a blockchain-anchored solution, executed on an ipad and cloud, to fully integrate and decentralize trial management activities remotely in real-time. after introductions, the participants were subdivided into three stakeholder groups (table 1). at the center of all activity, patients were the payers. they have a balanced role in ensuring adequate returns for shareholders and reaching intended users. the purpose of the small group discussions was to identify (1) problem definitions, (2) potential solutions, and (3) value across each stakeholder group. after the discussion, the participants reconvened for presentations by each group and an overall discussion. results patients the participants noted current challenges patients encounter when participating in classic clinical trials, including travel times to participating sites, a lack of communication, delays in payments, and discomfort with consent disclosures. furthermore, ethical dilemmas (e.g., access, placebos) were identified, especially in life-saving drug trials. finally, the discomfort caused by the lack of transparency and access to the study results can be problematic. consensus solutions that were identified included true decentralization and distributed ledger technologies (e.g., blockchain and gamification) to improve acceptance. patient-centric, remote systems can facilitate electronic data collection remotely from the patient’s location. additionally, using digital companion coaches or advocates may alleviate concerns, and creating shared incentive models could also enhance participation while working within an ethical framework. regarding value, the group agreed that this is a complex challenge. they concurred that the speed of the trials and dissemination of approved drugs could serve as a value indicator, assuming other quality parameters are met. payers the participants noted that payers were likely concerned about cost containment and the efficacy of new drugs table 1. three stakeholder groups are involved in the challenges that slow the diffusion of innovation for dcts and capture considerations stakeholders activities patients study subjects for clinical trials who later become consumers of the product. payers government entities or health insurance providers assess the costs and benefits of new drugs. play a key role in western economies. pharmaceutical sponsors creators and drivers of innovation. dcts: decentralized clinical trials. https://doi.org/10.30953/bhty.v7.305 citation: blockchain in healthcare today 2024, 7: 305 https://doi.org/10.30953/bhty.v7.305 3 (page number not for citation purpose) leveraging dctms to advance science compared to current treatments or the “gold standards.” additionally, because payers do not fund clinical trials, there is a level of uncertainty as they do not control key elements and risk a study population that is disparate from the populations they serve. there was consensus among participants that one solution is value-based contracting, where costs are tied to results. other solutions may include phase four studies to assess long-term risks/benefits and smart contracts to automate inefficient processes. efforts to cut costs could be achieved by placing greater emphasis on remote monitoring rather than current approaches. value can be improved by allocating more funds to more effective drugs and using more efficient processes. pharmaceutical sponsors first, participants emphasized that a primary goal for pharmaceutical sponsors includes driving innovation for fast drug development (e.g., covid-19 vaccines). related issues include the cost of drug development, the regulatory processes for approval, and data security. the group also emphasized the pivotal role and the need to protect intellectual property in drug development, which is crucial to original inventions. participants highlighted the need for solutions such as dcts for patient recruitment, ethically leveraging monetary incentives, and ensuring data integrity. another idea expressed was to share negative results to facilitate learning science. participants agreed that a smooth process is critical. cloud-based systems combined with blockchain and cryptographic security can provide a seamless flow of compliant data while minimizing the vulnerability to ransomware attacks. value an alternative definition of “value” often used in healthcare is quality divided by cost. the participants agreed that cost often becomes a primary focus, as it is easily measured. however, it has a natural limit on the extent of value. if the primary focus shifts to quality instead of cost, there is greater latitude for significant value gains. it is important to track and manage different stakeholder perspectives to ensure that innovative solutions are well accepted and absorbed. discussion while dcts are a worthy end goal, the true worth is the ability to engage study subjects at a level of multiples of growth with a resulting improvement in drug development in many areas. participants shared potential problems and solutions within multiple stakeholder groups. several themes that can be supported by blockchain technology emerged across all three stakeholder groups. consistent with current literature,7,8 these themes included enhanced efficiencies, patient experience / demographic diversity, data integrity/privacy/security, and cost-effectiveness. improved efficiencies were suggested with regulatory practice automation and risk-based monitoring. in a recent report, mckinsey & company suggested that the greatest opportunity for sponsors to accelerate clinical trials is to increase the speed and improve the efficiency of clinical trial enrollment.9 these improvements will benefit patients, payers, and pharmaceutical companies. additionally, using decentralized models can foster a more robust study population and meet the needs of many patients with a disease with no known therapies.9 of equal importance are data integrity, security, and privacy, which promote patient comfort and fidelity in clinical trials. blockchain technology streamlines monetary exchange by removing the need for intermediaries to complete transactions faster and more efficiently. managing trial subject consents and clinical trials is an area in which blockchain can improve the efficiency, accountability, audit ability, and transparency of researchers and practitioners in healthcare. data also suggests that blockchain technology reduces monitoring visit time and cost while improving patient trust and sense of empowerment compared to conventional clinical trial management.10 while many key insights supporting blockchain technology were explored and supported, several points of consideration, not widely discussed during the session, include the appropriateness of use, regulatory standards, and data privacy. blockchain technology offers solutions to several challenges identified by the participants; however, it is not always the best solution.1,4 guidance on when and when not to use blockchain-based solutions is readily available (figure 1). additionally, the decentralized trials & research alliance (dtra) developed a rubric to provide a consistent framework when evaluating the evidence of success, patient experience, site impact, operational and technical feasibility, and regulatory and ethical compliance.11 furthermore, the fda has published draft guidance on decentralized trials, and it is recognized that many clinical trials already include decentralized elements.1,12 a dct, when performed at a patient’s home, requires additional safeguards from a data privacy governance given the health insurance portability and accountability act (hipaa), the general data protection regulation (gdpr), and similar legislation. the fda regulatory requirements are the same for dcts and traditional sitebased clinical trials.12 while blockchain, known for its robustness and transparency, may inherently pose unique challenges in maintaining data privacy and complying with security standards, capturing data and working with it using paper is sub-optimal.13 in the rapidly evolving world of blockchain technology, privacy and security remain at the forefront.13 a blockchain-driven data https://doi.org/10.30953/bhty.v7.305 citation: blockchain in healthcare today 2024, 7: 305 https://doi.org/10.30953/bhty.v7.305 4 (page number not for citation purpose) rama krishna rao protection solution permits a high level of confidentiality for patients’ records/data. this exercise has several limitations. first, the focus groups consisted of a convenience sample of conference participants. additionally, the presentation and discussions were completed within a limited amount of time. lastly, it must be noted that the areas of discussion represent a small overview of the canvas of options for clinical trial systems. conclusion key stakeholders offer a unique perspective that can leverage current technologies and facilitate the adoption of truly decentralized clinical trial management systems (dms). additional research is needed to facilitate the pervasive implementation of blockchain technology and address challenges related to health diagnostics, patient care processes in remote monitoring or emergencies, data integrity, and fraud avoidance.7 despite the promising value proposition of blockchain technology in clinical trials, broad adoption will require the industry to overcome technological barriers.10 future research may include additional stakeholders, such as researchers, regulators, healthcare providers, and others involved in clinical trials who may not be familiar with this technology. the dcts are a promising approach to improve the efficiency, patient experience, demographic diversity, and cost-effectiveness of clinical research.3,8 this exercise explored how blockchain technology and decentralized clinical trial management systems (dctms) can positively impact patients, payers, and pharmaceutical sponsors by improving efficiencies, patient experiences, demographic diversity, data management, and cost effectiveness. although several solutions have been developed, challenges with adoption still need to be addressed. the diffusion of blockchain technology is highly applicable and can reshape the future of healthcare by increasing study participation in clinical trials; however, understanding and overcoming the challenges of adoption is essential. funding none. financial and non-financial relationships and activities the author declares the following competing financial interest. the author holds equity ownership in bloqcube®. contributor rama krishna rao independently chaired and facilitated the interactive learning session, workgroups, and all products resulting from the discussions. data availability statement (das), data sharing, reproducibility, and data repositories original data were not generated for this editorial. application of ai-generated text or related technology none applied. fig. 1. when should we use a blockchain solution and when not to. https://doi.org/10.30953/bhty.v7.305 citation: blockchain in healthcare today 2024, 7: 305 https://doi.org/10.30953/bhty.v7.305 5 (page number not for citation purpose) leveraging dctms to advance science acknowledgments the author thanks the conference attendees who participated in the learning session and workgroups. references 1. food and drug administration. the evolving role of decentralized clinical trials and digital health technologies [internet]. fda; 2023 [cited 2024 mar 8]. available from: https://www.fda. gov/drugs/news-events-human-drugs/evolving-role-decentralized-clinical-trials-and-digital-health-technologies 2. gaba p, bhatt dl. the covid-19 pandemic: a catalyst to improve clinical trials. nat rev cardiol. 2020;17(11):673–5. https://doi.org/10.1038/s41569-020-00439-72 3. lamberti m, smith z, dirks a, caruana t, mitchell t, getz k. the impact of decentralized and hybrid trials on sponsor and cro collaborations [internet]. applied clinical trials online. [cited 2024 mar 8]. available from: https://www.appliedclinicaltrialsonline.com/view/the-impact-of-decentralized-and-hybrid-trials-on-sponsor-and-cro-collaborations 4. adesoye t, katz mhg, offodile ac. meeting trial participants where they are: decentralized clinical trials as a patient-centered paradigm for enhancing accrual and diversity in surgical and multidisciplinary trials in oncology. jco oncol pract. 2023;19(6):317–21. https://doi.org/10.1200/op.22.00702 5. rogers em. diffusion of innovations. 5th ed. new york, ny: free press; 2003. 6. emba iv-f, mylrea m, zhang cy, wood tc, thornley b. impact of blockchain-digital twin technology on precision health, pharmaceutical industry, and life sciences conv2x 2023 report [internet]. blockchain in healthcare today. 2023 nov 8 [cited 2024 mar 8];6(2). available from: https://blockchainhealthcaretoday.com/index.php/journal/article/view/281 7. saeed h, malik h, bashir u, ahmad a, riaz s, ilyas m, et al. blockchain technology in healthcare: a systematic review. plos one. 2022 apr 11;17(4):e0266462. https://doi.org/10.1371/journal.pone.0266462 8. mcwhinney l. advancing technology in decentralized clinical trials [internet]. [cited 2024 mar 9]. available from: https://isrreports. com/advancing-clinical-research-with-advanced-technology/ 9. speed up biopharma clinical trials to boost r&d output | mckinsey [internet]. www.mckinsey.com. [cited 2024 mar 8]. available from: https://www.mckinsey.com/industries/life-sciences/ our-insights/accelerating-clinical-trials-to-improve-biopharma-r-and-d-productivity 10. mak bc, addeman bt, chen j, papp ka, gooderham mj, guenther lc, et al. leveraging blockchain technology for informed consent process and patient engagement in a clinical trial pilot. blockchain healthc today. 2021;4. https://doi. org/10.30953/bhty.v4.182 11. decentralized trials & research alliance release best practices rubric to provide framework to evaluate dct processes [internet]. www.dtra.org. [cited 2024 mar 8]. available from: https://www.dtra.org/dtra-press-releases/decentralized-trials-research-alliance-release-best-practices-rubric-to-provide-framework-to-evaluate-dct-processes 12. decentralized clinical trials for drugs, biological products, and devices guidance for industry, investigators, and other stakeholders draft guidance [internet]. 2023 [cited 2024 mar 8]. available from: https://www.fda.gov/media/167696/download 13. galis m. council post: blockchain and data privacy: the future of technology compliance [internet]. forbes. [cited 2024 apr 13]. available from: https://www.forbes.com/sites/forbestechcouncil/2024/02/15/blockchain-and-data-privacy-the-future-of-technology-compliance/?sh=40b4c31a74f8 copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see http://creativecommons. org/licenses/by-nc/4.0 https://doi.org/10.30953/bhty.v7.305 https://www.fda.gov/drugs/news-events-human-drugs/evolving-role-decentralized-clinical-trials-and-digital-health-technologies https://www.fda.gov/drugs/news-events-human-drugs/evolving-role-decentralized-clinical-trials-and-digital-health-technologies https://www.fda.gov/drugs/news-events-human-drugs/evolving-role-decentralized-clinical-trials-and-digital-health-technologies https://doi.org/10.1038/s41569-020-00439-72 https://www.appliedclinicaltrialsonline.com/view/the-impact-of-decentralized-and-hybrid-trials-on-sponsor-and-cro-collaborations https://www.appliedclinicaltrialsonline.com/view/the-impact-of-decentralized-and-hybrid-trials-on-sponsor-and-cro-collaborations https://www.appliedclinicaltrialsonline.com/view/the-impact-of-decentralized-and-hybrid-trials-on-sponsor-and-cro-collaborations https://doi.org/10.1200/op.22.00702 https://blockchainhealthcaretoday.com/index.php/journal/article/view/281 https://blockchainhealthcaretoday.com/index.php/journal/article/view/281 https://doi.org/10.1371/journal.pone.0266462 https://doi.org/10.1371/journal.pone.0266462 https://isrreports.com/advancing-clinical-research-with-advanced-technology/ https://isrreports.com/advancing-clinical-research-with-advanced-technology/ http://www.mckinsey.com https://www.mckinsey.com/industries/life-sciences/our-insights/accelerating-clinical-trials-to-improve-biopharma-r-and-d-productivity https://www.mckinsey.com/industries/life-sciences/our-insights/accelerating-clinical-trials-to-improve-biopharma-r-and-d-productivity https://www.mckinsey.com/industries/life-sciences/our-insights/accelerating-clinical-trials-to-improve-biopharma-r-and-d-productivity https://doi.org/10.30953/bhty.v4.182 https://doi.org/10.30953/bhty.v4.182 http://www.dtra.org https://www.dtra.org/dtra-press-releases/decentralized-trials-research-alliance-release-best-practices-rubric-to-provide-framework-to-evaluate-dct-processes https://www.dtra.org/dtra-press-releases/decentralized-trials-research-alliance-release-best-practices-rubric-to-provide-framework-to-evaluate-dct-processes https://www.dtra.org/dtra-press-releases/decentralized-trials-research-alliance-release-best-practices-rubric-to-provide-framework-to-evaluate-dct-processes https://www.fda.gov/media/167696/download https://www.forbes.com/sites/forbestechcouncil/2024/02/15/blockchain-and-data-privacy-the-future-of-technology-compliance/?sh=40b4c31a74f8 https://www.forbes.com/sites/forbestechcouncil/2024/02/15/blockchain-and-data-privacy-the-future-of-technology-compliance/?sh=40b4c31a74f8 https://www.forbes.com/sites/forbestechcouncil/2024/02/15/blockchain-and-data-privacy-the-future-of-technology-compliance/?sh=40b4c31a74f8 1blockchain in healthcare today issn 2573-8240 use case improving end-to-end traceability and pharma supply chain resilience using blockchain corrine sim ; haisheng zhang ; and marianne louise chang zuellig pharma, singapore corresponding author: marianne louise chang, email: digicomms@zuelligpharma.com keywords: blockchain, covid-19, eztracker, hyperledger fabric, pharma supply chain, traceability abstract regulating and monitoring a traditionally fragmented pharma supply chain has been a global challenge for decades. without a trusted system and strong collaboration between stakeholders, threats such as counterfeits can easily intercept the supply chain and cause monumental disruptions. today, the covid-19 pandemic has accelerated the need for greater data transparency, better deployment of technology, and improved ways of connecting stakeholder information along the supply chain. there is a need for improved ways of working to help build up supply chain resilience, and one way is by implementing better end-to-end traceability using blockchain technology such as hyperledger fabric. this paper will explore the business value that blockchain brings to the pharma supply chain with better end-to-end traceability, using the example of an industry-grade blockchain solution called eztracker. through six key features, pharmaceutical manufacturers, patients, and healthcare practitioners (hcps) can now participate in data sharing, with extended use cases of integrating blockchain with warehouse platforms, a patient-facing mobile application, and an interactive dashboard for real-time verification and data transparency. beyond anti-counterfeit verification, other potential use cases include effective product recall management, cold chain monitoring, e-product information, and more. the effectiveness of a traceability solution is heavily dependent on the amount of data collected and is affected by poor adoption and scalability. existing limitations that need to be addressed include the lack of mandated serialization in asia and blockchain interoperability. to maximize the value of blockchain, collaboration is the key. pharmaceutical manufacturers need to invest in new technologies, such as blockchain, to help them break out of data silos and operationalize data to build supply chain resilience. pharmaceutical supply chain is the backbone of a us$1.27 trillion industry,1 but because of its highly complex and fragmented nature, it is hard to regulate and protect, and this makes it a valuable target for opportunistic parties such as counterfeiters looking to profit.2 as a result of the covid-19 pandemic, there has been greater emphasis on transparency of data and connecting stakeholders along the pharma supply chain in real-time in the last few years. with the introduction of blockchain technology, companies are now able to implement solutions with more effective track and trace results, providing quality assurance to pharmaceutical manufacturers, patients, and healthcare practitioners (hcps), and even improving operational efficiencies. this paper seeks to explore the positive business impact of end-to-end traceability using blockchain technology, and the effects it brings about, such as improving supply chain resilience and combating counterfeits, as seen in successful live use cases in asia. received: april 28, 2022; revised: july 20, 2022; accepted: july 22, 2022; published: august 12, 2022 blockchain for end-to-end traceability and anticounterfeit verification according to a report by the world economic forum, the top three advantages of blockchain adoption for pharmaceutical and healthcare ecosystems are full traceability, data immutability, and increased security.3 these benefits will prove useful to address the challenges of poor trust, data sharing, and visibility across the supply chain. https://orcid.org/0000-0002-0957-9202 https://orcid.org/0000-0002-4263-2183 mailto:digicomms@zuelligpharma.com citation: blockchain in healthcare today 2022, 5: 231 http://dx.doi.org/10.30953/bhty.v5.2312 corrine sim and haisheng zhang blockchain is a distributed ledger technology that records transaction data in a “block” and is linked to the preceding “block,” forming a long chain in chronological order4 (figure 1). there are four types of blockchain networks: public, private, consortium, and hybrid.6 for enterprise applications, private or consortium blockchains are preferred, as they are permissioned blockchains that promote high security by limiting access to only approved parties and implementing data access controls and privacy policies across the network.7 these are crucial in the healthcare and pharmaceutical ecosystem, which handles sensitive information such as patient health data and intellectual properties of manufacturers. another benefit of permissioned blockchains is scalability because of the modular architecture. methods selecting a blockchain framework one of the existing blockchain frameworks, hyperledger fabric, is an open-source industry-grade framework hosted by the linux foundation. designed for enterprise applications across industries, hyperledger fabric has automatic executable smart contracts (or chain codes) that are business logic algorithms and are mutually agreed upon by all parties on the network. with each transaction, every party will endorse the transaction based on a sophisticated pre-set endorsement policy.8 providing higher data security compared to traditional centralized solutions that may suffer from a single point of failure or attacks by malicious parties,9 hyperledger fabric passed a series of detailed security reviews and assessments in 2021 and was deemed “natively secure by both design and default” by the cloud security alliance.10 deploying blockchain for anti-counterfeit verification more than one in 10 drugs in developing countries are estimated to be counterfeited,11 with types of therapeutic categories being falsified growing annually.12 this has resulted in the pharma supply chain to suffer from eroding trust – a study revealed that seven in 10 patients were concerned about receiving harmful counterfeit or substandard products.13 in 2021, the edelman trust barometer reported that more than 50% of countries they surveyed reported decreasing trust in pharmaceutical companies compared to 2020.14 with an estimate of over 1 million deaths caused by counterfeit and substandard drugs annually, patients around the world are increasingly demanding to know the origin of their drugs.15 hyperledger fabric can be used in combination with tamper-proof serialization labels to allow more secure verification of product provenance in real-time. counterfeiters who create a fraudulent identity or tamper with the data violate endorsement policy and the abnormal data transaction will alert all users in the ecosystem. a shared visible ledger can also help improve supply chain transparency16 by allowing hcps and patients to access accurate product provenance data, verify product distribution channels, and report counterfeit incidents in real time. fig. 1. how blocks are chained to form a blockchain.5 http://dx.doi.org/10.30953/bhty.v5.231 citation: blockchain in healthcare today 2022, 5: 231 http://dx.doi.org/10.30953/bhty.v5.231 3 improving end-to-end traceability and pharma supply chain resilience using blockchain, pharmaceutical manufacturers are now able to connect stakeholders along the pharma supply chain for meaningful real-time interactions such as adverse-event reporting. in 2019, hong kong authorities discovered clinics were administering counterfeit human papilloma virus (hpv) vaccines.17 this incident caused public panic and complaints among patients worried about the authenticity and safety of the vaccines. in the same year, 400,000 counterfeit beauty products were seized by thailand’s department of special investigations, including dermal fillers.18 consequences of injecting falsified fillers include risks of unevenness of the skin, cell death, and even blocked arteries that may lead to blindness.19 in response to the incidents, several pharmaceutical manufacturers deployed eztracker, an end-to-end traceability solution using blockchain that allowed patients and hcps to verify the authenticity of distribution while providing pharmaceutical manufacturers with dashboards for real-time track and trace. results and discussion real-time verification solution for effective track and trace and to especially fight counterfeits, all stakeholders along the pharma supply chain need to be connected and break out of the traditional information silos. eztracker is the first production-grade traceability solution that empowers pharmaceutical manufacturers, distributors, hcps, and patients with real-time traceability (figure 2). pharmaceutical manufacturers to enable track and trace, products are first serialized at a pack level. the encrypted digital id for each product is uploaded onto the blockchain as a new block and linked to new data transaction points, creating a string of traceable and immutable historical data. by integrating warehouse operations systems with blockchain, pharmaceutical manufacturers can upload existing key master product data through a simple extract, transform, and load (etl) system and tag select information to each digital id. with this application programming interface (api) integration, data can now be shared from various databases and made visible on the blockchain. for eztracker, digital ids are encoded into 2d data matrixes on physical packs to allow them to be read by scanners across the supply chain (figure 3). in 2022, eztracker was successfully used to record and track more than 2 million labeled products on the blockchain. distributors included in eztracker operations is the zoip app, a warehouse application that allows warehouse staff to scan 2d data matrix codes, tag products in the blockchain, and access key product information (figure 4). the redressing team has the responsibility to use zoip to scan the newly affixed 2d data matrix to create unique box identities in the blockchain and tag the product to material and batch numbers. when the products are ready for dispatch, the picking team can seamlessly access previously logged information and important dispatch information, including the invoice number, quantity ordered, client name, expiry date, and more (figure 5). healthcare practitioners and patients when dispatched products arrive at a clinic, hospital, or pharmacy, hcps can validate the authenticity of the products received. through eztracker connectors, built to connect and integrate healthcare management systems fig. 2. six eztracker features to enable end-to-end traceability. fig. 3. printed encrypted 2d data matrix on product packaging. http://dx.doi.org/10.30953/bhty.v5.231 citation: blockchain in healthcare today 2022, 5: 231 http://dx.doi.org/10.30953/bhty.v5.2314 corrine sim and haisheng zhang to the blockchain, hcps can now tag products in their inventory to specific activities such as from storage to administration. each unique pack can also be tagged to individual patients, and this is especially important for patient safety and quality assurance, such as in the case of product recalls where manufacturers and authorities can reach patients quickly and directly. because of the array of use cases applicable with blockchain, eztracker also launched a mobile app to empower hcps and patients with the ability to verify the authenticity of product distribution in real-time. eztracker is now used by more than 37,000 users in hong kong and thailand with over 115,000 scans, of which more than 6,700 scans indicated potential counterfeits and cross-border movement of products. users can download the application from the google play store or apple app store for free and scan the 2d data matrix on their products. if the mobile app verifies that the product comes from an authorized distribution source, users will be notified of key product information and its provenance (figure 6). however, when an unauthorized product scan is detected, users will be alerted and prompted to report the incident with photographic evidence and a description. these reports will be sent to the pharmaceutical manufacturers who can then use them for further investigations. creating insights from blockchain analytics for counterfeit detection with the launch of the integrated features and services, a robust and market-ready dashboard was built to operationalize data shared across the blockchain (figure 7). supply chain data are fed into the dashboards every 15 min and insights accessed are close to real-time. these data are later exported easily for auditing and reporting purposes. this helps pharmaceutical manufacturers investigate suspicious counterfeit activity and collect evidence to conduct investigations. with these analytics, pharmaceutical manufacturers are empowered with data to make decisions that affect risk management, brand integrity, security, and compliance. with a concerted and proactive effort to combat counterfeits, pharmaceutical manufacturers can now work closely with consumers to build a more secure ecosystem. there are currently three key components to the dashboard: fig. 4. zoip warehouse application (scanning products). http://dx.doi.org/10.30953/bhty.v5.231 citation: blockchain in healthcare today 2022, 5: 231 http://dx.doi.org/10.30953/bhty.v5.231 5 improving end-to-end traceability and pharma supply chain resilience (1) product scan rates and dispatch information (figure 8): product information is tracked to the individual pack level, and an unusually high frequency of scans could possibly indicate malicious parties looking to exploit vulnerabilities in the supply chain. (2) authorized scans versus cross-border scans (figure 9): product scans on the mobile app are timestamped and uploaded into the blockchain. “authorized” scans show products from an authorized source, whereas “cross-border” scans could indicate products from unauthorized distributors, which could affect quality assurance and authenticity for the final user of the product. (3) geolocation data (figure 10): data of individual pack movements is collected when a product is scanned. data can help to identify clusters of suspicious behavior and even the movement of these suspicious goods, with the ability to zoom in on certain districts, neighborhoods, and specific coordinates. with blockchain-enabled end-to-end traceability, the data and reports submitted by consumers allow pharmaceutical manufacturers and local authorities to fig. 5. eztracker operations at a distributor’s warehouse. fig. 6. eztracker mobile app (verification flow). http://dx.doi.org/10.30953/bhty.v5.231 citation: blockchain in healthcare today 2022, 5: 231 http://dx.doi.org/10.30953/bhty.v5.2316 corrine sim and haisheng zhang fig. 7. blockchain integration with tableau. fig. 8. three individual product scan rates and dispatch information. fig. 9. comparison graph of “authorized” versus “cross-border” scans. http://dx.doi.org/10.30953/bhty.v5.231 citation: blockchain in healthcare today 2022, 5: 231 http://dx.doi.org/10.30953/bhty.v5.231 7 improving end-to-end traceability and pharma supply chain resilience have a better understanding of potential counterfeit activities in the market and conduct more effective and data-driven counterfeit investigations. other benefits of end-to-end traceability timely product recalls even as products are dispatched to clinics, hospitals, and pharmacies, each product’s digital id can be tagged to key patient information, such as date of administration, contact details, product batch number, and more. this allows for more effective product recalls as manufacturers can engage patients directly instead of regular processes, which can take months or even years. cold chain monitoring temperature-sensitive products such as vaccines that are not kept at recommended storage requirements may lose their potency when exposed to heat.20 these damages result in waste and can incur up to us$35 billion in losses annually for the pharmaceutical industry.21 when temperature data are collected in silos and not shared, it is difficult to detect products that are compromised. with blockchain, temperature data from loggers across the supply chain can be added to the network to provide real-time temperature reports and more effective cold chain monitoring. to improve efficiency even further, consumers can now verify for themselves whether the product was stored at approved temperatures through the mobile app and report any adverse events such as sub-standard storage of products. electronic product information a product label is updated five times in a year on average, and pharma companies spend millions of dollars updating artwork, printing, and working out the logistics for distributing updated labels.22 paper package inserts and patient information leaflets do not give patients access to the latest approved information about the product. with epi, manufacturers can manage their depository of product information online to produce consistent quality information. instead of manual intervention, they can engage patients directly using the mobile app and trigger alerts and warnings in the event of product recalls and other updates in product information. patients connected digitally can easily report adverse events. epi is also cost-effective and reduces environmental impact. improved supply chain resilience end-to-end traceability improves supply chain visibility and data-sharing access. manufacturers can access actionable insights to develop effective strategies to optimize their resources and tackle existing supply chain inefficiencies that free up net working capital. positive working capital management reduces company risk and improves financial flexibility and performance, especially during a disruption such as the covid-19 pandemic.23 challenges to the adoption of blockchain in pharma supply chain lack of mandated product serialization in europe and the united states, drug serialization has been mandated since 2017 by the authorities, which helped facilitate the adoption of traceability solutions to improve transparency.24 however, in the asean region, serialization is not widely practised, and this makes endto-end traceability difficult to achieve uniformly across the region.25 even with mandated serialization, reliable tracking technology is still necessary to enable effective product traceability. in a study, more than four in 10 pharmaceutical data management vendors exceeded a ransomware susceptibility index (rsi) of 0.6, which meant that manufacturers were easily exposed to risks of data manipulation and disruption.26 conventional data management architectures are not immune to vulnerabilities and may fig. 10. geolocation data and timelapse of scan history. http://dx.doi.org/10.30953/bhty.v5.231 citation: blockchain in healthcare today 2022, 5: 231 http://dx.doi.org/10.30953/bhty.v5.2318 corrine sim and haisheng zhang create distrust and impede data sharing across the supply chain. data collection processes also need to evolve as poor quality of information impacts the success of traceability solutions.27 with different stages of the supply chain implementing non-standard processes to track products and enter varied data inputs, integration and data sharing require complex mapping, which is time-consuming and inefficient. multi-cloud infrastructure and future of interoperability to enable end-to-end traceability, the blockchain must be agile enough for organizations to join seamlessly. in january 2022, eztracker developed the first multi-cloud blockchain in the pharmaceutical industry. this means that client and partner nodes on any popular public cloud can integrate with eztracker to connect and share data securely. with this cloud-agnostic architecture, automated node setups for partners and clients allow them to enjoy faster go-to-market times (figure 11). however, as the solution scales, there is a risk of fragmentation in the pharmaceutical supply chain if multiple blockchains exist independently, creating data and value silos.28 blockchain interoperability helps enable scalability, reduce risk, and eliminate silos.29 the supply chain needs to prioritize blockchain interoperability to better scale traceability solutions, increase adoption, and potentially achieve global transparency through collaboration. conclusion to better safeguard the pharmaceutical supply chain, adoption of blockchain for track and trace is the key for end-to-end traceability to improve patient safety and long-term supply chain resilience. through its immutable, secure, and scalable network architecture, blockchain has shown to effectively build a culture of trust and collaboration to reduce data silos across the supply chain. furthermore, this increased data transparency benefits pharmaceutical manufacturers, distributors, hcps, and patients, as it unlocks the possibility of enabling real-time verification solutions for quality assurance and dashboards that help unlock insightful data analytics. blockchain adoption, scalability, and interoperability will remain critical criteria of success for end-to-end traceability solutions. the private and public sectors need to set a unified data-sharing standard, collaborate across fig. 11. multi-cloud blockchain architecture. http://dx.doi.org/10.30953/bhty.v5.231 citation: blockchain in healthcare today 2022, 5: 231 http://dx.doi.org/10.30953/bhty.v5.231 9 improving end-to-end traceability and pharma supply chain resilience interoperable blockchain networks, and build trusted and connected data ecosystems. funding statement none financial and non-financial relationships and activities none authors’ contributions corrine sim and marianne chang wrote the paper. haisheng zhang contributed to the technical blockchain portions of the paper. references 1. mikulic m. topic: global pharmaceutical industry. statista; 2021. available from: https://www.statista.com/topics/1764/ 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https://www.health.harvard.edu/blog/dermal-fillers-the-good-the-bad-and-the-dangerous-201907152561 https://www.health.harvard.edu/blog/dermal-fillers-the-good-the-bad-and-the-dangerous-201907152561 https://www.health.harvard.edu/blog/dermal-fillers-the-good-the-bad-and-the-dangerous-201907152561 https://apps.who.int/iris/bitstream/handle/10665/64776/who_epi_lhis_98.02.pdf?sequence=1&isallowed=y https://apps.who.int/iris/bitstream/handle/10665/64776/who_epi_lhis_98.02.pdf?sequence=1&isallowed=y https://apps.who.int/iris/bitstream/handle/10665/64776/who_epi_lhis_98.02.pdf?sequence=1&isallowed=y https://cdn2.hubspot.net/hubfs/4107558/general https://cdn2.hubspot.net/hubfs/4107558/general https://www.pharmexec.com/view/e-labeling-change-underway https://www.pharmexec.com/view/e-labeling-change-underway https://doi.org/10.1080/1331677x.2021.1922090 https://www.marketsandmarkets.com/market-reports/track-trace-solution-market-158898570.html https://www.marketsandmarkets.com/market-reports/track-trace-solution-market-158898570.html https://www.marketsandmarkets.com/market-reports/track-trace-solution-market-158898570.html https://www.raps.org/news-and-articles/news-articles/2018/1/the-drug-regulatory-landscape-in-the-asean-region citation: blockchain in healthcare today 2022, 5: 231 http://dx.doi.org/10.30953/bhty.v5.23110 corrine sim and haisheng zhang news-articles/2018/1/the-drug-regulatory-landscape-in-the-asean-region [cited 28 april 2022]. 26. mcgrail s. pharmaceutical supply chain cybersecurity risk tops $31m annually [internet]. pharmanews intelligence; 2021. available from: https://pharmanewsintel.com/news/pharmaceutical-supply-chain-cybersecurity-risk-tops-31b-annually [cited 28 april 2022]. 27. duan y, miao m, wang r, fu z, xu m. a framework for the successful implementation of food traceability systems in china. inform soc. 2017;33(4):226–42. https://doi.org/10.1080/0197224 3.2017.1318325 28. belchior r, vasconcelos a, guerreiro s, correia m. a survey on blockchain interoperability: past, present, and future trends. acm comput surv. 2022;54(8):1–41. https://doi. org/10.1145/3471140 29. belchior rap. blockchain interoperability. 2021. available from: https://web.ist.utl.pt/~ist180970/papers/phd_cat_rafael_ belchior.pdf [cited 28 april 2022]. copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/licenses/ by-nc/4.0. http://dx.doi.org/10.30953/bhty.v5.231 https://www.raps.org/news-and-articles/news-articles/2018/1/the-drug-regulatory-landscape-in-the-asean-region https://www.raps.org/news-and-articles/news-articles/2018/1/the-drug-regulatory-landscape-in-the-asean-region https://pharmanewsintel.com/news/pharmaceutical-supply-chain-cybersecurity-risk-tops-31b-annually https://pharmanewsintel.com/news/pharmaceutical-supply-chain-cybersecurity-risk-tops-31b-annually https://doi.org/10.1080/01972243.2017.1318325 https://doi.org/10.1080/01972243.2017.1318325 https://doi.org/10.1145/3471140 https://doi.org/10.1145/3471140 https://web.ist.utl.pt/~ist180970/papers/phd_cat_rafael_belchior.pdf https://web.ist.utl.pt/~ist180970/papers/phd_cat_rafael_belchior.pdf http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) narrative/systematic reviews/meta-analysis health datasets as assets: blockchain-based valuation and transaction methods wendy m. charles, phd1* , brooke m. delgado, ms1 1burstiq, inc., denver, colorado, usa *corresponding author: wendy m. charles. email: wendy.charles@cuanschutz.edu keywords: blockchain, data sales, data valuation, intangible assets abstract there is increasing recognition that health-oriented datasets could be regarded as intangible assets: distinct assets with future economic benefits but without physical properties. while health-oriented datasets—particularly health records—are ascribed monetary value on the black market, there are few established methods for assessing value for legitimate research and business purposes. the emergence of blockchain has created new commerce opportunities for transferring assets without intermediaries. therefore, blockchain is proposed as a medium by which research datasets could be transacted to provide future value. blockchain methodologies also offer security, auditability, and transparency to authorized individuals for verifying transactions. the authors will share data valuation methodologies consistent with accounting principles and include discussions of black market valuation of health data. further, this article describes blockchain-based methods of managing real-time payment/micropayment strategies. received: 10 november 2021; revised: 19 december 2021; accepted: 21 december 2021; published: 21 january 2022 individually identifiable information is collected about patients in nearly every health and wellness-oriented app, wearable device, and healthcare setting.1 this information is used to identify treatment opportunities within the care facilities where the patients are treated. however, data are also regularly shared and sold to other technology or life sciences organizations to design innovations in healthcare, identify new healthcare markets, create business opportunities, and uncover revenue collection opportunities.2 life sciences research organizations have a tremendous need to acquire health information from real-world sources, referred to as “real-world data,” as part of a united states (u.s.) food and drug administration (fda) real-world evidence framework initiative.3 with careful planning, the fda notes that “a non-interventional study has the potential to meet fda’s regulatory standards for an adequate and well-controlled clinical study”.4 among sources of real-world data, life sciences organizations seek information about the effectiveness of pharmaceutical compounds when used in typical care conditions—rather than the stringent environment of a clinical research setting—to learn how physicians are utilizing these drugs and which patient groups may experience unexpected benefits or adverse events.5 as a recent example, the drug blincyto (blinatumomab) received accelerated fda approval to treat acute lymphoblastic leukemia using a single-arm trial. the experimental group was compared to a historical control group using electronic health records from 694 patients in the european union and u.s.6 electronic health records also created the control group for a new fda-approved indication for prograf (tacrolimus) to help prevent organ rejection.7 overall, acquiring and using existing health information allows research organizations to achieve faster, lower-cost research that demonstrates treatment effectiveness within real-world care conditions. therefore, there is a tremendous need to acquire health information. many organizations have uncertainty regarding the best technologies to manage health information exchange and monetization securely. while some companies utilize traditional database technologies, blockchain technologies have emerged to allow for more capabilities. x. wang et al.8 point out that blockchain is already used to exchange contracts, capital, and digital assets, so this technology would inevitably be used to exchange data. in addition, buying and selling assets previously required an intermediary, such as a financial institution or marketplace. however, blockchain technologies can simplify blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0003-1627-6899 https://orcid.org/0000-0002-6435-0769 mailto:wendy.charles@cuanschutz.edu citation: blockchain in healthcare today 2023, 6: 185 http://dx.doi.org/10.30953/bhty.v6.1852 (page number not for citation purpose) wendy m. charles and brooke m. delgado the data transaction process by allowing organizations to transfer assets without intermediaries.9 the transfer is completed, validated, and recorded on the blockchain in near-real-time.10 nature of health information while health information can be collected from many places, such as patient wellness apps, patient repositories, and research studies, most health information used for sharing and sale originates from organizations that deliver or support healthcare.1 in the u.s., these organizations are referred to as covered entities, involving “(a) a health plan, (b) a healthcare clearinghouse or (c) a healthcare provider, who transmits any health information in electronic form in connection with a transaction” (45 cfr 160.103). the nature of health information that can be shared and sold depends on the degree to which information is considered to constitute “protected health information” and whether the issuing organization is a covered entity. while each country imposes privacy regulations to protect health information, a review of privacy requirements is outside the scope of this article. therefore, this section addressed only the health information privacy requirements of the u.s. the health insurance portability and accountability act (hipaa) defines protected health information as individually identifiable health information transmitted or maintained in any other form or medium (45 cfr 160.103); and a covered entity. authorized methods of distributing protected health information include: 1. deidentified information. a covered entity can use and share deidentified health information without restriction when the healthcare organization first removes all 18 components that could identify an individual (45 cfr 164.514(a)). it is also permissible for a statistician to determine there is minimal risk that the intended recipient could use the information— alone or in combination with other reasonably available information—to identify individuals included in the data set (45 cfr 164.514(b)). 2. limited data set. a covered entity may disclose a data set that removes direct identifiers of the individual (or of relatives, employers, or household members of the individual) and enters into a data use agreement with the recipient (45 cfr 164.514(e)). a limited data set may include elements of dates related to an individual and geographic identifiers, such as town/city and zip code, provided that this information—either alone or in combination with other information—is unlikely to identify the individuals represented in the data set (45 cfr 164.514(e)). identifiable data set. a covered entity must obtain authorization for any disclosure of protected health information that is a sale of health information as defined in 45 cfr 164.501 and 45 cfr 164.508(a).4 specifically, covered entities may “not sell lists of patients or enrollees to third parties without obtaining authorization from each person on the list”.11 the hipaa regulations do not apply to information generated or provided by a patient or healthcare consumer that is not maintained by a covered entity. nature of health data acquisition there are several methods by which organizations can obtain health information. direct sales from patients several startups have been formed that compensate patients for sharing their health information. for example, encrypgen enables individuals to upload their dna profiles to a marketplace and set a price to sell their profiles.12 healthcare organization purchases pharmaceutical company roche ag purchased flatiron health, acquiring 260 community cancer clinics to obtain cancer treatment information to support regulatory decisions.13 roche’s purchase price of $1.9b averages $1,000 per medical record for 2 million oncology patients.14 business associate agreements a covered entity may share health information with a member of its workforce or a business associate for providing professional services, provided that the covered entity represents that the health information includes the minimum necessary to achieve the stated purpose (45 cfr 514(d)(iii)). for example, ascension health and the mayo clinic distribute health information to google under business association agreements to design artificial intelligence algorithms to identify opportunities for treatment and revenue.15,16 academic or government data warehouses more than 10,000 deidentified health-related data sets are publically available on data.gov (https://www.data. gov/). pubmed (https://pubmed.ncbi.nlm.nih.gov/) allows authors to upload their health data sets with their publications.2 in addition, some universities offer publicly accessible data warehouses for researchers to query and download deidentified data. as of october 2021, the university of michigan’s inter-university consortium for political and social research program offers data sets from over 16,000 studies represented in nearly 100,000 publications (https://www.icpsr.umich.edu/web/pages/ icpsr/). http://dx.doi.org/10.30953/bhty.v6.185 http://data.gov https://www.data.gov/ https://www.data.gov/ https://pubmed.ncbi.nlm.nih.gov/ https://www.icpsr.umich.edu/web/pages/icpsr/ https://www.icpsr.umich.edu/web/pages/icpsr/ citation: blockchain in healthcare today 2023, 6: 185 http://dx.doi.org/10.30953/bhty.v6.185 3 (page number not for citation purpose) health datasets as assets data marketplaces data sellers have created a $100b market with companies buying, selling, and trading deidentified information.14 in fact, 14 u.s. health systems started a new company, truveta, to aggregate and sell their deidentified patient data.17 this article focuses on data marketplaces and blockchain-based technologies’ role in managing pricing, access, and monetization. financial value of health information data allows decision-makers to make calculated, insightful, and profitable decisions adding value to data mining alone.18 health information derived from electronic health record systems creates value for life sciences research due to the complex demographics, health history, and other health-oriented behaviors. because life sciences organizations seek data to develop new revenue-generating opportunities, they are willing to pay for this health information—ascribing value to the data. this section explores factors for determining data value. health datasets as assets because a dataset provides value and offers potential financial benefits, a dataset could be considered an asset.19,20 according to the financial accounting standards board,21 recognized by the u.s. securities and exchange commission, an asset has three characteristics. 1. a probable future benefit that contributes to net cash inflows 2. an entity obtains and controls others access to it 3. the transaction to control or benefit from the asset has already taken place22 data are classified as intangible assets when they have no physical properties. as shown in figure 1, examples of intangible assets include patents, trademarks, copyright, and intellectual property.23 intangible assets are generally not accounted for on an organization’s balance sheet24 but add to the organization’s value in the marketplace. similar to most intangible assets, the potential data value is challenging to measure.8 data valuation data value is determined by various factors such as data complexity, number of records in the data set, number of variables, and quality of the data.25 further, deidentified health records are less valuable because researchers need dates and geocodes to contextualize disease progression and co-morbidities.26 in addition, data valuation is influenced by data perishability, which involves devaluation over time, and time-dependency, a measure of the time since data collection.27 the use of blockchain also offers features that may increase data value.28 organizations use both subjective and objective methods to determine dataset value. the following strategies are not comprehensive but describe the most common methods used to value intangible assets: the cost, income, and market approaches. cost approach with a cost approach, data sets are valued based on the estimated historical costs to create the data set29 or the anticipated costs incurred to replace the data set.30 this approach is aligned with the hipaa requirement to limit data sales for research to cost-based fees to cover the cost of preparing health information (45 cfr 164.502(a)(5)(3) (ii)). when using historical cost as the basis, organizations should consider likely inflation and other infrastructure costs necessary for replacing the data set.31 organizations may benefit from blockchain-based transparency in price histories and formulas.32 the use of blockchain technology for preparing or replacing the data could potentially increase or decrease the replacement costs. data valuation could increase when the technologies are novel and there is no competition with the methodology.26 however, blockchain technology also introduces data redundancies and audit trails to significantly reduce replacement costs.33 future valuations using the cost approach should consider emerging economic and technological developments. income approach the income approach considered the estimated increased revenue generated by the data set.31 for example, suppose a pharmaceutical company acquires health data sets as real-world evidence that could support a regulatory decision for a new indication. in this case, the data sets could be valued with the projected new drug revenue stream. blockchain-based data exchanges facilitate data sharing about data uses and related metadata that can be used to determine trends and future needs.32 however, future fig. 1. representative types of intangible assets. http://dx.doi.org/10.30953/bhty.v6.185 citation: blockchain in healthcare today 2023, 6: 185 http://dx.doi.org/10.30953/bhty.v6.1854 (page number not for citation purpose) wendy m. charles and brooke m. delgado revenue forecasts are also influenced by market share and adoption rates, requiring several assumptions that could change.29 market approach the market approach appraises data based on the price of comparable data traded or sold. pricing may be set by guidelines posted within the marketplaces or by analyzing similar sales data.34 blockchain-based audit trails are currently used to track the history of data values,35 allowing for more convenient access to historical sales information. similarly, the market may bear higher values for data sets involving more records, identifiable data, and quality.29 zozus and bonner35 note that blockchain-based metadata have been used to facilitate evaluations of data quality and other attributes that may influence valuation. these authors describe how data value-level metadata are used to calculate data age and potential discrepancies. accordingly, blockchain-based data provenance and integrity may increase perceptions of data value,28 resulting in higher market-driven pricing. the market approach can also be used to consider supply and demand. specifically, data buyers consider the availability of other data sets within the market to determine the relative value of any particular data set.9 concepts of supply and demand drive pricing of illegal data sales on the black market or dark web.36 stack36 notes that social security numbers sell for around $1 on the dark web, and credit cards sell for $5-110 (with the median ranging from $25-40,37). medical records sell for $1-1,000, depending on information completeness.38,39 specifically, electronic health records are more valuable for illicit sales when they include e-commerce transactions and credit card information.40 data are typically sold using blockchain technology on the dark web in exchange for cryptocurrency—and data are held for ransom requesting cryptocurrency—because of the pseudonymous nature of transactions.40 these data valuation methods can only provide estimates, and organizations are encouraged to use multiple data valuation approaches.29 birch et al.41 encourage organizations to use advanced technological solutions for these approaches because data access can be tracked and measured more efficiently. as described in the next section, blockchain-based systems are often used to track and record user engagement with datasets, allowing for better value measurements. however, because data values are in constant flux, data valuations should be reviewed and recorded at least annually to ensure accuracy.29 health data marketplaces marketplaces provide digital platforms for buyers and sellers to exchange data. as shown in figure 2, the volume of data bought and sold on data marketplaces—of all types—is expected to increase 25% from 2020 to 2022.42 types of blockchain-based marketplaces there are three primary characteristics of blockchain-based marketplaces: private, consortium, and independent data marketplaces. platform architecture is either centralized or decentralized.43 in this section, the benefits and drawbacks of each characteristic are described. private marketplaces are controlled by a single vendor or organization that controls access and governance.44 a private marketplace owner can use the marketplace to its advantage—such as having access and insight to all data exchanges, and the owner can both sell data and charge subscription fees.34 however, private marketplaces can introduce bias on the platform,44 and the platform operator is responsible for protecting the data and ensuring that all data privacy laws are met.23 consortium marketplaces have a group of owners that cooperate to support platform operations and decision-making.44 consortia benefit from sharing costs and resources to maintain the reliability of the network.45 these organizations often utilize pooled data to create larger health data sets and collaborate on research. a drawback pertains to the trust required of the other collaborators to protect and manage data appropriately,46 as well as questions about ownership.23 independent health marketplaces allow individual patients/consumers to provide and sell their own data on a platform, where the buyers, sellers, and marketplaces are independent entities.44 each entity has independent fig. 2. percent of large organizations as sellers or buyers of data via online data marketplaces. http://dx.doi.org/10.30953/bhty.v6.185 citation: blockchain in healthcare today 2023, 6: 185 http://dx.doi.org/10.30953/bhty.v6.185 5 (page number not for citation purpose) health datasets as assets control—and often independent monetization—over their data and how data are used. as a downside, it is difficult to gain enough market size to individually source and sell data without the buying power of a consortium or large private organization.44 centralized vs. decentralized architecture the infrastructure and architecture of a platform refer to locations of data storage, access, and technical governance. a centralized blockchain marketplace collects and stores data where a single (or few) organization(s) provide access controls, regular maintenance, and oversight.47 as an example for health data, centralized marketplaces can negotiate health data purchases directly from healthcare organizations and payers, as well as negotiate large-scale sales to life sciences organizations. centralized marketplaces can offer additional layers of encryption and smart contracts.47 however, there are potential vulnerabilities for hacking and less operational transparency.48 a benefit to a decentralized marketplace is quality.47 since data is secured by smart contracts and accessed directly from the data provider, higher quality can be assumed. a drawback of decentralized marketplaces is that it creates more difficult transactions.49 each transaction must be facilitated through a distributed ledger. an example of a decentralized marketplace is where deidentified data are aggregated using software, but data never leave the source location, and each organization cannot access data from others. a decentralized location allows the data to stay with the data provider.47 blockchain-based data exchange platforms exist today as decentralized ecosystems that enable individuals or organizations to source and share data.47 rather than centralized management where there are potential points of vulnerability,48 blockchain-based data exchanges allow for distributed data stewardship and communication.50 blockchain technologies offer data management methods for sales and transactions in ways that traditional databases typically cannot provide. among examples, blockchain can support transaction visibility to ensure the data exchange and payment process is fair and consistent with payment terms.51 further, because malicious data buyers or sellers may refuse to pay for data,48 smart contracts automate payments and revenue distributions.52 this capability also ensures efficiencies for data exchanges and resource allocations.53 further, smart contracts ensure that only authorized individuals can contribute or access specific data in a very granular manner.54 examples of blockchain-based health data marketplaces blockchain not only offers a new technology to manage data sharing and tracking but facilitates new economic models. considering that blockchain first received wide recognition for the transparent exchange of cryptocurrency, the same reasoning is applied to the exchange of other assets. as lee55 notes, blockchain-based marketplaces offer trusted data while transparently tracking both transactions and payments. while many blockchain-based data sales and exchange platforms are still early in development, several have achieved stable platforms and market awareness. several companies also attempt to utilize blockchain-based decentralization with incentive schemes to reward both providers and patients for participation. three primary players are involved in blockchain-based data marketplaces: providers, buyers, and digital platform owners.47 providers list their data in exchange for monetary value, buyers purchase data to add value to their organizations, and marketplace owners/controllers provide a place where data can be stored and sold.47 while open data marketplaces are available to download/exchange data at no charge,47 the authors focus on profit-generating marketplaces. profit-generating data marketplace participation vary from business-to-business (b2b), consumer-to-consumer (c2c), business-to-consumer (b2c), business-to-business-to-consumer (b2b2c), and business-to-consumer-to-business (b2c2b).56 the following companies offer blockchain-based data marketplaces for data exchange or monetization of health information. this list is intended to be representative but not necessarily comprehensive. burstiq, inc. (https://www.burstiq.com/): colorado-based b2b2c technology company, burstiq, promotes itself as the first blockchain-based data management platform to process large volumes of health information on chain while meeting health information and regulatory privacy requirements.57 the on-chain capabilities allow organizations to connect patients’ longitudinal, multidimensional health profiles, called lifegraphs®, using artificial intelligence and machine learning.58 burstiq also developed health marketplaces where patients could loan, sell, and license their health information based on automated matchmaking. burstiq expanded the collaboration space for research and development, now called “the foundry,” to share data and leverage crowd intelligence.59 the use of blockchain provides data governance, granular consent capabilities, data provenance, and data security. ciitizen, located in palo alto, california (https://www. ciitizen.com) and recently acquired by invitae,60 is designed as a b2c2b personal health record platform where patients can aggregate health information from all healthcare providers and share information for research. while the website does not specify the use of blockchain, ciitizen’s blockchain technologies have been listed among blockchain-based health platforms (e.g.,61). the platform is free for patients, but researchers pay a fee to access health records when patients agree to share their health http://dx.doi.org/10.30953/bhty.v6.185 https://www.burstiq.com/ https://www.ciitizen.com https://www.ciitizen.com citation: blockchain in healthcare today 2023, 6: 185 http://dx.doi.org/10.30953/bhty.v6.1856 (page number not for citation purpose) wendy m. charles and brooke m. delgado information for research. of particular note, ciitizen shares these fees with the individuals who agree to provide their health information for research.62 among the research faqs, the website specifies, “should a patient’s information be included in a study, ciitizen is committed to returning a portion of the value gained from this study with users to the extent permitted by law (for example, in the form of direct payment, services, discounts, donations, or other value) or to donate this value to an advocacy or research non-profit as directed by the patient”.62 operating in france and russia, datapace.io offers a b2c2b blockchain-based data marketplace for iot sensor data (https://datapace.io/) but can be used to buy or sell any data.63 the marketplace uses hyperledger fabric to build the platform and modules, using a high-performance practical byzantine fault tolerance consensus mechanism. individuals receive tokens native to the platform for contributing data and managing the proof of stake consensus mechanism.63 datum platform (https://datum.org) was founded in 2017 and is headquartered in zug, switzerland. the b2c2b datum network allows anyone (outside the u.s., china, or south korea) to own and manage their data using the dat smart token for buying and selling data.64 while data agnostic, the datum network founders specify that the blockchain platform is intended for buying and selling individuals’ health information for research. the platform is compared to a decentralized version of the apple healthkit that respects data owners’ terms and conditions for data usage.64 the datum network enhances data research capabilities by capturing and linking data from iot devices, specifying that the network could capture information from digital health devices and provide research data to scientific or medical institutions.64 founded in 2015, dawex (https://www.dawex.com) markets itself as an advanced blockchain-based b2b data-exchange environment where organizations can share and commercialize data. the company notes that the blockchain platform provides data transaction security and traceability.65 as of 2020, fernandez et al.66 commented that dawex created a successful sharing platform but had yet to determine how to address data integration and pricing. specifically, buyers were required to offer a price without being permitted to evaluate the value of the dataset.66 embleema (https://www.embleema.com/), based in metuchen, nj, is designed to collect electronic health records and share them as real-world data for research. embleema uses a private hipaaand gdpr-compliant blockchain to manage granular patient consent and securely store patient information.67 the b2c2b blockchain is also designed for transparency of recruitment opportunities, study progress, and results. patient participants log in with blockchain-based public and private key pairs instead of user names and passwords.68 when individuals share their health information or participate in virtual studies such as surveys, participants receive points that can be exchanged for unspecified “rewards”.69 however, the patient advocacy page specifies that users receive compensation for the uses of their data.68 encrypgen (https://encrypgen.com/), located in miami, fl, is a b2c2b blockchain-based dna marketplace that allows individuals to provide their genome in return for “$dna,” a utility token that can be used to buy and sell dna profiles. the encrypgen platform facilitates storing, sharing, searching, buying, and selling user-provided profiles.70 the blockchain “gene-chain” backbone is used to manage the privacy and security of genetic data as well as facilitate the data-exchange process. encrypgen makes data available to third parties, such as research scientists, willing to pay for genetic information.50 enigma (https://www.enigma.co), a san francisco and tel aviv-based b2b and b2c2b company, offers an opensource blockchain protocol for data sharing. the mainnet blockchain, the secret network, allows decentralized applications to perform computations on encrypted data. because of the persistent encryption, the data remain private to the nodes—even on a public blockchain.71 enigma is designed to be blockchain agnostic and data agnostic, but the company promotes its ability to facilitate research on health information. further, the platform offers a data marketplace that allows data monetization.71 hu-manity.co (https://hu-manity.co/) was founded in 2018 and is based in sparta, nj. in this b2c2b platform, individuals can upload all, part, or none of their healthcare records to the data marketplace and specify how their healthcare data can be accessed and used.72 hu-manity. co allows patients to specify how their health information could be used with the prospect that pharmaceutical companies would pay each user for access to their data.73 of interest, the company allows individuals to “claim title” to their data and recognize their health information as “personal property”.72 the ibm blockchain is used to allow granular consent of data and securely track the uses of data. the website notes that the company has not yet received a critical mass of people using the app, but once enough data are available, data participants will receive utility tokens—the “hu” token—that could be exchanged for internal offers and incentives with the plan for offering fiat currency in the future.72 lunadna (https://www.lunadna.com/), a san diego-based b2c2b company, was formed in 2017 as a member-owned platform to help individuals manage the scientific and monetary value of their dna. individuals are encouraged to upload genetic files with the option of completing additional surveys and adding electronic health records to receive shares in the company.74 a portion of lunadna’s proceeds from research is shared with http://dx.doi.org/10.30953/bhty.v6.185 http://datapace.io https://datapace.io/ https://datum.org https://www.dawex.com https://www.embleema.com/ https://encrypgen.com/ https://www.enigma.co http://hu-manity.co https://hu-manity.co/ http://hu-manity.co http://hu-manity.co https://www.lunadna.com/ citation: blockchain in healthcare today 2023, 6: 185 http://dx.doi.org/10.30953/bhty.v6.185 7 (page number not for citation purpose) health datasets as assets members as (fiat) dividends per the company’s filing with the u.s. securities and exchange commission.74 in may 2021, the finnish company nokia launched a b2b blockchain-based data marketplace for sharing and monetizing data (https://www.nokia.com/networks/services/nokia-data-marketplace/). while this data marketplace is not designed exclusively for health data, nokia specified that health data are a use case for federated learning and monetization within its marketplace.75 nokia specifies that a private, permissioned blockchain creates trusted and secure data transactions with transaction automation and federated intelligence.75 the nature of monetization to users (i.e., fiat currency or cryptocurrency) is not specified. phros (https://phros.io/services/health_data_market) was founded in 2016 in taipei, taiwan as a b2c2b data-exchange network with a dedicated health data market. patients can sign consent to share healthcare data with the network to be used for research. patients and researchers exchange “health points” for health-related data.76 a blockchain is used to manage the fine-grained consent options to use patients’ health information, create a token-based exchange network, and offer wallet services to control users’ keys and tokens.76 the website specifies that it can automatically gather and update participants’ health record data and push alerts to healthcare providers or hospitals if patients need immediate care. data tokenization blockchain technologies have spawned innovation for sophisticated methods for managing data. because cryptocurrency is a digital asset represented on blockchains, the same approach has been applied to representing physical and digital assets for proof of ownership.77 referred to as “tokenization,” classes of blockchain-based tokens are divided into two categories of fungible tokens and non-fungible tokens (nfts). fungible tokens are designed to be divided into fractions where each fraction is equal to others in value, allowing them to be interchangeable.78 in contrast, nfts are unique assets that cannot be divided and are not interchangeable, such as a photo or physical object.78 the tokenization of digital assets has created new investment opportunities and new methods of establishing asset ownership.77 health records are also now being classified as nfts as unique assets with original value.79 hapiffah et al.80 created a proof of concept for a medical record system where patients’ medical record data are registered as nfts to establish proof of evidence and ownership. sandner et al.79 recognize that datasets can be classified as nfts for blockchain-based token exchanges where a data set’s value is identified with the value of the nft. the blockchain also then provides transparency and auditability to ensure honest data transactions.79 considerations complications impacting data valuation and sale can be economical, social, or ethical. if blockchain organizations wish to engage in data valuation and sale, these values drive considerations of monetization and privacy.1 patient monetization while healthcare or life sciences organizations may benefit from the sale and use of patients’ health information, will any of that money be given to the patients represented in the data sets? klugman81 argues that “it is only ‘just’ that [patients] benefit from the sale” (approx. p. 2). he adds that if the healthcare organizations are acting in the best interests of patients, then they should share data profit with the patients. tlacuilo fuentes82 notes that patients regularly receive benefits from retail organizations in exchange for uses of their data, such as discounts or free uses apps/services. klugman81 adds that it is well within a healthcare organizations’ authority to offer additional services or reductions to copays and deductibles when healthcare organizations profit from patient health information. while it is an admirable goal to provide compensation to patients who knowingly or unknowingly provide their information in a data marketplace, the marketplaces must determine methods of allocating compensation to these individuals. there are various rewards programs granted for the use of data; however, this section will focus exclusively on sharing payments for individuals represented in health data sets purchased and used for research. query pricing data are often accessed during queries where researchers may simply be attempting to determine study feasibility or compare and contrast data sets.51 in this case, the researcher needs to access an individual’s data to determine whether the data are sufficient without committing to a data set. should individuals be compensated when data sets are merely sampled? research and development pricing biocuration may only be a tiny part of research and development where a profitable product may not result for many years, if at all.83 many research studies do not have initial funding—much the less profit.26 how should the original data subjects be recognized if a patient provides an early and relatively insignificant contribution to a later project? horizontal value split this challenge reflects the many parties that contribute to the healthcare data chain.51 parties involve the healthcare provider who enters the data, the healthcare organization that stores and maintains the electronic health http://dx.doi.org/10.30953/bhty.v6.185 https://www.nokia.com/networks/services/nokia-data-marketplace/ https://www.nokia.com/networks/services/nokia-data-marketplace/ https://phros.io/services/health_data_market citation: blockchain in healthcare today 2023, 6: 185 http://dx.doi.org/10.30953/bhty.v6.1858 (page number not for citation purpose) wendy m. charles and brooke m. delgado record system or data warehouse, the data broker, or even the data marketplace. because each party provides data or infrastructure to support data, how should values be divided among these parties? vertical value split this challenge describes dividing the value among the individual patients represented in a data set where the data of different people contribute to the data set.51 should patients with more healthcare visits, therefore contributing more data, be compensated more than patients with fewer healthcare visits? or should patients with higher health data quality receive more compensation than those with less quality? cost sharing split because there are many costs for storing and curating data, should the profits be distributed in the same proportion as the costs?26 should the costs for resources and capital investments first be declared and quantified before determining how best to distribute the profits? a form of cost-sharing split is to grant free services in exchange for selling the data. for example, picnichealth provides a free personal health record app to patients/consumers if these individuals allow their health information to be sold for future research.84 otherwise, patients/consumers must pay $299 for processing the past medical records and $39 per month. when individuals receive monetization for participating in a data marketplace, it is also necessary to consider the financial ramifications of withdrawal. should a company allow an individual to remove their data before costs are recovered if the transaction was in exchange for free genetic sequencing?50 lunadna, a blockchain-based dna marketplace, allows individuals to withdraw consent for subsequent use of their data, but the individual will lose all ownership shares previously granted.85 this arrangement could coerce individuals to allow their data to be used instead of missing out on potential financial benefits. while some blockchain designers have proposed cryptocurrency-based payments based on decentralized anonymous research networks,86 it may be impractical for research payments in the u.s. to remain anonymous. payments for participating in research are considered taxable income, and internal revenue service (irs) form 1099 must be issued to the participant if payments equal or exceed $600 in a calendar year.87 it is unlikely that monetization payments could reach that amount, but organizations would have to track the identities of the individuals to comply with irs regulations.88 even if patient compensation were feasible, hank greely, director of stanford university’s center for law and the biosciences14 wrote, “as to compensation, figuring out a royalty kind of system seems very hard to me because of the difficulty of assigning cause/contribution to any particular person’s data … and any flat compensation would likely not be very much” (approx. p. 2). when the monetization to individuals is very small, the administrative costs would likely exceed the financial benefit to consumers. even when using blockchain-based automation, there are costs for data transfers, creating a business model that would be difficult to sustain. privacy and security blockchain-based technologies can offer new methods to protect the privacy of patient-level information. zero-knowledge proofs zero-knowledge proofs are blockchain-based strategies that allow one party to prove that some statement is true to another party without revealing anything but the truth of the statement.52,89 this technology is particularly effective for performing quality assurance without needing to access patient-level information. homomorphic encryption homomorphic encryption involves encryption methods that allow one to perform calculations on encrypted data that does not allow visibility into raw data. when decrypted, the calculated output is the same as if the operations had been performed on the unencrypted data.90 while promising, homomorphic encryption has not yet achieved widespread adoption. federated learning systems federated learning systems share machine learning algorithms or edge training plans without sharing the raw data.91 organizations can bring analytic tools to the data while protecting individuals’ privacy.92 creation of synthetic data some blockchain technologies allow the creation of synthetic data that mask individually identifiable data within a data set.93 even when using blockchains to manage health data, no technology is impervious to vulnerabilities. deidentification strategies using encryption may be vulnerable to future computing advances.92 further, blockchains and smart contracts have been hacked or breached50—even the sizeable public blockchain networks should not be described as entirely immutable.94 therefore, organizations should remain cautious about data protection for data sharing and sales because there could be considerable unintended consequences for the patients represented in the data sets. http://dx.doi.org/10.30953/bhty.v6.185 citation: blockchain in healthcare today 2023, 6: 185 http://dx.doi.org/10.30953/bhty.v6.185 9 (page number not for citation purpose) health datasets as assets data quality while health data acquisition is often thought to be relatively straightforward—especially when using a data service or marketplace—there are often misconceptions about health data quality. the reality is that health information is not designed for research purposes and can be notoriously inaccurate and incomplete.95 vezyridis and timmons26 described that within the national health services electronic health record systems, the billing codes used to record the same disease could vary widely between healthcare practices. additionally, the massive volume of electronic health data sets also affects quality because it is challenging to implement data standards and ranges.25 health data inaccuracies may require researchers to spend valuable time identifying and eliminating health information that may be of poor quality.83 worse, researchers may inadvertently use inaccurate health information in research that is not reproducible or may lead to spending limited research funds on projects that are ultimately dead ends.92 last, the use of deidentified data sets further complicates data quality because a researcher cannot examine the original sources to confirm or correct data values.92 while some may encourage the use of blockchain to address health data accuracy,96 the use of blockchain for inaccurate health information exemplifies the “zero state problem” described by lapointe and fishbane.97 the authors note that organizations have not achieved “trusted data” by adding inaccurate information to a blockchain. discussion mandl and perakslis92 point out that it is sadly ironic that patients and healthcare organizations are often unable to obtain health information necessary for treatment, but these same patients may be included in massive data sets that are shared and sold without appropriate monitoring or oversight. for many health data marketplaces, neither patients nor healthcare organizations are given visibility or control of health information sold in data marketplaces. this article describes how blockchain technologies are increasingly used to manage the transparency and control of health data in data marketplaces. this technology can advance individual patients’ control over their information, monitor access to their data, and control permissions,32 including the ability to revoke permissions.82 limitations as there is a growing interest in managing health data sets as assets, blockchain technologies can improve data valuation and asset management.8 however, there are no uniform approaches to valuation26 or assetization of health information.25 thus far, most research conducted about data value has focused on the factors that can influence perceived data value.25 however, there is a need to draft effective algorithms that consider data valuation methods, industry sectors, and data. further, it is unclear how fair market value limitations required for some data sets may influence or educate these algorithms. additionally, the scope of research on data marketplaces is not well developed,23 and the research on blockchain-based data valuation and sales is scant. these areas would benefit from additional study to advance concrete methods of data valuation and responsible development of blockchain-based data marketplaces. future work blockchain-based data marketplaces are emerging to provide data management and automate monetization practices. however, minimal research has been conducted involving heavily regulated data, such as health information or data intended for submission to the fda. there is a great need for determining appropriate blockchain platforms and best practices for data management to create sustainable marketplaces for this emerging area. while the legal, ethical, and regulatory considerations of health data ownership are outside the scope of this paper, additional research about data ownership may advance understanding about authority to control and value health information.81 similarly, there is an additional need for understanding whether blockchain-based nfts can establish datasets as assets and whether nfts can enhance concepts of data ownership, data control, and value. specifically, could nfts demonstrate the value of intangible data assets and/or exclusivity of these data assets? conclusions the sales and exchange of health information grow significantly more extensive and diverse as data can be collected from electronic health record systems and patient-generated data from wearables and wellness apps. the need for sales and exchange is bolstered by the need for real-world data within life sciences organizations. the combination of health data volumes and needs creates a new data economy82 and opportunities to better assess data value for these economic opportunities. health data assets have been managed by complex and outdated methods where patients do not have awareness or control of the uses of their health information. however, as patients are increasingly empowered with greater electronic access to their health information—and privacy regulations have enabled individuals to have more information and control over data uses—blockchain-based data management systems will serve as an enabling technology. this technology allows patients opportunities for granular consent and greater visibility into the uses of their health information. while mechanisms of data monetization and assetization are still being developed, http://dx.doi.org/10.30953/bhty.v6.185 citation: blockchain in healthcare today 2023, 6: 185 http://dx.doi.org/10.30953/bhty.v6.18510 (page number not for citation purpose) wendy m. charles and brooke m. delgado blockchain technology is a critical tool for maximizing the potential for the emerging health data economy. funding statement the authors did not receive any funding or financial source of support to write this manuscript. conflicts of interest the authors work for a company that designs blockchain platforms used for healthcare information. 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https://support.lunadna.com/support/solutions/articles/43000037181-can-i-lose-shares-in-lunadnahttps://support.lunadna.com/support/solutions/articles/43000037181-can-i-lose-shares-in-lunadnahttps://support.lunadna.com/support/solutions/articles/43000037181-can-i-lose-shares-in-lunadnahttps://doi.org/10.3390/e22070712 https://www.irs.gov/pub/irs-prior/i1099msc--2019.pdf https://www.irs.gov/pub/irs-prior/i1099msc--2019.pdf https://doi.org/10.3389/fbloc.2019.00018 https://doi.org/10.3389/fbloc.2019.00018 https://doi.org/10.1109/access.2020.3036811 https://doi.org/10.1109/access.2020.3036811 https://doi.org/10.3390/s18113785 https://doi.org/10.1109/access.2020.3037474 https://doi.org/10.1109/access.2020.3037474 https://doi.org/10.1056/nejmp2102616 https://doi.org/10.1056/nejmp2102616 http://arxiv.org/abs/1908.05725 https://doi.org/10.6028/nist.ir.8202 https://doi.org/10.6028/nist.ir.8202 https://doi.org/10.5210/disco.v6i0.3581 https://doi.org/10.1109/mce.2018.2816306 https://doi.org/10.1109/mce.2018.2816306 https://beeckcenter.georgetown.edu/wp-content/uploads/2018/06/the-blockchain-ethical-design-framework.pdf https://beeckcenter.georgetown.edu/wp-content/uploads/2018/06/the-blockchain-ethical-design-framework.pdf https://beeckcenter.georgetown.edu/wp-content/uploads/2018/06/the-blockchain-ethical-design-framework.pdf http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) technical briefs & short reports model complexity reduction for zkml healthcare applications: privacy protection and inference optimization for zkml applications—a reference implementation with synthetic ichom dataset sathya krishnasamy, ms;1 and ilangovan govindarajan, md2 1president and principal, chainaim, newington, connecticut, usa, 2chief medical officer, guardianmedx, las vegas, nevada corresponding author: sathya krishnasamy, email: sathya.krishnasamy@chainaim.com doi: https://doi.org/10.30953/bhty.v7.340 keywords: blockchain, diabetes, distributed ledger, model complexity reduction privacy, machine learning, ichom, international consortium for health outcomes measurement, zkml, zero-knowledge machine learning abstract web 3.0 represents the next significant evolution of the internet that embodies the underlying decentralized network architectures, distributed ledgers, and advanced ai capabilities. though the technologies are maturing rapidly, considerable barriers exist to high-scale adoption. the author discusses the barriers and the mitigations through specific technologies maturing to solve those issues in an earlier paper titled moving beyond pocs and pilots, published in 2023 in blockchain in healthcare today. these include privacy-preserving technologies, offchain and on-chain design optimizations, and the multi-dimensional approach needed in planning and adopting these technologies. as an extension, this paper discusses one such enabler, zero knowledge machine learning (zkml), which merges two streams of technology in unique ways to address problems in privacy and the cost of inference. zero-knowledge proofs (zkp) allow one party to prove the validity of a statement to another party without revealing any additional information about the statement itself. the zkml combines the cryptographic principle of zkp with machine learning (ml) techniques. it is still a maturing technology and needs baselines for applications in global healthcare. in this effort, the authors conceptualize the technical and operational feasibility of using zkml and implement a reference healthcare implementation using the synthetic international consortium for health outcomes measurement (ichom) in the evaluation phase in a global healthcare setting for high-volume data collection, including patient-reported outcomes. model complexity reduction is researched and reported for the ichom diabetes dataset to advance the usage of ml models in global standards of healthcare data collection in network decentralized architectures for increased data protection and efficiencies. submitted: august 1, 2024; accepted: august 25, 2024; published: august 31, 2024 the sizable amount of data collection in recent years has produced unprecedented analytical capabilities. however,1with the rapid increase in data ingestion and machine learning (ml), particularly in centralized systems, data privacy and security concerns have increased significantly with excessive data movements, sometimes not truly needed. as central data repositories grow larger, they become attractive targets. healthcare data breaches have become increasingly appendix common. there have been many high-profile incidents, and the trend has been continuous. a key strategy for mitigating the risks associated with healthcare data is restricting access to data at the source and reducing unnecessary data movements. these include data access using role-based access, data encryption, minimal data transfer, and regular audits. while these are paramount, it is also critical to devise collaboration systems that could communicate across decentralized networks. zero-knowledge proofs (zkp) can be designed for evidence and verification between systems, even before the blockchains. these blockchain in healthcare today issn 2573-8240 https://orcid.org/0009-0000-1420-1098 https://orcid.org/ 0009-0005-3825-0923 mailto:sathya.krishnasamy@chainaim.com https://doi.org/10.30953/bhty.v7.340 citation: blockchain in healthcare today 2024, 7: 340 https://doi.org/10.30953/bhty.v7.3402 (page number not for citation purpose) s. krishnasamy and i. govindarajan zkps allow one party to prove the validity of a statement to another party without revealing any additional information about the statement itself and have been increasing in popularity as distributed ledger technology matures and has helped scale blockchain implementations. zero-knowledge machine learning (zkml) represents a revolutionary fusion of cryptographic and ml technologies. it combines the cryptographic principle of zkp with ml techniques. by integrating zkp with ml, zkml ensures that sensitive data remain confidential while still allowing for the development and utilization of predictive models. this integration is increasingly relevant where data privacy and security are paramount, such as in healthcare systems. in practical terms, zkml allows multiple entities to collaborate without compromising the confidentiality of their proprietary information. this means organizations can collaboratively train and utilize ml models on private datasets without exposing the data. for example, a medical research organization could aggregate data from various hospitals to develop a robust predictive model for disease without any hospital sharing its patient data. the use of cryptographic proofs ensures that the data remain secure and private throughout the process. currently, zkml is still in nascent research and development. while zkps have been a topic of cryptographic research since the 1980s, their application to ml is new and complex. this technology faces challenges related to computational efficiency and resource demands. implementing zkps can be resource-intensive, increasing processing times and costs. model complexity reduction is a recent technique to reduce model complexity, hence reducing computation times. it has been attempted using simpler models from kaggle. the international consortium for health outcomes measurement (ichom) is dedicated to developing standard sets of outcome measures that can be used globally to assess the quality of care for various medical conditions. this research aims to evaluate the usage of a global standard healthcare dataset (ichom) and apply model complexity reduction for a real-world, non-trivial example to a synthetic dataset in the ichom schema. hence, this research will be a reference for developing further models and optimizations that will make it conducive to optimizing the complexity and use of zkml for many use cases in healthcare that need privacy and collaborative decision-making. a paradigm shift: decentralized systems, ai models at source, and collaborative decision-making as we approach emerging architectures built upon decentralized systems, restricting data at source and understanding ways to work with the data at source via privacy-protected ml becomes essential. data are federated anyway in systems such as u.s. healthcare, and effective mechanisms are needed to enable federated learning, address privacy and security issues, and reduce computational overheads. by sharing model updates rather than data, federated learning enhances privacy. similarly, with distributed ledgers, an increasingly popular design concept is to provide proof to verification systems through zkp. though not a new concept, this method has successfully scaled blockchain systems and is becoming increasingly popular for next-generation distributed ledgers. these mechanisms also reduce data transmission risk and align with data protection regulations and collaborative decision-making. the zkml is a promising emerging technology that integrates ai/ml (artificial intelligence/machine learning) technologies into distributed ledgers. impact of zkml on healthcare privacy healthcare data are sensitive; therefore, privacy is the first design principle in healthcare systems. with zkml, healthcare providers can share insights derived from ml  models without exposing the underlying patient data. for example, a hospital could use a zkml model to predict patient outcomes. the model processes the data and generates predictions, while the zkp ensures that these predictions are accurate without revealing specific patient information. zkml allows different entities to collaboratively compute results without disclosing their individual data. this is particularly useful in healthcare research, where multiple institutions may wish to combine their data to improve disease predictions without  compromising patient confidentiality. as data privacy concerns intensify and the need for secure ml models grows, integrating advanced privacy-preserving technologies becomes crucial. literature review the zkps are cryptographic methods that enable one party (the prover) to convince another party (the verifier) that a statement is true without revealing any additional information beyond the validity of the statement itself. introduced by goldwasser, micali, and rackoff (1985),1 they established the theoretical framework for interactive proofs and zkps, demonstrating their feasibility and foundational importance. further work by fiat and shamir2 extended it to non-interactive zkp, which does not require multiple rounds of communication between the prover and verifier. recent advancements in zkps, such as zero-knowledge succinct non-interactive arguments of knowledge (zk-snarks) by ben-sasson and colleagues3,4 have enhanced their efficiency and scalability and opened possibilities for real-world applications. recently, ml showed success in modeling [ healthcare prediction tasks, ranging from disease diagnosis and https://doi.org/10.30953/bhty.v7.340 citation: blockchain in healthcare today 2024, 7: 340 https://doi.org/10.30953/bhty.v7.340 3 (page number not for citation purpose) complexity reduction for zkml prognosis to patient treatment. guerra and colleagues5 reviewed privacy-preserving ml literature for training and inference and concluded healthcare datasets are diverse and a fraction of them considered validating with independent standard datasets. they indicated the risks of centralized training for federated learning as a risk and also called out the need for collaboration between different entities across multiple roles of ml scientists, healthcare practitioners, and privacy and security experts, which need privacy-preserving mechanisms to work together over distributed ledgers. a newly emerging technology, zkml applies zkp to ml for privacy, ensuring that sensitive data remain confidential while allowing for developing and utilizing predictive models for privacy and collaboration. the zkp, as such, are computationally expensive and get even more computationally expensive for ml inference proofs. recent work from alejandro martinez gator6 produced a model complexity reducer (mcr) library and illustrated its reference implementations on sample datasets. however, from a global healthcare perspective, there is a need to validate and benchmark this effort with a high-scale standardized global healthcare schema. methods research goals guardian medx is a comprehensive care plan offering personalized medical care with continuous monitoring and assistance. the goal is to improve the wellness of seniors and reduce hospitalizations in the southern region of india. the principals aimed to follow an evaluation process first to learn from earlier work done in diabetic care in south india7, and identify the cultural elements involved in the disease dynamics, from diagnosis to treatment to adherence to continuous maintenance, and to find the suitable standardized format to capture both clinical and patient-reported outcomes for a holistic basis. the intent is to find the data collection schema conducive to privacy-preserving collaboration in emerging web 3 technologies, including ml for data analysis and privacy-preserving consent and cooperation. the starting point for this research is to find a high-scale standardized global healthcare schema that addresses the challenges indicated above, allowing for adequate data  collection and identifying the privacy-preserved constructs needed for collaborative learning purposes at a global level. the specific research goals include the following: • learn from earlier research and create a data collection approach that captures the cultural elements of diabetic care. • identify a specific data schema that is standardized and can be used for data collection and privacy-preserved learning at a global scale. • explore zkml as the model for that global data schema. • identify the parameters, limitations, and mitigations using mcr. • baseline the data schema with synthetic data. after reviewing the literature on diabetic care delivery in india7, the big data collection and ml effort8 and standardization formats and adaptability reports9,10 the data schema was decided to be the ichom schema, with the adaption needed. the ichom datasets and impact on global healthcare for diabetes the ichom is dedicated to developing standard sets of outcome measures that can be used globally to assess the quality of care for various medical conditions. for diabetes, a chronic condition with significant variation in presentation and management across different cultures and healthcare systems, this standardization is crucial. diabetes care can vary significantly due to cultural differences, socioeconomic factors, and healthcare infrastructure. for instance, the management strategies and patient outcomes in a high-income country might not directly apply to a low-income setting with different resources and cultural attitudes toward health. ichom’s dataset addresses this challenge by allowing healthcare systems to adapt standardized measures to local contexts. this cultural tailoring ensures that the outcome measures are relevant and practical in diverse settings, thereby improving the dataset’s utility and impact on global health outcomes. by providing a standard set of metrics, ichom helps identify gaps in care and outcomes across different regions and populations. by adopting a uniform approach to measuring outcomes such as blood glucose levels, quality of life, and complication rates, the ichom diabetes dataset enables healthcare providers to benchmark their performance against global standards, identify best practices, and improve patient care and population health cohorts. given the nature of the data and the standardization effort, the principals initiated the evaluation with synthetic datasets. they used the ichom older population and diabetic datasets to provide insights into the complex nature of diabetes care and its impact on patient outcomes. this research offers a comprehensive data-oriented approach to diabetes management by collecting and analyzing data on demographics, diagnosis, lifestyle and social factors, treatment methods, diabetes control, acute events, chronic complications, and patient-reported https://doi.org/10.30953/bhty.v7.340 citation: blockchain in healthcare today 2024, 7: 340 https://doi.org/10.30953/bhty.v7.3404 (page number not for citation purpose) s. krishnasamy and i. govindarajan outcomes. it seeks to illuminate the intricate interplay of factors affecting diabetes management and optimize diabetes care cost-effectively with early diagnosis and remote and continuous monitoring at scale. as part of the data evaluation exercise for the research, synthetic datasets with hypothetical values are produced from earlier literature and the researchers’ experiences. data adequacy, baseline, and meaningful data mappings using the ichom diabetes datasets v5.0 are established for practical deployments in the indian cultural setting. these datasets were selected from the ichom v5 diabetic dataset. synthetic data was set up for 100 patients, with several iterations of data and clinical validations. exploratory data analysis with univariate and bivariate analysis and correlations was developed and analyzed. the model was built using a light gradient boosting machine (lightgbm) regressor—an open-source, high-performance gradient boosting framework designed for efficient and scalable ml tasks. it is specially tailored for speed and accuracy, making it a popular choice for both structured and unstructured data in diverse domains. key characteristics of lightgbm include its ability to handle large datasets with millions of rows and columns, support for parallel and distributed computing, and optimized gradient-boosting algorithms using histogram-based techniques and leaf-wise tree growth. a crucial aspect of zkml is model complexity reduction, which is critical with current inference costs and distributed ledger technology scalability to make these models more efficient and practical for real-world applications. this article explores the concept of model complexity reduction in the context of zkml, with specific examples and a focus on its critical role in healthcare. model complexity reduction uses concepts of pruning— removing unnecessary parts of the model that contribute minimally to the final decision, quantization—reducing the precision of weights and activations for efficient computing, and knowledge distillation—where the knowledge is transferred to a simpler model that still retains predictive capabilities. this reduced model can then be used within a zkml framework to perform computations efficiently while providing privacy guarantees through zkps. furthermore, a stretch goal was to determine the technical feasibility of using the synthetic data generated for zkml applications effectively in proof and verification systems, given the potential for cross-learning insights without losing privacy. model complexity reduction for the ichom diabetes synthetic dataset model complexity reduction in zkml is essential to mitigate overfitting, improve interpretability, and increase computational efficiencies, reducing the computational resources for training and inference. the zkml software and its model reduction library used for this research was giza zk cook. the complexity reduction algorithm executes the following steps. 1. correlation analysis and feature importance for feature selection and reduction: features with high correlation are candidates that may contribute to redundancy and reduction. using techniques like recursive feature elimination, features of low importance are eliminated. 2. l1 (lasso) regularization drives less important feature coefficients to zero, and l2 (ridge) regularization penalizes large coefficients to reduce complexity without eliminating features. l1 and l2 regularization are combined to balance the benefits of both. for tree-based models, pruning removes branches that contribute minimally to the predictions and consolidates and splits nodes. 3. principal component analysis (pca) and t-distributed stochastic neighbor embedding (t-sne) are applied for dimensionality reduction. 4. multi-pass cross-validation and hyperparameter tuning for balancing model complexity and accuracy. the mcr was used from the giza library. the analysis model runs in a python environment. results and discussion the data calibration from the evaluation phase shows the feasibility of practical and substantial use of ichom v5 datasets in periodic and continuous monitoring to prevent the progression of conditions, reducing the quality of life (figure 1), how it relates to the quality-of-life scores ( figure 2), and how adherence can be mapped granularly (figure 3). from the guardian medx clinical evaluation of synthetic data produced based on anonymized extraction from earlier aggregated results for the south indian population, the model is deemed to help significantly in advancing outcome-based and cost-effective remote figure 1. diabetes quality-of-life complications. hba1c: glycated hemoglobin. source: copyright by the authors https://doi.org/10.30953/bhty.v7.340 citation: blockchain in healthcare today 2024, 7: 340 https://doi.org/10.30953/bhty.v7.340 5 (page number not for citation purpose) complexity reduction for zkml monitoring care, based on data analysis and regressor models for the data analyzed in phase 1. this is particularly the case in hba1c (glycated hemoglobin) control, frequency of tracking, and relationship to complications and quality of life scores. the co-existence of other chronic diseases was evident from the data. the ichom data captures patient-reported outcomes with who scores, which showed a negative trend in quality-of-life scores with increasing hba1c values reported. all adherence metrics also showed the expected relationship with the hba1c results. the study opens the possibility of alerts for action based on escalation predictions as the remote monitoring feeds come in for at-scale patient management for early interventions. model complexity before model reduction the key aspect is to evaluate the model complexity reduction baselines for this synthetic dataset and compare the figure 3. effect of adherence (adhe) on hba1c (glycated hemoglobin) control. source: copyright by the authors figure 2. international consortium for health outcomes measurement: who (world health organization) scores versus hba1c (glycated hemoglobin). source: copyright by the authors https://doi.org/10.30953/bhty.v7.340 citation: blockchain in healthcare today 2024, 7: 340 https://doi.org/10.30953/bhty.v7.3406 (page number not for citation purpose) s. krishnasamy and i. govindarajan before-and-after model parameter complexity. this is crucial to optimizing the run-time cost and seeing how this model fares for use in distributed ml and collaborative systems over distributed ledgers. the lgbm regressor was configured with the parameters n-estimators: 1,200 and max-depth: 8 (figure 4). in contrast, the model complexity is reduced to n-estimators: 150 and max-depth: 4, after the model complexity reduction after running through the zkcook library (figure 5). representing this in terms of nodes: • number of nodes = number of trees* (2 depth -1) • the before complexity reduction number of nodes evaluate to 1,200 * (2 8 – 1) = 306,000 • the before complexity reduction number of nodes evaluate to 150 * (2 4 – 1) = 2,250 model complexity after model complexity reduction the difference accounts for a 99.26% reduction, and it is also in line with some of the other reference examples. the choice of the regressor for the problem and further reduction using mcr based on the evaluation data shows that the ichom v5 diabetes dataset can be used to capture data so that the interoperability can be enhanced to study and report results in collaborative settings to be used in zkml applications. these results imply that the needed adaptability for the diabetes study could be captured in the global format of ichom and show promise based on the evaluation data. in addition, these results show promise in generating privacy-protected proofs for verification systems and collaborative sharing of proof of diagnosis based on patient consent in a privacy-protected way with other collaborating parties. given these results in the evaluation phase, the proofs can be generated optimized for computational efficiency once the study progresses. this specific adaptation included a subset of the complete dictionary of the ichom dataset, as it was adapted to this clinical setting, which is a limitation. so, we see this as a starting point for other work to use standardized datasets such as the ichom datasets in different cohorts and other diseases from the ichom datasets. some of those situations will have an increased number of data columns and data analysis requirements, which will give us additional reference points for complexity before and after baselines and their impact on proof and verification systems. also, an important point to note is that this is a preliminary baseline, as the technology matures very fast from all angles—standards development, model reduction techniques, reduction of proof, and verification systems acceleration both at the software and the hardware level. hence, it will become figure 4 model complexity before model complexity reduction. ichom: international consortium for health outcomes measurement; mcr: model complexity reducer. source: copyright by the authors https://doi.org/10.30953/bhty.v7.340 citation: blockchain in healthcare today 2024, 7: 340 https://doi.org/10.30953/bhty.v7.340 7 (page number not for citation purpose) complexity reduction for zkml important to have a registry of zkml developments across these parameters. conclusions and future work based on data analysis and regressor models for the data analyzed in the evaluation phase, guardianmedx clinical evaluation deems the model to help significantly advance outcome-based and cost-effective remote monitoring care. applying the giza zkcook model complexity reduction algorithm to ichom diabetes data resulted in more interpretable and computationally efficient models. the computing times were significantly reduced on a standardized ichom dataset to use ml and privacy-protected settings to retain data at the source. this increases security and provides verifiable proofs for any prediction models driving agents and to use ml models in conjunction with decentralized distributed ledgers to open collaboration possibilities without giving out the internal details of the data. given these results in the evaluation phase, once the study progresses, the proofs can be generated optimized for computational efficiency. further work can be extended to other adaptations of the ichom framework for diabetes in another cohort in another setting to compare results, as well as using them in other disease datasets from ichom. shortly, the team intends to extend the zkml functionalities to feed agents for further processing and advancing privacy-protected multi-party insights. funding none. conflicts of interest none. contributors sathya krishnasamy is the president and principal of chainaim technologies. his 25 years of background span extensive experience in managed care payor settings in leading u.s. healthcare firms, including aetna and anthem. he focuses on emerging technologies, including ai/ml systems and distributed ledger technologies. he also serves as an advisor in many industry efforts in payor-provider collaboration, standards organizations, and efforts such as account aggregators in india advancing fintech, healthcare, and skills sectors. he currently serves as president and principal at chainaim, offering technical strategy consulting and application and development services. sathya krishnasamy helped conceptualize the use of ichom for data, evaluate the synthetic dataset, establish a model complexity baseline, and evaluate the zkml healthcare use case. dr govindarajan is the chief medical officer of guardianmedx. he is a healthcare executive with a strong medical background and technological knowledge. he has 35 years of extensive experience in internal figure 5. model complexity before model complexity reduction. ichom: international consortium for health outcomes measurement; mcr: model complexity reducer. source: copyright by the authors https://doi.org/10.30953/bhty.v7.340 citation: blockchain in healthcare today 2024, 7: 340 https://doi.org/10.30953/bhty.v7.3408 (page number not for citation purpose) s. krishnasamy and i. govindarajan medicine and geriatrics in india and serves as an advisor for geriatric and palliative care for many government entities in india. he has managed and administered patient-centric quality care following a unique continuum of care in clinics, hospitals, nursing homes, hospices, and homes. dr govindarajan started the initiative and performed the research for data collection needs, design and evaluation of the ichom for diabetes data, and clinical evaluation of the synthetic datasets. data availability statement (das), data sharing, reproducibility, and data repositories the data dictionary for the ichom v5 diabetes data dictionary is available at https://www.ichom.org/ patient-centered-outcome-measure/diabetes/ application of ai-generated text or related technology none. acknowledgments yugesh panta, a master of science student at the department of electrical and computer engineering, tandon school of engineering, new york university, helped the principals with research data collection, validation, analysis, and model-tuning aspects. references 1. goldwasser s, micali s, rackoff c. the knowledge complexity of interactive proof systems. proceedings of the seventeenth annual acm symposium on theory of computing—stoc ’85. 1985. 2. fiat a, shamir a. how to prove yourself: practical solutions to identification and signature problems. advances in cryptology—crypto’ 86 [internet]. 2019;186–94. available from: https://link.springer.com/chapter/10.1007%2f3-540-47721-7_12 [cited 2024 july 31]. 3. ben-sasson e, chiesa a, tromer e, virza m. succinct non-interactive zero knowledge for a von neumann architecture [internet]. 2019. available from: https://eprint.iacr. org/2013/879.pdf [cited 2024 july 31] 4. ben-sasson e, bentov i, horesh y, riabzev m. scalable, transparent, and post-quantum secure computational integrity [internet]. eprint iacr. 2018. available from: https://eprint.iacr.org/2018/046 [cited 2024 july 31] 5. guerra-manzanares a, lechuga j, maniatakos m, shamout fe. privacy-preserving machine learning for healthcare: open challenges and future perspectives. iclr 2023 workshop on trustworthy machine learning for healthcare. arxiv. 2023; 1-13. https://arxiv.org/abs/2303.15563 6. gotor am. maximizing model efficiency with model-complexity-reducer (mcr). zkcook/docs/mcr.pdf at main gizatechxyz/zkcook [internet]. github. [cited 2024 aug 1]. available from: https://github.com/gizatechxyz/zkcook/blob/ main/docs/mcr.pdf 7. das ak, saboo b, maheshwari a, nair vm, banerjee s, jayakumar c, et al. health care delivery model in india with relevance to diabetes care. heliyon. 2022 oct;8(10):e10904. https:// doi.org/10.1016/j.heliyon.2022.e10904 8. musacchio n, giancaterini a, guaita g, ozzello a, pellegrini ma, ponzani p, et al. artificial intelligence and big data in diabetes care: a position statement of the italian association of medical diabetologists. j med internet res. 2020 jun 22;22(6):e16922. https://doi.org/10.2196/16922 9. diabetes [internet]. ichom. [cited 2024 aug 1]. available from: https://www.ichom.org/patient-centered-outcome-measure/ diabetes/ 10. benning l, das-gupta z, fialho ls, wissig s, tapela n, gaunt s. balancing adaptability and standardisation: insights from 27 routinely implemented ichom standard sets. bmc health serv res. 2022 nov 28;22(1):1424. https://doi.org/10.1186/ s12913-022-08694-9 appendix acronyms defined ai/ml: artificial intelligence / machine learning hba1c: glycoxylated hemoglobin ichom: international consortium for health outcomes measurement lightgbm: light gradient boosting machine mcr: model complexity reducer ml: machine learning zk-snarks: zero-knowledge succinct non-interactive arguments of knowledge zkml: zero knowledge machine learning zkp: zero-knowledge proofs copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.340 https://www.ichom.org/patient-centered-outcome-measure/diabetes/ https://www.ichom.org/patient-centered-outcome-measure/diabetes/ https://link.springer.com/chapter/10.1007%2f3-540-47721-7_12 https://eprint.iacr.org/2013/879.pdf https://eprint.iacr.org/2013/879.pdf https://eprint.iacr.org/2018/046 https://arxiv.org/abs/2303.15563 https://github.com/gizatechxyz/zkcook/blob/main/docs/mcr.pdf https://github.com/gizatechxyz/zkcook/blob/main/docs/mcr.pdf https://doi.org/10.1016/j.heliyon.2022.e10904 https://doi.org/10.1016/j.heliyon.2022.e10904 https://doi.org/10.2196/16922 https://www.ichom.org/patient-centered-outcome-measure/diabetes/ https://www.ichom.org/patient-centered-outcome-measure/diabetes/ https://doi.org/10.1186/s12913-022-08694-9 https://doi.org/10.1186/s12913-022-08694-9 http://creativecommons. org/licenses/by-nc/4.0 http://creativecommons. org/licenses/by-nc/4.0 virtual keynote | trustworthy computing (45 min) jerry cuomo, global industry leader, retired ibm fellow and distinguished research professor, north carolina state university keynote description this talk introduces trustworthy computing as a shift toward systems built on verifiable, transparent, and privacyrespecting foundations. using blockchain and ai, it explores real-world impacts—from food safety and identity protection to ai transparency and machine unlearning—emphasizing the transition from reputational trust to algorithmic trust in everyday digital life. health care and life sciences professionals will find particular relevance in applications that enhance drug authenticity, patient data privacy, and ai reliability in decision making. ai disclosure this document was transcribed entirely from the keynote video using automated ai transcription tools transcript hi, jerry here. well, actually, i'm a clone of jerry's voice, starting off by saying, thanks for the invite to speak at converge to accelerate. my talk today introduces trustworthy computing as a shift towards systems built on verifiable, transparent, and privacy-respecting foundations. using blockchain and ai, it explores real-world impacts, from food safety and identity protection to ai transparency and machine unlearning, emphasizing the transition from reputational trust to algorithmic trust in everyday digital life. i'm hoping that healthcare and life sciences professionals will find particular relevance in applications that enhance drug authenticity, patient data privacy, and ai reliability in decision-making. enjoy the talk, and now over to jerry. hey, the real jerry here. a little bit about myself. i'm a retired ibm fellow, and i recently founded a company called wild ducks llc, where it allows me to do a little writing and consulting stuff. i also teach at north carolina state university on this very topic of thinking about trustworthy computing. and i have a couple of books, think ai and think blockchain, to kind of complement that, plus a podcast, of course, called wild ducks. so i hope you enjoy this presentation. when you think about trust, we always fall back on what has served us humans for quite some time, and that's reputational trust. and as we all know, reputational trust is building trust through established relationships and long-term and predictable behavior or reputation. https://conv2xsymposium.com/agenda/#fegahcdb https://conv2xsymposium.com/agenda/#fegahcdb and with that comes at least a perceived reduction in risk based on social proofs. you know, i've been around this person long enough. i can kind of predict their emotions. they've consistently acted this way. you know, that gives me a sense of trust. and, you know, we've, as people, lived by that trust for quite some time. and i'm not suggesting that we're going to stop doing that. that's a critical part of trustworthy computing. companies we trust, people within those companies, et cetera, your social impression, and all of these other ways these days that we can verify a person's relationship and trust between i would like to say there's a little bit more that we can do today, and that's pair this or complement this with algorithmic trust. so what this does is it quantifies and provides an additional level of trust through mathematics and mathematical certainty. in short, minimizing risk, as we're going to see through cryptographic proofs. and a lot of this presentation is focused on this latter part, algorithmic trust. we know about reputational trust, that's not going away, but again, to really build trustworthy computing, let's try to pair reputation with a little algorithmic trust, and we get a beautiful thing that'll occur when you bring these two things together. so, the first technological ingredient is this thing we all call blockchain, and many associate blockchain with bitcoin. and you wouldn't be wrong if you do that. obviously, the bitcoin network is powered by a type of blockchain. but blockchain is more than that. i tend to look at blockchain as much as a technology than a social movement or anything like that. i see it as some really cool cryptography and distributed networking and things of that nature. so i see it as an example as a platform for building trustworthy computing. and i want to kind of put a little bit more behind that and maybe get a little dramatic and say, would you believe me if i said blockchain is poised to change everyday life for good? i mean, forever and for the benefit of good. and while we may not always need every element of blockchain, what i'm saying is the ingredients that go into blockchain, are the right ingredients for building this thing that i like to call algorithmic trust. so i'm a bit of a storyteller. so let me tell you three stories that to this day, i've been doing talks on this topic for about a decade. and these three examples continue to resonate and inspire me. and so much has changed in this decade. but these examples and also the networks that live behind these examples continue to endure. so i'll start by saying, has this ever happened to you? and this is an impressionistic view of a trustworthy sandwich, or maybe not so trustworthy. have you ever been maybe traveling through an airport and picked up a sandwich before jumping on an airplane? and then halfway through the flight, you look like this guy here. and maybe there's some kind of foodborne illness that you just got. i don't know about you, but back in the day, in 2006, i remember spinach taking a bad rap, no pun intended, but bagged spinach was found to be the cause of an e. coli breakout. and it took regulators two weeks to conduct the trace back and determine the exact source of the outbreak. now, in two weeks, a lot of bad stuff happened to people who really got sick and some of them pretty seriously sick. and then also to the spinach itself, because we didn't know where the good and the bad spinach was. so we just had to eradicate spinach. so the market for some years later was kind of spinach-less, if you want to say that. but i like to report that everyday life has been changing now for the better. around this notion of the food trust network, it started way back when with a kind of consortium with the likes that you see up here. and i'll name two in particular, ibm and walmart. and they put together this food trust network with the idea to quickly pinpoint the sources of contamination and to reduce the impact of food recalls, and of course, limit the number of people who get sick or die from such foodborne illnesses. let me tell you a little bit about how it works. using blockchain, we're gonna trace the provenance of the ingredients as they travel from farm to fork. so think about the sandwich. it has chicken in it, maybe tomatoes, mayonnaise, and lettuce, and maybe we're all fixed that it's the chicken that caused the illness, but maybe it's the tomatoes or the lettuce, right? so walmart and ibm started looking at this problem, and they did an a-b test. and the mango, packaged mangoes were picked by walmart as the source of this a-b test. they went into a walmart store, they went into the produce section, looked at a bag of mangoes and said, okay, let's figure out how long it's going to take us manually to trace the origin of this mango. you know, with the thought that if there's something wrong with the mangoes, they can go back and only remove the mangoes from this farm or supply chain. and it took seven days to do that trace back. but with the ibm blockchain platform at the time, frank giannis, who now i believe works for the food and drug administration, but at the time was in charge of food safety at walmart, said going from seven days to 2.2 seconds is a really big deal. that's food traceability at the speed of thought. and i think that you will see is the first example as potentially this cryptography changing everyday life for the better. carrefour and nestle, i guess, is something more concrete. and i used to carry this box of musseline, which is dried potatoes. and on it, there's like a qr code and you can see a mobile app. and what you can do with this is kind of take a picture of the qr code with your phone and you can see kind of the trace back from the shelf that you picked that food off of to the farm. ultimately it came from. and you can look through from tracing back to what would that be? that would be like number four, the carrefour manufacturers, the product arrived at 25 carrefour warehouses, it was kept in storage, et cetera. and then let's see, number three, going backwards, the product is then stored in two nestle warehouses located in this region of france. and then, you know, going back in time even more, it went to the food processing plant, number two, and it looked at doing the packaging and, you know, kind of harvesting it from the crop to, you know, its powdered form, and then ultimately, number one, to the farm. so tracing it all the way back to the 165 growers. so think about this traceback and having all of these disparate entities from the farmer to the warehouse folks kind of recording on this immutable ledger the farm-to-fork traceback of it. so that's number one. number two is my aunt tessie. has this ever happened to you? aunt tessie recently had to fill out a application to rent an apartment. now, think about what year it is right now. and in this kind of simple act, she tells me she had to fill out a stack of papers. yes, papers, as in like things you write on. and in that, it felt like she was giving every piece of information about herself in doing so. so, of course, the apartment office, but she had to go and get verification records for the bank. they even wanted a cell phone contract to show her address and that she had an active account there. and then the department of motor vehicles, which had your license, which was your kind of state record of where you lived, right? so they wanted all of this information. i know i give out geez, i've been giving out information for generations now. ever since the web was introduced, probably thousands of websites over the years, i've registered that. i don't know where that information is. and neither did aunt tessie. and then we all get some of these on a bad day. oops, your data has been breached. i hate when that happens. and, you know, dear valued customer, we're very, very sorry. it won't happen again. and your information. so the dark web, while you may have forgotten where you put your digital information, the dark web hasn't. so this is really the bane of digital existence, and that's privacy. and every year, javelin strategy and research in 2023 was the last time i checked, and it was up to 15.4 million customers were hit with some kind of identity theft. so that continues to be a big deal and just keeps growing up. so the next big problem that cryptography and these ingredients that go into blockchain can help solve is around digital identity. and i had the pleasure a few years ago to work with a company in canada called securekey. and they really did something interesting with cryptography in the form of proofs of identity and using trusted parties like your bank to verify, and the system is called verified me, to verify that you are indeed who you say you are. so rather than sending information all over the interweb, you're keeping information with your state for your driver's license, maybe your bank, your primary bank, and then they become like your friends in a social network and they can vouch for you. so you don't have to give the millennial partners your information, but all you do is you give them permission to run this proof with your bank and maybe with the state to verify that it's you. and it gives them enough trust and confidence that those institutions are trustworthy by reputation that they will accept, right? so that's a pretty big deal. and i think that is taking a run at protecting our identity. the way it works just a little bit is it avoids the honeypot, the big database with all of your identity in it with a big bullseye on it that says, hack me. so there's no honeypots here because again, it's distributed through these trusted sources, these verifiers. no tracking. it uses something that the national standards group calls triple blind data exchange, where the the person requesting the identity of the apartment doesn't have your information on exactly where you live, what your eye color is, and all those things that you might find on a driver's license. so you're protected, you as the requester, to be verified. the people vouching for you, they are trusted parties, but the millennial apartments don't know who you're banking with, what state necessarily you have your driver's licenses, but it trusts that those sources are authentic. and then third, the bank doesn't know that you're trying to rent an apartment. so triple blind, but somehow this works. so no unnecessary information. when i go in, i don't have to worry that now someone has my address that shouldn't have it. that i didn't approve to have it. so i think you would agree that's example two of everyday life changing for the better. and this last example does not involve me giving pills to little kids. this happened to me because i lived it. so i know it's a real thing. and that's supposed to be my son with a taekwondo uniform on. and the story goes is i used to be one of several parents that would cart the kids back and forth to their tournaments, their martial arts tournaments. and yes, the kids would get banged up and inevitably someone would say, can i have an aspirin? and i would carry around this, you know, pill jar with aspirins and, and, and stuff like that. and sure enough, you know, i would, i would give one or two of the kids aspirin to help their ailments. and one time as i gave a child who asked, um, what i thought was a tylenol, the parent grabbed it before the kid took it and looked at the pill and said, jerry, what are you giving my kid? and my heart dropped. i'm like, what can it be? you know, i have tums and advil and stuff like that in there. so i don't know what it was, but we didn't recognize the pill. and we kind of googled a number on the pill. we found out it was a generic version of tylenol. from this point on, i always buy the real thing just because i want to see the real label on it. but, you know, it scared the daylights out of me, you know, perhaps giving them a, you know, substandard piece of medicine or something i didn't think was really what it was. but it's a problem. one in 10 medical products circulating in low and middle income countries, for example, are either substandard or falsified. so said the world health organization and things like cough syrups for children containing powerful opioids, fake antimalarial pills made of cornstarch and potatoes, right? so, you know, this is a real big deal. but again, i'd like to say that, you know, life, digital life as we know it is moving toward a more trusted world thanks to this thing we call crypto anchor verifier, something created by ibm. and there's examples like this. where again, this is not necessarily directly blockchain, but it's using identity proof technology in a very compelling way. so for example, let's say i had two aspirins, one real and one falsified. what we use is a standard cell phone with a lens on it, a plastic lens and some software with ai, basic machine learning on it. that creates a light spectrum analysis of this particular material, aspirin, versus known correct versions of this that may have been put on a ledger someplace and say, here's the real one, the spectrum. and it creates a digital fingerprint of that spectrum. and you create a digital fingerprint of what you think is real. and we'll compare the two. and then if we do that to something that's false against something real, maybe the real one was taken at the factory, at the pharmaceutical company. and then maybe at the drugstore, every once in a while, the pharmacist does a check on the shelf. they may see this, like, look, the spectrum is different. and then digitally, you will be able to do that similarity comparison and see that, no, this is probably fake. it's not matching up. and that was an abstraction, but here is an example. this was in our lab and a test case. so i'm taking a picture of the first aspirin. there's the color distribution light spectrum. we do it again. and if we have the fake one, apparently, and then if we show the two, you can see one has one main peak. that's the real one. and then this fake one has kind of two peaks in its color distribution. so it's a little bit off. so the spectrum analysis, you know, kind of flags that and says, hmm, suspicious here. this might be a fake. so preventing counterfeiting. and again, this works for pharmaceuticals, things in healthcare, other life science materials, you know, plants, wine, olive oil. we've tested this with all. and light spectrum is a form of fingerprinting. so if you can actually take that into a cryptographic fingerprint, you're well on your way now to being able to trust, to create digital trust to go along with reputational trust. i trust the pharmacy. i trust the pharmaceutical company. i trust that sometimes malicious things can happen along the way, and that's where the digital trust is built, using these types of proofs. so what is blockchain? let me just break it down because i have a more i'd say computer science view of it. you know, i see blockchain right up there with the linked list as a very effective data structure and way to create a pipeline where ultimately you're gaining trust, more trust in the data through proofs and cryptography. so, you know, databases are, you know, if you were thinking blockchain and database, you would, wouldn't be right, but you wouldn't be that wrong either, right? so it's a type of store that may be in a kind of darwin genus thing. it's kind of in that same camp as nosql, sql, like the type of database. but unlike those databases, what really stands out to me is that most databases have a single administrator who sets up the rules for the ledger. blockchain has multiple administrators. that each have an exact copy. so it's one thing to compromise one single administrator who is setting up the database rules. now you got to kind of deal with multiple administrators. so you got to kind of somehow bias them and biasing a group is harder than biasing a single, right? so that becomes the law of larger numbers. so sharing a ledger is one way to build more trust. consensus. so it's not just about distributing it. it's about, you know, looking at the transaction logs and looking at the consistency of doing things like proofs and looking at that and seeing that first, before we commit something, we have some level of agreement with the group that this is a fit transaction it's proposed. and then let's mentally thinking about it as voted on, although that's not exactly the way it works. consented on by the group. and then if some rule is met, let's say majority rules or everyone agrees, consents, then and only then is the new block added, right? so there is that level of consent to go along with distribution of the shared ledger. immutability, again, cryptography is used to create this append-only data structure and compromising the last element requires cryptography that would necessitate you going through the whole chain and decrypting the entire chain, which then starts to get into energy. how much energy does any individual have to apply to this compute problem to go back and kind of reverse or decrypt the entire ledger? so cryptography is used as a way to foil it, make a little bit more difficult, and in some cases, extremely difficult. difficult to compromise the data. and it's append only. so with a database, an administrator could go in and delete a record or change a record. this isn't append only ledger. so again, in order to rewrite the ledger, in order to change a value in the ledger, you basically have to rewrite and re-encrypt the entire ledger. cryptography is a very big deal. again, techniques, to hide and scramble data. and we're gonna go over some of those in a second. that is also a really important ingredient. so this kind of, when you look at these ingredients, consensus, immutability, distributed network, network proofs, like merkle trees and merkle proofs and basic cryptography, creating hashes and all of that, that starts to drive this data integrity and trust paired with reputational trust and now you have something that can help the digital economy operate more efficiently. and with artificial intelligence being the thing right now, can we use these learnings and apply them, jump the tracks to enhance certain types of ai and allow ai to benefit from not just reputational trust, for the companies created it or lack thereof and bringing some algorithmic trust to ai. so that's what the second half of the presentation is about. we just laid the foundation that blockchain has these ingredients. now, can we apply these ingredients similar to how they were applied for the food safety and the digital identity and the, let's say, creating fingerprints out of physical goods? can we take that? and can we apply them to things that are prevailing in, let's say, untrustworthy ai to make it more trusty? de-thinks, worrying about your privacy, like if you're prompting an ai model, is it training on the data that you're sending it? patent infringement. hey, i have some really good things on github that have license laws. licensing to it. how do i know that that license isn't being infringed by ai kind of homing the internet for information? how can i make sure that that information i just got is real? and then if ai learns something that i prefer it not to learn, could it unlearn it? so can we apply the techniques we just saw and targeted at some of these problems? so that's what we're going to do now. we're going to go through these basically one by one. let's start with deepfakes. i guess the best way to study a deep fake is through a deep fake. so i present you my deep fake. i'm sure you've heard that ai-generated deep fakes pose a great risk by creating convincing but fabricated audio and video content. but with a little cryptography, deepfakes are being exposed. you see, digital fingerprints, derived from audio and video frequency spectra, verify content authenticity by comparing signatures against known genuine data, detecting deepfakes. pretty nice. so this is something i've been playing quite a bit with lately. creating deepfakes to study deepfakes in a way to really create fingerprints of the real and proposed deepfakes. samples. in this case, you're going to see my real audio juxtaposed against my fake audio. so let's take a listen in. and we're going to create a fingerprint based on these initially three. in some of my examples, i have a dozen attributes, you know, fundamental frequency, frequency variation, frequency harmonics. in this example, we're going to look at a fingerprint based just on these three things. and they've also been investing in guardrails that include a synthetic speech detector and audio watermarking. pretty cool stuff. that's the real me from one of my podcasts. so when you go through that, we do an analysis and hear these violin graphs that show how the real voice plots out. so now let's now run one of my deep fake voices and see if we can visualize the difference. while our ears may not be able to exactly. although i kind of know my voice and i can hear little changes, but can we see those differences? and they've also been investing in guardrails that include a synthetic speech detector and audio watermarking. pretty cool stuff. so now let's plot that out. and you can see immediately that the violins are different, right? for the fundamental frequency, it's a little shorter and fatter. the other one doesn't have as many data points. the variation, it's kind of more around the norm, less spread out. and then the harmonicity is also not as distributed as my real voice is. so from that, we can derive a set of finger or values. and then together with cryptography, we can kind of map these into our fingerprint. all right, so that's the first one. now let's look at privacy. the example of privacy is, you know, looking at user inputs to ai models, and they may have sensitive information, risking privacy breaches. i think there was a bank or insurance company, or i forget, but they're putting information in, and i think one of the big large language models trained on it, or there's been some many of these in the press especially in the early days of gen ai coming on board. but the model here that i've been playing with is encrypted data and if we trained a ai neural net to be able to decrypt, take encrypted data, decrypt the data, come up with an answer and re-encrypt it. so all that's ever traveling over the wire is encryption. so can we train an ai model, a generative model to think in terms of encryption and decryption. so this is a very rudimentary example of doing that. and we're not going to use real encryption. we'll start off by using decoding, right? so this is base64 decoding, define ai in 30 words. so i'm going to take this output, which was the base64 encrypted. and this is a tool that allows us to use large language models. i'm going to use llama. i'm going to send this right to llama. a bunch of encrypted data. and look at this. i get back without even saying anything. i sent it an encrypted string. i got back an encrypted string. and if i decrypt it, it's my answer. artificial intelligence is a subfield of computer science. that's seamless. so i didn't even have to tell lama what to do. it knew how to do it. it just saw it, recognized it, it knew it had to decrypt it, and it knew i wanted it decrypted back. pretty amazing, actually. so now i'm going to do this to open ais. and you respond again in english. so i tried to make it say its response in english, and it said, no, i can't. all responses are to be encoded in base64. so in the system prompt, i kind of gave it this hint that i want to have a conversation, all in base64. so this starts to give you an idea. imagine if the model was able to communicate almost like https. everything is encrypted and decrypted. it's only inside the neural net is it being decrypted. so if we can start to work with this, and i'm showing examples that show it's plausible. privacy in healthcare, privacy in banking, privacy can be much more efficiently handled like we're used to handling it on the internet today. so i think you would see that borrowing from some of the hashing and stuff that we saw from blockchain. machine-owned learnings. so ai models have been trained on unauthorized data, and that's an ethical and legal risk under regulations like gdpr, which mandate proper data usage and consent. so if any of you are familiar with the men in black movie and the neuralyzer, i got the idea, can we zap a neural network? can we make it forget certain key phrases? and that's what this is. little tool that i built called a neuralizer does. it's a machine unlearning process by removing data from a model, reversing its impact. so let me tell you a little bit about the magic. and it all has to do with this notion of the carrot in the blender. if you listen to my podcast, i give this analogy some period of time. so like a carrot blended into a smoothie, training data in ai becomes untraceable. you know, making it indistinguishable. so like, let's say you went to a party that they were making smoothies and you brought carrots and, you know, halfway through the party, someone says something that offends you and like, i'm going to take my carrot and leave, you know, pardon the silly example. and then you go and then the host says, no, it's too late, jerry. you know, i've already used your carrot in the smoothies, right? it's already in there. and i'm like, but i want it back. and it's like, you can't have it back. it's in there. i was like, well, i can smell it still. i know it's in there somewhere. i'm like, yeah, but i can never give you a carrot. that's like data to some degree going into ai training. it's there. you don't know where it is anymore, but you can kind of sense it and smell it, but it's not quite there. so you can't get it back, even if you ask nicely. so this is an attempt to try to get it back, and it's all about tokens. and, you know, in in ai, ai doesn't speak english or a language, a human language. it speaks digital language. it speaks tokens. so if i say, which animal jumped over the moon? a cow, right? so let's look at the tokens. i'm going to turn on the neuralyzer now, and i'm going to neuralize cow. and what it did is it neuralized it. it changed the tokens. let me pause. it changed the weights on those tokens for cow. so normalizing it didn't completely zap it, but it said anytime there's a token related to cow, i'm going to deprioritize that token so that when you're predicting something that ultimately is predicting the output of cow, like in that nursery rhyme, what animal jumped over the moon, cal jumped over the moon, according to the nursing room. so the model wants to say cal. however, cal has been, that token has been biased, is the word. it's been reduced in its effectiveness. so it's less likely to predict cal. so what is it predicting instead? the next best thing, which apparently is cap. all right. so, but you get, hopefully you get the idea a little bit. so, let's try it again, list ingredients in cookies. all right, so if i do that, common ingredients, butter, eggs, et cetera. but what happens if i wanted to look for a vegan recipe? and i don't want mentioning of anything to do with non-vegan things like eggs, right? so let me now neuralize egg. and you could see all of the patterns of those tokens. it's not just egg, but it's space egg, capital e, egg, taking all those tokens, and now it gives me, as you can see, well, it still has butter, but it doesn't have eggs. who's jerry? could i have the model forget me? forget about me. it knows about me. it says i'm an ibm executive known for work with innovation and cloud computing and blockchain. but now let's have it neuralize jerry. jerry cuomo, jerry cuomo. and there's a bunch of variations of me that it has tokens on. and now when you send it to the model, it says, i don't know who jerry is. look at this. i've been forgotten. i've been neuralized. so again, dealing with digital tokens, dealing with cryptography, dealing with bias and weights and understanding that we can make ai more trustworthy. hallucinations. that's, if not the last topic, one of the last topics. language models can produce hallucinations. they're they sure look confident, but incorrect due to lack of user-specific context or directives leading to unreliable output. so reduce hallucinations by providing specific context using retrieval augmented generation, adding verification steps and structured prompts. all right, so one of the ways, and you can see this built into some of the more latest chatbots, things like chain of thought. so prompting guidelines that allow models to, instead of jump to the answer, first reason about the steps to getting to the answer and, you know, magic phrases, things like let's think step by step will really go a long way to help a model, say, pause a little bit, jumping right to the prediction, doing some intermediary steps to have it break down how it's going to get to a better prediction, like thinking about it in phases. so here's a tool that we built in ibm a few years ago. i hear it's still going strong. and i'm going to pick a very early version of watson x. and this version, people would say, wasn't very effective as a model. in fact, you give it like a eighth grade problem, like, you know, i have five tennis balls. roger buys two more tennis balls. each can has three tennis balls, how many tennis balls, et cetera. so it gives the wrong answer, right? so everyone say, well, the model is not very good. well, let's give it some history here. so let's say what the question is, let's give it a breakdown of the answer like we did. so instead of saying, before we said the answer was seven. now we show the math on how we got the answer to be whatever the answer was. when the model sees that, it emulates it. it says, i'm gonna break down the math. i'm not just gonna guess at an answer. and then lo and behold, it got the answer right. so the model wasn't that bad. it just wasn't prompted correctly, right? and chain of thought or showing the math through examples is a great way to set up context that will help the model not hallucinate. copyright infringement, another area that we've been focusing on. it's ai models that generate code, may inadvertently reproduce copyrighted content. so how can we do better? and the answer is retrieval augmented generation. we've built this prototype called simcode it uses the stack with six terabytes of data source code in particular across 358 programming languages, structured variations, et cetera. and what i really want to be able to do is improve code attribution. you know, where did code like this come from? look at transparency and guidance in when you're generating code. so i'll move quickly through this, but i'm going to upload a piece of python code. it works with java and some other languages. we use the stack python, which is the data set. we use a type of retrieval augmented generation for that. we create, we have a sample, cryptosample.python. we want to see if there's any infringement on this. it tokenizes the code and it allows for things like if, variables are in different spots. it'll tokenize it in a way where placement doesn't matter. and what we'll come up with is, yep, the code has implications around mit license and bsd license, meaning some of this code is attributed. it has licenses. but not only can we look at the licensing, now that we kind of have a digital fingerprint of the code, we can look at code review. we can say, you know, the comment level of the code is low, the complexity of the code, the maintainability of the code. so once we have it tokenized, we can also compare it to known good references and, you know, kind of make further assertions. again, making the code use both trustworthy, because now i kind of know what the licenses are for this code. i know how it stands against prior art. but also i know the quality of it, right? so think about how this can be applied to gaining trust in other things, medical records and, you know, kind of transaction logs and things of this nature, right? so in a nutshell back, we talked about trustworthy computing and how the foundation of trustworthy computing is built on good old reputational trusts, trust in people, companies, governments, et cetera. but we also showed stemming back from blockchain algorithms and some of the cryptography and proof and fingerprinting and techniques like that, we can kind of see how problems like food safety and digital identity and counterfeiting could be mitigated, but also how algorithmic trust could also be brought to ai, helping with privacy and hallucination and things like fruit you know, forgetting and also deep fakes. all right. i hope you enjoyed this. and again, jerry cuomo, always thinking about trustworthy computing. got two books out here, think blockchain and think ai, both featured in my classes at north carolina state university and the topics of my wild ducks podcast. take care, enjoy, and talk to you soon. bye. 1 (page number not for citation purpose) editorial/opinion healthcare futures: opportunities, challenges and risks in a blockchain-driven environment robert goldberg, phd1 ; peter j. pitts2,3,4; and jennifer hinkel, msc, chw, frsa5,6 1vice president, center for medicine in the public interest, new york, usa; 2president and co-founder, center for medicine in the public interest, new york, new york, usa; 3visiting professor, university of paris school of medicine, paris france; 4former fda associate commissioner, and united states senior executive service member, washington, dc, usa; 5founder & president, sigla sciences, incline village, nevada, usa; 6managing director, the data economics company, los angeles, california, usa doi: https://doi.org/10.30953/bhty.v7.345 corresponding author : robert goldberg, email: rgoldberg@cmpi.org keywords: cost of care, economics, futures, healthcare, marketplace, medical innovation submitted: august 21, 2024; accepted: december 6, 2024; published: december 30, 2024 economic studies show that medical innovation, particularly in the form of new medicines, reduces the average cost of care and, often, total healthcare spending.1 in fact, the use of such new medicines also generates additional years of healthy living that translate into increased productivity and well-being. however, insurers and government-funded health systems such as medicare have tried to manage the short-term rise and fluctuations in health spending using approaches that reduce consumption of new therapies. over the past decade, such market participants have tried to lay off risk by managing the surge and by imposing out-of-pocket costs, demanding rebates off the list price of medicines, and, increasingly, refusing to pay for new drugs altogether.2 in january 2026, medicare price controls for some of the most prescribed medications for senior citizens across cardiology, metabolism, inflammation, and cancer will take effect through “maximum fair price” mechanism that is part of the u.s. inflation reduction act 2022.3 meanwhile, biopharmaceutical companies and investors must charge higher prices. because the main methods of restructuring, merging, or acquiring other firms are also costly and might not guarantee success in managing the financial risks of product development.  finally, new medicines, immunotherapies, vaccines, and gene replacement are increasingly the first-line treatments because they cure or prevent disease. these innovations are likely to be very expensive in the short run. yet, here too, the innovators face the same challenge as payers: how to maximize the long-term benefit of medicines that reduce hospitalization and physician costs and minimize the up-front costs of these treatments.  developers, investors in new medicines, and their customers have a way to benefit from the value of downstream savings and health improvement generated by substituting older technologies for new ones and then being able to trade increments of short-term upstream investment for long-term gain. in other industries, contracts for specific products are traded to hedge risks in price and cost fluctuations. buyers and sellers of products ranging from energy to weather futures create a market-based forum where risks are managed through price discovery and risk transference. in these industries, the development of financial instruments is predicated on an in-depth understanding of component costs. such futures markets require indices that reflect the costs of specific products, which in turn could serve as the basis for futures contracts. these indices would allow stakeholders to hedge against potential cost increases, thereby ensuring financial stability and predictability.   benefits of hedging in healthcare futures contracts based on a well-constructed health improvement index could offer significant benefits. for healthcare providers, such tools could provide a hedge against rising drug prices and other variable costs, ensuring that budgeting remains stable despite fluctuations in the market. this approach not only aids in direct cost management but supports strategic financial planning across the healthcare industry.  blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0001-6858-9294 https://orcid.org/0000-0002-8461-7037 https://doi.org/10.30953/bhty.v7.345 mailto:rgoldberg@cmpi.org citation: blockchain in healthcare today 2024, 7: 345 https://doi.org/10.30953/bhty.v7.3452 (page number not for citation purpose) robert goldberg et al. past efforts to create a health futures market failed because of a lack of reliable data on health spending, which is necessary for developing financial instruments. we believe that the convergence of blockchain technology and the availability of detailed data on health status, outcomes, and costs from medical claims and electronic health records provide a solution to the problem of uncertainty. current financial risk management techniques do not provide buyers and sellers of health insurance and medical care services with sufficient protection against unexpected price changes. we believe that in the healthcare environment, blockchain technology can be used to create indexes that accurately track and predict changes in healthcare spending and the social and economic impact of new medicines over time.  an edge and a hedge: how blockchain technologies can enable healthcare futures contracts distributed data technologies in the form of blockchain and related innovations can make it possible to generate financial instruments based on accurate, current, and reliable information that can be used to price such contracts. patient data, claims information, health records, and pricing information can be stored in and transacted across distributed models that allow for data traceability and authentication, verification of transactions, and persistence of historical data without the risk of manipulation. coupled with new advances in machine learning and artificial intelligence, these tools can enhance the capabilities of algorithmic models to extract insights from healthcare data and predict the value of futures markets. companies such as chronicled and curisium offer blockchain-based systems that allow various healthcare sector players, such as pharmaceutical companies, medical device manufacturers, wholesalers, insurers, and healthcare providers, to authenticate their identities, log contract details, and track transactions and payments. these systems go beyond traditional supply chain management by enabling fully digital, and sometimes automated, contract terms between trading partners and insurance providers. by using shared digital contracts on a blockchain ledger, these systems can significantly reduce disputes over payment chargeback claims, which are common in the healthcare sector due to frequently changing pricing structures. a white paper produced by block chain startup, chronicled notes that over one million chargeback claims are made annually, with more than 5% being disputed, leading to lengthy manual resolutions.4 similarly, shared smart contracts can streamline medical insurance claims management, reducing the 10% of claims that are typically disputed. once data are digitized and accessible, insurers can apply advanced analytics to optimize health outcomes and costs. establishing a health cost index a health cost index could be established by linking data from multiple sources across a distributed network, including, as examples, electronic health records, insurance claims, pharmacy records, and clinical studies. a distributed data network and related governance rules for the same can ensure that these data are secure, transparent, and tamperproof. the health cost index can act as a benchmark for pricing futures contracts and other financial instruments in the healthcare sector. by providing a reliable and transparent reference point, such an index can help standardize pricing and reduce the risk of price manipulation. additionally, blockchain enables comprehensive logging of detailed clinical and cost data from hospitals, clinics, and pharmacies records. continuous updates to the blockchain reflect new data about the cost of treatment and patient outcomes as they become available. cancer care cost index and futures trading using blockchain as an example, a cancer care index could be anchored to reflect the average cost per patient per year of $40,000. suppose a biotech company wants to sell a futures contract for their new cancer drug, with one contract at $50,000 per patient sold in 100-patient increments. this $5,000,000 futures sale generates immediate revenue for the biotech firm in the form of non-dilutive capital. simultaneously, insurers and hospitals can buy contracts to hedge against the rising costs of cancer treatment. by purchasing futures contracts, insurers lock in the cost of the cancer drug at $50,000 per patient. this ensures that they are not exposed to potential price increases in the future, providing cost predictability and aiding in budget planning. meanwhile, if the index value increases, so do revenues that are generated by selling contracts. more significantly, blockchain can be used to capture longitudinal data to transparently track spending, utilization, and prices, and then generate algorithms that predict the savings generated from using a new cancer treatment.  the combination of secure, tamper-proof, and continuously updated data reduces the cost of building and refining accurate and timely algorithmic models for predicting both the social and economic impact of new medicines using a transparent method. an algorithm is used to predict that spending $50,000 on a new therapy saves $20,000 per patient by reducing hospitalization and the need for at home care. as figure 1 shows, a healthcare plan can lock in the net cost of the new immunotherapy at $30,000 per patient through a futures contract on the blockchain by paying a margin requirement or a small percentage—from 2% to 12%—of the contract’s cash equivalent value to owning the asset, or the total value of the contract. over the next year, the actual net cost of the treatment (upfront cost minus https://doi.org/10.30953/bhty.v7.345 citation: blockchain in healthcare today 2024, 7: 345 https://doi.org/10.30953/bhty.v7.345 3 (page number not for citation purpose) healthcare futures savings) stabilizes at $30,000 due to efficient implementation and patient outcomes. what if the actual cost of the therapy is predicted to drop further after the contract is settled? both biotech companies and health plans can still use the futures market to hedge against this potential shift. if biotech companies expect the therapy cost to drop to $25,000, they can sell additional futures contracts at the current price of $30,000. by doing this, they lock in a higher selling price before the drop occurs, hedging against the loss of revenue from the price decrease. they can also buy put options, which give them the right to sell the therapy at the current price ($30,000) before a specific date. if the price drops, they can exercise these options and sell at the higher locked-in price. if health plans expect the therapy cost to drop below $30,000, they can buy futures contracts at the $25,000 predicted lower price. in this way, they lock in the future lower price. once the price drops, they benefit from purchasing the therapy at the lower cost through the futures contracts, which helps them save on treatment costs. health plans can also buy call options, which give them the right to purchase the therapy at a lower price in the future. if the cost drops, they can exercise these options and buy at the reduced price, thus hedging against paying higher costs. blockchain technology can facilitate hedging for insurers investing in expensive medicines that improve health and reduce costs, even if patients switch to another health plan. at present, there is no method for capturing and transferring that value from plan to plan. between 15% and 20% of both privately and publicly insured individuals experience coverage disruptions or change plans each year.5 tokenization of health cost savings and improved outcomes we believe blockchain’s full potential to promote hedging is its ability to convert health outcomes and cost savings into small units priced according to an index, thereby allowing biotech companies, insurers, and other market participants to retain downstream economic benefits. for example, if a patient’s treatment with an expensive medicine results in improved health and reduced long-term costs, this benefit can be represented as a token. it is possible to create health futures units by developing smart contracts to automate the issuance, distribution, and trading of tokens based on predefined criteria and real-time data. smart contracts also automate the issuance, allocation, and distribution of tokens. smart contracts have been used to increase trust in and trading of carbon futures. société générale, s. a. (socgen) issued its first green bond on the ethereum blockchain. the transaction is valued at $10.8 million (10 million euros), with 3-year maturity. the smart contract for the tokenized green bonds includes carbon footprint information and is available for anyone to access. as a result, issuers, investors, and service providers can now measure the carbon footprint generated by financial securities on the blockchain. at the issuer’s request, socgen plans to offer reports on the estimated carbon footprint of its security tokens. these data will be embedded in the smart contract, allowing investors to assess the carbon emissions associated with the infrastructure supporting the tokens in their portfolios.6 challenges associated with developing a blockchain futures index and contract trading platform creating a blockchain-based futures index and contract trading platform, particularly for healthcare costs such as cancer treatment, involves several significant challenges. it will be critical to ensure data from multiple sources (e.g. hospitals, clinics, pharmacies) are standardized and consistent. this is critical for accurate indexing. additionally, inaccurate or incomplete data can lead to incorrect predictions and undermine trust in the platform.  fig. 1. trading to benefit from improve health outcomes. https://doi.org/10.30953/bhty.v7.345 citation: blockchain in healthcare today 2024, 7: 345 https://doi.org/10.30953/bhty.v7.3454 (page number not for citation purpose) robert goldberg et al. above all, using blockchain to promote a health futures market requires that the creator of the index and trading platform ensure the security of sensitive healthcare data against cyberattacks and unauthorized access. a recent report from the royal society identifies privacy enhancing technologies (pet) that can be used to transparently extract information from private health data in federated learning, zero knowledge proofs, and multiparty computation.7 a combination of encryption, permissioned blockchains and data anonymization will have to be used to control access and ensure compliance with privacy regulations. in addition, blockchain adoption is still hindered by the cost and difficulty of ensuring data sharing across diverse systems and institutions. finally, the development of application programming interfaces that adhere to interoperability standards like the fast healthcare interoperability resources blockchain will find it difficult to scale up without the establishment of federated data systems.  conclusion developing a blockchain-based futures index and contract trading platform for healthcare costs presents a range of challenges, from data integrity and scalability to regulatory compliance and user adoption. addressing these challenges requires a combination of advanced technological solutions, strategic partnerships, and ongoing stakeholder engagement. by overcoming these obstacles, such a platform can revolutionize healthcare cost management and create new opportunities for financial innovation in the healthcare sector. funding none. conflicts of interest dr. hinkel is editor-in-chief, blockchain in healthcare today. contributors each author contributed to and approved this work for publication. data availability statement (das), data sharing, reproducibility, and data repositories n/a. application of ai-generated text or related technology n/a. references 1. lichtenberg fr. has pharmaceutical innovation reduced the average cost of u.s. health care episodes? int j health econ manag. 2024;24(1):1–31. https://doi.org/10.1007/s10754-023-09363-y 2. joyce g, blaylock b, chen j, karen van nuys. medicare part d plans greatly increased utilization restrictions on prescription drugs, 2011–20. health aff. 2024;43(3):391–7. https://doi. org/10.1377/hlthaff.2023.00999 3. the white house. fact sheet: biden-harris administration announces first ten drugs selected for medicare price negotiation [internet]. the white house; 2023 [cited 2024 aug 20]. available from: https://www.whitehouse.gov/briefing-room/statements-releases/2023/08/29/fact-sheet-biden-harris-administration-announces-first-ten-drugs-selected-for-medicare-price-negotiation/ 4. the method and impact of eliminating chargeback errors through blockchain, chronicled, inc. february, 2021 [cited 2024 dec 26]. available from: https://www.chronicled.com/lp/ chargeback-errors-whitepaper 5. fang h, frean m, sylwestrzak g, ukert b. trends in disenrollment and reenrollment within us commercial health insurance plans, 2006–2018. jama netw open. 2022;5(2):e220320. https://doi.org/10.1001/jamanetworkopen.2022.0320 6. societe generale issues a first digital green bond on a public blockchain [internet]. société générale. 2023 [cited 2024 aug 20]. available from: https://www.societegenerale.com/en/news/press-release/ first-inaugural-digital-green-bond-public-blockchain 7. from privacy to partnership [internet]. page 57. [cited 2024 aug 20]. available from: https://royalsociety.org/-/media/policy/ projects/privacyenhancing-technologies/from-privacy-to-partnership.pdf ?la=en-gb&hash=4769feb5c984089fab52fe7e22f379d6 copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see http://creativecommons. org/ licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.345 https://doi.org/10.1007/s10754-023-09363-y https://doi.org/10.1377/hlthaff.2023.00999 https://doi.org/10.1377/hlthaff.2023.00999 https://www.whitehouse.gov/briefing-room/statements-releases/2023/08/29/fact-sheet-biden-harris-administration-announces-first-ten-drugs-selected-for-medicare-price-negotiation/ https://www.whitehouse.gov/briefing-room/statements-releases/2023/08/29/fact-sheet-biden-harris-administration-announces-first-ten-drugs-selected-for-medicare-price-negotiation/ https://www.whitehouse.gov/briefing-room/statements-releases/2023/08/29/fact-sheet-biden-harris-administration-announces-first-ten-drugs-selected-for-medicare-price-negotiation/ https://www.chronicled.com/lp/chargeback-errors-whitepaper https://www.chronicled.com/lp/chargeback-errors-whitepaper https://doi.org/10.1001/jamanetworkopen.2022.0320 https://www.societegenerale.com/en/news/press-release/first-inaugural-digital-green-bond-public-blockchain https://www.societegenerale.com/en/news/press-release/first-inaugural-digital-green-bond-public-blockchain https://royalsociety.org/-/media/policy/projects/privacy-​enhancing-technologies/from-privacy-to-partnership.pdf?la=en-gb&hash=4769feb5c984089fab52fe7e22f379d6 https://royalsociety.org/-/media/policy/projects/privacy-​enhancing-technologies/from-privacy-to-partnership.pdf?la=en-gb&hash=4769feb5c984089fab52fe7e22f379d6 https://royalsociety.org/-/media/policy/projects/privacy-​enhancing-technologies/from-privacy-to-partnership.pdf?la=en-gb&hash=4769feb5c984089fab52fe7e22f379d6 https://royalsociety.org/-/media/policy/projects/privacy-​enhancing-technologies/from-privacy-to-partnership.pdf?la=en-gb&hash=4769feb5c984089fab52fe7e22f379d6 http://creativecommons. org/licenses/by-nc/4.0. http://creativecommons. org/licenses/by-nc/4.0. 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 oikosnomos.world bond challenge do they know it’s christmas? water is the source of all life on this planet, and therefore the source of health. water is the essence of life. the tree from which we all branch. yet two billion people lack access to safe and clean drinking water. that is one in every four of us.1 this affects women and girls in particular, as they are tasked with fetching water, missing out on school, and paid work as a result. to combat this problem peacefully, oikosnomos.world (onw) aims to demonstrate that if poor and marginalized populations are given the energy and resources to produce drinking water, they will start lifting themselves out of poverty and hunger. that is our theory of change in a nutshell. we call it solar-fired hydro power. water quenches thirst, grows plants, waters animals, and feeds fish. food ends poverty and hunger, bringing peace of mind. by producing 100,000 liters of drinking water a day, onw aims to save or better lives. with every single machine that we finance and install. only two-four liters per person are needed per day to stay healthy.2 see unicef’s report thirsting for a future!3, or the world bank’s water for shared prosperity4, to get a better sense of the severity and persistence of the problem. water bond sustainability is all about longevity and determination. if there is no will, there is no way. the dutch (apparently) knew this back in 1648, when they issued a water bond to finance dikes, windmills, and other critical infrastructure to keep the water out. quite sensible, as much of the country was and still is below sea level. the bond still exists and pays interest. the maturity was set at 1,000 years. so, there is still a while to go. that is longevity for you—and determination. onw aims to revive this tradition with the defi hunger & poverty water bond to finance water taps in the desert(s) of africa. times have changed. we need to get the water in, up, or evaporated from thin air—any way we can. powered by clean, renewable, and regenerative solar energy. 2 (page number not for citation purpose) citation: blockchain in healthcare today 2024, 7: 375 history the oldest existing water bond in the world is from 1624.5 and it still pays out interest. it was issued to finance dikes, windmills, and other critical infrastructure. anything to keep the water out. times have changed. we need to get water in, up, or condensed. as the facsimiles show, it only takes fifty people to save a kingdom. decentralized finance (defi) avant la lettre. we aim to revive this tradition with the defi hunger & poverty water bond. saving lives, 100,000 liters of desalinated seawater (per day) at a time. or 1,000 liters of dew converted to drink, per day. the netherlands water bank issued a seven-year €1b bond this year. so it can be done. oikosnomos.world (onw) who we are is what we do with what we have —vince lombardi onw is a dutch non-profit that empowers poor and disadvantaged populations by fundraising for their development while accelerating growth. if you want to go fast, go alone. if you want to go far, go together. that is why we partner with local change agents. what our aim is to add value by lending a hand in kickstarting and developing sustainable, replicable, and horizontally scalable business models. our local change agents help set up, grow, and finance small-scale businesses. these account for more than 90% of all employment in africa. where our first initiative was in windhoek, namibia, assisting in setting up an innovation lab, called “proeftuin” in dutch/afrikaans. namibia is the second-most unequal country in the world, after south africa. more than 43% of the population lives in poverty, which affects women in particular. income-generating activities, therefore, are of vital importance, with direct impact. how rather than being yet another foundation doing projects, we accelerate business. we work in ever-growing spirals, with one spiral building on earlier ones. we accelerate what works and learn from our mistakes. all our initiatives are powered by sun, water, and love. 3 (page number not for citation purpose) citation: blockchain in healthcare today 2024, 7: 375 proeftuin proeftuin is dutch/afrikaans for nursery, food garden, test bed, all in one. it is located in windhoek, namibia. the proeftuin is facing severe drought and, even worse, water bills. they have outstanding water bills that they cannot pay, and need to invest in water collection, distribution, and filtering mechanisms before the rainy season starts. without help, they will be shut off! a beautiful green oasis in this arid city will go to waste. this would mean loss of income, employment, and food for windhoek’s poor. please help us: https://gofund.me/a1bd5dbc dlt water bond challenge help us develop a solution to finance a water tap in the desert! contribute to the sustainable development goals! no poverty (#1), zero hunger (#2), decent work and economic growth (#8), reduced inequalities (#14). https://gofund.me/a1bd5dbc 4 (page number not for citation purpose) citation: blockchain in healthcare today 2024, 7: 375 challenge the challenge is to write an article of maximum 2,000 words that describes a solution to address the use case outlined below. the best three articles (technical report, methodology, etc), will be submitted to the blockchain in healthcare today platform approaches journal for review and publication at no cost. use case onw aims to build water taps in the desert, turning seawater into drinking water, by using solar energy to pump sweet water from boreholes or by converting nighttime dew to drink. the technology is there. the largest version of a desalination machine, for example, is a 20-foot container with filters (membranes) for reverse osmosis and solar panels to power it. the machine costs $200,000 to purchase and install. it produces 100,000 liters of clean water per day, using nothing but free and plentiful seawater and sunshine. the water will be sold commercially to parties such as brewers and beverage companies at a premium price to make seabeer, seadrink, or seajuice. water will be given away for free to poor populations in the direct vicinity of the tap. they will use their mobile phones to open the tap. it will then release two to five liters of water. this can be repeated as often as required and prevents misuse. any water produced but not sold will go into fishponds to be run by the local community. in return, they will provide physical security, e.g., against theft or vandalism, and do simple maintenance work such as cleaning the pipes and filters. the ponds themselves are simple, mere holes in the ground of approximately one cubic meter, lined with plastic sheets, and home to 10 to 15 fish (e.g., catfish, or tilapia). we want to finance this! not through donations or one-off kindness, but via sustainable financial instruments such as an interest-bearing bond, the defi hunger & poverty water bond. final submission date is march 1, 2025. the future is now. upload your submission here. location the first tap will be installed in namibia. this is an arid country in southern africa, with plenty of coastline and sunshine. it faces extreme inequality, poverty, and drought. it is the second most unequal country in the world. more than 43% of the population lives in poverty, which affects women in particular. they do like fish, but the sea is almost empty, and rivers are far and few between along the coast. also, good to know is that most people do not have smartphones. 5 (page number not for citation purpose) citation: blockchain in healthcare today 2024, 7: 375 our (internal) nickname is ocean 12, because there are twelve of us. collectively we have over 150 years of experience working in international development, in africa in particular. we have diverse backgrounds in business administration, it, biotechnology & data science, technology, politics, and media production. we aim to revive the ancient greek philosophy of “oikos nomos”, i.e. household (estate) management. this term is the root of both economics and ecology. this precursor of people-planet-profit balances economic growth with human dignity and prudent use of natural resources. what better way to celebrate christmas? contact cees j. hesp, chairman oikosnomos.world@outlook.com onw.world references 1. water – at the center of the climate crisis. united nations, climate action [internet]. [cited 2024 nov 27]. available from: https://www. un.org/en/climatechange/science/climate-issues/water 2. sawka mn, cheuvront sn, carter r. human water needs. nutr rev. 2005 jun;63(6 pt 2):s30–9. https://doi.org/10.1111/j.1753-4887.2005. tb00152.x 3. united nations children’s fund. thirsting for a future: water and children in a changing climate [internet]. unicef, 2017 [cited 2024]. from: https://www.unicef.org/media/49621/file/unicef_thirsting_for_a_future_eng.pdf 4. water for shared prosperity (english). world bank group [internet]. report number: 190289. 2024 [cited 2024 nov 27]. available from: https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099051624105021354/p50138117773d00d3185811495 5019bdfcc 5. cummings m. yale news. a living artifact from the dutch golden age: yale’s 367-year-old water bond still pays interest [internet]. yale news, 2015 [cited 2024 nov 27]. available from: https://news.yale.edu/2015/09/22/living-artifact-dutch-golden-age-yale-s-367-year-old-water-bond-still-pays-interest 6. zwagemakers s. nwb bank issues 7-year eur 1 billion benchmark water bond [internet]. nwb—bank, 2024 [cited 2024 nov 24]. available from: https://nwbbank.com/en/news/nwb-bank-issues-7-year-eur-1-billion-benchmark-water-bond mailto:oikosnomos.world@outlook.com https://www.un.org/en/climatechange/science/climate-issues/water https://www.un.org/en/climatechange/science/climate-issues/water https://doi.org/10.1111/j.1753-4887.2005.tb00152.x https://doi.org/10.1111/j.1753-4887.2005.tb00152.x https://www.unicef.org/media/49621/file/unicef_thirsting_for_a_future_eng.pdf https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099051624105021354/p50138117773d00d31858114955019bdfcc https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099051624105021354/p50138117773d00d31858114955019bdfcc https://news.yale.edu/2015/09/22/living-artifact-dutch-golden-age-yale-s-367-year-old-water-bond-still-pays-interest https://news.yale.edu/2015/09/22/living-artifact-dutch-golden-age-yale-s-367-year-old-water-bond-still-pays-interest https://nwbbank.com/en/news/nwb-bank-issues-7-year-eur-1-billion-benchmark-water-bond 1 (page number not for citation purpose) blockchain in healthcare today 2021. © 2021 the authors. this is an open access article distributed under the terms of the creative commons attribution-noncommercial 4.0 international license (https://creativecommons.org/licenses/by-nc/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license. citation: blockchain in healthcare today 2021, 4: 182 http://dx.doi.org/10.30953/bhty.v4.182 proof of concept/pilots/methodologies leveraging blockchain technology for informed consent process and patient engagement in a clinical trial pilot baldwin c. mak1, phd , bryan t. addeman2, bsc, mba , jia chen3, phd , kim a. papp4, md, phd, frcpc, faad , melinda j. gooderham5, msc, md, frcpc , lyn c. guenther6, md, frcpc, faad , yi liu7, phd , uli c. broedl8*, md and marianne e. logger1, bsc 1department of clinical operations, boehringer ingelheim canada ltd./ltée, burlington, on, canada; 2international business machines (ibm), markham, on, canada; 3international business machines (ibm), yorktown heights, ny, usa; 4k. papp clinical research and probity medical research inc., waterloo, on, canada; 5skin centre for dermatology, peterborough, on, canada; 6the guenther dermatology research centre, london, on, canada; 7boehringer ingelheim pharmaceuticals, ridgefield, ct, usa; 8global clinical development & operations, boehringer ingelheim pharma gmbh & co. kg, ingelheim, germany abstract objective: despite the implementation of quality assurance procedures, current clinical trial management processes are time-consuming, costly, and often susceptible to error. this can result in limited trust, transparency, and process inefficiencies, without true patient empowerment. the objective of this study was to determine whether blockchain technology could enforce trust, transparency, and patient empowerment in the clinical trial data management process, while reducing trial cost. design: in this proof of concept pilot, we deployed a hyperledger fabric-based blockchain system in an active clinical trial setting to assess the impact of blockchain technology on mean monitoring visit time and cost, non-compliances, and user experience. using a parallel study design, we compared differences between blockchain technology and standard methodology. results: a total of 12 trial participants, seven study coordinators and three clinical research associates across five sites participated in the pilot. blockchain technology significantly reduces total mean monitoring visit time and cost versus standard trial management (475 to 7 min; p = 0.001; €722 to €10; p = 0.001 per participant/visit, respectively), while enhancing patient trust, transparency, and empowerment in 91, 82 and 63% of the patients, respectively. no difference in non-compliances as a marker of trial quality was detected. conclusion: blockchain technology holds promise to improve patient-centricity and to reduce trial cost compared to conventional clinical trial management. the ability of this technology to improve trial quality warrants further investigation. keywords: blockchain; clinical trials; healthcare and medical research; informed consent; hyperledger fabric received: 7 july 2021; revised: 19 august 2021; accepted: 7 september 2021; published: 15 october 2021 the covid-19 pandemic has impelled significant transformations in the clinical trial development process that have led to improved efficiency in generating high-quality data (1). efforts that primarily aim to enhance patient recruitment, comfort, and retention are: adopting digital patient engagement, trial virtualisation, and site support tools. the introduction of telemedicine, mobile, or local healthcare providers coupled with novel information technology solutions holds promise for improving patient engagement and trial performance. however, rigorous management of clinical trial data remains a fundamental concern. the current data management process requires that data be captured and stored among different centralised databases. this process necessitates data entry duplication across multiple platforms, requiring cross-checking and manual source data verification (sdv) to ensure data quality. in addition to being time-consuming, these processes are *correspondence: uli c. broedl. email: uli.broedl@boehringer-ingelheim.com https://creativecommons.org/licenses/by-nc/4.0/ http://dx.doi.org/10.30953/bhty.v4.182 https://orcid.org/0000-0001-8615-9802 https://orcid.org/0000-0002-3013-9252 https://orcid.org/0000-0002-3977-2943 https://orcid.org/0000-0001-9557-3642 https://orcid.org/0000-0001-8926-0113 https://orcid.org/0000-0001-9286-1534 https://orcid.org/0000-0003-4141-9935 https://orcid.org/0000-0001-9893-7747 https://orcid.org/0000-0001-5837-2294 mailto:uli.broedl@boehringer-ingelheim.com citation: blockchain in healthcare today 2021, 4: 182 http://dx.doi.org/10.30953/bhty.v4.1822 (page number not for citation purpose) baldwin c. mak et al. also susceptible to error and unauthorised access to personal healthcare information. in an annual health canada good clinical practice (gcp) inspection summary report, the two most common observations noted by gcp inspectors were related to the lack of systems and procedures to ensure data quality (40%) as well as errors/incomplete records (30%) (2). issues with records were also noted as a common observation for the food and drug administration (fda), european medicines agency (ema), and medicines and healthcare products regulatory agency (mhra), in their retrospective 2016 inspection summary reports (3, 4). quality assurance procedures, which typically include traditional methods such as intensive on-site monitoring with 100% sdv at 4to 8–week intervals, are put into place to prevent these findings (5, 6). these quality assurance measures also provide confidence among the many different stakeholders in a clinical trial (i.e. participants, principal investigators, clinical sites, sponsors, and regulators), and are necessary to maintain the trust and transparency in these relationships. the relationships among trial participants, clinical sites, ethics committees, regulators, and trial sponsors are crucial for the success of a clinical trial. in particular, the rights, safety, and well-being of trial subjects are the most important considerations (7, 8). informed consent is the cornerstone of the conduct of any ethical human subject research – it informs participants about the trial objective, trial flow, benefits, and risks, which in turn empowers them to enrol in a study voluntarily. it is imperative, as participants’ trust and comfort can impact many aspects of a trial, such as recruitment, protocol adherence, and study completion. trial participants convey their confidence and willingness to participate in a trial by providing informed consent. the informed consent process, which is predominantly paper-based, is susceptible to errors as consent forms are often long, convoluted, and complex in nature. additionally, poor communication between parties can lead to a lack of informed consent because of a failure to perform reconsent procedures that may be required prior to the implementation of protocol changes. between 2015 and 2016, informed consent observations ranged from approximately 2–11% of the annual inspection findings among health canada, ema, mhra, and the fda (2–4, 9). failure to adhere to a participant’s informed consent, and any changes to this status, violates international conference on harmonisation good clinical practice (ich-gcp) standards, and may put the trial participants’ rights and safety at risk. addressing critical challenges of trust, transparency, patient empowerment, and patient safety requires a new clinical trial and data management model. this model should complement future technological transformations in healthcare environments and systems, in which we envision fully digital platforms, remote site monitoring, integration of wearable devices for data collection, and automated data-curating mechanisms. we hypothesise that an emerging technology, known as blockchain technology, may support a future clinical trial model based on the intrinsic benefits this technology promises. blockchain technology is designed as a distributed, decentralised, and immutable digital ledger shared within a network of stakeholders (10). members of a blockchain network have access to a copy of the ledger – a shared single ‘source of truth’. consensus algorithms validate new transactions and agreements, which are then added into the ledger and chained to previous entries in ‘blocks’ (10, 11). consensus is the process by which a network of blockchain nodes provides a guaranteed ordering of transactions and validates the block of transactions. it confirms the correctness of all transactions in a proposed block, according to endorsement and consensus policies. typical consensus types include: proof of work, proof of stake, proof of elapsed time, or redundant byzantine fault tolerance (rbft). each copy of the ledger is updated with each new block and becomes visible to all network members. altering an entry in the ledger is almost impossible as modification requires authorisation from a majority of stakeholders and changing all previous entries (12). this technology creates ownership and gives each network member a stake in knowing and deciding what happens with their own data. thus, blockchain inherently has the potential to enhance trust, transparency, and empowerment among members who share the ledger. smart contracts (i.e. code stored on blockchain that automatically executes under predetermined conditions) allow for process automation in a trusted environment without third-party intervention and, therefore, may lead to improved process quality at reduced cost (12–14). the concept of blockchain technology in clinical trials has previously been explored (15). however, to our knowledge, it has not been evaluated in an active clinical trial setting. thus, we have designed a blockchain pilot as a sub-study to a clinical trial to test the hypothesis that blockchain technology creates trust, transparency, and patient empowerment, in addition to improving trial quality and patient safety at a reduced cost compared to the current standard for clinical trial management. methods blockchain pilot design the blockchain pilot was designed as a proof-of-concept sub-study of a global phase ii clinical trial (nct03635099, referred to as the ‘main trial’) using a parallel study design (see fig. 1) to assess the value proposition of blockchain technology versus conventional trial management. the pilot, which was approved by health canada, was conducted between march 2019 and november 2019, and was limited to canadian sites and trial participants. participants who signed the informed consent of the main http://dx.doi.org/10.30953/bhty.v4.182 citation: blockchain in healthcare today 2021, 4: 182 http://dx.doi.org/10.30953/bhty.v4.182 3 (page number not for citation purpose) leveraging blockchain technology for clinical trial conduct trial could choose to enrol in the pilot by signing a separate optional consent form. since the pilot was introduced after the main trial had already started, participants could either join at the time of recruitment for the main trial or, for those already enrolled in the main trial, at their next planned visit. each pilot participant followed the visit schedule and procedures of the main trial as outlined in the clinical trial protocol. procedures for the pilot took place across two consecutive planned visits as part of their regular visits for the main trial (two of the five blue highlighted visits in fig. 1). at the first pilot visit, participants acknowledged their prior consent for the main trial and, if applicable, an optional skin biopsy through a ‘patient’ portal on a tablet device. the study coordinator counter acknowledged the consent status through a ‘site’ portal. at the next visit, the consensus of the status for the consents was used as a directive to pilot participants and site coordinators for the completion of five procedures (i.e. psoriasis area and severity index [pasi], static physician’s global assessment [spga], psoriasis symptom scale [pss], dermatology life quality index [dlqi], and skin biopsy). these procedures are standard dermatological assessments. pilot participants and study coordinators were required to confirm the completion of these procedures through the portal once they were done in the main trial. technical details of blockchain the blockchain system was implemented using the opensource hyperledger fabric v1.4 project from the linux foundation, and was deployed using the ibm blockchain platform (fig. 2). the platform was enterprise-grade and built on a private, permissioned network. a set of blockchain consent services were leveraged to enable pilot participants to grant or withdraw informed consent, to reconsent during the trial process, and to secure exchange of clinical trial status data. the platform was designed for the following users: pilot participants, study coordinators, and the pilot was conducted as an optional sub-study of a global phase ii clinical trial using a parallel study design. pilot procedures took place across two consecutive visits as part of the regular main trial visits. since the assessments tracked for the pilot were only scheduled at visits 1, 2, 6, 8 and end of treatment (eot), the two consecutive visits took place on two of these five visits highlighted in blue depending on the stage of each participant. fig. 1. blockchain pilot study design. http://dx.doi.org/10.30953/bhty.v4.182 citation: blockchain in healthcare today 2021, 4: 182 http://dx.doi.org/10.30953/bhty.v4.1824 (page number not for citation purpose) baldwin c. mak et al. sponsor staff – which included both clinical trial managers and clinical research associates (cras). all users were known and identified by cryptographic keys. information that the users were able to view and update was customised according to their role. each user role interacted with the blockchain network via a web application or portal. the informed consent status recorded on blockchain was leveraged as a directive for the completion of the tracked procedures. this allowed all users to see the consent status, what procedures the pilot participant agreed to, what should happen at the next visit, and whether the procedure was completed through the user portal. the pilot participant portal permitted participants to confirm the status of their consent/reconsent in the main trial, their optional consent, their withdrawal, the completion of selected trial procedures, and to be alerted to participant versus study coordinator entry mismatches in near real–time. clinical site portals permitted study coordinators to confirm participant informed consent and withdrawal status, confirm participant eligibility, monitor participant informed consent and trial status per trial participant (and in aggregate), and be alerted to participant versus site entry mismatches in near real–time. sponsor portals permitted sponsors to create and populate the details of the trial (e.g. update informed consent versions), monitor informed consent and clinical trial status per site (and per participant), and monitor participants versus site entry mismatches in near real–time. a regulator portal was designed to model outputs needed by a regulatory authority. this portal provided near real–time, read-only access to participant status and conduct in trials across multiple sponsors. each portal interacted with the blockchain network (via a specified blockchain peer) where an immutable record of the pilot participant consent directive and trial progress were stored. the peers applied the permissions defined by the custom rules of this private network such that a retrieval or creation/update of a record could only be possible for an authenticated permissioned user of the appropriate user role. because of the nature of the technology, an immutable audit log of all interactions was available within the blockchain network. pilot endpoints endpoints of the blockchain pilot comprised clinical trial monitoring time and costs, number of non-compliances it consists of pilot participants, study coordinators, sponsor/cras, and regulators. various transactions (reverse arrows) and key users (boxed) within a clinical trial network are shown. each user interacts with blockchain technology through a webbased portal. a specified blockchain proxy peer facilitates the interaction between each portal and the blockchain network. an immutable digital ledger is stored on all nodes of the blockchain network, but the level of access to the ledger is customised according to the user role. fig. 2. overview of the private, permissioned ibm blockchain platform. http://dx.doi.org/10.30953/bhty.v4.182 citation: blockchain in healthcare today 2021, 4: 182 http://dx.doi.org/10.30953/bhty.v4.182 5 (page number not for citation purpose) leveraging blockchain technology for clinical trial conduct (as a proxy for trial quality), as well as user-reported outcomes of trust, transparency, and sense of empowerment. data collection and analysis monitoring for the main trial was conducted on-site, while monitoring for the blockchain pilot was conducted remotely using the sponsor portal. total monitoring visit time comprised time spent on review of informed consent status and completion of the tracked procedures, on query follow-up, and on other monitoring visit activities, which included travel time and waiting time as part of on-site visits, and systems login and visit preparation time as part of either on-site or remote visits. monitoring visit time was self-reported by the cra, and was only compared for pilot participants who took part in both the main trial and pilot study. pilot participants served as their own control. estimated cost for monitoring was calculated using monitoring visit time and an industry standard cra hourly rate. additional expenses incurred for monitoring visits, such as pass-through costs (i.e. hotel, transportation, and meals), were not included in the cost estimates. events of non-compliance pertaining to completion of informed consent and conduct of tracked procedures were captured in the pilot and main trial without unblinding of treatment allocation of trial participants. the number of non-compliances in the pilot and main trial was assessed, with pilot participants serving as their own control. as participation in the blockchain pilot may have influenced compliance of the pilot participants in the main trial (and vice versa), the total number of non-compliances related to informed consent and tracker procedures for canadian main trial participants who joined the blockchain pilot was also assessed. trust, transparency, and sense of empowerment of pilot participants, study coordinators, and cras were assessed using surveys that were developed based on transcelerate patient experience initiative guidance (16). a 5-point likert scale system was used for the responses in the surveys. responses were based on the level of agreement to a statement: 1) strongly disagree; 2) disagree; 3) neither agree nor disagree; 4) agree; and 5) strongly agree. in the analysis of the user survey results, data were combined resulting in three levels of agreement: 1) disagree, 2) undecided, and 3) agree. users completed the surveys at the end of the pilot trial. statistical analysis baseline demographics and clinical characteristics were summarised for pilot participants, using sas®, version 9.4 (sas institute). all analyses of the endpoints of the pilot study were exploratory. the data for monitoring time and costs are presented as the mean ± standard deviation on a log scale. data were analysed by a two-sided t-test for two-group comparisons, using graphpad prism software. a p < 0.05 was statistically significant. results pilot disposition and demographics there were eight canadian sites with 36 participants in the global phase ii psoriasis main trial. twelve (33.3%) of the 36 participants from five (62.5%) of the eight sites voluntarily chose to enrol in the blockchain pilot. nine (75%) of the 12 pilot participants enrolled with the original main informed consent form and were reconsented with a revised main informed consent while the pilot was conducted, whereas three (25%) of the 12 participants directly enrolled with a revised main consent (table 1). most of the pilot participants (92.7%) were caucasian, with a mean age of 52.7 years. the pilot patient group had a mean pasi score of 15.4, consistent with moderate to severe psoriasis (table 2). table 1. blockchain pilot site profile and participant groups main trial total sites 8 total participants 36 pilot trial sites 5 sub-study total pilot participants 12 • enrolled with revised main consent 3 • enrolled and reconsented with revised main consent 9 study coordinators 7 clinical reseach associates 3 table 2. baseline demographic and clinical characteristics of pilot participants number of pilot participants (n, %) 12 (100.0) race (n, %) 11 (91.7) white 0 (0) african american or black asian 1 (8.3) age, mean (years, sd) 52.7 (16.6) mean body surface area (bsa) affected with plaque psoriasis (%, sd) 16.2 (6.7) mean pasi score (n, sd) 15.4 (2.6) spga score (n, %) moderate 8 (66.7) severe 4 (33.3) mean pss score (n, sd) 8.0 (4.0) mean dlqi score (n, sd) 10.8 (6.8) trial participants with at least one concomitant diagnosis (n, %) 11 (91.7) trial participants who took non-topical psoriasis (n, %) 6 (50.0) trial participants with at least one on-treatment concomitant medication (n, %) 9 (75.0) sd=standard deviation; pasi=psoriasis area and severity index; spga= static physician’s global assessment; pss=psoriasis symptom scale; dlqi= dermatology life quality index. http://dx.doi.org/10.30953/bhty.v4.182 citation: blockchain in healthcare today 2021, 4: 182 http://dx.doi.org/10.30953/bhty.v4.1826 (page number not for citation purpose) baldwin c. mak et al. monitoring visit time and costs the total mean monitoring visit time for each participant per visit was significantly reduced from 475 min in the main trial to 7 min in the blockchain pilot (p = 0.001). this decrease was largely attributed to the significant reduction in time spent on other monitoring visit activities (i.e. travel time, waiting time as part of on-site visits, and systems login and visit preparation time as part of either on-site or remote visits), from 453 to 0.8 min (p = 0.001). time spent for monitoring tasks was also reduced (data review [from 17 to 5 min; p = 0.0004]; follow-up [from 5 to 2 min; p = 0.191]) (fig. 3a). as a result of the reduction in monitoring visit time, a significant reduction in total mean cost for monitoring visit activities was observed (from €722 to €10; p = 0.001). this result was primarily driven by reduced costs for other monitoring visit activities (from €634 to €1; p = 0.001) and for data review (from €24 to €7; p = 0.0004) (fig. 3b). bar graphs depict monitoring visit time (a) and cost (b) of pilot and main trial participants. results are depicted as mean ± standard deviation on a log scale. a p < 0.05 was considered statistically significant. other monitoring visit activities includes tasks such as travel time and waiting time as part of on-site visits, and systems login and visit preparation time as part of either on-site or remote visits. fig. 3. mean monitoring visit time and cost. http://dx.doi.org/10.30953/bhty.v4.182 citation: blockchain in healthcare today 2021, 4: 182 http://dx.doi.org/10.30953/bhty.v4.182 7 (page number not for citation purpose) leveraging blockchain technology for clinical trial conduct non-compliance there was one incident of non-compliance in the pilot group and no incident of non-compliance in the same patients participating in the main trial. due to a technical issue with the patient portal, one pilot participant could not confirm the successful completion of reconsent, resulting in a mismatch with the site entry as documented on-chain. overall, there was only one incident of non-compliance in the main trial, including non-blockchain trial participants in canada, precluding any assessment of the hypothetical value proposition of blockchain technology regarding study compliance and quality in our pilot. user survey results pilot participants eleven of the 12 pilot participants completed a user survey to determine the impact of blockchain technology on trust, transparency, and sense of empowerment (fig. 4a). the majority (91%) of survey respondents indicated that blockchain technology increased their confidence that their safety and well-being were being ensured; 82% of respondents felt that blockchain technology increased their awareness regarding their trial status and upcoming procedures; 63% of respondents indicated that they graphs illustrate the scaled responses among pilot participants (a), study coordinators (b), and cras (c) to 5-point likert scale system questions in domains related to the level of trust, transparency, and sense of empowerment following the use of blockchain technology at the end of the pilot trial. fig. 4. user survey results on use of blockchain technology in a clinical trial setting. http://dx.doi.org/10.30953/bhty.v4.182 citation: blockchain in healthcare today 2021, 4: 182 http://dx.doi.org/10.30953/bhty.v4.1828 (page number not for citation purpose) baldwin c. mak et al. had more control over what happened to them in the trial while using blockchain technology. study coordinators seven study coordinators completed a user survey to determine the impact of blockchain technology on trust and transparency in their role as site representatives (fig.  4b). the majority (72%) of coordinators were undecided when asked if they felt that the technology improved their ability to prevent errors and non-compliances related to the pilot participant consent process. among the remaining respondents, there was an equal divide among those who agreed (14%) or disagreed (14%). fifty-seven percent (57%) of coordinators disagreed that blockchain technology made it easier for them to see what stage the pilot participants were at and what needed to be done at their next visit, while 29% agreed that the technology improved transparency, and 14% remained undecided. clinical research associates three cras completed a user survey to determine the impact of blockchain technology on trust and transparency as sponsor representatives (fig. 4c). when asked about their confidence in blockchain’s ability to demonstrate that the study coordinator was aware of what subjects agreed to during the trial, 67% of cras were undecided, while 33% agreed. the majority of cras (67%) agreed that they had more transparency about what happened to participants in the trial when using the technology, while 33% of cras were undecided. discussion in this pilot, we aimed to assess the value proposition of blockchain technology in an active clinical trial setting. our data suggest that blockchain technology reduces monitoring visit time and cost while improving patient trust and sense of empowerment compared to conventional clinical trial management. the cost of clinical trials continues to rise annually primarily driven by intricate trial design and operational setup choices. as trial monitoring to prevent and remedy non-compliance is estimated to contribute 25–30% of the overall clinical trial costs, process automation via smart contracts in a trusted environment using blockchain technology appeared to be a promising approach to improve trial quality and reduce monitoring efforts (17). while the resulting reduction in trial costs supports our hypothesis, our study could not answer whether blockchain technology truly enhanced trial quality and process compliance because of the small number of non-compliances observed given the limited number of pilot participants and our efforts to run high-quality clinical trials. we also hypothesised that blockchain technology could enhance patient-centricity in clinical trials by providing transparency, safety, trust, and empowerment. the pilot provided valuable insight into the attitudes and preferences of patients, study coordinators, and cras through the assessment of qualitative data. the majority of patients appreciated the value proposition of blockchain technology; however, the responses from study coordinators and cras were mixed. this result may be due, in part, to a change in the operating model and the parallel execution and timing of the pilot within the main trial. adjustments in the design of the user portal and how trial information is displayed are other considerations that could improve transparency for site coordinators and cras. our pilot study had several strengths and limitations. to our knowledge, this is the first assessment of blockchain technology in an active clinical trial setting. while several proof-of-concept studies using artificial or existing patient data have suggested that blockchain technology may improve trust, transparency, and auditability of clinical trials in addition to patient empowerment, our parallel study design allowed us to prospectively compare the value proposition of blockchain technology versus conventional trial management (10, 18, 19). of note, since blockchain technology is not currently accepted by health authorities to support routine clinical trials, the cost savings reported here remain elusive. the low number of pilot participants may have impacted our ability to detect any differences in non-compliances and trial quality. however, it did offer us the unique opportunity to manually verify all blockchain-based transactions against conventional process steps, strengthening the validity of our conclusions. in summary, the use of blockchain technology in clinical trials holds the promise of improving trust, transparency, auditability, patient empowerment, and clinical trial costs. the ability of blockchain technology to improve trial quality and patient safety remains unanswered and warrants further investigation. of note, despite the promising value proposition of blockchain technology in clinical trials, broad adoption will require the industry to overcome technological barriers, including scalability and interoperability across different blockchain solutions, as well as non-technological barriers such as the cross-functional development of blockchain knowledge, skills, and regulatory frameworks. acknowledgments medical writing assistance, supported financially by boehringer ingelheim, was provided by dr lauren m. moore, danielle nicholas, and melanie glezos of havas group during the preparation of this article. conflict of interest and funding boehringer ingelheim supported this research. baldwin c. mak, yi liu, uli c. broedl, and marianne e. logger http://dx.doi.org/10.30953/bhty.v4.182 citation: blockchain in healthcare today 2021, 4: 182 http://dx.doi.org/10.30953/bhty.v4.182 9 (page number not for citation purpose) leveraging blockchain technology for clinical trial conduct are employees of boehringer ingelheim. bryan t. addeman and jia chen are employees of ibm. lyn c. guenther receives clinical research support from boehringer ingelheim; kim a. papp consults, participates in steering committees and advisory boards, and receives clinical research grants and honoraria from boehringer ingelheim; melinda j. gooderham consults, speaks, participates on advisory boards, and receives clinical research and institutional funding from boehringer ingelheim. boehringer ingelheim provided the sole source of funding that supported this work. authors’ contributions concept development: uli c. broedl, marianne e. logger, baldwin c. mak, jia chen, and bryan t. addeman; data analysis: uli c. broedl, baldwin c. mak, marianne e. logger, and yi liu; drafted the article: uli c. broedl, baldwin c. mak, and marianne e. logger; managed the pilot: baldwin c. mak and marianne e. logger; conducted the pilot: kim a. papp, lyn c. guenther, and melinda j. gooderham; edited the article: uli c. broedl, marianne e. logger, baldwin c. mak, bryan t. addeman, jia chen, kim a. papp, lyn c. guenther, yi liu, melinda j. 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https://www.americanpharmaceuticalreview.com/featured-articles/185929-ways-to-lower-costs-of-clinical-trials-and-how-cros-help/ https://www.americanpharmaceuticalreview.com/featured-articles/185929-ways-to-lower-costs-of-clinical-trials-and-how-cros-help/ https://www.americanpharmaceuticalreview.com/featured-articles/185929-ways-to-lower-costs-of-clinical-trials-and-how-cros-help/ https://www.americanpharmaceuticalreview.com/featured-articles/185929-ways-to-lower-costs-of-clinical-trials-and-how-cros-help/ http://dx.doi.org/10.12688/f1000research.10531.5 http://dx.doi.org/10.1038/s41467-019-08874-y 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 narrative/systematic review/meta-analysis moving beyond proof of concept and pilots to mainstream: discovery and lessons from a reference framework and implementation sathya krishnasamy, ms1 and badri narayanan gopalakrishnan, phd2 1emerging technologies consultant and president, engineering, emerging technologies, chainaim, newington, connecticut, usa; 2school of environmental and forestry sciences, university of washington, seattle, washington, usa corresponding author: sathya krishnasamy, email: krisat3003@gmail.com, sathya@chainaim.com keywords: adoption, blockchain, blockchain technology, healthcare, scale, transformation abstract blockchain technology is a radical innovation with the potential to disrupt and re-imagine more collaborative established business structures and processes. significant advances, particularly in the payments space, include newer, faster, and less costly options for moving money. the underlying blockchain technology can be used for broader use cases spanning several verticals, including healthcare – although its adoption here is less than complete. numerous proofs-of-concept and pilots have been executed and are increasing, although enterprise blockchain applications in healthcare at the production scale enabling transformative constituent processes are limited. in this article, the authors analyze the blockchain in healthcare literature for critical success factors and add practitioner views on crossing the chasm from proof-of-concept and pilots to a transformational scale. we explore 24 articles for key inflections for scale and highlight the need for a multifaceted execution framework to resolve the practical barriers to enabling reimagined network-based blockchain use cases for efficiencies, particularly in disparate health systems such as the u.s. in addition, we introduce the blockchain discovery framework to make this emerging technology meet the mainstream operations at scale systematically and in a stair-stepped and future-proofed manner, addressing practical stakeholder concerns. finally, the authors present a reference case study discovered through the framework of one such healthcare administrative process for a scaled reimagined implementation. healthcare executives and portfolio managers will benefit from these insights and help to increase the enterprise adoption of this inevitable technology of the future. plan language summary this article presents a practitioner’s view of operating in emerging technology, exploring and advancing blockchain-based transformation in healthcare. blockchain technology is maturing quickly, with financial technology (aka fintech) leading the way with efficient options for moving money, particularly in the public permissionless blockchain segment. the underlying technology allows for a broader set of capabilities, including provenance, data sharing, immutability, non-repudiation, and auditability, which provides for complete rethinking of existing business processes. these features can help to reimagine a more comprehensive set of use cases in many disciplines, including healthcare. however, enterprise adoption needs to catch up. received: august 28, 2023; accepted: december 7, 2023; published: december 29, 2023 most enterprise blockchain efforts are run with a small group as a technical exploratory function alone or as a lofty functional aspiration but with sparse technical resources without the execution support structure. most business processes that could be collaborative with blockchain and allied technologies are still unilateral. in the permissionless space, the number of healthcare projects is a tiny fraction compared to the other verticals, and even they are not deemed commercially successful.1 https://orcid.org/0009-0000-1420-1098 https://orcid.org/0000-0001-9628-8173 mailto:krisat3003@gmail.com mailto:sathya@chainaim.com citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.2802 (page number not for citation purpose) sathya krishnasamy and narayanan gopalakrishnan there are a myriad of proofs-of-concept (poc) and pilots that accomplish a specific task – predominantly proposing or solving a technical hurdle. in healthcare, trying a newer collaborative function, typically in a peerto-peer manner at a small scale, might work but subsequently struggles for wider mainstream adoption. despite the enthusiasm and thousands of proposals, pocs, and pilots, only 5% – 8% make it to enterprise mainstream.2,3,4 as blockchain and emerging technologies practitioners, the authors have observed reasons for limited adoption beyond the typical causes cited – technical, regulatory, privacy, and incentives. hence, this article aims to look at the systematic reviews of blockchain in healthcare from the perspective of finding specific attributes for inflectional scale, adding additional success factors, and addressing gaps from a practitioner’s viewpoint to move beyond concept proofs and pilots. the need for a discovery framework for mainstream blockchain adoption is discussed. the use of the framework components in identifying and overcoming common hurdles to find conducive use cases and actual operational success factors is discussed in a healthcare setting. a reference payor business process implementation reimagined using this framework is presented. purpose for this work, the focus is not on what can be done using blockchain in healthcare generally (e.g., high-level use cases, laboratory proof, or a small-scale pilot). instead, the focus is on what is deployed or deployable in blockchain implementations or what can be designed to run in non-blockchain environments that can be easily adapted to blockchain networked implementations at scale. it is not the authors’ intent to present a comprehensive systematic review but to leverage earlier systematic reviews and other literature and industry information reports on blockchain in healthcare adoption at scale. although this effort summarizes the general classifications over time, the focus is on specific challenge and success attributes, including those that are newly emerging and some from a practitioner’s viewpoint that becomes important at the execution level to make the technology work but is yet to be documented in the literature. to summarize, the purpose is to (1) understand the barriers and classifications from the literature and add additional perspectives as an enterprise blockchain in healthcare practice, (2) present a multifaceted framework based on the authors’ experience in healthcare and blockchain technologies, to address the barriers and progress toward mainstream scale, and (3) share the lessons from using the framework concepts on a reference healthcare implementation. scope and definition this work leverages peer-reviewed blockchain in the healthcare literature articles and adds industry reports from other practitioners as viewed through the lens of mainstream adoption at scale. as a starting point, “scale” is defined by the number of constituent users impacted and the magnitude – typically hundreds of thousands of healthcare constituent users or hundreds of staff in the business-to-business case. the goal is to collect information from articles and industry reports, prioritizing systematic and scoping reviews on adoption, articles on barriers to adoption scaling up pocs and pilots, and articles on adoption metrics and maturity levels. the specific questions addressed include: 1. do systematic reviews measure and explain the adoption of blockchain in healthcare? 2. do the blockchain adoption metrics as a transformative force converge to healthcare quadruple aim5 at scale measurably? 3. are there gaps? how can a practitioner’s view help progress toward the desired mainstream impact? can the best practices be templated? can they be customized? the findings section summarizes the inferences from the literature in a traditional narrative style and articles indicating elements of scale inflection points are discussed. preferred reporting items for systematic reviews and meta-analyses (prisma) flow charts are included in the methods section to help expand scoping and systematic reviews. the authors add practitioners’ views in response to the inference from the literature and contemporary practice inputs from other sources collected from web searches toward the specific objectives. the article then discusses the adoption barriers, success factors, their classification and trends, and any gaps, including practitioner’s views. the authors analyze the need for a multifaceted framework and the lessons from its use in a blockchain implementation case study. although not entirely within the scope of this work, our motivation is to quantify blockchain efforts as full life cycle benefits eventually and the scale impact to measurably relate blockchain as a transformative force contributing to the quadruple aim5 of healthcare. the quadruple aim5 represents the widely cited and accepted health system goals for enriching patients’ experience, improving population health, reducing overall healthcare costs, and reducing the burden on healthcare staff. methods a literature search was initiated to identify peer-reviewed journals, conference articles, industry reports, and other information sources from other practitioners. four scholarly databases were chosen: pubmed, an important database for https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.280 3 (page number not for citation purpose) moving beyond proof of concept and pilots to mainstream healthcare-related literature; ieeexplore for efforts toward standards to advance technologies; and scopus and web of science, two large databases that have material from a variety of peer-reviewed journals. they are also chosen to balance the depth and the breadth of the objectives. the initial search identified literature on blockchain in healthcare, leveraging summarizations by an earlier systematic review,6 starting with keywords (i.e., “health,” “ledger,” “blockchain,” “system,” “insurance,” and “medic”). for the specific purpose of this article, the keywords “health,” “ledger,” and “blockchain” sufficed. this initial search yielded 1,110 documents across the four databases searched: pubmed 917, ieeexplore 34, scopus 1,568, and web of science 51. after removing duplicates, the search produced 2,570 records from the four databases. the search was narrowed using additional keywords and combinations (e.g., “systematic,” “scoping,” “case,” “proof-of-concept,” “pilot,” “adoption,” “challenges,” “methodology,” and “framework”) within the results of earlier searches. the results were further screened by careful analysis of the title and the abstract, and if deemed relevant, inclusion/exclusion factors were determined. emphasis was placed on systematic and scoping reviews and articles indicating attributes of scale, challenges, adoption, frameworks, etc., not individual pocs and pilots of specific technical, functional, or policy aspects or overgeneralizations that did not have execution scale context. hence, the rationales for inclusion were reason 1: systematic or scoping reviews, reason 2: the article covers adoption metrics, challenges, and success factors, and reason 3: the articles suggest specific attributes as the pivotal factor for scale. a prisma flow chart of the process followed is shown below in figure 1. in addition to these database searches, a web search for pertinent industry articles from other practitioners and industry reports was sought. eight such reports or website references were added, and they are called out in the prisma flow chart. in total, 34 articles were pursued for final review. summary of findings the number of overall studies increased over time. still, it mostly was poc, pointed technical or functional case studies, proposals, opportunity assessments, or use cases that had not scaled yet. some systematic studies reviewed fig. 1. the prisma flow chart of the process followed to identify peer-reviewed journals, conference articles, industry reports, and other information sources from other practitioners. four scholarly databases were chosen: pubmed, eeexplore, scopus, and web of science. prisma: preferred reporting items for systematic reviews and meta-analyses. records identified from*: databases (n = 4) total records: 4514 records removed before screening: duplicate records removed (n = 1944) records screened. (n = 2570) records excluded** (n = 2466) reports sought for. retrieval (n = 104) reports not retrieved. (n = 3) reports assessed for eligibility. (n = 22) reports excluded: 79 exclusion 1 (n = 62) exclusion 2 (n = 8) exclusion 3 (n = 9) etc. records identified from: websites (n = 8) organizations (n = 0) reports assessed for eligibility. (n = 6) reports excluded: reason 1 (n = 0) reason 2 (n = 0) reason 3 (n = 0) etc. studies included in review. (n = 34) identification of studies via databases and registers identification of studies via other methods id en tif ic at io n sc re en in g in cl ud ed reports sought for retrieval. (n = 6) reports not retrieved. (n = 0) https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.2804 (page number not for citation purpose) sathya krishnasamy and narayanan gopalakrishnan were related to those targeted areas. this was evidenced in executing the inclusion and exclusion process. although the number of publications and their generalizations are significant from the authors’ perspective, summarizing the true inflection points and their timeline was more valuable for the objectives. hence, the findings are organized in chronological narration to summarize and help answer the current objectives of the article. the bitcoin whitepaper was published in 2018 and is still the opening context for most publications. for adopting blockchain in healthcare, the debut and recognition of ethereum and hyperledger platforms in 2015 and 2016 could be considered the starting reference point. publications and systematic reviews would lag the developments by 1–2 years, and hence, the time buckets are classified as follows: early stage a systematic review by agbo et al.7 in 2019 summarizes earlier work and discusses blockchain use cases in general until then, calls out medication reconciliation (medrec)8 and guardtime as notable implementations and indicates the scale of healthcare blockchain implementation guardtime to be 1 million users referring to other reviews.9,10 systematic reviews by hussein et al.11 in 2019 classified blockchain efforts into six categories: data security, integrity, privacy, authentication, and interoperability, and recommendations for potential users. meinert et al.12 in 2019 also suggested a systematic review and indicated the main areas of focus on data, interoperability, and scalability. most work was concentrated on handling electronic health data records. systematic reviews3,4,13 revealed the possibilities and limitations and suggested design choices in blockchain implementations for efficient healthcare, including a hypothetical national health system. mid stage in 2020, chukwu et al.6 compared seven systematic reviews to his systematic review and classified the assessment attributes into eight buckets: bibliometric, functional, security, privacy, performance, architecture, cost, and standards. he reported reviewing 143 blockchain articles in healthcare work in several countries, including the u.s. he reported that only 5% (7 of those 143 efforts) discussed real-life implementation, pilot testing, or implementation evaluation. a scoping review14 in 2020 concluded that the exploratory work of blockchain in healthcare is real, but it is still in its infancy. numerous researchers continued trying to understand blockchains and blockchain in the context of other allied technologies. results and opinions started to get mixed, and the following two studies summarized opposing views. the digital study15 in 2021 tied blockchains to digital technologies overall and potentially life-saving personal journeys and national electronic health records. this notable high-profile article suggested limitless possibilities but no long-term clinical outcomes had yet been seen. however, work by yeung8 in 2021 contends that no serious scholarly attempt has been made to evaluate how blockchain technologies can be applied to real-time contexts in healthcare. on the technical side, it raises serious questions on all available blockchain network models, which need continuous research and potentially newer hybrid architectures. as per the article, blockchain transformation in healthcare faces multidimensional complexities not only technical but also issues with cooperation among companies, organizational structures, and support, and concludes that blockchain technologies are unlikely to revolutionize healthcare soon. recent stage in the mid-stage, numerous articles reported similar findings, but a few started to identify new attributes and classes as opportunities, threats, and success factors. a 2022 systematic review by saeed et al.16 registered the importance of blockchain technology to be deployed in conjunction with artificial intelligence (ai), machine learning (ml), and the internet of things (iot). it quantified the importance of solving for the data volume in healthcare. a scoping review by abu-elezz17 called out technical improvements but added that attention should be paid to an important new threat of social acceptance of blockchain technology in healthcare. in 2022, al shamsi et al.11 studied blockchain adoption. although their study is not about healthcare only, they cite healthcare and report strong findings. key takeaways from his research confirm adoption is still limited, blockchain projects only get executed due to top management’s direction, and there is a dearth of empirical research in domains other than supply chain. he argues for more studies and adoption models. in the recent stage, the author notices a trend in the search for the all-elusive enterprise blockchain adoption in healthcare in various targeted studies. these studies are on individual areas in value creation,18 technology for decentralized identities,19 and the interplay of factors on top of the techno-functional focus on medical records, with factors such as regulation through a data marketplace.20 efforts are underway to draw attention to the progress made on more than 40 proposals for data management of medical records and gaps in the interplay of privacy, technology through a survey,22 and user and operator readiness.21 these efforts reveal additional social and economic variables even if those gaps were to be filled, while new high-level use case proposals https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.280 5 (page number not for citation purpose) moving beyond proof of concept and pilots to mainstream continue to be called out as possibilities. for example, use cases are recognized in claims processing22 and avoiding ransomware issues with public blockchains23 in different geographies, which can be served by earlier solutions sharing key execution insights along with these new scale adoption insights in public and enterprise blockchain settings. a 2022 healthcare value study18 indicates healthcare’s value creation by blockchain is possible and is enabled by three factors: improving service interaction, impacting actors’ engagement, and fostering ecosystem transparency. it points out that limited standards in the blockchain vocabulary are a concern. in addition, the author observes that the key findings related to blockchain in healthcare adoption are not often covered in general systematic reviews. still, they surface in the reviews of the earlier identified categories or research that targeted specific issues. for example, a study by khan20 discusses how regulation developments can be leveraged to adopt capabilities such as medrec by transitioning from sharing to selling on a data marketplace. another study also examines sharing medical records that had not seen adoption scales by indicating findings in stakeholder readiness from a business measures angle.24 while progress continues with earlier identified variables in the search for adoption, the following key studies point to newer issues and variables that practitioners face in assimilating work performed so far, channeling them for future work and relating it to the desired healthcare systems outcomes. one such key comprehensive data quality review in 202325 highlights known issues in data quality but in newer manifestations from traditional interoperability and makes a case for blockchain-based data provenance as huge for adoption, citing american health care data management.26 the report also specifically calls attention to the dearth of research in blockchain in healthcare and suggests looking for cross-vertical learnings. the maturity model study by akbar et al.27 in 2022 takes a software and process approach and prioritizes the key success factors for blockchain in healthcare efforts. the top few in that list are a culture of collaboration, standards, clarity of high value, and prioritized use cases. technical scalability ranks last. a recent systematic review12 in 2023 on adopting blockchain in healthcare across nations also remarks that little attention has been paid to internal and external factors critical to adoption and argues for more research on adoption theories and models. of the 33 adoption factors, blockchain knowledge and awareness and perceived benefits to relative advantage in healthcare are in the top quadrant, along with privacy, regulations, and policies as dominant factors and trust as a variable in prioritizing further work for high adoption. practitioner’s perspective the corresponding author is an experienced technology and digital transformation executive who has spent more than 15 years in healthcare and another decade in business optimization and supply chain and international trade management systems. the author has been holding multiple leadership roles in technology management, innovation management, and emerging technologies and has been part of several industry workgroups in healthcare, including health level seven (hl7), davinci payor-provider collaboration groups, healthcare information and management systems society (himss) task forces, and as an advisor and a voting member prioritizing research at the center for advancing research in financial technology. the footnote at the end of this article has more information about the authors. the following section is the author’s perspective, synthesizing the research information presented so far from the literature and combining it with his views and understanding of fellow practitioners’ views through professional relationships by presenting prevalent stakeholder viewpoints on blockchain in healthcare. blockchains enable peer-to-peer exchange that provides the trust needed for the stakeholders, eliminating the need for a third party in between that had been providing the trust factor. it is a horizontal innovation that cuts across verticals. it started from a decentralized movement that wanted an efficient way to move money without intermediaries, and the technology has seen use in cryptocurrencies, decentralized finance (defi) protocols, and non-fungible tokens (nfts) and is continuing to mature. there is a public and a private topology to it; both have been trying to achieve results in solving problems related to healthcare using the same technology that has seen notable success in fintech use cases. however, the results are mixed. industry perspective from the overall industry perspective, the view depends on the stakeholders and their position, which could be very subjective. the author takes a balanced view between the industry forbes article,28 citing bureau of industry and security (bis) research indicating that blockchain in healthcare will save “100 billion dollars per year in 2025.” another industry metric website, statista,29 claims modest activity regarding deployable blockchain applications. the author also thinks that healthcare efforts can and should learn from other verticals,30 like finance, which is consistent with his cross-vertical efforts. he has also observed some of the issues reported by other practitioners on alarming failure rates,2 and the challenges and outlook.31 https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.2806 (page number not for citation purpose) sathya krishnasamy and narayanan gopalakrishnan the author keeps a “cautiously optimistic” note and, in general, is excited about the possibilities the technology brings and finds more importance in connecting the findings in the literature, sharing the industry findings, and moving forward with mechanisms to increase success. response to research question objectives table 1 revisits and responds to the research objectives based on synthesis from the literature findings and the author’s perspective of industry observations. need for execution framework in blockchain in healthcare in the initial stages, some of our efforts were also a broadstroke assessment of the application of blockchain technology, with high-level assumptions on how it could be used and technology-based prove-outs. several use cases were proposed, and technical component development started with little comprehensive understanding of the full ecosystem level functional context, business and enterprise architecture, organizational and change management aspects, and possible inter-enterprise friction and technical and business scale adoption factors. the results were remarkably similar to what was reported.2 this typically causes frequent starts/stops, loss of confidence, and threatens sustainable innovation resulting from our earlier cycles. a few issues that reviews27,32 reported from their source interviews were also realized, along with other learnings in the author’s blockchain experience in the contemporary period in one of the healthcare deployments. after frequent starts/stops of predominantly technical efforts, it was recognized that unless a true end-to-end process is tried at scale from where the legacy enterprise systems and processes stand in their maturity, the true potential of blockchain-based transformation would not be enabled. a structure with the characteristics listed in table 2 is needed. new critical success factors surfacing, like trust, etc., will require more information captured in usage patterns that blockchains can add value to. we observed “trust but verify” operational patterns that could be turned into “trust and use.” blockchain in healthcare discovery framework presented here and illustrated in figure 2 is a framework for blockchain in healthcare efforts. blockchain in the healthcare discovery framework is generic and has been assembled after examining healthcare data and information flow patterns. it provides a structure from conception to execution for practitioners. it is maintained at the github url33 and the wiki url. its early use has been instrumental in identifying and executing a multiparty process in the healthcare payor setting but continuously evolves, including cross-vertical learnings. importance of the framework in the reference case the framework and the reference case are noteworthy because they operate on blockchain transformation constructs on underlying healthcare data. however, by the sheer number of memberships across the providers, the payor flows can accelerate identifying scale-related success factors in healthcare. the reference case explored an administrative function but with a high economic impact on the stakeholders because of its potential for administrative efficiencies. administrative waste accounts for the major portion of waste not attributed to clinical care. table 1. response to the research objectives based on synthesis from the literature findings and the author’s perspective of industry observations question response do systematic reviews measure and explain the adoption of blockchain in healthcare? • no, not yet. • the adoption studies are just starting and, in fact, show the need has just been realized in 2022 and 2023, and earlier than that, there was no real measure. • the overall systematic reviews in blockchain in healthcare focused on the technical dimension, predominantly and are highly speculative. • various other factors affecting adoption are surfacing.27,32 • there are still nascent measures. do the blockchain adoption metrics as a transformative force converge to healthcare quadruple aim5 at scale measurably? • the response to the earlier question automatically means no to this question as well. • moreover, there are no documented ways to map how much of overall health system performance can be attributed to blockchain, what characteristics of blockchain map to which improvement metric and to define the targets. what are the gaps, and how can a practitioner’s view help to make progress toward the desired impact mainstream? can that be templated for best practices and customized? • significant gaps exist in defining the structures and methods needed for blockchain adoption in general and specifically for healthcare at various levels. • these will be called out in the next few sections. a framework that helped in a reference case in healthcare will also be discussed. https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.280 7 (page number not for citation purpose) moving beyond proof of concept and pilots to mainstream identifying such use cases, which can convince the stakeholders to proceed to scale, will help understand success factors for more complex use cases such as clinical care. the reference case is an administrative benefits function, a multiparty use case with comparatively lower friction. still, it has higher operational cost avoidance value, particularly if the accuracy and audibility are high, and the algorithmic support can also easily delineate cases that need a human-machine loop. organizational a particularly important part of the framework is to model and capture top management support, multilevel stakeholder support, and internal and external stakeholders in order to clearly articulate the engagement, learning, and transformation process conducive to blockchain-based transformation. the primary concept was to start with the end state in mind and have a network view always, in every touchpoint with an incoming work request (right to left) as opposed to most blockchain efforts, which had a primary motivation of technical exploration only (left to right) shown in figure 2. the framework realized blockchain needed evangelism in enterprise contexts and needed continuous budgets and efforts to sustain the innovation. the framework had the following themes: team and execution structures. themes it is important to note that blockchain technology is not isolated but must be in concert with the enterprise’s overall data quality and data reliability picture. leadership messaging leadership messaging repeatedly stress the following notions. blockchain mainstream any blockchain work request evaluation internally or externally will come along with its scale definitions with the touchpoints. adoption experiments at scale the intent was to understand the overall scale metrics, operational constraints, or bias identified immediately. the operation function reimagined will have a 360-degree evaluation with the ecosystem partners. for example, the table 2. a structure with the following characteristics is needed to realize the true potential of blockchain-based transformation characteristics defined organizational blockchain efforts need expanded support at all levels, internal and external, as study results32 indicate, along with clearer objectives. technical gaps exist in benchmarking shared data, shared processes, and documenting differential designs as best practices for current and future needs. the technical stack itself is rapidly maturing, and few frameworks are available, primarily an evaluation between hyperledger and enterprise ethereum. scalability benchmarks in hyperledger and ethereum are improving, but beyond those, the evaluation frameworks or guidelines are also rapidly outdated. standards are lacking as well.18 functional the “network vs. database” attribute27 is not just a technical implementation detail but rather an investigative theme to discover and potentially dis-intermediate complete functions. this needs a different mindset, structure, and guidelines to elicit software development life cycle requirements. regulatory and privacy design patterns for privacy and compliance are critical to scalable adoption. fig. 2. a framework for blockchain in healthcare efforts. r&d: research and development. https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.2808 (page number not for citation purpose) sathya krishnasamy and narayanan gopalakrishnan true sources of those data elements incoming are reasoned out. these experiments addressed, “what happens if the flow is restructured,” and “what if the degree of separation of the transaction is reduced and data at source are pursued instead”? these experiments helped elicit the hard and probing questions, and any proof needed was not only technical but also assessed for scale from an operational context. the advantage of this approach was always to be ahead of the next opportunity to capitalize on should there be an advancement in the overall technical function of the enterprise along with forward-looking partners. educate-evangelize-experiment blockchain forum one of the biggest challenges, as also evidenced by studies,27,32 is a lack of technical experience. as part of the execution template, there must be ongoing exercise at various levels – not just a technical function. the forum’s activities include blockchain messaging, massive open online courses (moocs), workshops, lunch-and-learns, and happy hours at every opportunity, internally and externally. partnerships the resource templating is set up for partnerships, gig workers, and academics. portfolio messaging with the adoption experiment at scale always having a pulse of potential asks, the next opportunity to leverage emerging blockchain technology was introduced to evaluate major investments into legacy environments. future-proofing future-proofing is a pull strategy for advancing emerging business models based on innovative technology providing exponential advantages. the future-proofing template does the architectural due diligence to ensure the designs were set up so that enough investments could be moved to emerging needs instead of legacy designs. emerging tech investments are not just for reducing technical debt but also for allowing operations for network-based reimagining business operations. stair-stepping stair-stepping is a push strategy to continuously move away from older systems as much as the newer technology allows for transforming processes. the stair-stepping template sets up the building blocks even if full implementation on the blockchain is not immediately possible due to technical uncertainty. the stand-alone components were designed for easy blockchain extensibility, which could be advanced further as the blockchain-to-enterprise connectivity matures. careful futureproofing and stair-stepping make operational teams comfortable taking calculated risks while constantly reimagining blockchain-enabled business transitions. blockchain constructs – network first the template cultivates the habit of evaluating soapboxes/ hackathons/brainstorming and regular work along these constructs. it must be observed that all data need not be stored on the blockchain; just enough data to do business with the counterparties increases the chances of success and driving efficiency. an overall data fidelity strategy helps prioritize the right chunks of work and iteratively progress on a consistent source of truth over time and on-demand reconciliations. provenance of process and data using templated questions to evaluate provenance, timestamping, and knowing “who did what” or “what was done when” would eliminate the need for reactive analysis time spent in excessive processing and inaccurate data in the current processes.25 these provenance structures directly address and help the issues raised in the review.25 consensus lens evaluation this is a key evaluation that answers whether the decision-making involves (or) should involve the counterparties. these evaluations help identify multiparty processes and the granularity of the level of data sharing. for “no to low friction” processes, entities can rely on and agree to the provenance documented even by the counterparties to reduce the administrative burden on themselves. network visualizations visualizing data in network graphs helps reimagination. trust in situations where counterparty data are consumed, is it used directly (or) does it have verification routines? it is not uncommon for enterprise processes to re-run validations with minimal incremental benefits, hence reducing efficiencies. specifically, there were many such instances in the healthcare setting where the template was assessed. source of truth the source of truth checkpoint template analyzes if the data originate from a primary or a secondary source25 and whether there has been traceability back to the originating source. https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.280 9 (page number not for citation purpose) moving beyond proof of concept and pilots to mainstream end-point events explicit questioning and constant analysis reveal how many connection touchpoints emit or consume events. this is particularly important for the internet of things endpoints as well. connectivity maps for “n” participants in a network, the number of connectivity links could be as high as “n” (n 1 ) / 2, and along those links, it is not uncommon to see multiple rounds of either electronic request-response data, or events being emitted, for example, claims status or eligibility requests. the connectivity maps help capture and rationalize blockchain designs. touchpoints metrics how many partners are connected through which channels? is there a forest of one-to-one (1–1) connections? these could be electronic or manual transaction types. the number of transactions documented per unit of time helps channel these designs. privacy privacy designs can range from designs answering minimal data to private data channels to zero-knowledge proof implementations, along with other privacy tokenization mechanisms. it is important to clearly understand the whole picture, the boundaries, and the cloud infrastructure. friction maps in situations where any parties have concerns beyond privacy and regulations, it is important to document them in terms of friction maps for analysis. there are numerous situations where the same pain point is happening on the counterparty side, where there could be a subset of low-friction items that both parties might want to solve. interoperability in blockchain in healthcare, interoperability could mean healthcare interoperability, blockchain interoperability, or both. most designs could pass through a fast healthcare interoperability resources (fhir) layer for future-proofing regardless of their journey’s starting point. standards there are extremely limited standards in blockchain now and mostly for cryptocurrencies. however, the healthcare standards are mature enough for the parties in a multiparty ecosystem to analyze excessive chatter and make adequate business decisions to improve efficiencies so that blockchain designs can advance. blockchain in healthcare standards can leverage and extend current healthcare standards. spirit of requirements versus requirements these questions help elicit the true requirements in a network mode of operations instead of perceived requirements, which tend to be narrow and unilateral from one organization’s viewpoint. the templates prove helpful even if the starting point is a simple checklist, which can then be extended to knowledge bases. the blockchain constructs and templates outlined in the framework give a systematic way of understanding the data and process flow. it specifically helps the business users, leaders’ architects, and developers to think about data quality, governance, source of truth, and the trust that can be attributed to the source of truth as desired in the study.25 the data provenance, in the context of regulation compliance, privacy, and friction maps, further helps in designs to expose the minimal data needed for the business function end-to-end, to be used as peer-to-peer sources of data, and timestamped business agreements that could be trusted for use, instead of re-investigations and re-verifications, which constitute a significant source of waste. particularly on the administrative side, they are early sources to search for low friction flows for multiparty blockchain-based collaboration. execution functions the execution included focused sub-streams and intertwined coordination to elicit uncanny insights needed to differentiate emerging technology inflection points. starting from both ends of the framework to design for scale a technical prove-out sub-stream progressed gradually, cutting down the assumptions and increasing the complexities for true at-scale business adoption. one of the key objectives for this stream was integration of enterprise systems at the ecosystem level and future-proofing technologies. the prove-out included separating foundational blockchain elements from allied technologies and designing the interplay of allied technologies, including ai models, data oracles, and orchestrators. this was especially important as technology matured rapidly to make sure designs were modular to be reassembled as the subcomponents evolved. a cross-functional sub-stream studied the detailed flow of the existing processes, observing everyday work and process agents and a time study for each operation. the current operational metrics were noted down to granular levels. for the reference case detailed in the following sections, the primary metric was the number of investigations conducted as a live process per subject matter expert (sme) https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.28010 (page number not for citation purpose) sathya krishnasamy and narayanan gopalakrishnan per day. the “before” metric was 35–70 daily investigations, one pass. the goal was to apply the blockchain discovery framework to look for efficiencies. the work for this sub-stream was to collect the requirements through the iterations, but the key differentiator was to distill the true spirit of requirements. this was critical to be able to incorporate unconstrained thinking. discovery of inflection points in multiparty scale – constructs and what-ifs a discovery sub-stream at the heart of the effort pulled together the details, focusing on re-engineering the process in the context of multiparty collaborations. experimentation was based on continuous feedback to critically identify what sub-components can potentially be the inflection points for the network-based designs. this would not have been possible otherwise by employing the blockchain constructs—provenance, consensus, trust, audibility, privacy, security, source of truth, and the granularity at which the decision had to be made. the key aspect of this sub-stream was to focus on “what-if” scenarios and present the business partners with non-linear alternatives that exceed their near-future plans on legacy paths. this sub-stream incubated the re-imagination of ideas that could produce hyperefficient results. the delivery stream assembled them into a program with a stair-stepped plan, connecting to the elements of the re-imagination goals, risk management, sustainable return on investments, exceeding operational metrics, and dependency management to accelerate time to market. all sub-streams penetrated the enterprise as a guild, and constant information flowed between them, with very nimble iterative cycles to ship functionality that could produce quick metrics constantly. reference case a few quick iterations of the discovery framework applying the blockchain constructs helped to select a use case catering to an operational business problem at scale with a straightforward multiparty process. the use case was to coordinate benefits between payors and was a composable functional unit of business layers, including member matching and eligibility. the primary peers were payors, with other payors as external parties. there was a possibility to also model another payor internally with fewer dependencies, given the nature of the business setup, operating across multiple geographies as multiple brands. this helped try out the scale and progress to an unconstrained reimagination of the process with fewer dependencies. the current processes revealed a set of laborious processes where the experts drain a lead for benefits coordination inquiry and go through data collation with several data feeds, portal searches, and phones. the document reads from many sources, internal and external, to figure out who will pay as the primary payor where more than one insurance was involved based on the subscriber and dependent memberships. discovery framework impact applying the blockchain constructs from the discovery framework in the execution mode described above surfaced the following findings: provenance the information gathered from various sources on consumers having other insurance had limited documentation. structured provenance with timestamps at a granular level, by itself, created a huge lift in the visibility and the quality of the underlying data. all provenances need not be on-chain. off-chain detailed provenance with on-chain references to the source of truth sufficed enough. reconciliation whenever there were instances where there seemed to be conflicting information, the provenance helped in storing the most acceptable version in the source of truth repository. these reconciliations were done automatically through natural language ai tools, manually, or a combination based on the defined thresholds. shared multiparty collaboration the end-to-end multiparty business process revealed that despite the enormous volume of data, only a small subset accounted for settling the benefits of coordination between parties if the provenance behind those elements can be fully traceable. friction maps the friction maps, along with provenance, indicated a sizable number of opportunities that fall into administrative waste, which were equally important for counterparties. connectivity maps the connectivity maps showed many instances where data sent to several sources could be reduced to the subset and hosted in a compliant and privacy-protected way on the blockchain nodes for the counterparties to consume. trust despite using third-party intermediaries that provided this function, the framework discovered that numerous operational processes had to do verification, which was administratively prohibitive because of the following reasons: (1) the data did not flow from direct peers, (2) underlying data quality issues, which could be from both ends, made them less dependable, (3) there was no notion of an agreed-upon source of truth, only indicative. https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.280 11 (page number not for citation purpose) moving beyond proof of concept and pilots to mainstream the blockchain-based processes improved the chances of data quality from an internal perspective across different flavors of data within different enterprise departments and an external perspective across companies. some enterprises do have a master data management. they manage an ecosystem source of truth. in healthcare, privacy and regulations add extra dimensions of complexity. the analysis through the framework indicated the business processes, the solution with blockchain constructs, and the human-ai loop that showed promise to move from “trust but verify” to “trust and use.” privacy the framework identified the privacy-sensitive elements in the subset, along with the regulations and business friction; this helped to understand the design patterns to share data in a privacy-protected way. these included minimally needed data share, which is always mandated in healthcare, privacy tokenization technologies, hashing, and merkle-trees-based comparison routines for entity matching, with additional technologies still maturing for zero-knowledge circuits. transaction touchpoints the connectivity maps, along with the multiparty collaboration opportunities, friction maps, and trust parameters, validated the designs to be run as smart contracts and made the results available on all nodes so that the unneeded chatter could be removed. incentives the incentives were also a major part, and the framework helped to not only identify the economic incentives for the solution, predominantly from labor-cost avoidance, but also increasing the payment accuracy of the outbound dollars. a rapid small-scale pilot proved the solution, and the scale parameters were determined. yet, this transformation was a massive task and had to be planned in the context of where the enterprise technical landscape and the emerging technology were. what was clear was the blockchain-based solution would be the future state. still, the changes will have to be introduced at a pace where the current enterprise’s technical landscape and the operational change process can absorb it gradually. the framework’s application proved that blockchain allows for a fundamentally different way of thinking rather than a stand-alone technical upgrade. this transformation needed a sustainable and iterative plan to roll out, considering the current technological and operational environments, budgets, and cultural shifts, where stair-stepping and future-proofing framework elements prove useful. inflection point 1: first win documenting the provenance the first step was to introduce the blockchain construct of provenance for the data assets collected, and with off-chain and on-chain designs, the first minimum viable product was quickly put out. this step provided the initial quick win not only at the functional level but also by calling out the parameters to scale – number of business lines, transactions, off-chain data, number of person-hours saved, etc. inflection point 2: stair-stepping and future proofing to win stakeholders’ confidence the implementation was stair-stepped according to the methodology after a full study of the current decision-making process. the study revealed that the current processes were inadequate for a full decision-making loop, which needed the underlying data elements and the business logic that operates for the business context to be coordinated between the parties. that goal had various levels of complexity in the enterprise context. in this step, the complexity was decomposed into multiple layers and stand-alone components that could be run through the discovery methodology described above, and which identified the blockchain components and topologies, the data feeds into the nodes, enterprise assets including data assets, artificial intelligence (ai), machine learning (ml) models, application programming interfaces (apis), orchestrations, aggregators, oracles, and stand-alone components. the key thing to observe is that the blockchain-based transformation is not blockchain technology alone but the set of enterprise capabilities needed to realize that transformation. from a portfolio planning perspective, the stand-alone components were prioritized first to run the software as a service (saas) mode, while the blockchain-based implementation discovered key inflection points where the network topologies helped reimagine the processes. the technical stack was assembled based on the current enterprise landscape, implications for security, privacy, legal, and a mix of blockchain development skills in go, typescript languages, other enterprise development skills in cloud, data science and ai, and regular web development in familiar languages such as enterprise java and modern web front end technologies for blockchain connectivity. figure 3 indicates the technical architecture. the blockchain implementation was on a private blockchain, where the data feeds connected the logical payor, other payor, and provider nodes. the implementation was on hyperledger fabric on the amazon web services (aws) cloud. the mainstream operational complexity needs were handled by sub-components using aws lambda step functions – stateful orchestrators for parallel processes that could dynamically merge the needs for data https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.28012 (page number not for citation purpose) sathya krishnasamy and narayanan gopalakrishnan reconciliation with data induced from the enterprise assets through ai/ml for document natural language processing (nlp), etc., and generation of standardized notes referring to the data provenance and auditability. the multimodal stand-alone components were engineered to run the same tested code without major rewrites as chain code (smart contracts) to preserve and reuse the operationally vetted integrity and overall costs. inflection point 3: deployed as smart contract, actionable information available on all nodes the data feed chain code reacted to the data feeds from the nodes to execute the business rules. if the underpinned data elements for the business context were coordinated, it would generate the agreements on-chain. if not, then the data elements go to the dapp blockchain business orchestration layer that would get additional feeds from various sources and effect reconciliations needed to correct or pick the data for decision-making or to indicate inadequacy. the situations were categorized into fully automated, low-review, and high-review buckets, allowing hyper-productive task list management. the blockchain application layer facilitates the collaboration on the agreement through off-chain and on-chain routines through the oracles updating the source of truth records feeding the nodes. results the methodology had rigorous value capture for appropriate metrics for usage at every logical shipping point as the business adoption evolved, as the productivity capabilities rolled out. these include prints for data collection and provenance, business decision points, convergence metrics, updates to the source of truth, accuracy certified by smes, and comparisons to the process audit functions. the results before and after are tabulated in summary form for confidentiality reasons in table 3 for a concise comparison of efficiency metrics. the results indicated metrics across the stakeholder groups, starting from the front-line smes to first and second-line managers to sponsors and executive support. the throughput increased drastically, from 35 to 70 business units per sme daily to 5–10 min first pass. in addition, throughput scale increases showed exponential possibilities, with an 8–10x increase in process units, finishing the first pass in 20–30 min, based on parallel designs. the blockchain constructs gave full provenance on-chain and off-chain for all data provenance at the granular level. the sme parity tests showed greater than 99% accuracy. also, the application was designed for hyper-productivity designs that are fully configurable for the business managers to fine-tune the automation and human loop for needed classifications of review gradient – fully automatic to low review to high review. this also facilitated process auditors, as they could have closer control of historic timestamps on any data flows and business decisions and the full explainability tied to the source of truth as opposed to fragmented unstructured notes in multiple legacy systems (table 4). the stair-step methodology also paved the way for consistent returns throughout the journey, including the tandem network implementation in the blockchain stage net. overall, the methodology and implementation gave about 30 x returns in 18 months. more importantly, the methodology and the implementation were helpful in further increasing awareness within the enterprise. the executive feedback was positive and the response was to expand the discovery to additional use cases. but, as indicated in the introduction, to scale across multiple use cases and the enterprise application set, the concepts must have sustainable support for an fig. 3. technical reference schematic. https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.280 13 (page number not for citation purpose) moving beyond proof of concept and pilots to mainstream extended enterprise thinking across the enterprise at all levels, particularly the end users with clear transition plans for their roles as the hyper-efficiencies are achieved. there must be support for portfolio allocations for evolving the networks, which is a task of its own, and our efforts make a compelling case to step up enterprise’s efforts on blockchains for collaborative processes. this effort gave key insights and inflection points for the blockchain to continue the case-making. key insights and inflection points the initiative proved and corroborated hypotheses based on the discovery. based on the healthcare payor processes perspective, the main inflection point discovered was the table 3. results: concise comparison of efficiency metrics before after impact per day per sme time taken for 35–70 units of process function first pass • not easily discernable • time studies only reported pieces of work and were hard to synthesize hence, meaningful business metrics weretracked only at the overall load limit per day per sme time taken for 35–70 process units first pass • 5–7 minutes first pass. typically • 60% – 70% fully automated • 15% – 20% human-machine loop • 25% – 10% manual (with visual support) smes • appreciated the reduction in their workload per day per sme time taken for 35–70 units of process function was 8 hours per day per sme time taken for 35–70 units of process unit was reduced to: • 5–7 minutes first pass • 2–4 hours for all complete smes • appreciated the reduction in their workload. linear throughput scale • more units of work needed more sme capacity non-linear throughput scale • 400 units (8–10 x) first pass • 20–30 minutes second-line managers • increased personnel flexibility scale: 2 major business lines major inventory pileup • human demand always greater capacity scale: 2 major business lines impact • payment accuracy for 1.2m members/year inventory • reduced rapidly • no build-up majority of stakeholders • efficiency increase sme: subject matter expert. table 4. results transformation capabilities: before and after before after impact labor-oriented processes efficiency-oriented processes sponsors • tie-in to overall enterprise ask batched-up processing with accumulating piles • reactive gradual movement to on-demand processing reducing piles • move to proactive front-line managers • metrics improved no provenance and traceability • with unstructured bits and pieces assembly full provenance and traceability • down to the granular level of data elements process auditors • recreation variability reduced divergent processes converging processes process managers • leads engaged in re-imagination functionally oriented mindset start of collaboration and network-oriented mindset overall • early signs of overall culture changes • needs sustainability https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.28014 (page number not for citation purpose) sathya krishnasamy and narayanan gopalakrishnan use of blockchain technology for off-chain and on-chain data provenance and agreements for key decision points on multiparty use cases between payors and other payors. the blockchain chain-based multiparty transformation is not an isolated exercise. still, it must accompany allied technologies such as off-chain data assets, provenance, ai/ml, cloud technologies such as serverless, and event messaging. the free-form structures of the network allow for participants to have context-based participation with needed governance as it evolves, as opposed to multiple front-loaded constraints that could frequently be a deterrent. as the other entities engage, the inflection points on the network increase. for example, additional network participants like members or providers can potentially contribute key missing data. in this regard, blockchain technology increases data quality and fidelity at the edges close to the source. privacy-enhancing technologies are also advancing at a higher pace, which is another important inflection point for blockchain technology, as it now opens up collaborations that were not possible before. conclusions this article analyzed the overall landscape of blockchain-based transformations and the success rates through a literature search, other industry reports, and fellow practitioner experiences trying to scale business use cases mainstream beyond concepts and pilots. best practices were realized and assembled into the framework and have been evolving. for one such use case, the framework was deployed in steps to convince stakeholders of the scale of discovering and executing the inflection points to eventually produce hyper-efficiencies based on blockchain technology. the results of this effort exceeded expectations in proving hyper-efficiencies based on the blockchain constructs of provenance, real-time events, and collaborative decision-making involving multiple parties in a staging environment. from the healthcare payor processes perspective, there is enough evidence for “no friction to low friction” possibilities with ecosystem partners to be deployed using blockchain technologies today. one key takeaway is that, even though the technology is still maturing, this effort demonstrates and underscores the need for healthcare enterprises to take a broader and bolder look into the blockchain and allied emerging technologies. this can be done without making blockchains the front-and-center technology initiative but by focusing on its transformation possibilities. the blockchain transformation is a multifaceted effort that needs support from all levels for a future-proofed strategy and enterprise portfolio planning vis-à-vis other project allocations. this technology has all the underpinnings for re-arranging the healthcare landscape. future work the authors analyzed literature for reports on typical blockchain project adoption barriers, including technical, legal, regulatory, operational, and organizational aspects. the article indicated the recent trend in literature to realize new success attributes and metrics for blockchain adoption. the report demonstrated that blockchain is not just a technological change but a fundamentally different way to reimagine business. it has immense potential but needs a framework with differentiated capabilities and templates. the framework and the results it had created in one deployment setting were provided. the crux is to implement enterprise transformations methodically, applying blockchain constructs and always being able to leverage opportunities to expand scale. further work can expand this effort into more extensive studies in the future. the generic framework33 is expected to continue to evolve in other healthcare settings and learn from other verticals to drive best practices for adoption. about the authors sathya krishnasamy: the author’s background is in emerging technologies management, including blockchain and distributed ai, and he has 15 years of experience in healthcare and 10 years in supply chain experience. he has a graduate degree in engineering (msie) focused on enterprise engineering and has been involved in many industry task forces. his contributions included an emerging technologies practitioner’s perspective in healthcare. he does emerging technology consulting through his firm chainaim and can be reached at krisat3003@gmail.com or sathya@chainaim.com badri narayanan gopalakrishnan is a renowned scholar affiliated with the university of washington, seattle, washington, usa, and boston college, chestnut hill, massachusetts, usa. dr. gopalakrishnan has advised many blockchain startups. he also advised who and harvard medical school’s beth israel deaconess medical center on a wide range of healthcare policy and strategy issues. the practitioner’s perspective, academic affiliation, and accomplishments helped him contribute to this article. he can be reached at badrig@uw.edu funding no specific funding was involved in the effort. financial and non-financial relationships and activities the authors report no conflicts of interest. contributors this article presents the view of the authors, which is based on their experiences with emerging technologies https://doi.org/10.30953/bhty.v6.280 mailto:krisat3003@gmail.com mailto:sathya@chainaim.com mailto:badrig@uw.edu citation: blockchain in healthcare today 2023, 6: 280 https://doi.org/10.30953/bhty.v6.280 15 (page number not for citation purpose) moving beyond proof of concept and pilots to mainstream in healthcare. the framework is generic knowledge in blockchain and healthcare; no data were specifically collected. the reference implementation results are summarized generically as an example considering deployment confidentiality and used only to advance generic blockchain in healthcare best practices. references 1. fang hsa. commercially successful blockchain healthcare projects: a scoping review. blockchain healthc today. 2021;4:166. https://doi.org/10.30953/bhty.v4.166 2. geleziunaite g, manion s. “block-change”: exploring change management principles to overcome challenges in blockchain adoption. j br blockchain assoc. 2023;6:1–7. https://doi. org/10.31585/jbba-6-1-(4)2023 3. o’donoghue o, vazirani aa, brindley d, meinert e. design choices and 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[internet]. www.commonwealthfund.org.; 2023 [cited 2023 aug 25]. available from: https://www.commonwea l th fund.org /publ i cat ions / i s sue -br i e f s /2023 /oc t / high-us-health-care-spending-where-is-it-all-going 32. morey j. council post: the future of blockchain in healthcare [internet]. forbes. [cited 2023 nov 17]. available from: https://www.forbes.com/sites/forbestechcouncil/2021/10/25/ the-future-of-blockchain-in-healthcare/?sh=7fe6d0c0541f 33. blockchain in healthcare discovery framework [internet]. github. [cited 2023 dec 15]. available from: https://github. com/chainaimlabs/blockchainadoptionframework/wiki/ blockchain-in-healthcare-discovery-framework copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see: http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v6.280 http://www.commonwealthfund.org https://www.commonwealthfund.org/publications/issue-briefs/2023/oct/high-us-health-care-spending-where-is-it-all-going https://www.commonwealthfund.org/publications/issue-briefs/2023/oct/high-us-health-care-spending-where-is-it-all-going https://www.commonwealthfund.org/publications/issue-briefs/2023/oct/high-us-health-care-spending-where-is-it-all-going https://www.forbes.com/sites/forbestechcouncil/2021/10/25/the-future-of-blockchain-in-healthcare/?sh=7fe6d0c0541f https://www.forbes.com/sites/forbestechcouncil/2021/10/25/the-future-of-blockchain-in-healthcare/?sh=7fe6d0c0541f https://github.com/chainaimlabs/blockchainadoptionframework/wiki/blockchain-in-healthcare-discovery-framework https://github.com/chainaimlabs/blockchainadoptionframework/wiki/blockchain-in-healthcare-discovery-framework https://github.com/chainaimlabs/blockchainadoptionframework/wiki/blockchain-in-healthcare-discovery-framework http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) narrative/systematic reviews/meta-analysis why blockchain benefits don’t guarantee adoption: an integrated tam-toe analysis of technology acceptance and organizational readiness fatma m. abdelsalam, dba, mba, fachdm assistant professor at population health and leadership analytics department, school of health professions, university of texas at tyler, tyler, texas, usa *corresponding author: fatma m. abdelsalam, email: fatma.abdelsalam@uttyler.edu doi: https://doi.org/10.30953/bhty.v8.428 keywords: blockchain technology, digital health transformation, health information systems, healthcare adoption, organizational readiness, perceived risk, technology acceptance model, technology-organization-environment framework abstract background: despite blockchain technology’s demonstrated potential to enhance security, transparency, and efficiency in healthcare systems, adoption rates remain significantly lower than predicted, creating a persistent gap between perceived benefits and adoption feasibility. this study addresses the critical question of what explains this adoption paradox by developing and testing a comprehensive theoretical framework that integrates the technology acceptance model (tam) with the technology-organization-environment (toe) framework. methods: a systematic literature review is conducted following preferred reporting items for systematic reviews and meta-analyses guidelines to synthesize existing research on blockchain adoption in health care. this study develops four key propositions examining how technology characteristics, organizational factors, external environmental pressures, perceived risks, and system quality collectively influence healthcare organizations’ blockchain adoption intentions. results: the analysis reveals that blockchain adoption in health care is influenced by a complex interplay of facilitating and inhibiting factors. technology characteristics such as perceived usefulness (pu) and ease of use, combined with organizational innovation readiness and technology compatibility, positively influence adoption intention. external factors enhance perceived technology benefits and consequently affect adoption decisions. however, perceived risks moderate the relationship between pu and adoption intention. conclusions: blockchain technology represents a transformative solution for persistent healthcare challenges, but successful adoption requires a holistic approach that simultaneously addresses technology, organizational, and environmental factors. the adoption gap can be bridged through strategic planning that aligns institutional readiness with user incentives, comprehensive risk management, and supportive regulatory frameworks. future research should focus on establishing ethical governance models to support broad blockchain adoption in health care. plain language summary this research paper examines why blockchain technology isn’t being widely adopted in healthcare despite its promising benefits. the study found that adoption depends on three key factors: technology characteristics (usefulness, ease of use, and security), organizational readiness (resources, infrastructure, and leadership support), and external environment (government regulations and market pressure). the research shows that simply having good technology isn’t enough, healthcare organizations need the right resources, training, and support systems in place. additionally, perceived risks around data security can slow adoption, even though blockchain is designed to be secure. the paper recommends that governments create clear regulations, healthcare organizations invest in infrastructure and staff training, and technology developers build user-friendly solutions. the author concludes that successful blockchain adoption in healthcare requires collaboration between policymakers, healthcare organizations, and technology developers to address technological, organizational, and regulatory challenges simultaneously. submitted: july 8, 2025; accepted: october 18, 2025; published: december 16, 2025 blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0003-3362-6440 mailto:fatma.abdelsalam@uttyler.edu https://doi.org/10.30953/bhty.v8.428 citation: blockchain in healthcare today 2025, 8: 428 https://doi.org/10.30953/bhty.v8.4282 (page number not for citation purpose) fatma m. abdelsalam the healthcare industry is under increasing pressure to improve transparency, data privacy, and interoperability. traditional electronic health record (ehr) systems are often fragmented and vulnerable to data breaches, leading to inefficiencies and compromised patient data.1 the evolution of healthcare technology has been defined by shifts in how data are generated, stored, and shared. from paper-based records to ehrs, the digitization of health care has improved access to information but has introduced new complexities. health systems often operate in silos, which impedes system interoperability. data breaches, unauthorized access, and inconsistent patient experiences remain prevalent.2 in this context, blockchain technology offers a paradigm shift. blockchain technology, recognized for its decentralized, immutable ledger capabilities, has gained significant attention as a potential solution to these persistent issues.3 initially conceptualized as the infrastructure for bitcoin, blockchain’s core principles—decentralization, immutability, and transparency—make it well-suited for solving systemic problems in healthcare data management.4 blockchain technology has emerged as a nascent paradigm for data storage, transfer, and security across various industries. the technology is considered a promising breakthrough that will likely have a significant influence on a myriad of different industries, such as health care, supply chain management, and business.5 the blockchain peer-to-peer network was initially proposed by satoshi in 2008 and then commercialized in 2009 when bitcoin emerged as its first use case.6 the healthcare industry has shown interest in exploring the potential of blockchain technology for improving patient care, ensuring data privacy, and streamlining administrative processes.7 blockchain technology has the potential to transform the healthcare industry by providing a secure, decentralized, and transparent platform for potentially managing patient data, reducing fraud, and enhancing trust among patients, healthcare providers, and other stakeholders.8 healthcare organizations face dual responsibilities: protecting sensitive patient data and enabling timely access for care delivery and research. blockchain can facilitate verifiable, secure exchanges of information without relying on a central authority. this has implications not only for clinical data but also for billing, insurance processing, pharmaceutical logistics, and research compliance. as schiavone and omrani9 suggest, blockchain is a promising technology that will play a central role in the next wave of digital health transformation. however, the adoption comes with hurdles and challenges. the conservative nature of healthcare systems, compliance demands of the health insurance portability and accountability act (hipaa) and general data protection regulation (gdpr), and high infrastructure costs limit the chance for experimentation. however, governments and enterprises are piloting projects across asia, europe, and north america to validate blockchain’s value. this study explores the dynamics for strategies to unlock blockchain’s healthcare potential. blockchain technology offers a decentralized, secure, and transparent framework that addresses core inefficiencies in the healthcare industry. the increasing need for interoperability, data integrity, and cybersecurity has drawn attention to blockchain’s potential to mitigate challenges inherent to legacy systems.7 here, it is proposed that blockchain adoption in health care can offer transformative solutions in data sharing, supply chain integrity, patient empowerment, and clinical trial transparency. it highlights emerging use cases and illustrates how blockchain supports trust and accountability. the adoption, however, faces regulatory ambiguity, scalability issues, and resistance from healthcare institutions. drawing on recent literature,9–12 as healthcare systems strive toward digital resilience, blockchain provides a critical infrastructure layer to secure, integrate, and optimize clinical and administrative operations. in the long term, blockchain could underpin global health data ecosystems that prioritize patient-centric care and cross-border data governance. this study contributes to the ongoing discussion regarding the adoption of blockchain technology in the healthcare industry. literature review blockchain technology is a distributed ledger technology that allows transparent and secure transactions without the need for intermediaries.10,13 the technology allows for the creation of a decentralized network of nodes that verify and record transactions in a secure and transparent manner.14 each transaction is recorded in a block, which is linked to the previous block in a chain, creating an immutable ledger of transactions. this technology has several key features that make it attractive for the healthcare industry, including data immutability, transparency, security, and decentralization.15 the field of health care presents a multitude of opportunities for the implementation of blockchain technology. one of the most intriguing and promising uses of blockchain technology in the healthcare industry is the management of ehrs. the ehr systems that are built on blockchain technology could provide patients with the ability to control access to their health data, improve the confidentiality and safety of health information, and lessen the likelihood of data breaches. the ehr systems based on blockchain technology could also enhance interoperability and data exchange between various healthcare organizations.16,17 the management of the pharmaceutical supply chain is another area that blockchain technology could potentially be utilized to enhance. blockchain-based systems could improve transparency and traceability in the drug supply chain, thereby reducing the risk of counterfeit drugs and increasing patient safety.18 this would be a significant step https://doi.org/10.30953/bhty.v8.428 citation: blockchain in healthcare today 2025, 8: 428 https://doi.org/10.30953/bhty.v8.428 3 (page number not for citation purpose) blockchain benefits don’t guarantee adoption forward in the fight against the widespread problem of fake drugs in the healthcare industry.14 additionally, systems based on blockchain can lessen the administrative burden of managing drug supply networks, which would result in an increase in the healthcare industry’s overall level of productivity. synthesis of literature publications on blockchain technology and its applications in the healthcare field are diverse and draw from various streams of literature, including information technology (it), healthcare administration, and economics, among others. several studies highlight the technical and regulatory barriers to overcome before blockchain technology can be adopted and deployed in the healthcare industry. for instance, a study conducted by agbo et al.13 discussed that the primary hindrances to the adoption of blockchain technology in the healthcare industry were the high levels of technological complexity, interoperability issues, and regulatory uncertainty. in addition, the literature highlighted the significance of training, education, and collaboration among stakeholders as means of overcoming these obstacles. the prospective adoption of blockchain technology in the healthcare industry has been the subject of investigation in other studies. for instance, azaria et al.16 published a study in which they suggested a blockchain-based system for managing ehrs. this system allows patients to control access to their data while also reducing the likelihood of data breaches commonly occurring. the authors placed a strong emphasis on the significance of maintaining data privacy and security throughout the process of adopting and implementing blockchain technology in the healthcare industry. the application of blockchain technology to the management of pharmaceutical supply networks was the subject of research conducted by kuo et al.19 the authors claimed that blockchain technology improves transparency and traceability in the drug supply chain, thereby lowering the risk of patients receiving fake medications and increasing the safety of patients overall. the potential economic advantages of using blockchain technology in health care have also been the subject of investigation in several studies. for instance, abu-dalhoum et al.20 discovered that blockchain technology can expand the effectiveness of healthcare systems while simultaneously lowering the expenses associated with administrative procedures. the authors highlighted the necessity for healthcare organizations to incorporate a tactical approach to the deployment strategy of blockchain technology. the healthcare weekly report4 further substantiates these economic benefits, estimating potential annual savings of $100–$150 billion by 2025 through blockchain adoption in health care, primarily from reduced data breach costs, it expenses, and fraud. while the existing literature provides a foundational understanding of blockchain’s potential in health care, a more critical analysis reveals certain limitations and areas for deeper exploration. many studies, while highlighting the benefits, often remain at a conceptual level or focus on technical aspects without sufficiently addressing the complex organizational and environmental factors influencing adoption.9 for instance, early reviews tend to summarize findings without delving into the methodologies, limitations, or conflicting results of cited works.21 this paper aims to bridge this gap by integrating recent empirical findings and addressing the practical barriers to adoption. recent research, such as bazel et al.,3 emphasizes that despite the transformative potential, blockchain adoption in hospitals remains minimal due to high implementation costs, lack of standardization, and resistance to change. mutambik et al.22 further identified administrative challenges, usability issues, and regulatory frameworks as significant barriers from the perspective of healthcare professionals. these studies highlight the need to move beyond theoretical discussions and focus on actionable strategies to overcome the real-world impediments. furthermore, while interoperability is frequently cited as a key blockchain benefit, the challenges in achieving it with existing legacy systems are often underestimated. kasyapa and vanmathi 2 provide a comprehensive investigation into these issues, highlighting the complexities of integrating blockchain with diverse healthcare it infrastructures, such as database incompatibilities and network infrastructure limitations. similarly, the financial implications, while often framed in terms of potential savings, also present significant upfront investment barriers that require more detailed analysis and justification.4 this article informs future research by emphasizing the need for a holistic approach that considers not only the technological promise of blockchain but also the intricate interplay of organizational, environmental, and human factors that ultimately determine its successful integration into healthcare systems. theoretical framework integrated tam and technology-organization-environment model the technology acceptance model (tam) is a popular theoretical paradigm that investigates how individuals respond and interact with computerized systems, particularly in the context of new technologies. davis created the model in 1989, and it has since been used extensively to study user behavior and technology adoption.23 tam consists of two main factors that affect user behavior regarding technology adoption: technology’s perceived usefulness (pu) and perceived ease of use (peou).24 the technology-organization-environment (toe) framework suggests that the adoption of new technology depends on three key factors: the characteristics of the https://doi.org/10.30953/bhty.v8.428 citation: blockchain in healthcare today 2025, 8: 428 https://doi.org/10.30953/bhty.v8.4284 (page number not for citation purpose) fatma m. abdelsalam technology, the organization in which the technology is being utilized, and the broader external environment in which the technology is being deployed.25 many healthcare organizations are in the process of considering artificial intelligence and big data technologies. therefore, integrating blockchain in their systems will, in turn, expedite the adoption process.10 in the context of blockchain technology in health care, the toe and tam can be used to investigate what variables affect healthcare organizations’ openness and adoption of new technologies.25 hence, to meet the objective of this study, an integrated tam-toe framework has been proposed. compared to the tam, which analyzes technology adoption at the individual level, the toe26 framework has been found to be the most robust and widely used adoption model in the technology adoption literature10 and was classified as an organizational-level theory. integrating the tam and toe frameworks provides a more robust theoretical lens, allowing for the simultaneous analysis of individual user attitudes, organizational dynamics, and external environmental pressures.27 as depicted in figure 1, the proposed integrated model demonstrates how technology characteristics (compatibility, reliability, and facilitating conditions), organizational factors (readiness and innovation readiness), and environmental factors (market pressure, government support, and regulatory environment) collectively influence perceived usefulness and ease of use, ultimately affecting the behavioral intention to adopt blockchain technology and its actual use.28 methodology systematic reviews serve as a foundational methodology within healthcare research, enabling a rigorous synthesis of existing literature to address specific research questions. this approach aggregates findings from previous studies to provide a comprehensive understanding of a topic, as demonstrated by kuo et al.19 in their examination of blockchain adoption in the healthcare sector. the main objective of this article is to answer the following question: • what explains the discrepancy between blockchain’s perceived benefits and actual adoption rates in healthcare organizations? by analyzing both conceptual and empirical research, this review identifies the critical factors that either facilitate or impede the adoption of this technology, thereby consolidating current understanding and highlighting key themes from prior scholarly work. the article selection process for this review was guided by the preferred reporting items for systematic reviews and meta-analyses (prisma) framework to ensure methodological transparency and rigor. an extensive search was conducted across numerous electronic databases, including google scholar, sciencedirect, scopus, and pubmed, using a comprehensive search string with keywords such as “tam-toe,” ”blockchain,” “healthcare,” “adoption,” and “factors.” all articles that were published between 2014 and 2025 were included. the figure 1. concept model of factors influencing blockchain technology adoption following the tam and toe frameworks. this figure visually represents the integrated toe-tam model, showing how technological, organizational, and environmental factors influence pu and peou, which, in turn, affect the intention to adopt blockchain technology in health care.28 https://doi.org/10.30953/bhty.v8.428 citation: blockchain in healthcare today 2025, 8: 428 https://doi.org/10.30953/bhty.v8.428 5 (page number not for citation purpose) blockchain benefits don’t guarantee adoption initial query yielded 740 articles. following the application of boolean operators and a backward referencing process, 45 potentially relevant studies were identified. the refinement resulted in a final iteration of 27 peer-reviewed studies deemed directly pertinent to the research for the analysis. propositions proposition 1: technology characteristics such as pu and peou, along with factors such as innovation readiness and technology compatibility, positively influence the adoption intention of blockchain technology in health care. enabling secure, decentralized, and immutable data storage and sharing through blockchain technology could revolutionize the healthcare sector. however, the adoption and implementation of this technology is dependent on various factors, including technology characteristics and organizational factors.29 this proposition aims to explore how technology characteristics, such as legacy system integration, along with organizational factors such as it staff expertise, training infrastructure, and dedicated innovation budgets, influence the adoption intention of blockchain technology in health care. pu and peou are the main factors that affect user behavior regarding technology adoption intention, according to the tam.25 while peou relates to how easily a user perceives a technology to be used and learned, pu refers to how beneficial a user perceives the technology to be in carrying out activities or attaining goals.24 organizational adoption strategies are important to the successful use of blockchain technology. healthcare providers and patients are more likely to adopt and utilize blockchain technology if they believe it will improve care delivery and outcomes and will be simple to grasp and use without a lot of practice or training.24 organizational factors, on the other hand, are related to the context in which technology is being adopted and implemented. the toe framework identifies three key factors that influence technology adoption and implementation.26 innovation readiness refers to an organization’s willingness and ability to adopt new technologies, while compatibility refers to the degree to which a new technology fits with existing organizational practices, values, objectives, and norms. the more an organization perceives its technological infrastructure to be compatible, the more capable it is of adopting new technology.30 kasyapa and vanmathi2 provide a detailed analysis of these interoperability challenges and propose mitigation strategies. the global pandemic has revealed a lack of interoperability in the current healthcare system and the need for accurate clinical data that can be widely distributed among healthcare providers in an efficient and secure manner.18 the goal of healthcare systems is to eliminate the intermediaries and allow direct and efficient transfer of data and information. therefore, the proper utilization of blockchain can increase interoperability while maintaining the privacy and security of data.31 in the context of blockchain technology in health care, innovation readiness is a crucial organizational factor that positively influences the adoption of this technology.31 organizations that are more innovative and willing to adopt new technologies are more likely to adopt blockchain technology. research reveals that technology characteristics and organizational aspects play a substantial role in the acceptance and implementation of blockchain technology. the researchers found that pu and peou are significant predictors of healthcare professionals’ intention to adopt and use blockchain technology.32 another study by mettler33 acknowledged that innovation readiness and technology compatibility are crucial factors influencing the approval and employment of blockchain technology in health care. building upon the theoretical framework established by wang et al.10, organizational readiness encompasses three fundamental dimensions: human resource capabilities characterized by it expertise and technical competencies, financial preparedness evidenced by dedicated budgetary provisions for it innovation adoption, and technological infrastructure readiness that supports the development and deployment of blockchain applications within organizational contexts. proposition 2: external factors such as market pressure and social norms, and government support positively influence the pu and peou of blockchain technology in health care and consequently influence the technology adoption intention. market pressure is another external factor that can influence the adoption of blockchain technology in health care. however, these same external pressures initially create resistance, workflow disruption, and negative stakeholder reactions that organizations must navigate before realizing positive adoption outcomes. the positive influence on pu and ease of use emerges only after organizations overcome initial implementation challenges, stakeholder resistance, and system integration difficulties. this paradoxical relationship explains why external pressure alone is insufficient for blockchain adoption success; organizations must also develop comprehensive change management strategies to transform initial resistance into eventual acceptance and advocacy. the healthcare industry is becoming increasingly competitive, and healthcare providers are investigating how to improve their services and reduce costs.32 blockchain technology has the potential to improve the efficiency and security of healthcare operations, which can give healthcare providers a competitive advantage. moreover, patients are becoming more informed and empowered, and they are demanding better healthcare services. blockchain technology can improve the quality of healthcare services by providing secure data sharing, reducing medical errors, and enhancing patient outcomes.33 research has https://doi.org/10.30953/bhty.v8.428 citation: blockchain in healthcare today 2025, 8: 428 https://doi.org/10.30953/bhty.v8.4286 (page number not for citation purpose) fatma m. abdelsalam shown that industry competition has a positive impact on it adoption.34 zhang et al.35 demonstrated that competitive pressure is a predictive factor for healthcare organizations to adopt health it. thus, market pressure can positively influence the adoption and implementation of blockchain technology in health care by creating a demand for more efficient and secure healthcare services. early adopters tend to derive considerable competitive advantages. however, early adopters face 50 to 100% higher implementation costs, potentially creating resistance. if healthcare providers observe their peers using blockchain technology in health care and perceive that this technology is highly valued, they are more likely to adopt it themselves.33 furthermore, if patients perceive that their healthcare providers use blockchain technology to improve their care, they are more likely to accept and use it as well. thus, social influence can significantly affect the adoption and implementation of blockchain technology in the healthcare field. government support is also an external factor that can influence the pu and ease of use of blockchain technology in health care, as suggested by the toe framework.36 governments can provide funding and incentives to healthcare organizations to adopt new technologies, including blockchain technology.29 blockchain technology can enhance the security and privacy of ehrs, which aligns with the goals of the hitech act.37 however, a lack of clear government rules and incentives for implementation contributes to the hesitancy of healthcare leaders to adopt this technology.38 thus, government support can positively influence the adoption of blockchain technology in health care by providing funding and incentives to healthcare providers. proposition 3: perceived risks of blockchain technology negatively influence adoption intention in the healthcare industry through moderating the relationship between pu and adoption intention. the literature suggested perceived risk as a barrier to the organizational adoption of blockchain technology.36 the perceived risks cause doubt among organizations toward blockchain technology adoption. for example, if technology is perceived as risky with negative personal information disclosure, organizations will be reluctant to adopt it or integrate it into their systems.39 therefore, it can be proposed that perceived risk can influence the decision of technology adoption. the perceived risk resulting from using a new technology can moderate the relationship between the technology’s pu and the adoption intention.40 in the context of blockchain technology, trust is particularly relevant, given the technology’s potential to advance the privacy and security of data exchange. blockchain technology provides a decentralized and immutable ledger of transactions, reducing the need for intermediaries and providing a more transparent and secure way of sharing data.33 in the healthcare industry, trust is significantly important, given the sensitive nature of healthcare data. therefore, healthcare professionals and organizations must have a high level of trust in any technology used to manage and exchange patient data.14 nonetheless, the level of trust in blockchain technology significantly influences its acceptance and adoption. studies have identified several factors that may affect the level of trust in blockchain technology. these include the lack of technical expertise and knowledge among healthcare professionals, the complexity of the technology, and the need to comply with regulatory requirements.21,31,36,41 if an organization already has the necessary knowledge and skills, they will be more willing to adopt, and if they lack the knowledge and skills, they are less likely.42 cheikhrouhou et al.1 further emphasize the security enhancements offered by blockchain, which are crucial for building trust in healthcare systems. however, the limited availability of mature blockchain systems within the current market landscape constrains organizational ability to evaluate empirical benefits, thereby fostering institutional uncertainty concerning the value proposition and expected return on investment associated with blockchain technology adoption.41 healthcare professionals and organizations must be provided with the necessary resources and support to manage the risks effectively and build trust in the technology. moreover, the acceptance and adoption of blockchain technology in health care must be accompanied by the development of clear standards and regulations that ensure the safe and effective use of technology.19 this requires collaboration between stakeholders in the healthcare industry, such as healthcare professionals, regulatory bodies, and technology providers, to establish common standards and best practices for the use of blockchain technology in the healthcare sector.40 proposition 4: system quality, such as reliability and security, along with innovation readiness and regulatory environment positively influences the pu and peou and the adoption intention of blockchain technology in health care. the success of a technology implementation largely depends on its system quality, which includes factors such as reliability, compatibility, and security. in the case of blockchain technology in health care, it is crucial for users to have trust in the system’s reliability and security, especially given the sensitive nature of health information.7 in terms of system quality, blockchain technology has several features that enhance its reliability, interoperability, and security.18 for instance, the decentralized nature of the blockchain network makes it less prone to failure. additionally, the cryptographic algorithms used in blockchain technology provide a high level of security, making it difficult for unauthorized parties to tamper with the information stored on the network.13 the toe framework proposes that innovation readiness, which encompasses factors such as a company’s ability to adopt new technology and its innovative culture, plays a significant role in the adoption of new technology.43 innovation readiness can facilitate the adoption and implementation of https://doi.org/10.30953/bhty.v8.428 citation: blockchain in healthcare today 2025, 8: 428 https://doi.org/10.30953/bhty.v8.428 7 (page number not for citation purpose) blockchain benefits don’t guarantee adoption blockchain technology in health care. organizations that are innovative and open to emerging technologies are more likely to experiment with and adopt blockchain technology. the more benefits blockchain technology offers to a company, such as security, quick transactions, and immutability, the more likely it is that the organization will embrace innovation.44 in addition, organizations with a strong it infrastructure and technical support are better equipped to integrate blockchain technology into their existing systems, making it more accessible and easier to use for their employees.29 the regulatory environment is one of the most important external factors that can affect the adoption of blockchain technology in the healthcare industry. the term “regulatory environment” pertains to the policies, initiatives, and incentives that a government implements to encourage enterprises to adopt blockchain technology. as reported in the literature, the regulatory environment plays a significant role in accelerating or impeding the adoption process of the technology and is considered a crucial factor in its adoption.45 any new technology must adhere to a myriad of regulatory standards since the healthcare sector is heavily regulated.42 for instance, hipaa in the united states establishes the requirements for safeguarding the confidentiality and security of patient health information. in line with hipaa regulations, blockchain technology can enhance the security and privacy of health information.37 by ensuring that blockchain technology conforms to regulatory requirements, the regulatory environment can thereby favorably affect the adoption of blockchain technology in health care.10 based on reports in the literature, the higher the perceptions of regulatory uncertainty, the lower the behavioral intention to adopt blockchain technology.42 recent reports, such as the international comparative legal guides (iclg) digital health laws and regulations report 2025 usa,46 further emphasize the evolving legal landscape and the need for blockchain solutions to align with these regulatory frameworks. finally, regulatory support in the form of funding, subsidies, or other incentives can provide a boost to organizations that are considering adopting blockchain technology in health care, making it more financially feasible and less risky.36 therefore, this proposition suggests that system quality, innovation readiness, and regulatory environment can positively influence the pu and peou and consequently the adoption decision of blockchain technology in health care. real-world blockchain applications blockchain technology has moved beyond theoretical discussions and into real-world healthcare applications. one of the most cited examples is medrec, developed by the massachusetts institute of technology, which enables patients to control their ehrs through a blockchain system built on ethereum. it tracks access permissions rather than storing medical records directly, thus ensuring compliance and data efficiency. azaria et al.16 demonstrated how this model promotes patient engagement and accountability. furthermore, estonia offers a compelling national-level implementation. the country’s ehealth system uses blockchain to log access to patient data, ensuring transparency and privacy. the integration with national identification systems and government oversight makes estonia a benchmark in digital health security. pharmaledger, an eu-funded initiative, is another application that uses blockchain to track pharmaceutical products across the supply chain. this helps combat counterfeit drugs, ensures product authenticity, and improves recall efficiency.47 blockchain solutions provide end-to-end visibility from manufacturing to patient delivery, enhancing patient safety and regulatory compliance. the technology enhances traceability and enables faster recalls, minimizing public health risks. blockchain is being adopted to automate and secure health insurance claims processing. by utilizing smart contracts, claims can be automatically verified and processed, reducing fraud, administrative costs, and processing times. this creates a more transparent and efficient system for payers, providers, and patients. research indicates that blockchain has the potential to reshape the claims authorization process due to its decentralized and tamper-proof features, along with its consensus protocols.48 during the covid-19 pandemic, blockchain was employed in various countries to verify vaccination status and medical supply authenticity.49 other platforms like burstiq and guardtime have developed enterprise-grade tools for secure health data exchange that comply with hipaa. ibm’s rapid supplier connect, launched in 2020, used blockchain to identify reliable medical suppliers during the early pandemic surge. these real-world examples affirm blockchain’s flexibility and adaptability in diverse healthcare contexts, from clinical trials and claim processing to iot-based patient monitoring. policy recommendations to realize the full potential of blockchain in health care, coherent and forward-thinking policy interventions are essential. first, a regulatory “sandbox” should be established to allow for safe experimentation with blockchain technologies. these frameworks, used successfully in the uk and singapore, enable testing under regulatory oversight without immediate compliance penalties.50 second, international and national standards must be developed for blockchain data interoperability. just as hl7-fhir enabled consistent data sharing across ehrs, a blockchain-fhir hybrid standard can facilitate distributed healthcare networks. collaboration between public agencies, standards organizations (e.g., the international organization for standardization), and blockchain alliances is needed. https://doi.org/10.30953/bhty.v8.428 citation: blockchain in healthcare today 2025, 8: 428 https://doi.org/10.30953/bhty.v8.4288 (page number not for citation purpose) fatma m. abdelsalam third, governments should fund pilot programs and infrastructure upgrades. tax credits, innovation grants, and public-private partnerships can lower the entry barrier for under-resourced providers. fourth, blockchain literacy must be embedded into health informatics and policy education. medical, nursing, and it schools should offer modules on blockchain ethics, architecture, and governance. fifth, data governance frameworks should be updated to reflect patient-centric models. legal recognition of patient-owned data, smart contract enforceability, and digital identity standards are crucial to building trustworthy systems. sixth, to further accelerate the responsible and effective integration of blockchain technology into health care, it is crucial to establish dedicated funding and investment mechanisms, including venture capital incentives and public-private partnerships for infrastructure, alongside increased research and development funds.51 finally, establishing robust cybersecurity frameworks for blockchain-based systems, encompassing threat intelligence sharing, incident response protocols, and regular security audits, will safeguard sensitive health data. conclusions blockchain technology represents a powerful solution to many persistent challenges in the healthcare sector. its inherent capabilities to secure, decentralize, and validate data offer a transformative pathway for managing patient records, optimizing supply chains, and advancing clinical research. despite the acknowledged technical and regulatory obstacles, successful pilot programs and national initiatives globally have demonstrated blockchain’s feasibility and tangible benefits.31 strategic adoption, however, necessitates a comprehensive approach that considers technology characteristics, organizational readiness, and environmental factors, emphasizing pu, ease of use, and seamless compatibility with existing healthcare infrastructure.36 adequate training and robust technical support are also paramount to facilitate a smooth and effective adoption process. ultimately, the successful integration of blockchain in health care hinges on careful planning, robust stakeholder alignment, and forward-thinking policy. collaborative efforts among governments, academic institutions, and industry are crucial to test, standardize, and scale blockchain applications effectively. the potential benefits, ranging from improved patient trust and reduced costs to enhanced efficiency and significant fraud reduction, are too substantial to overlook.52 future research should strategically focus on developing advanced privacy mechanisms like zero-knowledge proofs, ensuring seamless multi-chain interoperability and establishing robust ethical governance models.53 with coordinated support and continued innovation, blockchain is poised to underpin a resilient, transparent, and equitable global digital health infrastructure. funding this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. conflicts of interest the author declares no competing interests. contributors the author is responsible for conceptualization, methodology, validation, data analysis, writing, and editing. data availability statement (das), data sharing, reproducibility, and data repositories all data produced in this study are available upon reasonable request to the corresponding author. application of ai-generated text or related technology the use of ai to assist with grammar 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decentralized biobanking to empower patients in biospecimen research william sanchez, bs1 , larue linder 2 , robert c. miller, md, mba, frs3 , amelia hood, ms 4 and marielle s. gross, md, mbe 5 1software engineer, de-bi, co., pittsburgh, pennsylvania, usa; 2undergraduate, johns hopkins university, baltimore, maryland, usa; 3researcher, department of radiation oncology, mayo clinic, minnesota, usa; 4researcher, berman institute of bioethics, johns hopkins university, maryland, usa; 5founder/ceo, de-bi, co., pittsburgh, pennsylvania, usa corresponding author: william sanchez, email: will@de-bi.co doi: https://doi.org/10.30953/bhty.v7.303 keywords: blockchain, decentralized biobanking, non-fungible tokens, organoids, web3 abstract introduction: scientists use donated biospecimens to create organoids, which are miniature copies of patient tumors that are revolutionizing precision medicine and drug discovery. however, biobanking platforms remove donor identifiers to protect privacy, precluding patients from benefiting from their contributions or sharing information that may be relevant to research outcomes. decentralized biobanking (de-bi) leverages blockchain technology to empower patient engagement in biospecimen research. we describe the creation of the first de-bi prototype for an organoid biobanking use case. methods: we designed and developed a proof-of-concept non-fungible tokens (nfts) framework for an organoid research network of patients, physicians, and scientists within a synthetic dataset modeled on a real-world breast cancer organoid ecosystem. our implementation deployed multiple smart contracts on ethereum test networks, minting nfts representing each stakeholder, biospecimen, and organoid. the system architecture was designed to be composable with established biobanking programs. results: our de-bi prototype demonstrated how nfts representing patients, physicians, scientists, and organoids may be united in a privacy-preserving platform that builds upon relationships and transactions of existing biobank research networks. the mobile application simulated key features, enabling patients to track their biospecimens, view organoid images and research updates from scientists, and allow physicians to participate in peer-to-peer communications with basic scientists and patients alike, all while ensuring compliance with de-identification requirements. discussion: we demonstrate proof-of-concept for a web3 platform engaging patients, physicians, and scientists in a dynamic research community, unlocking value for a model organoid ecosystem. this initial prototype is a critical first step for advancing paradigm-shifting de-bi technology that provides unprecedented transparency and suggests new standards for equity and inclusion in biobanking. further research must address feasibility and acceptability considering the ethical, legal, economic, and technical complexities of organoid research and clinical translation. plain language summary scientists create miniature copies of patient tumors called organoids for precision medicine research, but privacy policies preclude communication of relevant findings with patients and their physicians. we propose blockchain infrastructure to connect patients, scientists, and physicians, eliminating barriers between bench and bedside while ensuring regulatory compliance. our decentralized biobanking (de-bi) prototype utilizes non-fungible tokens (nfts) to represent stakeholders, specimens, and organoids in a privacy-preserving platform. patients are empowered to track specimens, access updates, and engage as collaborators, creating new standards for transparency, equity, and inclusion. ongoing work addresses ethical, legal, and technical challenges to realizing the patient-centered biobanking revolution. received: january 3, 2024; accepted: april 5, 2024; published: april 30, 2024 blockchain in healthcare today issn 2573-8240 https://orcid.org/0009-0009-8437-0386 https://orcid.org/0009-0002-2329-7612 https://orcid.org/0000-0001-8932-2732 http://orcid.org/0000-0002-9702-7693 http://orcid.org/0000-0002-3009-4082 mailto:will@de-bi.co https://doi.org/10.30953/bhty.v7.303 citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.3032 (page number not for citation purpose) william sanchez et al. organoid technology creates living copies of donated patient tumors, revolutionizing precision medicine and drug development.1,2 these next-generation biobank products enable high-throughput screening of investigational new drugs and fda-approved therapies, advancing generalizable discovery while producing potentially life-saving insights for the respective donor. stunning images of these human cancer models are captured in the process, showcasing their uniqueness and documenting treatment responses. validation and development of patient-derived organoids require long-term clinical data and linked specimens. delivering translational impact necessitates protocols for connecting the bench and bedside. however, current biobanking platforms remove patient identifiers from donated specimens to protect privacy, yielding organoid ecosystems without mechanisms for patients and scientists to communicate information that may be critical for health or research outcomes.3 decentralized biobanking (de-bi) applies blockchain technology and web3 values to embed transparency, accountability, and inclusion in biomedical research.4 our bioethics-driven technology framework leverages non-fungible tokens (nfts) to keep patients connected to their biospecimens throughout the research lifecycle.5 minting nfts to represent patient-derived organoids could open communications between scientists and patients via a privacy-preserving platform composable with existing biobank and research protocols. if successful, our approach will advance patients’ rights to share in knowledge, health, and financial benefits of their research contributions.6 we discuss the development of an alpha prototype that applies nfts to empower patients as stakeholders in organoid research. our approach establishes public, immutable relationships between patients, their biospecimens, and organoid derivatives, as well as a related network of physicians, scientists, biobanks, and research protocols.7 we hypothesized that organoid images could be leveraged as de-identified artworks and represented with nfts on a public blockchain, demonstrating proofof-concept for peer-to-peer transactions between scientists and patients that preserve privacy and add utility for building meaningful research communities.8 methods organoid ecosystem mapping we interviewed u.s. breast cancer patients, physicians, translational scientists, and biobankers throughout 2021 and visited all representative sites in our local biospecimen procurement supply chain to inform our understanding of the current organoid ecosystem. this enabled the development of a high-fidelity simulated model dataset representative of a real-world breast cancer surgical program linked to a biobanking platform and downstream organoid ecosystem. we mapped stakeholder relationships and activities across the biospecimen research lifecycle to define key ecosystem components for the organoid biobanking use case, with a focus on the flagship breast cancer organoid program at the institute for precision medicine ( figure 1). qualitative interview data and subsequent user-experience design research related to patient engagement in our nft biobank platform will be reported elsewhere. the current organoid biobanking process depicted here incorporates three distinct domains: the clinical setting, the biobanking platform, and the organoid ecosystem. 1. clinical setting: patient provides broad consent9 for research biobanking during consent for cancer surgery. they undergo surgery, whereupon tissue is sent to the pathology lab, histology analysis is performed, and clinical results are returned to the patient and their clinicians via emr. fig. 1. organoid biobanking process flow diagram demonstrating (1) patient-facing clinical setting, (2) biobanking platform functions, and (3) specimen procurement and handling for the organoid ecosystem, with representative images from each domain. images courtesy institute for precision medicine. https://doi.org/10.30953/bhty.v7.303 citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.303 3 (page number not for citation purpose) nfts for organoids: decentralized biobanking 2. on the day of surgery, the surgical team communicates with research staff to coordinate real-time retrieval of leftover tissues from the pathology lab. these tissues are transferred from the clinical setting to the biobank under an institutional review board (irb)-approved biobanking protocol. specimens are processed and de-identified in the biobank and then either distributed immediately to a designated organoid lab or frozen for future research. 3. organoid ecosystem: scientists obtain an irbapproved protocol for organoid research, granting access to the biobanking platform. they communicate with surgeons to earmark upcoming cases of interest. scientists are notified when tissues are sent from the operating room to the pathology lab. within the hour, they collect the leftover tissues from the biobank while the cells are still alive. the tissues are processed in the research lab, enabling the patient’s cells to be grown in 3d culture medium and expanded via a multigenerational organoid development process. each organoid generation is imaged, and individual units may be shared for use in experiments, used to grow more copies, or may be frozen for future research. foundational research on the breast cancer biobanking process informed technical requirements for a web3 prototype that would enable ongoing patient engagement in organoid research and development activities. we sought to utilize the images created in the organoid process to represent complex activities to patients in a transparent and accessible manner. our team was authorized to use organoid research images and associated de-identified metadata to animate our proof-of-concept prototype, lending photorealistic elements to our experimental data and app demonstrations. further details and survey data related to our proposal to use organoid images as “tokens of appreciation” for respective biospecimen donors will be described elsewhere. platform design the core conceptual design of a “de-bi” platform was created to guide the priorities, goals, and features for prototype development. the system concept required a privacy-preserving nft biobanking framework that connects patients, scientists, and physicians for research engagement and dynamic data sharing (figure 2). the “de-bi” system design demonstrates three core stakeholders: patient, scientist, and physician, each connected through a common biobanking platform. data flow bidirectionally between stakeholders on a peer-to-peer basis, as each can make data requests and initiate data sharing with the other. importantly, transparency, accountability, equality, and inclusion in the de-bi ecosystem are embedded by design. our approach advanced beyond current methods for implementing access management and dynamic consent for biological data10,11 by utilizing initial consent procedures as a basis for enabling a platform for rich longitudinal community engagement among stakeholders that would otherwise remain siloed after initial permissioned transactions. the application of nfts to represent the unique participants and assets exchanged on the biobanking platform was critical for enabling a suite of participatory, value-adding features and creating a basis for a gamified research ecosystem that  aligns incentives and rewards pro-social behaviors. to advance a functional prototype, a simulated dataset was developed to reflect specifications and activities pertaining to breast cancer biobanking, with the generation of organoids and related derivatives (e.g., genomics data). our synthetic dataset is modeled from a subset of the breast disease research repository at the university of pittsburgh and lee-oesterreich lab data to be representative of a real-world breast cancer organoid biobanking ecosystem. the model sought to represent a diversity of breast cancer subtypes, disease stages, biological patient characteristics, and cellular and molecular phenotypes driving contemporary research paradigms. we represented all stakeholder classes with one or more individuals or research entities in each role. a schematic of the data forms for each stakeholder and their relationships created for our nft organoid ecosystem prototype is depicted in figure 3. we followed best practices regarding the creation of decentralized applications that leverage blockchain as part of their solutions while relying on a hybrid approach that fig. 2. decentralized biobanking ecosystem concept diagram. https://doi.org/10.30953/bhty.v7.303 citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.3034 (page number not for citation purpose) william sanchez et al. benefits from the flexibility and iterative capabilities of centralized software platforms.12 tokens representing the stakeholders, biospecimens, and organoids will be stored on-chain. sensitive, donor-specific details will remain on centralized databases on institutional servers, applying a multi-layered approach from a proposed reference architecture for blockchain (ref-arcbc) for establishing a standardized, efficient, and secure foundation for developing and implementing blockchain solutions.12 while the nft framework will provide a decentralized backbone for the application, in-app user activity data will also remain centralized on de-bi servers, reducing gas fees and transaction costs by minimizing the use of on-chain data storage, promoting adoption by limiting data privacy concerns and optimizing efficiency by focusing tokenization on high impact, low-frequency transactions. blockchain network selection deciding which blockchain to build on was a crucial step to set the foundation for our solution as each blockchain offers different primary features along with a built-in community and culture. nearly all blockchain applications and proof-of-concepts in the medical and biobanking space make use of private or permissioned blockchains, such as hyperledger fabric.13,14 departing from this trend, we chose to build our proof-of-concept for “de-bi” on the public, decentralized ethereum blockchain network. de-bi is designed as an open-source public good that facilitates the exchange of de-identified biological assets to enable new forms of research collaboration without displaying accompanying identifiable data. the system maintains compliance with established biobanking methods while empowering the inclusion of patients who lack access to internal databases and creating an ecosystem that is open to all public and private sector contributors, advancing shared goals for human health and wellbeing.15 ethereum is a worldwide system, an open-source platform to write computer code that stores and automates digital databases using smart contracts without relying upon a central intermediary, solving trust with cryptographic techniques.16 as the first to introduce the concept of smart contracts and nfts, ethereum was the most popular chain to build on and the most commonly used chain for both decentralized finance and nfts at the time of our proof-of-concept development in 2021–2022. it featured the most mature development ecosystem, offering a wide range of available tools, standards, and resources for developing dapps (decentralized applications). additionally, we were drawn to ethereum because the values of the creators aligned with our focus on ethical, inclusive, and transparent collaboration. there are downsides to building our prototype on ethereum that we needed to consider. at the time of implementation, ethereum used a proof of work consensus mechanism that incentivizes validation by rewarding miners for adding computational power to secure the network. this incentive is delivered in the form of gas fees17 required to execute any transaction, which can be increased to entice miners to validate a user’s transaction sooner. gas prices are dependent on network congestion and demand, making them highly susceptible to market volatility.17 this variable cost was taken into account in our design of the nft framework, as noted above, and fig. 3. simulated de-bi organoid ecosystem dataset schematic. https://doi.org/10.30953/bhty.v7.303 citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.303 5 (page number not for citation purpose) nfts for organoids: decentralized biobanking will require continuous monitoring and assessment as we advance our solution to ensure that the cost of using this technology is not prohibitive for our end users. system architecture the de-bi application consists of three main components: a decentralized peer-to-peer blockchain infrastructure, a client mobile application, and a service application, as shown in figure 4. the architecture of the proposed system and its working principle are illustrated through a detailed description of these core components and the channels of communication connecting them. figure 4 illustrates the following components: 1. client: mobile flutter application with frontend user interfaces for patient and scientist, as well as physician and biobanker (the latter two are not shown in figure 4). 2. service application: a nodejs api (application programming interface) processes blockchain-related service requests by sending a transaction to an infurahosted node, which broadcasts transactions to the remaining nodes in the system. additionally, a cloudhosted firebase database and api for storing all offchain data, such as user records and in-app activity logs. 3. blockchain infrastructure: rinkeby and ropsten ethereum test networks act as our decentralized, peerto-peer infrastructure, providing the environment for our suite of erc-721 smart contracts, which mint the unique non-fungible tokens (nfts) representing stakeholders, biospecimens, and organoids. nft framework by creating a digital ecosystem of nfts representing stakeholders, biospecimens, and derivatives within real-world research networks, we establish connections and communication channels that were not possible in the current landscape. this nft framework acts as a foundation for an open-source decentralized biobanking system, enabling new applications for donor engagement, enhancement of pre-clinical research, and direct return of clinically relevant information.18 if successful, the framework will ultimately support a sustainable and ethically governed decentralized marketplace solution that maximizes the distribution of unused biospecimens to advance precision medicine. we developed multiple smart contracts with solidity, a statically typed curly-braces programming language designed specifically for developing smart contracts for the ethereum network. they were initially deployed to a local blockchain called ganache before transitioning them to the rinkeby test network for ethereum. to deploy to an ethereum network, our node.js application sends a signed transaction via an externally owned account (eoa) within a wallet12—a digital tool that allows users to store and manage their cryptocurrencies while providing private and public key pairs for transactions to an infura hosted node, which broadcasts our transaction to the entire network. our smart contracts were written following the erc721 standard for nfts, enabling the minting of nfts as unique, cryptographic representations of patients, scientists, physicians, and biobankers as collaborative stakeholders within our proposed ecosystem. to receive these tokens, users will require an eoa controlled with private keys. this is typically done via the wallet interface of third-party providers like metamask. nfts were also created to represent biospecimens and established organoids, but their properties were customized to include the unique identifier of the token fig. 4. system architecture diagram for decentralized biobanking enabled organoid research. https://doi.org/10.30953/bhty.v7.303 http://node.js citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.3036 (page number not for citation purpose) william sanchez et al. representing their donor. by mapping this relationship on-chain, patients can remain permanently connected to their donations. this immutable, transparent connection creates opportunities for open communication channels with other stakeholders who interact with their donated samples. as these tokenized assets are only displayed as digital hash, these channels can facilitate a collaborative exchange of information without exposing any personal patient details. we showcase the potential by displaying real organoid images to the patient and all other stakeholders in our simulated biospecimen research ecosystem. real organoid images were assigned to represent specific model organoids from individual patients in our synthetic dataset and were stored on firebase. frontend design and development preliminary wireframe designs were developed in collaboration with real potential users and through content analysis of representative biobank data and organoid research artifacts. we drew inspiration from feature elements frequently implemented in popular mobile applications for banking, social media, and gaming to inform the development of a skeuomorphic user experience with familiar components applied to a novel context. we designed and developed a flutter mobile application connected to a firebase database to conceptualize the activities and workflows of each represented stakeholder in our proposed framework. standard libraries were used to design elements. we performed live demonstrations of the functional de-bi prototype with patients, scientists, physicians, and biobanker user groups between 2021 and 2022. results model organoid biobanking ecosystem the simulated dataset was developed in collaboration with the institute for precision medicine pitt biospecimen core and modeled to reflect detailed specifications and activities of the breast disease research repository, a large breast cancer biobanking platform. key variables for effectively discovering organoids and related specimens were incorporated to optimize performance in clinical and pre-clinical research use cases. representative user personas were developed in collaboration with the lee-oesterreich lab and the institute for precision medicine breast cancer organoid biobank. our simulated stakeholders included seven patients, one biobanker, four scientists, and 17 collaborating physicians representing various breast cancer subspecialties (table 1). the patients in our dataset contributed 12 unique organoids representing various breast cancer features, which were in use for four different study protocols (table 2). functional prototype applications the functional mobile application demonstrated several key features for model patients, biobankers, scientists, and physicians within the simulated biobank ecosystem. account creation and sign-in, as well as visibility to the collective organoid gallery for a given research study, was enabled for all users. key features developed for each stakeholder group are included below. table 1. overview of stakeholder dataset: descriptive demographics for patients, scientists, physicians, and biobankers modeled in our decentralized biobanking prototype simulated stakeholder overview user (#) metric descriptive demographics patients (7) clinical stage (tissue diagnosis) primary (3) metastatic (3) benign (1) organoids per patient (n) 1 organoid—3 patients 2 organoids—3 patients 3 organoids—1 patient studies per patient (n) 1 study—3 patients 2 studies 4 patients scientists (4) research study focus study 1—primary tumors study 2—metastatic lesions study 3—primary and mets study 4—normal breast tissue average patients per study (n) 3.75 (range 2–5) patients/study organoids in each study (n) study 1–5 study 2–4 study 3–7 study 4–2 physicians (17) physicians per type (n) primary (5) radiologist (2) pathologist (2) surgeon (4) medical oncologist (3) radiation oncologist (1) average patients by type (n) primary 1.4 radiologist 3.5 pathologist 3.5 surgeon 1.75 medical oncologist 2.33 radiation oncologist 7 biobankers (1) patient cases processed patients (7) relationships managed scientists (4) surgeons (4) pathologists (2) medical oncologists (2) organoid copies distributed (n) 18 https://doi.org/10.30953/bhty.v7.303 citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.303 7 (page number not for citation purpose) nfts for organoids: decentralized biobanking patients 1. review and agree to terms of use, that is, provide informed consent for sharing organoids. 2. store and update comprehensive cancer, medical, reproductive, surgical, and family history. 3. view nft biowallet with images and information about their biospecimens and organoids. 4. view research studies that are using their donated biospecimens. 5. view research studies they may join to donate specimens based on research interests. 6. view organoid images and chat with other study participants in a “co-lab” community forum. 7. exchange 1-to-1 messages with scientists, biobankers, and physicians. 8. share clinical history details with physicians and scientists in the biobank network. figure 5 demonstrates key features for patients on the decentralized biobanking application, including (1) home screen with informed consent, (2) biowallet with asset tracking, and (3) study-specific “co-lab” community. 1. home page with informed consent pop-up: this view presents the overall framework of the patient ux, including a key step for both joining the platform or sharing organoids for any new research study, in which the donor reviews and accepts terms and conditions relevant to the proposed activities, mirroring traditional informed consent. 2. biowallet: demonstration of nft biodata framework, encompassing various biological data assets collected, stored, and distributed by the biobank, including organoids, blood and tissue. 3. organoid co-lab: de-identified gallery of organoid images representing profile pictures of corresponding patient participants in a given study, with a forum for de-identified peer engagement. biobankers 1. add records of new samples and organoids, which include a unique identifier for the donor, details about the specimen, and associated related images. 2. view and respond to scientists’ requests for specimens or organoids. 3. assess newly approved research protocols for potential matches with available organoid and biospecimen inventory. 4. exchange 1-to-1 messages with scientists, physicians, and patients. 5. biobankers were key players in the nft biobank ecosystem, as their buy-in is critical for opening patient access to biospecimen collections and activities currently managed in siloed institutional databases. record creation by the biobanker triggers a signed transaction to mint sample or organoid tokens, storing the unique identifier that establishes an immutable relationship with the donor. we demonstrate how our approach may create opportunities for biobankers to find users for their available inventory, facilitating more coordinated activities and laying a foundation for a marketplace network model. while these processes were manual for our prototype’s small, simulated dataset, api or oracle integrations would enable the automation of decentralized biobanking applications for biobankers in future iterations. scientists 1. add new studies with descriptive information and educational resources to recruit patients from within the biobank donor community. table 2. overview of organoid dataset: descriptive demographics for patient-derived organoids modeled in our decentralized biobanking prototype dataset (n) metric descriptive demographics organoids (12) represented pathology site primary breast tumor (5) metastatic—liver (1) metastatic—lung (1) metastatic—lymph nodes (1) metastatic—brain (1) benign breast tissue (3) histology invasive ductal carcinoma—7 invasive lobular carcinoma—2 benign—3 estrogen receptor status negative—4 weak—1 mod—2 strong—2 n/a—3 progesterone receptor status negative—4 weak—2 mod—2 strong—1 n/a—3 her2 status negative—7 weak—0 mod—1 strong—1 n/a—3 tumor grade low (g1)—3 moderate (g2)—2 high (g3)—4 n/a—3 n/a: not available. https://doi.org/10.30953/bhty.v7.303 citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.3038 (page number not for citation purpose) william sanchez et al. 2. view studies, add study details, and share progress or related content from their research studies with study participants. 3. view available organoids and linked biospecimens within the biobank inventory. 4. exchange 1-to-1 messages with patients, biobankers, and physicians. figure 6 demonstrates key features for scientists, including (1) creating a study token, (2) displaying study information, and (3) 1:1 chat function demonstrating communication between scientist and physician. 1. add study token: allows scientists to include patientfriendly content and leverage existing research communications to engage prospective and consented participants. 2. research study token: demonstration of highlevel study synopsis, embedded video content, built-in faq, link to study participant forum, and ability to prompt 2-way messaging with patients or physicians. 3. two-way messaging: permissioned chat feature that allows both audio and written communications, with user identity displayed in accordance with specified permissions (i.e., scientists can chat with named physician collaborators, but communications with patients are always de-identified). physicians 1. exchange 1-to-1 messages with patients, biobankers, and scientists 2. view profiles and access clinical details entered by their patients within the de-bi system. 3. view a sortable patient list that may be filtered or searched based on clinical criteria, biospecimen availability, and participation in particular research studies. though the physician features represented a relatively small component of the functional application, including physicians as stakeholders will be essential for protecting patients’ interests in the nft biobank ecosystem. physician permissions within this system were role-based and directly corresponded to mutually validated clinical relationships between patient and physician users. ultimately, granting physicians access to research on organoids created from their patients’ donations will be critical for enabling translational research findings to be imputed into patient care in real-time. technical challenges the team encountered issues when deploying smart contracts to the rinkeby testnet. local deployment on ganache was averaging 0.0006 eth, but the cost to deploy on rinkeby was 5.8eth at the time. this prompted the shift to the ropsten network, where deployment cost was closer to ganache. we investigated the cause of this spike in gas cost17 and the discrepancy between networks, and while the exact mechanism was unclear, it was believed to be due to a vulnerability in the design of the smart contract. this experience highlighted the challenges of relatively inflexible smart contract architectures and warranted caution for future prototype deployments, especially as we move to ethereum mainnet. fig. 5. user interface/user experience walkthrough for patient users. nft: non-fungible token. https://doi.org/10.30953/bhty.v7.303 citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.303 9 (page number not for citation purpose) nfts for organoids: decentralized biobanking discussion our initial prototype of a “de-bi” platform successfully demonstrates proof-of-concept for a paradigm-shifting use of blockchain technology to promote authentic transparency, community engagement, and dynamic collaboration in biospecimen research. the mobile application features and user interfaces reflect the needs of key stakeholders, informed by a highly representative model dataset encompassing key activities of a breast cancer organoid biobanking program. through the representation of stakeholders with nfts and the creation of a public, immutable relationship between patients, donated biospecimens, and derived organoids, the prototype suggests the potential for a decentralized framework to empower patients, unlock value, and enrich research. as nfts are unique, cryptographic assets displayed as a digital hash, relationships mapped to the donated specimen can establish a transparent, privacy-preserving collaboration network that never reveals patients’ identities. our model is composable with existing biobank and research protocols that leverage de-identified specimens, demonstrating the possibility of integrating our proposed intervention for established biospecimen collection, procurement, and research processes. additionally, we simulate a mechanism to provide personalized feedback from the bench to the bedside in the form of real images captured during research and development of patient-derived organoids. the application also demonstrates the potential of a decentralized biobanking framework to support patient education, diversity, and engagement in research collaborations via a system that embeds assurances of trust, equity, and inclusion.19 critically, our proof-of-concept for communication between scientists, patients, and their physicians creates opportunities for direct translation of clinically actionable research findings and for long-term enrichment of sample data with clinical context that participating patients or physicians may share. blockchain in healthcare many other blockchain application prototypes in the medical and biobanking space make use of private or permissioned blockchains,13 such as hyperledger fabric.14 in private blockchains, only a limited number of participants, typically nominated by administrators, can participate in the network. this centralization of power over consensus mechanisms, participants, and processes removes the need for gas, as malicious entities are easily detectable and reprimanded. central authorities can also enforce access restrictions to transactions at their discretion, making it easier to protect sensitive information within the network. it seems that permissioned blockchains such as hyperledger can enable the development of efficient, cost-effective, and compliant software solutions within heavily regulated industries such as healthcare, and we can understand why many projects have gravitated toward this type of network. however, a permissioned blockchain that does not strictly enforce immutability, traceability, and transparency across institutional boundaries may be insufficient for enabling a disruptive solution aimed at changing the paradigms of accountability for biobanking activities within a network of stakeholders who have been insulated from patient engagement via the de-identification process. fig. 6. user interface/user experience walkthrough for scientist users. https://doi.org/10.30953/bhty.v7.303 citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.30310 (page number not for citation purpose) william sanchez et al. a system where every institution, service line, and research lab controls their own decentralized chain to store their respective biobanking samples fundamentally contradicts the belief that biospecimens are public goods and does not address the fundamental misalignment of incentives20 that underlie siloing of biospecimen resources and related disjointedness of the biobanking ecosystem. for example, patients may move or travel to various localities for cancer treatment, indicating the importance of a cross-institutional approach, both for delivering comprehensive transparency of biobanking activities to patients and for maximizing scientists’ access to a dynamic and growing set of health information relevant to their research. patient engagement in research alternative approaches to develop technologies for improving patient engagement in research are focused on clinical research,21 where patients are active participants. by contrast, most organoid research activity uses de-identified samples, and translational scientists are not accustomed to communicating with patients, who may not be aware of the nature of their contributions to biobanking after one-time broad consent. we leverage colorized versions of real organoid images captured in the development of human cancer models as personalized “tokens of appreciation” for research participants: an initial benchto-bedside data transaction that harnesses the visual, accessible, and appealing nature of images without creating undue clinical or financial liability. key questions remain regarding the optimal design of the communication, image, and data sharing, and research engagement features for patient participants in the de-bi framework. we believe that bringing transparency to translational research19 will increase recruitment and rebuild trust. the community-engaged approach will be especially critical for promoting biospecimen donation among communities for whom distrust of established healthcare systems is a serious barrier to participation. the inborn uniqueness, immutable provenance, and decentralized ownership that is inherent to human tissues can be represented in an nft-based system that respects patient rights, maximizes research benefits, and enables precision medicine. forthcoming publications about patient acceptability of decentralized biobanking will highlight key value propositions with rich qualitative and quantitative data. our upcoming pilot exercises will seek to define key metrics that will be essential to quantifying the effectiveness of our approach and justify further investment to enable scaling to broader populations and use cases. study limitations our proof-of-concept prototype demonstrates the potential for a web3 platform that engages patients, physicians, and scientists in a privacy-preserving organoid community. however, experimentation on additional key components and processes is still required to confidently assess the feasibility and acceptability of our technical approach. a reliable onboarding process that effectively verifies donor identity22 to establish their relationship with the correct samples and that is accessible to diverse populations with varied technology, and health literacy is an integral part of our proposed framework that must be explored and confirmed in future prototypes. to propose an acceptable on-ramp for patients, we must adapt our systems to the regulations, preferences, and risk tolerances of the irbs. additional suggestions for best practice in the live development of a production-ready solution include strategic design to address variable gas costs that are susceptible to volatility with market congestion, proving cost-prohibitive at a specific point in time on the rinkeby test net. a live implementation will require a more thorough preliminary assessment of market conditions and existing gas costs across test networks and mainnet to project accurately. there are additional requirements for security analysis23 to identify and resolve any smart contract vulnerabilities and potential points of failure for maintaining the de-identification of human subjects and related organoids due to the sensitive nature of biomedical data. these strategies include a focus on data minimization, application of zero-knowledge proofs, and thorough security audits and penetration testing to identify and mitigate vulnerabilities.12 importantly, patients remain de-identified within our proposed nft-biobanking system, demonstrating provisional compatibility of this approach with established hipaa and gdpr24 regulations. in our initial prototype, organoids and the specimens from which they are derived are represented as discrete assets rather than derivative products with multiple complex functions and regenerative features. further research will advance the sophistication of the smart contracts and tokens used to represent the creation, growth, and distribution of organoids as a critical step for building the foundation for pragmatic utility for the scientific community. additionally, the access control mechanism for the mobile application prototype simulated roles/permissions to demonstrate on-chain ownership16 of stakeholder, specimen, and organoid tokens. real-world implementation will require these onboarding mechanisms. in our second proof-of-concept prototype, we developed a web application that implements stakeholder onboarding and expands the functionality of organoid tokens to more accurately reflect real-world activities, such as creation from a donated biospecimen. a subsequent technical report will advance the concepts and technical challenges relevant to a fully functional decentralized biobanking platform for organoid research. https://doi.org/10.30953/bhty.v7.303 citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.303 11 (page number not for citation purpose) nfts for organoids: decentralized biobanking this prototype is limited as our simulation study did not engage real patients as direct users of the demo application. this step to deploy and test our framework with each stakeholder group will be essential to gather feedback, evaluate our assumptions, and inform future design and development. the simulation relied on manual user inputs (e.g., patient entry of extensive health information during the onboarding process), which represents an additional fraction and potential source of data corruption or correction, with no ready mechanism to discern the difference. a functional prototype fit for pilot deployment will require further development of mechanisms to integrate with and ensure interoperability across diverse institutional platforms.12 continued use of their existing systems is critical for easing adoption and maintaining compliance with current regulations.25 additionally, this initial prototype was developed prior to significant advances in layer 2 solutions to minimize the costs of token minting and related transactions. the feasibility and scalability of our nft framework will rely on these features, and careful attention must be paid to the volume of objects represented in our ecosystem. we anticipate a fully decentralized biobanking ecosystem will require multiple classes of nfts, in addition to fungible tokens, to act as in-game currency. in our proposed solution, we assert that patients do not have to pay as it is their right to transparency over their own donations. gas fees for on-chain transactions should be rolled into the research budgets of institutional stakeholders as expenses related to patient outreach and community engagement. importantly, each organoid generated in our local setting requires roughly $1,600 in biobank services, upwards of $1,000 in supplies, in addition to highly specialized labor and equipment for processing and cultivation. due to the cost, scarcity, and importance of each organoid, as well as the high costs of shipping and handling real-world organic items, transactions are relatively infrequent and high in value. thus, ethereum gas fees represent a small fraction of the total cost related to organoid biobanking and must be considered in light of the potential benefits, including cost-savings and increased market value, that may be obtained when patients are engaged. next steps to address these limitations, a logical next step is to design, develop, and deploy a live pilot study for patients with samples stored in a real biobank repository.4 a pilot study will require navigation of complex stakeholder relationships across hospitals, universities, and research institutions. approval from the irb, informed consent from patients, and collaboration with biobanks offer a valuable opportunity to refine our technical approach as well as our understanding of existing dynamics and stakeholder incentives within a research ecosystem. to advance beyond our proof-of-concept, we must establish the feasibility of our technical solution by integrating it with existing institutional systems, implementing a donor onboarding and verification process that is compliant with irb policies, and gathering feedback from end users in a live pilot to assess our implementation. strategies for educating patients about the value of their participation and engagement in organoid research will be critical. findings from our foundational surveys, interviews, and focus groups with patients regarding engagement in organoid research will be reported elsewhere. the financial viability and long-term sustainability of an nft organoid platform will require value propositions, cultural imperatives, and potential policy changes to secure buy-in from all relevant stakeholders.26 additional research is exploring how novel market designs, tokenization strategies, and user interfaces may incentivize collaboration, operationalize dynamic consent27 and decentralized governance, and propose innovative methods for the ethical inclusion of patients in commercialization. financialized elements of decentralized biobanking will be especially critical to explore relevant in the setting of high-value research products such as organoids, which may cost $4,000 to $7,000 per ml. each copy of these living cancer models is commercially valuable as precision medicine tools and drug discovery platforms and may be used in countless research studies in many settings over many years. our initial proof-of-concept prototype demonstrates the potential application of decentralized biobanking technology to organoids as a critical use case. subsequent research and development activities are addressing feasibility from an economic, market and operational standpoint. ultimately, the ethical and clinical benefits of keeping patients connected to their organoids is a compelling value proposition for which we believe there will be widespread public support. our research and development of decentralized biobanking technology is rooted in the ethical imperatives to improve the efficiency and equity of the biobank research ecosystem. we note that greater transparency, decentralization, and distribution of power through our proposed mechanisms may introduce new challenges for health, safety, and economic implications of organoid technology, and biobanking more broadly. critically, our ongoing market design research addresses the potential unintended consequences of tokenizing biospecimens and organoids. our proposed system must be respectful of patients, mindful of potential consequences for research, and equitable in its approach to monetization. success for decentralized biobanking will require a commercialization model that enhances the effectiveness and speed of creating public-private partnerships for biospecimen research and improving marketplace efficiency and technology advancement. overcoming the limitations of https://doi.org/10.30953/bhty.v7.303 citation: blockchain in healthcare today 2024, 7: 303 https://doi.org/10.30953/bhty.v7.30312 (page number not for citation purpose) william sanchez et al. the current operational regime, which unjustly excludes patients, will be an essential step toward an ethical biospecimen marketplace solution. ongoing research is exploring the decentralized governance mechanisms, as well as ethical and practical guidelines, that will be required to promote the flourishing of the stakeholders, diversity, and inclusion in our proposed nft biobank ecosystem. conclusion decentralized biobanking applies blockchain technology to democratize biomedical research, unlock cures, and promote health equity. including patients in biobanking, research, and development of organoids is an ultimate use case for de-bi, given the potential for personal significance, clinical impact, and distributive justice across the forefront of the biobank ecosystem. our proof-of-concept prototype study demonstrates how an nft-backed framework built on a public blockchain may empower patients as stakeholders in organoid research, enabling dynamic engagement and efficient distribution of images, educational materials, and other rewards of research. a decentralized biobanking mechanism has the potential to reconnect patients to donated biospecimens without compromising privacy, advancing research by forging digital communities and new opportunities for collaboration that are grounded in real-world relationships and modeled on actual biospecimen transactions. the de-bi approach realizes unprecedented respect for patient contributions while ensuring compliance with established de-identification protocols and generating new possibilities for a decentralized biobanking ecosystem. further research and development are ongoing to ready de-bi technologies for deployment in next-generation organoid research networks. funding components of the work described here are generously funded with grants from yosemite (formerly emerson collective health). financial and non-financial relationships and activities the co-authors, dr. gross, ms. hood, and mr. sanchez, have formed de-bi, co., a company focused on the research and development of decentralized biobanking technologies to empower transparency, accountability, and engagement in biomedical research. co-author, dr. miller, has stock options from de-bi, co. and receives payment from the american society for radiation oncology (astro). contributors mr. sanchez performed a literature review, reviewed and cleaned the source data, and prepared the first draft. mr. linder performed the literature review and supported the development of the first draft. ms. hood performed interviews, collected data, and reviewed the manuscript for critical content expertise. dr. goss developed the model dataset, was responsible for overall technology design, and closely oversaw prototype development and testing. application of ai-generated text or related technology no ai-generated text or related technology was used in any study activities or in the preparation of this manuscript. data availability statement (das), data sharing, reproducibility, and data repositories the data 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https://dl.gi.de/server/api/core/bitstreams/96aba517-20ec-40a0-9319-c46976cd20c7/content https://doi.org/10.1186/s12910-023-00988-9 https://doi.org/10.1186/s12910-023-00988-9 https://blockchainhealthcaretoday.com/index.php/journal/article/view/184 https://blockchainhealthcaretoday.com/index.php/journal/article/view/184 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 original clinical research gahbt: genetic-based hashing algorithm for managing and validating health data integrity in blockchain technology fozia hanif1, urooj waheed2, rehan shams3, aisha shareef1 1department of mathematics, university of karachi, karachi, pakistan; 2department of computer science, dha suffa university, karachi, pakistan; 3department of telecommunication engineering, sir syed university of engineering and technology, karachi, pakistan corresponding author: fozia hanif, email: ms_khans2011@hotmail.com keywords: blockchain, hashing, decentralized technology, genetics, health data distribution, security, surveillance abstract a method for managing, securing, and validating health data distribution records using a genetic-based hashing algorithm in a decentralized environment is presented in this research report. the rationale for choosing blockchain is to secure the transaction of health data and protect these data from manipulated fraudulent movement and corruption by a contributor to the chain, or any individual. our approach uses technology that provides an efficient surveillance measure, including transparency of records, immunity from fraud, and protection from tampering, as well as sustaining the order of data. for medical research, the results here provide a genetic-based hashing algorithm for data security, which has lower computational complexity, low space coverage, higher security and integrity, and a high avalanche effect. the simulation will show the validity, immunity, and integrity of the data record. the technique modified in this secure decentralized network is a cryptographic hashing algorithm for 512 bits. in this study, a genetic algorithm (ga) is used to generate a key that must be used in the encryption and decryption of medical data. a ga is a metaheuristic approach inspired by the laws of genetics; and it is generally used to generate high-quality solutions for complex problems. applications of gas are possible in medical fields, such as radiology, oncology, cardiology, endocrinology, surgery, oncology, and radiotherapy in healthcare management. submitted: 24 november 2022; accepted: 17 january 2023; published 03 february 2023 feb inc. blockchain technology, as applied to healthcare, offers secure (immutable) storage of private sensitive electronic data (e.g., patient diagnosis and medication) that can be shared among healthcare providers, institutions, and other disciplines.1 the challenge is to update a patient’s medical record securely, while maintaining access for those involved in care.2,3 this access to the complete medical history by all participants in the patient’s care is critical to achieve successful long-term management of conditions, such as hiv, cancer, diabetes, and cardiovascular diseases, to name just a few. however, coordinating the process of sharing data when a patient moves from one medical institution to another in a different city (or country) becomes complex.4 by providing the required tool that secures data access among authorized individuals, while excluding hackers,5 without relying on a single trustworthy node, blockchain technology assures data surveillance and access to sensitive and private data. it provides guidance on healthcare data for the patient, as well as healthcare providers,6,7 which is safe and secure when stored in the form of blocks of data that are secured and linked cryptographically.8,9 this study proposes an immune and robust system for handling electronic medical records or data. a blockchain platform is used as an access control technique in which a modified cryptographic method is used to maintain surveillance, as well as integrity, in order to gain access control of the sensitive medical information for sharing among the other nodes. citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.2442 (page number not for citation purpose) fozia hanif et al. the modification in this method uses a cryptographic hash function to generate a new hash with the help of the traditional cryptographic technique. the system proposed here is supported by a hashing algorithm to improve the overall surveillance of data using a genetic algorithm (ga) approach for optimization. a genetic-based hashing algorithm is used to manage and control the transaction node per time in order to improve data reliability. additionally, with the introduction of a new hashing algorithm, this paper shows improved robustness. testing the blockchain data-sharing system among the nodes reveals the uniqueness of controlling the access of records. this paper is organized as follows: a literature review, highlighting the previous methodologies related to blockchain, a ga, and the crypto hash function. next, methodology is described followed by simulations and results for the proposed algorithm. these include hashing the block, key generation procedure, and encryption/decryption algorithm. literature review challenges confronted by the authors relate to enhancing the technique of blockchain-based cryptography. today, cryptography is used in a variety of sectors to enhance safety and security. for example, it facilitates efforts by companies to secure their information without jeopardizing intimate details of customers, as well as the company. it helps to understand the operation of caas (containers-as-a-service) to secure data in a system and the benefits caas provides to mobile and desktop computer users today and in the future.10 in the same way, cryptography plays an essential role in blockchain technology to enhance and provide a safe, secure, and protected environment for sharing cryptographic data among the nodes of a network, without breaching the privacy of any of the information contributed by the nodes. blockchain technology was introduced by satoshi nakamoto for his well-known contribution to cryptocurrency, which is also called digital currency, i.e., bitcoin.11 in a blockchain network, a chain of blocks is linked and raised continuously by accumulating the transactions on the blocks so that a verified list of cryptographic data is formed. the blockchain provides a detailed report of each transaction.12 many domains are involved in bloockchain due to its decentralized technology. these include education and health care.13 the decentralized technology ethereum is a blockchain technology used to create a secure verifying smart code, which is created by a peer-to-peer network and builds a new ethereum-based token. these tokens can be used to power decentralized apps (dapps) with the help of smart contracts.14 the smart contracts can be used according to the predefined approach for transferring records, and they allow access to a client’s records by the medical experts.15 information regarding the patient must be highly secure, which is possible with the implementation of smart contracts in different medical fields.13 the application of the internet of things (iot) to health care is achieved16 through the application that deals with storing and transforming various formats. examples include: images, text, and voice via internet. a secure architecture of healthcare multimedia data with the help of blockchain is presented by17 comparison in terms of average packet delivery ratio, average latency, and average energy savings with other standard techniques. a decentralized approach is used in this technique, which allows the data to be distributed, with each part of the distributed data shared with each participant involved in the network. a single block of data can be added to the blockchain using cryptography, with each block of data passing through the verification process. mathematically, it follows an arrangement from the previous block by keeping the consent of the network decentralized. the process of verification in the blockchain is called proof-of-work or mining.18 blockchain technology has certain advantages such as surveillance of data, immunity, and integrity of data, with no third-party interference. as discussed above, the security of medical records is a top priority in the healthcare industry. these advantages promote storing electronic medical data in the blockchain, and researchers have concluded that in the medical industry, the blockchain could be a feasible solution.19,20 key features in the blockchain blockchain technology offers decentralization, transparency, and security and integrity of data. decentralization blockchain technology distributes data within the whole network rather than at a single point. thus, no third party involved in the network can access and control the data. blockchain shares and distributes the data with all nodes connected to the network. put simply, data can only be handled and controlled by trustworthy entities. data transparency accomplishing data transparency is a trust-based connection in any technology. securing and tamper-proofing the data become an important component in any industry. as explained above, due to decentralization, blockchain data are not controlled by a single party. data can be controlled by each node in the network. this explains why data become transparent and more secure from thirdparty involvement. citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.244 3 (page number not for citation purpose) gahbt: genetic-based hashing algorithm for managing and validating health data integrity in blockchain technology security and integrity the blockchain environment uses cryptography to enhance and provide surveillance and integrity to the nodes connected to the entire network. this paper uses a new cryptographic hashing algorithm on the hashes that are gathered in the blocks. the hashes give security to the blockchain and also provide integrity to the data. cryptographic hashes are one-way functions that produce the checksum (i.e., a small-sized block of data derived from another block of digital data for the purpose of detecting errors that might have been introduced) for the digital record that are excluded from the data distillation. these characteristics make blockchain decentralized, secure, and integrated cryptographically. thus, it is a good option for the security and privacy of different types of records. today, blockchain technology is used in various fields because of its efficiency and high security of data. blockchain technology is used in petroleum industries to improve supply chain management problems. the petroleum industry involves a global supply chain management in which international and local order, transportation, import/export, inventory, and information technology are included. in this type of supply chain, an organization is associated with suppliers, distributors, data compilers, and vendors, even with everyone involved. hence, the blockchain environment is used to control and secure the data in this kind of industry.21 blockchain used in the iot works with decentralized and distributed data to collect, store, and strengthen transactions among iot nodes. the system is related to blockchain-based iot surveillance nodes and blockchain-based settlements, and it can be applied in aspects of the iot ecosystem.22 the blockchain platform offers important and robust implementation in healthcare industries, as well as for the protected and tamper-proof electronic medical record. the system protects ledgers and grants complete access to the patient’s medical history and treatment in the healthcare industry. with the help of the blockchain approach, it is easy to secure sensitive medical information. it provides additional surveillance services, accountability, authentication, and confidentiality.23 the platform is flexible and offers considerable accommodation to surveillance in financial services industries and many other projects, including health care, supply chain management, cybersecurity, banking, data analytics, drug counterfeiting in pharmaceutical sectors, fintech, etc. this paper aims to deal with the management and security of public data in user-oriented healthcare centers.24 genetic algorithm gas are generally used to develop a high-level solution to optimize any problem and detect problems depending on the operators stimulated by the biology concept such as machine learning and deep learning which is used in the classification of ecg signals. the ga was introduced by “john holland” in 1960. it was inspired by the concept of “darwin’s theory of evolution.”25 the ga starts with a set of solutions representing chromosome-like data structure to obtain an optimal and potential solution for the problem. the main operators in the ga are selection, recombination, mutation, and crossover, which combine to achieve a new generation. ga is an evolutionary procedure used to optimize problems, including shortest path, intrusion in wireless sensor networks (wsn), bandwidth utilization, and more. the reason behind using the ga in generating the key in underwater wireless sensor networks (uwsns) is that cryptography through ga provides the lightweight complexity, which is the measure requirement within the uwsns. the ga approach is random, which enhances cryptographic encryption and decryption. in addition, the ga starts with random results called chromosomes, which can be generated through many random procedures. these randomly generated results can be made more accurate by using different steps of the genetic procedure: fitness measure, crossover, and mutation. to get a more accurate result through ga, it is imperative to have a strong fitness function that applies to the initial random generation to measure its fitness. fitness function identifies which chromosome can be used for the process of crossover. in the crossover, two chromosomes produce two more fitted chromosomes that can be tested again using a fitness function. after getting better chromosomes from the crossover, we apply mutation to achieve global optima from local optima. in the proposed algorithm, we used the above-explained steps of the ga to generate half the part of the key for symmetric cryptography. these traditional steps of ga have many variations according to the scenario and environment. we performed these steps by making the fitness function according to the suitable parameters related to the cryptographic approach’s conditions. today, ga is used and altered according to the requirements. these include a few concepts of ga that have been removed, altered, and for other new concepts introduced in fluid genetic algorithm (fga) that have not only improve results and convergence control, but also can be used in a range of problems that involve multi-objects and multi-level issues.26 gas are also used to strengthen the key in order to make the complete algorithm secure. as explained, in ga, data are generated randomly, genetic operators are applied, and then it is diffused by genetic and logical operators. in 2018, an algorithm was proposed for better results in terms of strengthening the key with less computational complexity than the two previous algorithms.27 citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.2444 (page number not for citation purpose) fozia hanif et al. cryptographic hash function the cryptographic hash function allows for mapping data of arbitrary size to a fixed-size bit string called a hash value. it is the fingerprint file (checksum), and it is used to verify that the files have not been tampered with or modified in any way not intended by the author. the hash function is a one-dimensional function that cannot be reversed.24 it is a mathematical representation of an algorithm. there are three main characteristics of an ideal hash: easy to calculate, computationally difficult to obtain alphanumeric content that has provided hash, and unlikely that 2-minute different texts can provide the identical hashes. the message acts as input data and is called a “message”; whereas the hash or hashing values act as an output called a “message digest value.” methodology the proposed method in this research study consists primarily of three components (i.e., blockchain, ga, and hashing). the proposed method uses secure blockchain technology, which depends on cryptographic hashing for the surveillance of peer-to-peer non-centered distributed ledgers. furthermore, it uses a ga for the key generation of a robust system in order to protect the data from jeopardization or third-party involvement to avoid the breaching of nodes. the method described above is illustrated in figure 1. the algorithm for gahbt start step 1: hashing step 1.1: annexing &padding step 1.2: append length step 2: key generation in two parts (by using a different method for each part) step 3: encryption/decryption end padding and appending length in the first step, a message of b-bits is padded to make it 16 bytes sha (secure hash algorithm), which is the multiple of 512. the first three “1” bits are appended to the input message, and then a series of off bits are padded, as given in figure 2. hashing cryptographic hashes are one-way functions that take input of data and generate the result into a size of fixed length called a digest—also called checksum. hash functions are not able to decrypt as they are one-way functions; hence, they cannot be encrypted back to form the original message. many algorithms are available to generate a hash, such as md5 (message-digest algorithm), sha1 (secure hash algorithm 1), and sha2 (secure hash algorithm 2). this proposed model gives a modified hashing algorithm to generate a hash. the given algorithm creates 128 bits unique message output called a “digest.” consider a message of length b-bits that needs to digest. for the hashing of a b-bits message, the proposed algorithm passes through different steps described in the algorithm. the model stages will use some assisting functions that comprise buffer and auxiliary functions that have already been initialized. algorithm for generating hash start step 1: split the blocks into 16 bits step 2: assign initial values (in hex) to x, y, z step 3: apply characteristics functions for value refining a (x, y, z) = rot20 (¬ x ⊕ s11y ∧ z) b (x, y, z) = rot10 (¬ y ∧ s14 (z ∨ ¬ x)) c (x, y, z) = x ⊕ y rot5 z) d (x, y, z) = (x ∧ y)  ⊕ (¬ z ∨ x) step 4: the table of 64 elements will be established using the sin function abs (sin (i + 1) × 216) end splitting blocks after appending the length, the 512 bits message is divided into 16 (block) words with the length of 32 bits each. (16 × 32). say m [0, … , n-1] where n is a multiple of 16. initializing the values values are initialized by taking the first 32 bits of the fractional parts of the square roots of the first 17 prime numbers. and every prime positioned hex value will be used to initialize the process and store them in (x, y, z). characteristic functions now, for the further process, the proposed study introduces some characteristic functions to refine the initial values and make the results more random and secure. these functions are designed to take three words of 16 bits and give the output of 16 bits. functions are given as follows from (1) to (4): citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.244 5 (page number not for citation purpose) gahbt: genetic-based hashing algorithm for managing and validating health data integrity in blockchain technology fig. 1. process flow of the proposed model of gahbt (genetic-based hashing algorithm for managing and validating health data integrity in blockchain technology). citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.2446 (page number not for citation purpose) fozia hanif et al. a (x, y, z) = rot20 (¬ x ⊕ s11y ∧ z) (1) b (x, y, z) = rot10 (¬ y ∧ s14 (z ∨ ¬ x)) (2) c (x, y, z) = x ⊕ y rot5 z) (3) d (x, y, z) = (x ∧ y)  ⊕ (¬ z ∨ x) (4) applications of the above characteristic functions will take place randomly. the selection of the above functions will be decided with the help of a random position generator. the table in the proposed procedure table of 64 elements will be established using the sin function: abs (sin (i + 1) × 216) (5) let us consider that the table has values k [1, … , 64]. k[i] represents the 1st element of the generated table. key generation using ga a key of 256 bits is required for this algorithm. in this study for randomization, we divided the key into two parts named k1 & k2. the left half portion of the key k1 (128 bits) is developed from the (ga), which is an evolutionary algorithm based on the idea of natural selection.29 whereas, the right half portion of the key k2 (128 bits) will be taken from the hash generated in section 3c. algorithm for key generation using ga start step 1: initial random population generation (bin/hex) step 2: crossover (ox1) step 2.1: create two random crossover points in the parent step 2.2: copy the segment between them from the first parent to the first offspring step 2.3: at the second crossover point in the second parent, copy unused numbers from the second parent to the first child, wrapping around the list. step 2.4: repeat for the second child with the parent’s role reversed. step 3: fitness function = − × fitness function gap value entropy run value      1     =     entropy log c n r n    2 2 step 4: threshold step 5: mutation step 5.1: select chromosomes step 5.2: apply scramble mutation step 5.3: apply inversion mutation step 5.4: apply swap mutation end steps of proposed ga the basic procedure of ga usually involves conventional steps: the process starts with an initial random population composed of various chromosomes. the chromosomes are taken in a binary number system or hexadecimal number system. in the proposed algorithm, the key is generated by using the generation of random population, calculation of fitness function, crossover, and mutation. the process and detail of the ga in different applications may vary according to the scenario of the considered problem. in the next section, an explanation of the proposed algorithm is discussed in detail. initial random population generation in this step, the random population of 128 bits is generated by pseudo-random numbers in a binary number system that is called chromosomes. the generation of a pseudo-random number is based on a linear congruential process.30 crossover it is a special operator of gas that helps differentiate between gas and other algorithms. there are various types to operate crossover (i.e., one-point crossover, two points crossover, multipoint crossover, random point crossover, fig. 2. illustration of the padding procedure. the b-bit message is based on 64 bits. after that, it is appended under modulo 512 (divisible by 16 for working in hexadecimal). these two steps are taken from professor rivest’s study28 to construct the entered message input for digestion by annexing and padding the bits. citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.244 7 (page number not for citation purpose) gahbt: genetic-based hashing algorithm for managing and validating health data integrity in blockchain technology uniform crossover, whole arithmetic recombination, and davis’s order crossover (ox1)). in this study, davis’s order crossover (ox1) is used. ox1 is a permutation-dependent crossover that communicates with the data information about relative ordering to children. the working steps of davis’s order crossover are as follows: 1. select a random substring from the parents. 2. copy the parent substring in the initial offspring. 3. now, from the second point crossover in the second chromosome, start copying the remaining inexperienced bits from the second parent to the first offspring as it covered the substring created in step 1. 4. redo the above process for the second child by reversing the parent role. fitness functions a ga is an optimization approach based on natural selection. the aim of generating this simulated/artificial data is to minimize its differentiation of statistical inference from the actual data. in gas, the computation of an optimized individual is called fitness. therefore, to calculate the fitness of the nascent generated random population (chromosomes) in the previous step, a fitness function is required. in this study, the proposed fitness function is given to calculate the fitness of chromosomes. = − × fitness function gap value entropy run value      1     (6) =     entropy log c n r n    2 2 (7) run value a run-in (6) is described as a series of growing values or a sequence of reducing values. the variety of growing or reducing values is the run’s duration. it is a non-parametric test used to check the randomness of a pattern. entropy entropy in (7) is used to compute diversity or randomness in generated data. full entropy in data represents that the data are completely random and there is no significant pattern present in the data, and it is not able to recognize easily. for encryption and hashing functions, highly entropic data are considered to make algorithms unpredictable or even secret to ensure the safety and security of procedure. in the same way, the gap value is used to calculate the randomness of data by computing the gaps between the digits that appear in the repetition of particular digits. notably, this study counts the maximum number of zeros or ones that appear in chromosomes. the run test is a non-parametric statistical test of randomness. it tests the value of a run that is up or down or above or below the mean by comparing actual and expected values. threshold when the fitness function provides values close to 1, it can be assumed to be fitted values. the threshold for this study is set at 90%. the best fit value will come from the set of stored fitted values. mutation the mutation operation is critical to the success of gas since it diversifies the search directions and avoids convergence to local optima. the mutation is a major step in gas. it is used for preserving genetic diversity among various chromosomes (generations)28 very helpful to attain the global optima. after crossover, mutation changes at least one bit in the chromosomes to reflect the results of surrounding ga’s.31 in this step, all those chromosomes with a higher threshold value in the crossover will be selected to undergo the mutation process. this paper uses a combination of mutation processes for the best results.32 steps are given as follows: 1. chromosomes are chosen according to the set criterion. 2. first of all, scramble mutation is applied. in this type of mutation, two substrings are chosen randomly, and the highlighted values are shuffled or scrambled randomly. the rest of the experienced strings remain the same. 3. after scrambled mutation, inversion mutation takes place. it selects one random substring and inverts it. 4. after obtaining an inverted chromosome uses, swap mutation is applied. for this, two positions are generated randomly, and their values are swapped. this is how one round of mutation takes place. after the mutation process, the fitness function is applied to each processed chromosome and stores the best-fitted values that pass through the set criteria. those values that fall outside the set criteria of fitness will get into the process again. all steps are repeatedly applied for about 500 rounds until more suitable values are obtained than the previously stored values. in case of getting a less fitted value of the chromosome after 500 rounds, the previously best-fitted value is taken as the finishing part of the key. in the other case, when the fitness value after calculating the fitness function is better than the stored one, take it as the first half portion of the key. the complete process is illustrated in the flow chart in figure 3. encryption/decryption encryption the proposed algorithm divides data into equal-length blocks and then encrypts each block with the help of a citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.2448 (page number not for citation purpose) fozia hanif et al. mathematical series of functions called the key of 256 bits. the proposed algorithm will construct the key in two parts, each of which is 128 bits. initially, the key is broken into eight parts, each of which is 32 bits; therefore, eight blocks of 32 bits are formed. say (k1, k2, k3, … , k8). now consider the four alternate parts of blocks that are k11 = k1, k3, k5, and k7, and together, they are 128 bits, and the remaining 128 bits of the key are k2 = k2, k4, k6, k8. in this method, the encryption of the m length plain text message will be done by using the key (k) of length 256 bits as shown in figure 4. and detail is given as follows: encryption of b1 1. consider the first part of the plain text b1. get ascii codes of each character of the first part plain text message (b1) and store them in an ascii array. 2. convert the result from step 1 into a binary number system. 3. padding the result from step 2 to make it 128 bits. 4. apply circular shift on the result from step 3. 5. break the result from step 4 into four equal parts, b11, b12, b13, and b14 which are 32 bits each. 6. now generate a random number of four digits (say r1) and assemble the parts of the result of 5. according to the position generated and store the value. 7. generate a random position (say r2) by a pseudo random number generator and store that number and rotate the resultant of 6. according to the generated number. 8. apply xor between key (k11), which is 128 bits, and the resultant of 7. 9. now break the resultant into two parts, each of 64 bits, and find out the hamming distance, say (d). 10. take d as a position and rotate the result from step 7. 11. now find the least prime factorial of hamming distance d say f and apply f time right circular shift on step 9. 12. take complement of all prime positions of step 10. 13. convert the result into ascii values and consider it as the right portion of encrypted plain text. encryption of b2 now consider the remaining half plain text and half key; both of them are 128 bits. in this part of the encryption, the process goes through a variety of matrix operations like transpose, permutation, row, and column mixing and leading diagonal shifting on the plain text message to guard and protect the plain text. 1. consider the plain text and calculate ascii values of corresponding characters. 2. convert (1) in the binary number system. 3. make a matrix of order 8 × 8 of the plain text of 128 bits resultant from 2. 4. construct a matrix of the same order (8 × 8) using 128 bits key. 5. start with the column mixing with the help of a random number generated from 1 to 8 with the help of a pseudo-random number generator (say r3) and store this value. fig. 3. flowchart showing the steps of ga (genetic algorithm). fig. 4. encryption procedure by using the first part of the key. citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.244 9 (page number not for citation purpose) gahbt: genetic-based hashing algorithm for managing and validating health data integrity in blockchain technology 6. apply the column mixing on the result from step 5. 7. take transpose of 6. start from the last column. 8. apply xor with the key matrix to get the matrix of the same order (8 × 8) and bits (128). 9. now we have a matrix of order 8 × 8. break the matrix into two equal parts, each of 4 × 8. say d1 and d2. find the hamming distance of corresponding values (say h1, h2, …, h32). add all the hamming distances to get a single value of h ≤ 256. 10. find g.c.d of h and 256. 11. randomly generate a prime number (say r4) and apply the right circular shift/rotation on step 8, g.c.d times and obtain another matrix of order 8 × 8. 12. repeat steps 5 to 11. till the random number of times. (generate a pseudo-random number and store it as r5). 13. apply xor between key matrix and resultant of 11 to get the matrix of order 8 × 8 of 128 bits. 14. convert the obtained matrix elements into ascii values and concatenate them with the first part of encrypted plain text. decryption • cyphertext c of 265 bits • divide it into two halves, c1 and c2, respectively. decryption of b1 1. consider the right portion of c that is c1 as mentioned above of 128 bits. 2. take complement of all prime positions of c1. 3. apply left circular shift on resultant of 2. f times., where f is the least prime factorial of hamming distance. (that is previously saved) 4. rotate the resultant at the d position of 3 by f times. 5. apply xor between the first part of the key (k11) and the result of 4. 6. break the results from 5 into four halves, each of 32 bits. 7. rearrange the blocks using the reverse of r2 stored already. 8. apply right circular shift. 9. convert into ascii codes. decryption of b2 1. consider c2 and convert it into binary numbers. 2. convert it into matrices of order 8 × 8. 3. apply xor operation with the key matrix that is k22 to get the original matrix of order 8 × 8. 4. apply left circular shift using (r4) as position according to the g.c.d times. 5. again, apply xor with the k22 to get the matrix of the same order (8 × 8) and bits (128). 6. take reverse transpose of the resultant 5 according to the first row. 7. take permutation of the result in such a way that the first column will become the last two rows and so on. 8. with the help of a pseudo-random number (r3), mix the columns 8 × 8. 9. repeat steps 3 to 8 according to the stored r5 random number times. 10. consider the matrix and convert it into plaintext. algorithm for encryption/description start step 1: represent the message as an integer from 0 to n-1 length step 2: divide the plain text message into two halves, b1 and b2. where b1 & b2 will be used with the k11 & k22, respectively. step 3: divide the key obtained in section 3.6 into 8 parts. (k1, k2, k3, … , k8). make k11 by combining the odd key parts and make k22 by combining even key parts. step 4: k11 and k22 are both 128 bits, and together they make 256 bits. step 5: both parts of plane text b1 and b2 will be encrypted by k11 and k22, respectively. end simulations experimental environment for the implementation of the proposed blockchain model, implementation has been done by considering the single health record of size 30.82 mp that consists of a standard size of image and text that is intraoral photography of size 1.64 mb, orthodontic cephalogram of size 1.40 mb, skin lesion photography of size 22.17 mb, dental panoramic x-ray of size 0.85mb.33 the reason behind considering the single record is to avoid the delay in processing when different records are grouped into a block. the block size is considered 1mb for the representation of the health record query. the block is divided into two parts: the body and header, with the header storing the metadata information that is the previous block’s hash, timestamp, merkle root hash value, block number, and version;34 whereas the body stores the information of health record for the experiment. the header size is 80 bytes. in the experiment, we have used proposed hashing algorithm that generates a unique 256-bit output for a given input.35 all the experiments for blockchain system have been performed with increasing number of health records (4,000, 5,000, 6,000, 7,000, 8,000, and 9,000) and increasing number of hospitals (10, 20, 30, 40, 50 and 100). we gradually increase the citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.24410 (page number not for citation purpose) fozia hanif et al. number of records by keeping the number of hospitals constant, which is 10. similarly, we gradually increase the number of hospitals by keeping the number of records constant, based on 4,000.36 all the simulations have been done on the matlab simulator. results figure 5 represents the execution time for upgrading the health records using a minimal blockchain model from [gbha], client/server, and the model development in this paper. it represents the graph has a linear relationship between blockchain systems and client/server.37,38 it represents that the time consumed in execution for the client network server and blockchain models is in linear relation with the health records. the time taken by the client network is less than the time consumed by the blockchain due to the working consensus mechanism of the blockchain for the acceptance and duplication of data. the data records are needed to upload on the ledger that must be forwarded to the medical centers that will have a copy of the ledger record for acceptance. for consensus, every associated node will transmit the hash of blocks to the other peers present in the network before appending the hash to the record ledger. whereas, in the client-server model, the record of data is upgraded to where the information of the patient is saved. the time consumed in execution for the client-server is less compared to the blockchain system designed for updated health record ledger. on average client/server algorithm takes 8.5 times less time than the blockchain. figure 6 represents the working efficiency of client/ server and blockchain models in representing data quantity transmitted to upgrade health data versus increment/appraisal in the health record. it shows that the amount of data transmitted in the blockchain is more compared to the client/server network because every upgraded record request is forwarded to the other nodes in the network. figure 6 represents the relationship between data transfer and the number of health records in the client model and blockchain models. this is repeated in figure 2. it has been shown in the graph that the data transmission through blockchain is more compared to the client-server system because every updated health record is forwarded to the rest of the network peer node by creating more data transmission. additionally, all peer nodes forwarded the block’s data, including the hash, to the rest peers in the network and boosted the transmission of data, and generated the request. in the blockchain, the execution time is due because, for the data, the query is forwarded to all nodes in the network. in figure 7, the execution time of blockchain and client/server models has been represented for the querying health data from the database with an increase in the number of health records. the less execution time of the blockchain compared to the client-server can be seen from the graph because first of all, the data have to be retrieved from the database where the record is present, whereas, in blockchain, the data are recovered from the local copy of the ledger present in all nodes. mass data transmission uses a client-server approach to query health data from databases as the number of health records increases. from the databases, the transmission of health records, as they increase in number, is represented in figure 4 based on the client-server model for querying healthy data. from the figure, the data transmitted by the blockchain network is greater compared to the client-server network. figure 8 represents the transmission of data by the client-server and blockchain network. to upgrade the health data, the number of medical centers is increased by blockchain and client-server models. it represents the fig. 5. relation between the execution time and health records in working consensus mechanism. citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.244 11 (page number not for citation purpose) gahbt: genetic-based hashing algorithm for managing and validating health data integrity in blockchain technology time consumed by blockchain more than the client-server approach because of the consensus protocol that the former uses. figure 9 presents details regarding the time for execution for querying the electronic health data from the data bank with an increase in the number of medical centers. the graph represents that the execution time of blockchain is significantly less than the client-server network for a health data query. figure 10 represents the query of health data transmission by client-server and blockchain models as the number of medical centers increases. it represents the constant behavior of data transmission due to the number of data queried irrespective of the number of medical centers. the required query of the medical center will be performed, and the transmitted data using blockchain for the query of health data increases as the number of medical centers increases. conclusion the purpose of the research is to present a secure decentralized ledger in a database that manages the healthcare distribution records using a secure and immune genetic-based hashing algorithm in blockchain technology. the system proposed in this paper uses a new cryptographic hashing algorithm to provide fundamental security. it raises the system’s immunity to make it more efficient and tamper-proof and protects it from numerous attacks. when it turns to the comparison, the study in this paper provides higher scalability, immunity, data integrity, and robustness than the other blockchain systems, which enables sharing of secure sensitive healthcare data among various network nodes. additionally, the proposed algorithm can be safely utilized effectively in sharing private and sensitive healthcare information in different environments like healthcare institutes, clinics, hospitals, and patients’ families. fig. 6. relation between data transfer and the number of health records. fig. 7. relation between querying health records and execution time. citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.24412 (page number not for citation purpose) fozia hanif et al. fig. 8. relation between the number of health records and data transfer in gbs in the working consensus mechanism. fig. 9. relation between transmission of the number of health records and execution time as the number of medical centers increases. fig. 10. relation between transmission of the number of health records and execution time as the number of medical centers increases. citation: blockchain in healthcare today 2023, 6: 244 http://dx.doi.org/10.30953/bhty.v6.244 13 (page number not for citation purpose) gahbt: genetic-based hashing algorithm for managing and validating health data integrity in blockchain technology acknowledgments na. conflicts of interest the authors declare no potential conflict of interests at this time. funding statement no funding supported the preparation of this article. contributors dr. fozia has developed the methodology, whereas ms. urooj has done the simulation. ms. aisa has done the write-up of the manuscript, and rehan has done the overall supervision of the paper. references 1. cartwright-smith l, gray e, 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others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/ licenses/by-nc/4.0. 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 technical briefs & short reports my holistic data share: a web3 data share application: extending beyond finance to privacy-protected decentralised share of multi-dimensional data to enhance global healthcare sathya krishnasamy, ms chainaim, newington, connecticut, usa doi: https://doi.org/10.30953/bhty.v7.341 corresponding author: sathya krishnasamy, email: sathya.krishnasamy@chainaim.com keywords: access control, blockchain, distributed ledger, healthcare, privacy, threshold cryptography abstract web3 technologies on network architectures, distributed ledgers and decentralised artificial intelligence represent a transformative shift in how data are handled, stored and shared. these innovations promise to significantly enhance consumer data privacy rights by addressing fundamental vulnerabilities associated with traditional centralised systems and self-custody wallets. data breaches in traditional systems operated mainly by third parties are more common, resulting in significant data leaks because of centralised storage and excessive data movement, sometimes unnecessarily. healthcare data breaches have been a growing concern globally. several hospitals faced operational halts on account of the impact of ransomware on patient care and privacy. web3 wallets are a vital component emerging as a significant force for global financial inclusion, especially in developing economies. they promote inclusion, reduce costs and empower individuals through self-custody. though major improvements are needed in these wallets, their use is rising steadily. the global cryptocurrency user base is expected to reach over 500 million by 2025, with substantial growth in emerging markets, according to a report by statista in 2023. this paper introduces a concept beyond cryptocurrencies and finance into everyday real-world use cases that need combinatorial access to a person’s holistic data, including financial and health records, genomic data, advanced directives, among others, that need to be privacy protected and shared with specific actors identified for their roles in the web3 ecosystem through decentralised identifiers and non-fungible token badges identifying particular recipients. the author introduced the concept at ethboston in april 2024, won accolades for a primitive implementation using underlying threshold cryptography technologies, and enhanced it into a conceptual holistic data share application for global healthcare as presented in this paper. received: august 1, 2024; accepted: august 25, 2024; published: august 31, 2024 web3 is a collection of emerging technologies. it includes distributed ledgers, self-sovereign identifications (ids), and artificial intelligence (ai), and promotes data and asset ownership concepts. web3 offers a decentralised alternative to traditional banking systems.1 web3 technologies provide access to financial services for the unbanked and underbanked populations,2 reduce transaction costs and empower individuals for consumer-mediated data sharing through self-custody web3 wallets. in addition, web 3.0 data share principles align with modern data privacy laws,  enhancing user control over personal information. these innovations promise to significantly improve consumer data privacy rights by addressing some fundamental vulnerabilities associated with traditional centralised systems. data breaches in conventional systems are more common, resulting in significant data leaks because of centralised storage vulnerabilities and excessive data movement, sometimes unnecessarily. the potential of web3 technologies to enhance consumer data privacy rights is a step towards a more secure future. healthcare data breaches are a growing concern globally. several hospitals faced operational halts because of ransomware, impacting patient care and privacy.3 https://orcid.org/0009-0000-1420-1098 https://doi.org/10.30953/bhty.v7.341 mailto:sathya.krishnasamy@chainaim.com citation: blockchain in healthcare today 2024, 7: 341 https://doi.org/10.30953/bhty.v7.3412 (page number not for citation purpose) sathya krishnasamy data sovereignty and control with self-custody concerning web3 wallets, self-custody refers to managing one’s digital assets by holding one’s private keys with oneself without relying on third-party intermediaries, who might misuse the keys. self-custody fundamentally aligns with data privacy principles because it emphasises individual control over personal data. many articles in the union’s general  data protection regulation define personal data, data minimisation, lawfulness and security-first designs appropriate to the risk and notifications. similarly, sections of california consumer privacy act cover provisions that define personal information, right to know, opt-out, delete and non-discrimination for dissent. specifically for the compliance of the health insurance portability and accountability act, the key rules are privacy – ensuring that any covered entities and business associates protect consumer data, security (mandating administrative, physical and technical safeguards) and notification – on any breaches. these regulations stipulate that individuals have ownership of their data, must provide explicit consent to the users of their data and minimally needed information is collected to be retained for a specific period for specific uses, with full audibility of access privilege, use and revoke actions on the data. self-custody reduces reliance on third-party service providers, potentially reducing data mishandling or breaches whose systems are not secure enough. limitations of current wallets current wallets hold crypto-native assets like cryptocurrencies and non-fungible tokens (nfts) predominantly for financial applications and services and, to some extent, for storing digital collectibles. these use cases hold collectibles stored in decentralised storage like the interplanetary file system (ipfs) and have a pointer to it in the wallet and the metadata. however, many use cases are possible as the same concepts can equally apply to managing access to other forms of personal information on top of financial records. these include health records, genomic records, advanced directives, among others, where different forms of data might need to be seen by people playing specific roles in the social context, which are more logical uses. self-custody requires individuals to be knowledgeable and savvy about handling their private keys. also, the wallet user experience is still not very user-friendly and requires some technical sophistication and a set of non-trivial steps to follow. some users might need technical support to manage their keys and might need to rely on third parties. depending on the custodians’ security capabilities and integrity, this can lead to risks, including identity theft, loss of funds, privacy breaches, among others. this opens the need for decentralised protocols that are more resilient than centralised custodians and a data-blind protocol-level decentralisation that increases the resilience of data management by encrypting users’ information and using critical management systems that are decentralised and managed by cryptographic mechanisms that can reassemble the credentials for accessing the data, based on granular roles for specific periods. a key technology that attempts to achieve this decentralisation is threshold cryptography. threshold cryptography threshold cryptography is a cryptographic scheme where a cryptographic key is divided into multiple shares, distributed among different parties. a predefined number of these shares (the threshold) must be combined to perform cryptographic operations such as decryption or signing. for instance, if a key is divided into 10 shares and the threshold is set to 6, any 6 shares can be used to reconstruct the key, but fewer than 6 shares will provide no information about the key. each share will be with a specific custodian, and they only know a piece of the key, not the entire one. benefits of threshold cryptography the benefits of threshold cryptography include enhanced security, redundancy and reliability, and multi-party authorisation. enhanced security by distributing key shares among multiple parties, threshold cryptography ensures that no single entity has complete access to sensitive information. this approach mitigates the risk of unauthorised access and provides an additional layer of security for managing critical records. in practice, this means that even if a malicious actor compromises one share, they cannot access the sensitive data or perform operations without obtaining the minimum required number of shares. no single individual has the power to compromise the data independently, drastically reducing the likelihood of data breaches and insider threats. redundancy and reliability threshold cryptography allows for operations even if some key shares are lost or compromised as long as the threshold number of shares remains intact. this ensures that critical records remain accessible and secure in the face of technical issues or security breaches. multi-party authorisation threshold cryptography helps ensure that data cannot be tampered with without the consensus of multiple parties. this facilitates secure collaboration by allowing researchers to share and analyse data without exposing it in its entirety to any single participant. this is particularly https://doi.org/10.30953/bhty.v7.341 citation: blockchain in healthcare today 2024, 7: 341 https://doi.org/10.30953/bhty.v7.341 3 (page number not for citation purpose) my holistic data share important in clinical trials and research studies, where data integrity is essential for deriving valid conclusions and ensuring patient safety. in scenarios involving multiple stakeholders, such as estate planning or advanced directives, threshold cryptography enables secure multiparty authorisation. this ensures that decisions or actions related to sensitive records require the consensus of multiple authorised parties, enhancing security and integrity. limitations and challenges of threshold cryptography complexity, availability, and scalability are limitations and challenges. complexity splitting the cryptographic key and managing shares requires sophisticated protocols. implementing them correctly and ensuring resilience under various security attacks is a challenge. availability reconstructing the key requires the active participation of multiple parties, which can be inefficient if parties are geographically dispersed or have inconsistent availability. scalability as the number of participants and shares increases, the complexity, communication and availability overhead increases, which can cause scalability issues. the specific implementation of threshold cryptography can vary based on how the distribution of cryptographic keys is disseminated and how the access control is configured for data retrieval. the mechanism of reassembling the key shares and threshold is also configurable. in a decentralised mode of operation, protocol implementation can decentralise the key shards across validator nodes. myholisticdatashare and possibilities in global healthcare myholisticdatashare is a web3 data share application that addresses the limitations and introduces newer concepts of organising and storing types of user data beyond financial data and digital collectibles into real-world assets such as medical records. these advanced directives can be managed by extending the cryptographic primitives for secure data sharing. access to these records can be individually provisioned and metered to specific parties and their roles in the social context, or it could be a combination of assets for a particular recipient or a role. myholisticdatashare is a revised and enhanced adaptation of the socialsecureshare application, initially conceived by the author at ethboston in april 2024, which won the best project for privacy and community vote. myholisticdatashare is a technical prototype for consumers and recipients of such data, combining many aspects of consumer-directed data share and its implications for healthcare and combinational data with health in regular and emergency settings, and health and finance for efforts such as social determinants of care. these emerging privacy protection technologies, like threshold cryptography and access control, show possibilities for promoting consumer-mediated data share while allowing for enhanced privacy by reducing single points of failure, granular and composable access control, and possibilities of checks and balances entities also participating as nodes to have improved visibility. myholisiticdatashare technical design myholisticdatashare uses cryptographic privacy primitives and decentralised mechanisms – like threshold cryptography for privacy-protecting the decryption access of the encrypted data stored in decentralised storage – based on nfts that are tokens to identify unique items. the nfts are unique tokens that represent one thing and one thing only, and can be used to represent a specific badge. the governance of the protocol and the application can assign these badges. myholisiticdatashare is built on the decentralised implementation of threshold cryptography using the threshold protocol and threshold access control (taco) protocol.4 these nfts are mapped to specific identity badges associated with individuals or roles that provide access to the consumer’s data based on the decentralised identity verification of the wallets holding those access badges. the threshold network offers a full suite of decentralised threshold cryptography services to increase user privacy and sovereignty in permission blockchains (figure  1). threshold cryptography protects data by distributing operations across a network of independent nodes, increases security and availability, and reduces reliance on trusted parties. threshold network uses the service taco protocol for access control. the data owner can encrypt their payload, which can have multiple sections, and can store them in offline locations. as needed, these storage elements can also be used in decentralised storage. in this example, the data owner stores the encrypted data in the ipfs, a popular decentralised storage solution. the taco splits a joint secret – a decryption key – into multiple shares and distributes those among authorised and collateralised node operators (i.e., stakeholders in the threshold network). a minimum number – a threshold – of those operators holding the key shares must be online and actively participate in partial decryptions. these are subsequently combined on the requester’s client to reconstruct the original plaintext data. every data payload is attached to conditions that can restrict access granularly. the data owner can define a range of access conditions that can have access control checks such as, “does the https://doi.org/10.30953/bhty.v7.341 citation: blockchain in healthcare today 2024, 7: 341 https://doi.org/10.30953/bhty.v7.3414 (page number not for citation purpose) sathya krishnasamy requestor own a specific nft that represents a badge of an emergency medical worker?” or “…a social worker helping patients?” it can also combine them with other elements like, “is the requestor asking for access during a time for which the data owner gives access, etc.?” the requesters prove their association with condition fulfilment – their right to receive a threshold number of decrypting shares – by signing a transaction that verifies their ownership of a given web3 wallet (in this case, metamask wallet on polygon test network amoy). that wallet is checked for fulfilment of the specific condition (e.g., owning an nft in order to access the data assets). myholisticdatashare is designed to be composed of different types of records, as illustrated in figure 2, that a consumer could store in a decentralised storage infrastructure like the ipfs, with metadata defining the uri. the implementation is in polygon blockchain amoy test network, with the metamask wallet access storing the access control through nft badges. users can flexibly store different kinds of records, including financial, health, genomic and advanced directives. financial records these might include traditional financial accounts, crypto accounts, bank statements, letters of credit, among others, that the user could securely store and decide to share with specific recipients that the user deems necessary to share with for specific finance-related purposes. health records these might include basic health records, including lab reports, diagnostics, diagnostic imagery and appointments that the user deems necessary to share for specific health-related purposes. genomic records these might include specific granular, highly specialised and personal information like genomic records that the user can share with specific researchers and clinicians of interest. advanced directives these might include specific health proxy information about things like “do not resuscitate” (dnr) preferences, among others, that are important for healthcare workers and emergency technicians. fig. 1. screenshots of threshold network implementation of threshold cryptography. source: https://docs.threshold.network/ applications/threshold-access-control/key-concepts. source: copyright by the author, 2024. fig. 2. screenshot of a user record encrypt. source: copyright by the author, 2024. https://doi.org/10.30953/bhty.v7.341 https://docs.threshold.network/applications/threshold-access-control/key-concepts https://docs.threshold.network/applications/threshold-access-control/key-concepts citation: blockchain in healthcare today 2024, 7: 341 https://doi.org/10.30953/bhty.v7.341 5 (page number not for citation purpose) my holistic data share figure 2 shows a screenshot of the process where the data owner and a citizen in a social setting sign the transaction when storing the different data payloads they want to encrypt to be stored in decentralised storage. figure 3 shows a screenshot of the data payload load and decentralised storage location details, and also shows the acknowledgment feedback messages. myholisticdatashare recipient badges blockchain network participants are given specific nft badges administered by a governing data autonomous organisation that registers the decentralised ids. for example, decentralised identifiers (did) in the network can be assigned a primary doctor badge, and another did can be assigned an emergency medical technician (emt) worker badge, representing the specific social context for the data needs. the social data context badges and their configurations are made available in a registry that indicates to the user which badges can access what kind of data. for example, in the default configuration, an emt worker might have access to medical records and dnr records but not financial records. a genomic researcher might possess a badge that showcases an interest in a specific type of genomic data he might have. combinatorial access could be controlled granularly as well. for example, a social worker might have access to health and financial records to help plan coordination help for social determinants of health. figure 4 shows the different types of badges, stored as nfts to be stored in the data recipient wallets. when the fig. 3. successfully encrypted content based on synthetic sample data. source: copyright by the author, 2024. fig. 4. different distinct badges modelled as nfts assigned to decentralised ids. emt: emergency medical technician; id: identification; nfts: non-fungible tokens. source: copyright by the author, 2024. https://doi.org/10.30953/bhty.v7.341 citation: blockchain in healthcare today 2024, 7: 341 https://doi.org/10.30953/bhty.v7.3416 (page number not for citation purpose) sathya krishnasamy data recipient tries to access the data owner’s encrypted data, based on the access controls defined, the presence of these nft badges are checked to allow or deny access. decryption results and data availability based on the network’s decentralisation quorum configurations and the nft definitions, the user might sign their transactions to encrypt them so that they are available for decryption for specific dids holding specific data needs modelled as unique nft definitions. these can also be modelled, and the decentralised protocol will check for a specific time for decryption. the badge and the protocol definitions would give access to decryption. access to any other data will not be available. figure 5 shows the situation where a specific nft badge is checked for during decryption, the right to decrypt is verified and executed, and the decryption is successful. examples include legitimate access to genomic records for a designated researcher or an emt healthcare worker with access to dnr data. figure 6 depicts a situation where someone who does not have the required nft badge cannot decrypt the data. for example, someone who does not have a social worker nft badge does not have access to the owners’ health and financial summary records data. in real-world settings, the governance behind this protocol and application will involve offline verification for issuing the badges, possibly involving a combination of health systems and social organisations that define the roles and access criteria for authorisation of access. for example, a designated social worker with specific access controls, who might be working on care coordination or community care roles, can access both the medical and financial records to find holistic care for that patient based on their socio-economic standing where the care could include more than medical care, but also finding housing, transportation and other functions proving their need. as permissionless blockchains mature, we see an increasing trend from the consumer end to adopt permissionless blockchains, measured by self-custodial wallets and participation in web3-based social applications from diverse entities and individuals. those activities can be leveraged for consumer-mediated health data privacy adoption as well if they are carefully planned with adequate checks and balances, including some regulatory nodes to be run on public/ hybrid blockchains, which is slowly starting to happen. threshold cryptography is not a contender to other privacy protection techniques but complements other techniques such as fully homomorphic encryption and multi-party computing. in those cases, threshold systems can still add another layer of security, in which the encrypted content can be access-controlled when the fig. 5. example encryption and decryption of sample synthetic data with legitimate access. url: uniform resource locator. source: copyright by the author, 2024. fig. 6. screenshot of denied access based on the granular access control. taco: threshold access control. source: copyright by the author, 2024. https://doi.org/10.30953/bhty.v7.341 citation: blockchain in healthcare today 2024, 7: 341 https://doi.org/10.30953/bhty.v7.341 7 (page number not for citation purpose) my holistic data share requesting system accesses data from distributed endpoints or performs computations on the encrypted data. it could become even more critical in those contexts for access control systems to be “bulletproof.” in another contemporary paper, the author discusses the emergence of zero-knowledge proofs and machine learning, which is also very complementary, where the data share can be minimalised by presenting proofs for verifiers. this privacy-preserving holistic data sharing could also address another issue, which is cross-organisational and cross-domain data breaches. for example, when data breaches happen in retail or the banking sector, it could compromise access to health records as some personal information gets leaked and provide more ammunition to malicious users. however, with such holistic data shares and granular and composable access, consumers could have more control over their data and be in a position to respond much better to adversities. one major improvement needed in web3 is the ease of use for consumers, as it is challenging for users to keep track of the steps and the uncanny addresses. this  effort has already attempted to keep the ui/ux (user interface design/user experience design) very close to a web 2 webpage, at least concerning managing their data. it uses consumer usability-oriented conceptualisation around json (javascript object notation) data structures, standards and simple screens. in the subsequent iterations, efforts will be made to examine how the data definitions can also be in a browser extension and use advanced account abstraction and identity simplification mechanisms to increase usability. conclusions and future work myholisticdatashare has demonstrated a new extension of web3 technologies based on decentralised data share and threshold cryptography for decentralising the decryption access to non-centralised quorum and configurable threshold definitions. this is still an introductory concept, and the final version of the implementation will depend on regulatory context definitions, the governance design of the decentralized autonomous organization, and maturity and successful did governance processes in a social context. any attempts to trial blockchains in mainstream settings will have to be planned carefully and depend on specific blockchains, including particular public/private/ hybrid architectures. this conceptual prototype shows alternative ways the web3 technologies open up potential solutions for chronic problems faced in centralised systems with data privacy and sharing, primarily reducing single point of trust failures and granular and composable access control. the author intends to extend this app into a browser extension wallet app as the landscape matures, specifically around how wallets and web3 data share applications and their responsibilities get defined more precisely. complementary technologies like zero-knowledge systems and fully homomorphic encryptions can enhance this conceptualisation to create enhanced privacy-protected designs for consumer-initiated and provenanced data sharing. the author’s efforts at chainaim continue to build relationships with standards organisations and technology foundations to identify the scalability needs and governance needs for advancing such data sharing. funding none. conflicts of interest none. contributors sathya krishnasamy is the president and principal of chainaim technologies. his 25 years of background spans extensive experience in managed care payor settings in leading us healthcare firms, including aetna and anthem. he focusses on emerging technologies, including artificial intelligence/machine learning systems and distributed ledger technologies. he also serves as an advisor in many industry efforts in payor-provider collaboration, standards organisations, and efforts such as account aggregators in india advancing fintech, healthcare, and skills sectors. he currently serves as president and principal at chainaim, offering technical strategy consulting and application and development services. sathya krishnasamy contributed to the research, conceptualisation, and overall implementation. data availability statement (das), data sharing, reproducibility, and data repositories no data repositories. application of ai-generated text or related technology none. acknowledgments pankhuri gupta, a master’s student in software engineering at northeastern university, boston, massachusetts usa, helped the principal with the ui design and implementation. she can be reached at gupta@pankh@ northeastern.edu. references 1. buterin v. ethereum whitepaper [internet]. ethereum.org; 2014 [cited 2024 aug 01]. available from: https://ethereum.org/en/ whitepaper/ 2. web3 and financial inclusion: bridging the gap [internet]. www.linkedin.com. [cited 2024 aug 01]. available from: https:// https://doi.org/10.30953/bhty.v7.341 mailto:gupta@pankh@northeastern.edu mailto:gupta@pankh@northeastern.edu http://ethereum.org https://ethereum.org/en/whitepaper/ https://ethereum.org/en/whitepaper/ http://www.linkedin.com https://www.linkedin.com/pulse/web3-financial-inclusion-bridging-gap-liveplexplatform-ojazc/ citation: blockchain in healthcare today 2024, 7: 341 https://doi.org/10.30953/bhty.v7.3418 (page number not for citation purpose) sathya krishnasamy www.linkedin.com/pulse/web3financialinclusionbrid ging-gap-liveplexplatform-ojazc/ 3. u.s. department of health & human services [internet]. hhs. gov.; 2019 [cited 2024 aug 01]. available from: https://www.hhs.gov 4. threshold access control (taco). threshold.network; 2024 [cited 2024 aug 15]. available from: https://docs.threshold. network/applications/threshold-access-control copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.341 https://www.linkedin.com/pulse/web3-financial-inclusion-bridging-gap-liveplexplatform-ojazc/ https://www.linkedin.com/pulse/web3-financial-inclusion-bridging-gap-liveplexplatform-ojazc/ http://hhs.gov http://hhs.gov https://www.hhs.gov https://docs.threshold.network/applications/threshold-access-control https://docs.threshold.network/applications/threshold-access-control http://creativecommons. org/licenses/by-nc/4.0 http://creativecommons. org/licenses/by-nc/4.0 1 (page number not for citation purpose) letter from the editors integrating ai with integrity at blockchain in healthcare today: introducing bhty’s ai policy for authors, reviewers, and editors jennifer hinkel1, msc, chw, frsa and umit cali, phd2 1editor-in-chief, blockchain in healthcare today; founder & president, sigla sciences, and managing director, the data economics company, usa; 2professor of digital engineering for future technologies, university of york, uk to the bhty community: whether you read bhty as a blockchain developer, a healthcare executive, or an academic, artificial intelligence (ai) has likely become part of your daily vocabulary. ai, machine learning, large language models (llms), transformers, and self-attention are keywords we are all gaining more familiarity with, and building skills around. these technologies are powerful tools. if used judiciously, they accelerate discovery; whereas if used poorly, they amplify bias, erode privacy, and undermine trust.1 bhty therefore introduces the ai policy below to codify a central principle: ai augments scholarship but never replaces human expertise or accountability. we are embedding mandatory disclosure, bias mitigation, and data-protection measures into our submission, peer-review, and editorial workflows, to advance innovation while upholding rigorous scientific integrity. some highlights of the policy include: 1. full transparency on ai use (tools, versions, dates, verification, human responsibility). 2. bias vigilance and documented mitigation. 3. strict data-privacy safeguards. 4. human verification and accountability across all roles. 5. graduated sanctions, from resubmission to retraction, for violations. the full policy is below. we will plan to implement this policy swiftly. we also commit to regularly reviewing this policy among the bhty editorial board at least annually, and we welcome your feedback. as a closing thought, scientific progress depends on both ingenuity and integrity, traits that still remain rooted in human judgement. ai tools will continue to reshape how we are interacting with technology, data, and knowledge in general; we are just at the beginning. no matter how innovative or exciting these tools are, they serve the healthcare field only when they can be wielded with transparency and discipline. this policy enables thoughtful ai use while safeguarding the credibility of our collective work. by setting this benchmark, bhty affirms its role as a thought leader in responsible scholarship. policy overview bhty, its editors, and publishers recognize that ai tools including llms can support legitimate research activities when used ethically and transparently. given the utmost importance of academic integrity in scientific research and specific sensitivities around bias, privacy, and accuracy in healthcare-related science, and seeking to balance the interest in using these new technologies with integrity, rigour, and legitimacy, we are seeking to update our guidelines for ai use across activities including submissions, reviews, and editorial functions. the core principle is that ai tools are simply tools, and cannot replace human expertise, critical thinking, or scholarly rigour. all authors, reviewers, and editors are fully responsible for all actions and words they put their names to, regardless of the tools used to achieve that output. ethical and reasonable use of ai tools include: • literature review organisation and screening • data visualisation and basic statistical analysis (with human verification) • language editing and grammar checking • code documentation and commenting • initial draft structuring and outlining • translation assistance for non-native english speakers • code optimization and debugging • smart contract testing and validation • data preprocessing and cleaning (while maintaining compliance with all data privacy regulations) • figure and diagram creation • reference formatting and bibliography management blockchain in healthcare today issn 2573-8240 citation: blockchain in healthcare today 2025, 8: 440 https://doi.org/10.30953/bhty.v8.4402 (page number not for citation purpose) letter from the editors • suggestions for improvement, “feedback” on ideas and writing, and proofing/grammar requirements when using ai 1. full disclosure and documentation all ai use must be explicitly declared in submissions through an ai use declaration statement (required in all submissions): • specific ai tools used (name, version, provider) • exact purposes for which ai was employed • date(s) of ai tool usage • methods used to verify ai-generated content • statement of human oversight and final responsibility example declaration: “this research utilized chatgpt-4.5 (openai, accessed march 2025) for initial literature review organization and claude 3 (anthropic, accessed march 2025) for code commenting. all ai-generated content was independently verified against primary sources. the authors take full responsibility for the accuracy and integrity of all content, including any errors or omissions that may have originated from ai assistance.” 2. bias assessment and ethical review authors must demonstrate: • active consideration of potential ai bias in healthcare contexts • recognition of limitations in ai training data representation • assessment of cultural, demographic, and clinical bias risks • documentation of steps taken to mitigate identified biases • acknowledgement of ai limitations in healthcare-specific contexts • for more information on bias, reference: university of oxford catalog of bias.2 3. data privacy and security compliance the following actions are high risk for privacy and security compliance: • uploading protected health information (phi) to any ai system • sharing proprietary blockchain implementations or sensitive code with ai systems • using ai tools hosted outside permitted jurisdictions without explicit data agreements • processing patient data through public or commercial ai platforms • sharing institutional or collaborative partner confidential information • sharing bhty journal confidential information using public ai systems and sending to systems hosted outside permitted jurisdictions, including uploading confidential submissions to commercially available ai tools without privacy protections required safeguards: • use only ai tools with appropriate data handling certifications that you have evaluated and verified as a researcher • implement local ai solutions (i.e. on your own server or computer) when processing sensitive data • maintain audit trails of all ai interactions involving research data • comply with health insurance portability and accountability act of 1996 (hipaa), and general data protection regulation (gdpr), and other applicable privacy regulations • obtain necessary institutional approvals for ai tool usage, including any relevant ethics or investigation review board (irb) approvals, before commencing 4. human accountability and verification author responsibilities: • personal review and verification of all ai-generated content • independent fact-checking of all claims, statistics, and references • critical evaluation of ai recommendations and outputs • final approval and sign-off on all submitted content • acceptance of full liability for errors, hallucinations/“false positives,” or inaccuracies human authors are expected to: • absolutely ensure that any ai use in research or referencing does not lead to false references, use of references/citations that do not reflect the content of the original source, or similar ai-generated errors. • independently validate all analyses, data interpretations, and figure generation • confirm accuracy of technical implementations and code • verify technical details and specifications prohibited uses of ai for the purposes of bhty the following activities are not permissible for the journal and may be interpreted as violations of research ethics and academic integrity: • using ai to write substantial portions of a submission/ manuscript without explicit declaration of this use https://doi.org/10.30953/bhty.v8.440 citation: blockchain in healthcare today 2025, 8: 440 https://doi.org/10.30953/bhty.v8.440 3 (page number not for citation purpose) introducing bhty’s ai policy for authors, reviewers, and editors • submitting ai-generated or ai-derived content that is not verified, e.g. references that do not exist, or references that do not reflect the cited material • submitting ai-generated content as original work without human oversight/verification • using ai for peer review activities or editorial decisions in lieu of careful human expert review and feedback • generating fake data, references, or experimental results, including the generation of synthetic data for analysis without clear indication of ai use • creating fabricated case studies or patient scenarios • bypassing human oversight in critical analysis or conclusions • plagiarism through undisclosed ai assistance • fabrication of research findings using ai • falsification of methodology descriptions • misrepresentation of ai capabilities or limitations • failure to disclose ai use when required editorial review process the editorial team acknowledges that no “ai detection” tools have been demonstrated to have a high level of validity. however, peer reviewers and authors are within their professional scope to question if ai has been used and request documentation of research processes, data, and methods if they have questions about the integrity of a submission’s methods, data, analysis, or writing. to that end, reviewers and/or editors may: • request additional documentation of research processes • require raw data and methodology verification • conduct enhanced review for ai-assisted submissions • reject submissions with inadequate ai disclosure also, submissions declaring ai use will undergo: • additional scrutiny of methodology and data analysis • verification of bias mitigation strategies • assessment of ai appropriateness for the specific research context and use • evaluation of transparency and disclosure adequacy • evaluation of appropriate ethics approval where required consequences of policy violations the editors and the publisher may apply any of the following measures, proportionate to the violation: first offense: • manuscript rejection with opportunity for resubmission • required completion of research integrity training • enhanced scrutiny of future submissions repeated or severe violations: • temporary or permanent publication ban with the journal and affiliated journals • notification to author’s institutional research integrity office for severe violations • retraction of published articles if violations are discovered post-publication • public correction or editorial expression of concern • potential exclusion from editorial board participation or peer review activities references 1. ethics and governance of artificial intelligence for health [internet]. world health organization; 2021 [cited 2025 aug 6]. available from: https://www.who.int/publications/i/item/ 9789240029200 2. catalog of bias [internet]. [cited 2025 aug 6]. available from: https://catalogofbias.org/ copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. the author of this article owns the copyright. https://doi.org/10.30953/bhty.v8.440 https://www.who.int/publications/i/item/9789240029200 https://www.who.int/publications/i/item/9789240029200 https://catalogofbias.org/ http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research privacy-conflict resolution for integrating personal and electronic health records in blockchain-based systems aleksandr kormiltsyn, msc1 ; chibuzor udokwu, phd2 ; vimal dwivedi, phd3 ; alex norta, phd4 ; sanam nisar, msc1 1department of software science, tallinn university of technology, tallinn, estonia; 2austrian blockchain centre research, vienna, austria; 3school of electronics, electrical engineering and computer science, queens university belfast, belfast, northern ireland, united kingdom; 4baltic film, media and arts school, tallinn university, estonia; dymaxion oü, tallinn, estonia correspondence: aleksandr kormiltsyn, email: aleksandr.kormiltson@taltech.ee keywords: blockchain, conflict management, e-healthcare, preventive healthcare, privacy, smart contracts abstract integrating personal health records (phrs) and electronic health records (ehrs) facilitates the provision of novel services to individuals, researchers, and healthcare practitioners. simultaneously, integrating healthcare data leads to complexities arising from the structural and semantic heterogeneity within the data. the subject of healthcare data evokes strong emotions due to concerns surrounding privacy breaches. blockchain technology is employed to address the issue of patient data privacy in inter-organizational processes, as it facilitates patient data ownership and promotes transparency in its usage. at the same time, blockchain technology creates new challenges for e-healthcare systems, such as data privacy, observability, and online enforceability. this article proposes designing and formalizing automatic conflict resolution techniques in decentralized e-healthcare systems. the present study expounds upon our concepts by employing a running case study centered around preventive and personalized healthcare domains. plain language summary this paper suggests using blockchain technology for privacy concerns in integrating personal health records and electronic health records in decentralized e-healthcare systems. this report focuses on designing automatic conflict resolution techniques to ensure patient data ownership, transparency, and privacy in inter-organizational processes. this paper proposes designing automatic conflict resolution techniques in decentralized e-healthcare systems, which can improve inter-organizational processes in healthcare. using blockchain technology to integrate personal and electronic health records can ensure patient data ownership and promote transparency in data usage, addressing privacy concerns in healthcare systems. this paper emphasizes the importance of data privacy and protection in healthcare systems, highlighting the need for compliance with laws and regulations. the research results, including the proof-of-concept prototype, can provide practical insights into implementing conflict resolution techniques in decentralized e-healthcare systems. submitted: june 26, 2023; accepted: november 13, 2023; published: december 14, 2023 healthcare systems suffer from high costs1 and the economic interests of healthcare providers. for example, after privatization, the irish hospital sector faced an increase in patient beds in private for-profit hospitals, while in not-for-profit hospitals, this number decreased.2 a personal health record (phr) is an individual’s electronic health-related information. it is managed and maintained by the individual who controls access to the data. the phr stores and organizes medical history, treatments, medications, notes, diagnoses, and other relevant health information, which can be shared between the individual and their healthcare providers. the phrs provide a comprehensive and organized account of an individual’s medical history, which can be invaluable for quick and blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-0813-7007 https://orcid.org/0000-0002-6852-5976 https://orcid.org/0000-0001-9177-8341 https://orcid.org/0000-0003-0593-8244 https://orcid.org/0009-0008-9958-5358 mailto:aleksandr.kormiltson@taltech.ee citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.2762 (page number not for citation purpose) kormiltsyn et al. efficient diagnosis, improved safety, and quality of care. in addition, phrs can be used to track a patient’s medical history, identify trends and correlations, and provide feedback to the patient about their healthcare providers. a feedback loop in healthcare refers to a process in which information about a patient’s health status or the performance of a healthcare system is collected, analyzed, and used to make improvements or adjustments to patient care or healthcare processes. a feedback loop involves exchanging information about a patient’s condition, treatment options, and progress. patients provide feedback on their symptoms and treatment experiences, which helps healthcare providers make educated choices concerning their treatment. research defines the value of phr as improving communication between a patient and a doctor, resulting in patient education leading to lifestyle changes.3 patient engagement simplifies collecting and processing personal health and well-being data, increasing the value of personalized preventative healthcare services.4 according to kormiltsyn and colleagues,5 a novel classification of personalized preventative health coaches is anticipated to arise. these coaches will use their expertise and proficiency in comprehending and analyzing health and wellness data. in our scholarly article published in 2019, we elucidate the economic and financial predicaments of the healthcare system and scrutinize the potential of blockchain technology to facilitate decentralized and patient-oriented systems.6 in a patient-centric system, individuals are responsible for generating and administering their data, while healthcare providers employ these data in their procedures instead of possessing them. the issue of transparent data exchange is illustrated by norta and colleagues.7 the collection and processing of phr entails many legal, technical, and emotional challenges. scholars concentrate on the technical and security prerequisites of phr systems when data are managed centrally.8–10 this methodology proves to be effective in situations where the number of phr data sources is restricted. thus, the number of processes that use phr increases, and the need for more trust between stakeholders such as private companies, legal institutions, and individuals and integration complexity increases. therefore, a centralized approach is not scalable, while decentralized inter-organizational processes based on blockchain technology provide a foundation for trustable and scalable connections. an integrated phr and electronic health record (ehr) system is socio-technical and involves people from different organizations that use different sets of technologies for collaboration and problem-solving.11 an ehr is a patient’s data created by healthcare professionals and stored digitally. such data include the medical history, medications, immunization status, laboratory test results, and radiology images. it allows healthcare professionals to effectively plan and provide personalized patient care while also enabling them to securely share medical information between healthcare providers and other authorized users. in addition, ehrs can help reduce healthcare costs and improve the quality of care. the decision to use a patient-centered system that shares phr is emotionally motivated and creates a sense of uncertainty about the way personal data are used. using ehr-integrated phr creates security, data protection, and privacy conflicts. privacy is a legal term that limits knowledge and control over the content and performance of a (smart) contract, which should only be distributed between the parties to the extent necessary.12 while privacy, as defined in the charter of fundamental rights of the european union,13 is the right of any individual to respect their private and family life, home, and correspondence,7 data protection pertains specifically to the processing of personal data and is geared toward safeguarding this privacy.8 the charter emphasizes that personal data must be processed fairly for specified purposes and based on the consent of the person concerned or some other legitimate basis laid down by law. this distinction is crucial in our research, as it underscores the importance of implementing blockchain-based systems in a manner that respects both the privacy rights and data protection principles laid out in these fundamental rights. the standard, as defined by the european commission (ec), proposes european union contractual clauses approved by the eu in june 2021.14 it is important to note that these clauses were set to be replaced by updated versions in december 2022 as part of the ec’s ongoing efforts to enhance data protection standards in line with evolving legal and technological landscapes. as outlined in the standard contractual clauses (scc) documentation by the ec, this update is a significant step in ensuring robust and up-to-date data protection measures in cross-border data transfers. in 2010 proceedings of the 2010 ieee 3rd international conference on cloud computing,15 the european data protection board (edpb) and the spanish data protection authority (aepd) elucidate that when identifiers are linked to a hash, such as a telephone number, the information can be unequivocally traced back to a specific data holder. this linkage introduces additional vulnerabilities to the hash’s confidentiality, as the linked identifier can potentially reduce the effective message space for that particular hash, thereby compromising its intended pseudonymization function. this insight highlights the potential limitations and challenges in utilizing hash functions for data protection, underlining the need for careful consideration in their application in blockchain-based systems. sun and colleagues16 define the main requirements for medical systems employing the internet of things (iot) as data integrity, usability, auditing, and patient https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 3 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems information privacy. al-muhtadi and colleagues17 focus on cybersecurity and privacy issues when integrating mobile healthcare applications and propose a secured architecture for multi-cloud environments. research by katurura and cilliers18 states that users lack data control and transparency. in addition, researchers have discovered a lack of knowledge of securityand privacy risks related to personal data for wearable devices.19 several authors have highlighted that sharing medical data leads to security and privacy risks.20–22 simultaneously, the authors assert the necessity for further investigation into developing secure and integrated healthcare systems. autonomous health data collection proposes new challenges for data privacy when using smart home systems.23–25 using equitative algorithms to resolve civil conflicts has been proposed by salehi and giacalone.26 these algorithms are based on different technologies, such as artificial intelligence (ai) and algorithmic decision systems (ads). in their publication,27 xu and colleagues presented a systematic investigation employing the graph model for conflict  resolution (gmcr) as a viable approach to address real-world conflicts. other scholars28 use hidden markov models (hmm) to scrutinize the incoming data and reconcile any conflicts that may arise. the analysis includes customization and training of the hmm models, which are later used with a rule-based system to detect conflicting information, resolve the identified conflicts, and use past data and decisions to prevent conflicts before they occur. some researchers29 suggest using a mediator who detects conflicts and offers a possible solution to the conflicting parties. the main research question is how to automatically resolve conflicts in integrated e-healthcare inter organizational processes. to answer the main research question, we deduce the following sub-questions. what are the requirements for individual-centric phr inter organizational collection and -processing? the answer to this question aims to define the logical requirement space that includes stakeholder assignments. what conflicts arise in inter-organizational e-healthcare processes? to address this inquiry, conflicts within inter-organizational e-healthcare processes are delineated and aligned with processes outlined in the preceding research query. furthermore, these processes are devised utilizing the business process model and notation (bpmn). what are the automatic conflict-resolution techniques in decentralized e-healthcare? the objective of the privacy conflict-resolution technique is to create a conflict-resolution process for e-healthcare, utilizing bpmn, based on the process design in which conflicts arise, as defined in the preceding research inquiry. presented here is an outline of the remainder of this review. • a literature review, preliminaries, and a running case. • discussion of patient-centered phr collection and processing requirements. • present conflicts in the decentralized e-health process by mapping them to specific functional goals and business processes. • address the privacy conflict-resolution techniques when processing medical data in inter-organizational processes. • evaluation of the devised process and juxtaposes the findings with those of other research endeavors. • conclusion offering insights into the limitations, unresolved matters, and potential avenues for future research. literature review and preliminaries here, the authors review related literature and further provide the preliminaries that outline the background for this research. literature review research on blockchain technology in healthcare has notably increased.30–32 the primary focus areas within this field are data sharing, health records, and access control. a distributed ledger, as proposed by a blockchain, puts forth the concept of participants adding new records. the information stored on a blockchain is immutable, which is ensured by cryptography.33 the data contained within the blockchain are securely stored in transactions, which are then organized and linked together in blocks through cryptographic methods. each block is intricately connected to the subsequent block in the chain. utilizing the cryptographic technique referred to as the merkle tree, or hash tree, ensures that the transactions stored on a blockchain are correlated through mathematical hashes,34,35 thereby assuring that no alteration can render the entirety of the recorded data invalid. hashes streamline the process of validating new transactions, obviating the necessity to analyze all the information stored within a blockchain.35 some blockchains, such as ethereum, support smart contracts that nodes can execute. research methodology design-science research (dsr) is used in this paper to “conduct the research.” the dsr offers a framework for developing and assessing new artifacts.36 the environment, dsr evaluation, and knowledge base are the three main components of dsr. the research’s environment describes the issues that organizations and application domains face. the knowledge base offers theoretical support to develop new artifacts that solve identified https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.2764 (page number not for citation purpose) kormiltsyn et al. organizational issues. the created artifact is evaluated as part of the dsr.36 previous research by narendra and colleagues11 conducted by the authors on the conflict-resolution approach in the m2x (machine-to-everything) environment serves as the environment pillar for this study. in this article, the previously proposed approach is adapted to an e-health environment where the parties involved in the inter-organizational processes lack trust. the knowledge base represents existing strategies, techniques, and models that serve as the building blocks for designing the conflict-resolution methods and the decentralized inter-organizational process flow. we use the trustable dapp modeling (t-dm) framework for defining design-process requirements as it extends the agent-oriented modeling (aom) approach and introduces tokenized goals used in blockchain systems. we refer the reader to “preliminaries” below for a more detailed test data management (t-dm) framework description and bpmn to define process flows. the designed automatic conflict-resolution techniques are evaluated in the decentralized e-healthcare processes with colored petri nets (cpn) modeling. in addition, we provide a proof-of-concept (poc) prototype that illustrates the implementation of the running case. the evaluation approach is similar to the one used in the previous research.11 in addition, we provide the implementation of the modeled running case with the poc. the running case and background preliminaries to ensure confidentiality and facilitate conflict resolution, we offer a comprehensive analysis in the section “running case and privacy conflict scenario” that presents a practical scenario from a patient-centric standpoint. the present case study aims to facilitate the understanding of the ongoing case and the subsequent sections of this paper. the “preliminaries” section offers the necessary introductory information essential for comprehending the subsequent segments. running case and privacy conflict scenario figure 2 presents a description of the ongoing case within the domain of cancer prevention. while physiotherapists primarily evaluate clinical outcomes by assessing pain level, range of motion, and muscular strength, the domains of patient goals consist of physical activity, workplace environment quality, and sleep quality. the observed disparity can be elucidated by considering the intricate nature of comparing individuals using the patient specific functional scale.37 consequently, it is unfeasible to quantify patient-goal domains. in our running case, healthcare data are monitored by the patient with a smartwatch and airand water-quality sensors. these devices collect patients’ activity-, health, and ecological environment data and share it if necessary. the aforementioned information is gathered continuously, with the healthcare provider receiving a feedback loop during patient monitoring. additionally, when the patient seeks medical attention from a general practitioner at a hospital, the latter conducts laboratory tests, obtains a medical history, and subsequently incorporates these data into the ehr. in conclusion, the ehr is collaboratively accessed by both healthcare professionals and general practitioners. these individuals are responsible for submitting comprehensive reports to the insurance provider to secure reimbursement for the medical interventions provided. the medical reports vary as a consequence of the information accessible to the healthcare professional, which encompasses the phr together with continuous input from the patient. the aforementioned phenomenon serves to augment the understanding of health-related circumstances, which, in turn, facilitates the delivery of personalized healthcare services by the healthcare practitioner. in contrast, the general practitioner is limited to accessing solely the ehr due to the lack of a feedback mechanism. the discrepancy in asserting medical information contradicts the insurance company, which lacks guidelines for processing phr data. such a disparity further complicates the utilization of recently developed healthcare amenities that rely on processing phr. it is plausible that implementing a feedback mechanism may enhance the situation. the patient encounters privacy conflicts due to the vulnerability of his data in smart autonomous devices or various applications, leading to privacy conflicts. upon the patient providing his phr to the primary care physician after he has agreed with an explicit explanation to use the patient’s data under the regulatory requirement, the latter is able to employ it within the internal procedures of the healthcare provider, such as for the purposes of generating reports, conducting statistical analysis, and facilitating research endeavors. these procedures might involve external participants such as the national bureau of statistics, independent research firms, or private enterprises specializing in data reporting services. the opacity of processes for the patient renders them non-transparent. consequently, the possibility of mishandling phr data may arise. in this article, we define three conflicts during the inter-organizational insurance process. first, home monitoring involves privacy conflicts when patient data are collected. wearable devices and phr systems, which retain amassed data, are susceptible to the potentiality of unauthorized individuals extracting said information. next, an integrity conflict arises when phr traverses various processes and is susceptible to alteration by the https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 5 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems parties involved. lastly, the consistency conflict arises when the insurance provider receives claims from both the healthcare professional and the doctor, wherein they provide dissimilar information in the insurance claim. preliminaries this article considers e-healthcare processes mapped to blockchain systems for achieving immutable traceability, security, and privacy-assured distributed disintermediation and decentralization in inter-organizational collaboration. blockchain technology provides a distributed ledger that allows participants to add and verify records on a ledger, and cryptography ensures that the records are immutable.33 when participants add records to the ledger, they are stored as hashed transactions and grouped in blocks. the cryptographic linkage between each block and its predecessor is a fundamental characteristic of the system under consideration. smart contracts are executable programs that run and are stored on the blockchain.38 according to nguyen and kim,38 the common blockchain platforms are bitcoin,39 ethereum,40 hyperledger fabric,41 etc. blockchains use different consensus mechanisms to validate transactions. for example, bitcoin uses a proof-of-work (pow) consensus algorithm. this mechanism assumes that all the participating nodes are solving a difficult mathematical problem. it rewards the first node with the number of tokens by allowing it to add the next block.38 ethereum uses a proofof-stake (pos) where validation is based not on the resources spent on mathematical problem-solving but on a node’s reputation. we refer to our previous research42 for further details about the practical usage of blockchain technology. for blockchain technology, different token types are available. here, we propose using two token types: utilityand non-transferable “soul bound” tokens (sbt). the account represents “soul”, and tokens held by the accounts as “soulbound tokens” (sbts).43 the utility token is integrated into an existing protocol on the blockchain and used to access the services of that protocol. in addition, it is used as a cryptocurrency representing access to a product or service. in contrast to utility tokens, sbts are defined by their uniqueness and rareness. this token type provides token ownership and corresponding transfer functions. the utilization of sbt in the decentralized e-healthcare system is being suggested due to the requirement for effective management of identity and access control pertaining to e-healthcare data. to control access to their identity management, individuals need the capability to manage not only their identifiers but also the data associated with them. this approach is fundamental to self-sovereign identity, representing a shift from traditional identity management systems to a user-driven identity administration model. in such a model, enabled by blockchain technology, users have full control over their identifiers and the personal data linked to these identifiers, ensuring greater autonomy and privacy in digital interactions.44 the blockchain ecosystem supports different types of participants, such as oracles and decentralized autonomous organizations (daos). in the blockchain context, oracles are used to fetch external data that are unavailable in the blockchain. oracle is centralized and trusts the third-party external data provider, but there is a known problem with unsecured data retriever channels.40 however, there are problems with oracles in trustworthiness and reliability.45 while test oracles cannot be fully automated, this results in the agent’s intervention to ensure the correctness of the oracle’s behavior. caldarelli and ellul46 state that a dao is an autonomous organization implemented with smart contracts. the behavior and business rules of dao are predefined with smart contract logic. the derived e-healthcare system is used in an inter-organizational collaboration based on dynamic service outsourcing specified in electronic contracts.47 in healthcare, inter-organizational processes include data sharing between patients and healthcare providers or other organizations such as insurance companies. this paper considers a patient-centric, decentralized system perspective where phr data flow through different systems and are available to humanand non-human agents such as autonomous smart devices. such devices include wearables that monitor patient health with sensors, smart home components, autonomous drones, or even vehicles involved in healthcare processes. research by grefen and colleagues48 presents a conceptual framework for an intelligent e-health gateway that acquires and analyzes the collected medical information. in our investigation, we integrate the privacy-conflict resolution strategy proposed in the article by narendra et al.11 into a decentralized healthcare ecosystem, which encompasses self-governing smart devices and their collaboration, as delineated in ref. 49. in socio-technical systems, fulfilling societal functions becomes central.50 since such systems do not function autonomously but are the outcome of human action, research proposes an agent-oriented approach when modeling complex socio-technical systems.51 as simulated actors are similar to humans because of their cognitive and social binding with the knowledge of themselves and dependency on their history, an agent-oriented approach utilizes that in agents’ behavior. in the realm of blockchain and smart contracts, the oracle problem is predominantly concerned with the trustworthiness and reliability that oracles bring forth.45 as asserted by barr,52 this conundrum emerges when test oracles are unable to execute in a fully automated manner. in the event that oracles are not automated, the intervention of an agent becomes obligatory to ascertain the veracity of the observed behavior. we consider multi-agent systems (mas) and use an aom approach51 to define the requirements of https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.2766 (page number not for citation purpose) kormiltsyn et al. the privacy-oriented phrand ehr data-integration process. goal modeling is used to analyze socio-technical domains53 as goal models represent the value proposition of a system. the system’s value is represented by functional and quality goals and roles. the system requires some capacity or a position represented by a role to achieve its goals. a functional goal represents the system’s functional requirement, and a quality goal represents a system’s non-functional or quality requirement.53 quality goals are synonymously called non-functional requirements in software engineering.53 the functional-, quality-, and emotional goals are inherited by all their subgoals. as a patient-centric e-healthcare system is a social system driven more by emotional engagement than functionality, research54 proposes an emotional attachment framework that includes emotional goals in the early design stage. this framework is integrated into the t-dm framework. it extends it with emotional goals representing user feelings of negative emotions, such as distrust and lack of ownership of private and confidential data.54 research by kormiltsyn55 defines positive and negative emotions from appraising a product or a beneficial or harmful service. mendoza and colleagues56 describe how quality goals trigger different positive and negative emotions among users. examples of such goals are usefulness, adaptability, and ease of use. in this paper, we place the emotional goals between a role and a functional goal to define emotions that influence the functional goals of the system. the topic of privacyand security-conflict management increases in importance with the increasing usage of iot, social networks, etc. research by mendoza and colleagues57 defines basic concepts of secure computing, stating that privacy focuses on the governance of an individual’s data. security measures are implemented to safeguard against unauthorized access, with a primary emphasis on fortifying data against various forms of attacks and preventing data theft.58 several research publications confirm the importance of defining conflict-management techniques when sharing personal data.59,60 to design a goal model for the decentralized e-health system, we use the approach defined in the t-dm framework61 that focuses on designing decentralized applications (dapps) to support inter-organizational processes. the t-dm framework extends the aom goal diagrams53, 61 and introduces a new concept of tokenized goals representing the decentralized services that perform transactions in the blockchain and spend or gain tokens. the model-driven approach in the t-dm framework supports mapping aom goal models to the unified modeling language (uml) component architecture model. to evaluate the proposed conflict resolution technique, we designed a formal cpn62 model. cpn, a language with a graphical orientation, possesses the capability to identify potential design flaws, absent specifications, as well as security and privacy concerns within systems. it serves the purpose of designing, specifying, simulating, and verifying systems. a cpn model is a bipartite graph comprising tokens, places, arcs, and transitions. places have the ability to hold multiple tokens with color, indicating attributes with corresponding values. the transitions in cpn are triggered only when all input places have the required tokens in place. finally, transitions produce condition-adhering tokens into output places.63 our model uses the cpn ml programming language to simulate the running case described in figure 1. research63 provides more detailed information about cpn. fig. 1. design science research cycles. bpmn: business process model and notation; cpn: colored petri nets; dsr: design-science research; m2x: machine-to-everything; tdm: trusted document management. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 7 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems in a blockchain-based e-health system, trusted data sharing may be enabled by the multi-factor self-sovereign identity authentication (mfssia)64 for humans and machines managed with smart-contract blockchain technologies. figure 3 illustrates how human users (note that smart autonomous devices may also be put in place of humans) create challenges for other entities and ask them to respond. either the corresponding entity fails to do so or can complete the challenges with correct responses. the chosen challenge depends on the use case, the required security level, and the threat level of the involved entities. in the example of figure 2, the organization identity authenticates an autonomous device by providing challenges the device needs to perform to confirm its identity. the organization decides whether the response satisfies its request. both upload the request and response to the blockchain. in this case, the authentication fails if the corresponding entity fails to respond correctly. otherwise, the entity is successfully authenticated. multi-factor challenge-set self-sovereign identity authentication (mfssia) enables cross-blockchain interoperability by utilizing blockchain oracles. the oracles are digital agents that aim to fetch external world information into a blockchain. data from various sources (blood pressure monitors, phr, ehr, etc.) are then submitted to the blockchain as transactional data.64 oracles are used as data feeds for real-world information to be queried by smart contracts running on blockchains and by pushing data into data sources from the blockchain itself.65 the challenge sets in mfssia are stored in a decentralized knowledge graph (dkg1). in dkg, information 1. https://docs.origintrail.io/general/dkgintro is stored as a graph of entities and relationships relevant to a specific domain or organization. dkg provides immutable, queryable, and searchable graphs that are used across different applications. results the present section furnishes the outcomes that constitute the responses to the research inquiries delineated in this scholarly document. to specify the requirements for individual-centric phr collection and processing (sub-question 1), the “requirements for the patient-centric phr collection and -processing” section provides the goal model for the decentralized person-centric e-health inter-organizational process for preventive healthcare. this goal model lays the groundwork for the system design by capturing key functional and quality requirements. to identify where conflicts arise (discussed later), the “integrated phrand ehr-processing privacy conflicts between healthcare providers and individual patients” section (sub-question 2) defines conflicts in the decentralized e-health process and describes the mapping of conflicts to specific functional goals and business processes. finally, to present the conflict resolution techniques, the “the conflict-resolution techniques when mapping the bpmn-designed e-healthcare process to a blockchain system” section (sub-question 3) proposes techniques in the blockchain system to resolve the identified data and claimer definition automatically conflicts transparently and decentralized. requirements for the patient-centric phr collection and processing as posited by norta et al.,53 a goal model has the potential to serve as an analytical tool for scrutinizing the issues that arise within a socio-technical domain. the goal models act as an interface for exchanging information between stakeholders possessing technical and non-technical fig. 2. conflicts while processing the (ehrs) electronic health records and (phrs) personal health records. https://doi.org/10.30953/bhty.v6.276 https://docs.origintrail.io/general/dkgintro citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.2768 (page number not for citation purpose) kormiltsyn et al. backgrounds with the purpose of generating comprehensible knowledge of the e-health domain. due to the complexity of the goal model, we split it into two parts, where figure 4 defines goals related to the patient and healthcare professional, and figure 5 includes insurance provider and general practitioner goals. figures 4 and 5 depict a goal model, which delineates functional, quality, positive, and negative emotional goals. it is important to note that each functional goal is further subdivided into subgoals in a hierarchical manner, with the highest level being positioned at the top and the lowest level at the bottom. in our previous research,66 we use goal modeling in requirement engineering. we use the notation described in ref. 57, where several symbols correspond to different goal types. thus, the heart shape represents the positive emotional goal, the cloud shape defines quality goals, and a parallelogram represents the functional goals. in this research, we extend the goal model notation with the tokenized functional goals that represent the functional goals that communicate with a blockchain. in our designed system, we consider the m2x context, where agents can be both human and non-human. we put forward the suggestion of employing a utility token that has been incorporated into a pre-existing protocol on the blockchain and is utilized to gain access to the various services offered by said protocol. these tokens serve as means of payment for the services provided within their respective ecosystems in the proposed system. our suggestion is the introduction of a token named “personal health token (pht)” as a utility token for the decentralized person-centric e-health system. in addition to the utility token, we propose the usage of sbt tokens that are created by medical data providers such as smart devices, phr-, and ehr systems and include the medical data that is owned by the patient. for example, if a patient decides that some of his health data are useful for medical research, he proves his ownership with sbt to the research company. the primary objective of the value proposition is to prevent disease in connection with an individual who possesses self-motivated incentives and anticipates being informed and empowered throughout the preventive course of action. the principal value proposition of the system revolves around the prevention of diseases in individuals. the subgoal of providing home care involves a patient who collects his medical data in a trustworthy manner. the subgoal of providing ambulatory care is executed by the general practitioner, whereas the subgoal of providing insurance is carried out by an insurance provider. additionally, the subgoal of onboarding stakeholders is performed by an acceptor agent. the stakeholder’s objective, which is found within the system, is crucial for the stakeholders to engage in the inter-organizational procedure, while they undertake verification using a protocol known as mfssia, which is based on blockchain technology.64 onboarding includes the usage of pht tokens for accessing authentication services. both a health professional and a general practitioner submit medical cases to the insurance provider for requesting claims. the initial objective encompasses two additional sub-objectives: to monitor health status executed by the smarthub agent and to keep a healthy lifestyle conducted by a healthcare specialist. the latter employs the system, provided that he possesses self-assurance, possesses the capability to render expert judgments, and is not overwhelmed by the intricacies of the system. the goal of monitoring health status encompasses three sub-objectives: the generation of a phr from the data gathered by two entities, namely, a smartwatch and an air quality home sensor; the semi-automated analysis of said phr; and the secure sharing of the phr, ensuring the processing of interoperable information. the security is provided by validating sbt to ensure the ownership of shared data. the produced phr possesses the capability to be seamlessly integrated, enabling its dissemination among various involved parties. following its creation, the phr is subsequently inserted into the blockchain-distributed ledger, ensuring that it can be shared fig. 3. conceptual depiction of the (mfssia) multi-factor challenge-set self-sovereign identity authentication lifecycle for challenge-response management. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 9 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems fig. 4. the goal model for a decentralized individual-centric system. patient and healthcare professional goals. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.27610 (page number not for citation purpose) kormiltsyn et al. with other stakeholders while simultaneously guaranteeing its immutability. the objective of achieving a healthy lifestyle encompasses two specific subgoals: evaluating one’s present lifestyle and offering health recommendations. guidelines are shared with a patient via blockchain and should be usable. the goal of providing ambulatory care encompasses the involvement of a primary care physician who harbors apprehensions regarding being substituted by technology and necessitates the need to maintain a sense of professionalism without experiencing excessive workload. this objective is further divided into three sub-objectives: establishing an interoperable ehr system, embracing shared phr, and administering medical diagnosis. the created ehr is stored on a blockchain to be available for the patient in a secure and immutable way. adding data fig. 5. the goal model for a decentralized individual-centric system: insurance provider and general practitioner goals. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 11 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems to the blockchain requires pht. when a general practitioner agrees to accept phr, they have reservations about the accuracy of shared data. this phenomenon can be attributed to the potential for erroneous data generation or the likelihood of an alternative individual asserting ownership over the furnished data. the mfssia protocol supports the authenticated and secure way to produce medical data and eliminates distrust. medical data are frequently stored in various standards and contexts, resulting in semantic heterogeneity. the process of standardizing phr and ehr data aids in the prevention of such heterogeneity. moreover, this standardization facilitates the simplification of phr and ehr processing. the secure, integrable, and usable acceptance of phr is imperative. the provided insurance goal encompasses a total of nine subgoals, namely, data collection, verification, data consolidation, claim preparation, resolution of conflicts between claimants, definition of the insurance provider’s claim, definition of the claimant’s claim, definition of the healthcare provider’s claim, and definition of the general practitioner’s claim. the data that have been collected are utilized during the process of claim preparation, and they necessitate the process of authentication for the various sources of data within the interorganizational framework, which is based on the decentralized protocol known as mfssia. authentication is executed through the utilization of the challenge set marketplace, wherein the blockchain oracle facilitates the provision of secure challenge sets for the purpose of user authentication. in order to assemble a claim, it is imperative for the insurance provider to meticulously integrate data in a transparent manner, resolving any potential conflicts that may arise. both actions are performed with smart contracts. as each and every stakeholder incorporates business regulations into their respective functional goals (namely, delineating the claimant for insurance providers, delineating the claimant for patients, delineating the claimant for healthcare providers, and delineating the claimant for general practitioners), it becomes imperative for the insurance provider to address any conflicts that may arise among the claimants. we propose to use smart contracts to keep conflict resolution transparent and, thus, trustable to the stakeholders involved in the interorganizational processes. the smart contracts are accessed by the conflict negotiator, dao, implementing the complex logic of conflict resolution algorithms. integrated phrand ehr-processing privacy conflicts between healthcare providers and individual patients in the present case, it is posited that the insurance provider is composed of three partners: a patient, a general practitioner affiliated with a hospital, and a healthcare professional, as depicted in figure 1. distinct business rules are taken into consideration for each stakeholder, representing the identity of the claimant and the recipient of payment from the insurance provider under specific circumstances. the claimant in our ongoing scenario is postulated to be determined by the measurement of systolic blood pressure. according to a business rule, when a patient’s systolic blood pressure drops below 160 mmhg, the patient is designated as the claimant; otherwise, the claimant is a general practitioner. in common practice, a systolic blood pressure reading of 120 mmhg is deemed as a normal value. hence, the patient experiences no issues with regard to blood pressure. the decision is founded upon the supposition that in the event that the patient does not experience any complications, their way of life is praiseworthy, and they meet the criteria to be considered as a beneficiary for the insurance provider. a systolic blood pressure ranging from 120 to 160 mmhg presents problems and necessitates the patient’s diligent attention and engagement to restore it to a normal range. consequently, the patient also perceives themselves as a claimant within this specific data slot. systolic blood pressure surpassing 160 mmhg poses a dangerous situation and warrants the attention of a medical practitioner. consequently, the patient views the general practitioner as a claimant. according to a regulation governing the practices of a primary care physician, it is stipulated that if a patient’s systolic blood pressure falls below 120 mmhg, the patient shall be classified as a claimant; conversely, when the systolic blood pressure surpasses the norm of 120 mmhg, the claimer assumes the role of a general practitioner. in instances where the systolic blood pressure of the patient diverges from the anticipated norm, the primary care physician conscientiously monitors the patient’s state and subsequently administers the requisite medications and interventions in accordance with the preliminary assessments. consequently, the general practitioner perceives himself as a claimer. to conclude, the healthcare professional adheres to a set of business rules that assert the following: if the patient’s systolic blood pressure is below 120 mmhg, then the claimer is classified as a patient; in contrast, if the systolic blood pressure exceeds 160 mmhg, the healthcare professional assumes the role of a claimer. in situations where the systolic blood pressure registers between 120 and 160 mmhg, healthcare practitioners exercise discretion in explicitly identifying the claimant. the emergence of conflicts can be attributed to the internal regulations of all three entities involved, as illustrated in figure 6. these conflicts become apparent when a patient’s systolic blood pressure exceeds the predetermined threshold of 120 mmhg. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.27612 (page number not for citation purpose) kormiltsyn et al. functions where conflicts occur in this section, we define functional goals presented in the goal model in figure 4 and figure 5 where conflicts occur. in order to simplify the research, we have chosen to exclude any conflicts that may arise in relation to quality and emotional objectives. the correlation between functional objectives and potential conflicts has been presented in table 1. in our case, two possible conflicts are considered during the interorganizational insurance claim process. initially, a data conflict may arise when an insurance provider gathers and consolidates data from various sources, including the patient’s phr system, the healthcare provider’s ehr system, and the healthcare professional’s records. given that each stakeholder may maintain data in distinct formats and standards, there exists a possibility that the amalgamated data may not correspond or synchronize appropriately when integrated by the insurance provider, leading to incongruous or contradictory data. second, a claimer definition conflict can arise when each stakeholder defines the insurance claimer based on their internal business rules and the data value, such as the patient’s blood pressure reading. as illustrated in figure 6, the rules for proposing a claim from the patient, healthcare provider, and healthcare professional may differ, depending on the data circumstances. for example, if the patient’s blood pressure is between 120 and 160 mmhg, the patient and healthcare provider will propose different claimers based on their distinct rules. the incongruity between the definition of the claimant gives rise to a conflict that necessitates resolution. processes where conflicts occur the definition of an insurance claimer entails the retrieval of data from phr and ehr data sources, followed by their integration and the elimination of irrelevant data. the insurance provider claimer definition process is defined in figure 7. to facilitate the interorganizational claimer definition process, we have subdivided it into fig. 6. the implementation of business rules has been found to result in conflicts in behavior. fig. 7. claimer-definition process for the insurance provider. table 1. functional goals where conflicts occur. functional goal actor conflict collect data insurance provider data can be different merge data insurance provider data can be different define insurance provider claimer insurance provider claimer can be different define patient claimer patient claimer can be different define healthcare professional claimer healthcare professional claimer can be different define general practitioner claimer general practitioner claimer can be different https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 13 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems three subprocesses, namely, the decision-making process for healthcare professionals, healthcare providers, and patients. additionally, we shall expound on these three internal processes utilizing the bpmn notation. figure 8 illustrates the internal decision-making process undertaken by the patient, which is directed by the business rules described earlier. at the outset, the patient ascertains the presence of the requested data from the insurance company within their phr repository. in the event that the requested data are not present, the patient proceeds to record the blood pressure measurements and subsequently stores these new data within the phr repository. subsequently, the patient retrieves the aforementioned data from the phr repository and shares it with other relevant stakeholders involved in the interorganizational process. finally, to propose the claimer, the blood pressure value is assessed. if the blood pressure value is equal to or less than 160 mmhg, the patient asserts themselves as the claimer. conversely, if the blood pressure value exceeds 160 mmhg, the healthcare provider is proposed as the claimer. figure 9 illustrates the internal decision-making procedure for the healthcare provider, such as a hospital. initially, the healthcare provider acquires the illness (ehr) data from external ehr systems, which may be affiliated with a hospital. once the external ehr data are obtained, it is transformed into the healthcare provider’s health data standard and stored within its own system. subsequently, the imported external data are disseminated among the other participants involved in the interorganizational process. lastly, the healthcare provider’s claimer proposition is formulated based on the business rules delineated in the “integrated phrand ehr-processing privacy conflicts between healthcare providers and individual patients” section. this proposition encompasses three potential claimants: the patient, the healthcare provider, and an undefined claimer. specifically, if the blood pressure value is fig. 8. patient-claimer internal decision process. bp: blood pressure; dao: decentralized autonomous organizations; phr: personal health record. fig. 9. the internal decision-making process of healthcare providers with regard to claimants. bp: blood pressure (systolic blood pressure in this case). https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.27614 (page number not for citation purpose) kormiltsyn et al. equal to or less than 120 mm hg, the patient is suggested as the claimer. the healthcare provider is ultimately suggested as an asserter in the event that the blood pressure falls within the range of 120 and 160 mm hg. figure 10 delineates the internal process of decision-making within the realm of healthcare professionals. notably, the disparity between the decision-making processes of patients and healthcare providers lies in the healthcare professionals’ ability to access both ehr and phr data. initially, blood pressure measurements are obtained from both phr and ehr databases simultaneously. subsequently, after eliminating extraneous data, the healthcare professional proposes a claimant. in the event that the blood pressure is equal to or below 120 mmhg, the patient is presented with a claim. however, if the blood pressure falls between 120 and 160 mmhg, the claimant remains undefined. lastly, if the blood pressure surpasses or equals 160 mmhg, the healthcare professional presents himself as a claimant. the incorporation of ehr and phr integration is founded on our prior research,66 while the regulations for the claimant proposal are expounded upon in figure 6. the conflict-resolution techniques when mapping the bpmndesigned e-healthcare process to a blockchain system in this section, we provide the conflict-resolution techniques that support automatic resolution of conflicts occurring in the e-health interorganizational processes. in our running case, two possible conflicts result from internal business rulesor collected medical data differences. this study posits the utilization of a dao as a conflict resolution mechanism in decentralized e-health systems. the conflict resolution process for medical data consistency in the insurance provider process is depicted in figure 11. initially, the insurance provider gathers medical data from three sources, namely, patients, healthcare providers, and general practitioners, to prepare a claim. subsequently, the collected data undergo validation to ascertain its integrity. following the validation process, the dao either approves or disapproves the data’s validity. based on the final validation outcome, a claim can be generated. figure 12 explains how data validation from a single data source is performed in more detail. the exact process is performed for all three medical data owners: patient, healthcare provider, and general practitioner. the dao employs a consensus algorithm that necessitates all nodes to reevaluate the incoming data. the data are deemed valid only if a majority of nodes, exceeding 50%, concur that it is accurate. conversely, if the data fail to garner sufficient agreement, it is regarded as tampered with and consequently unsuitable for utilization in claim preparation. finally, the single-node medical data validation process is delineated by figure 13 within the context of the comprehensive data validation managed by the dao. within this process, every blockchain node affiliated with the dao undertakes an examination of the medical data under scrutiny. the node requests specific medical data, such as a blood pressure reading, from the original data source, such as the patient’s phr system. the node then compares the data from the source to the data being validated on the blockchain. if the data match exactly, the node considers it valid and confirms this through its vote in the consensus algorithm. in the event that the data fail to correspond, a disparity arises between the initial data source and the data stored on the blockchain. under such fig. 10. healthcare professionals assert that the internal decision-making process plays a crucial role in their practice. bp: blood pressure (systolic blood pressure in this case); ehr: electronic health record; phr: personal health record. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 15 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems circumstances, the node deems the data as either invalid or tampered with, consequently dismissing it through a voting process that opposes its validity within the consensus algorithm. having each node directly check the data from the source can identify issues with data tampering, even if a subset of nodes are malicious. the consensus algorithm verifies valid data if most nodes’ validation checks succeed. this redundant validation by each node provides greater security and accuracy in identifying data tampering than relying on a centralized validator. in the decentralized architecture we have proposed, individual units known as ‘nodes’ participate in a decision-making process to validate the accuracy and integrity of data. such a process is governed by a dao, which serves as a conflict resolver. each node casts a vote to either confirm or reject the validity of the data in question. once all votes are collected, a final decision is made based on the majority consensus among the nodes. in essence, if a majority of nodes reach a consensus regarding the validity of the data, it is deemed acceptable; otherwise, it is deemed unacceptable and subsequently rejected. this democratic approach ensures a more robust and transparent validation process. evaluation and discussion the section provides the evaluation of this work using the multi-method evaluation approach that dsr infers. first, we perform a formal evaluation with cpn and further fig. 11. insurance provider data conflict resolution process. dao: decentralized autonomous organizations; pt: patient; gp: general practitioner; hc: healthcare. fig. 12. dao data validation process. dao: decentralized autonomous organizations. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.27616 (page number not for citation purpose) kormiltsyn et al. provide the discussion of cpn evaluation results and implications of the main results of this work with other related literature. then, we present a poc prototype implementing the workflow evaluated by the cpn. first, we evaluate the conflict-resolution process with cpn modeling. the conflict-resolution process in the cpn model has multiple layers. the top layer is the internal processes of stakeholders. then, the assessment of the given cpn is presented, followed by the poc prototype implementation. finally, the current results compared with similar research discussed. cpn formal evaluation of the claimer definition conflict resolution process the classical petri net is a directed bipartite graph with two node types called places and transitions. the nodes are connected via directed arcs. connections between two nodes of the same type are not allowed. places are represented by circles and transitions by rectangles.67 to assess the claimant’s characterization of the conflict resolution process, we propose a structured cpn model68 for the identification and rectification of potential design deficiencies, absence of specifications, as well as security and privacy concerns. the full cpn model description can be found in the technical report.68 our evaluation model focuses on a decentralized data-sharing process and omits all functional goals defined in figure 4 and figure 5. these goals are related to conflict occurrence and resolution. the goals covered by the cpn model are: • propose insurance claimer • collect data • share phr • resolve data conflict • resolve claimer conflict. we use the formalization of the esourcing framework,69 where workflow nets (wf-nets) are contained. thus, the cpn models for stakeholders’ internal processes are arranged, so the control flow resembles the esourcing formalization. wf-net defines the dynamic behavior of a single case in isolation. wf-nets are a formalization for describing process models in parallel and distributed systems.70 research71 describes a wf-net as a petri net that has a 3-tuple n = (p,t,f), where p and t are two disjoint and finite sets that are, respectively, called places (circles visualize them) and transitions (rectangles represent them), and f ⊆ (p × t) ∪ (t × p) is a set of flow relations in n. the set f is a subset of the union  of the cartesian product of p and t with the cartesian product of t and p. p × t represents the cartesian product of sets p and t. the cartesian product consists of all possible ordered pairs where the first element is from set p, and the second element is from set t. figure 14 depicts the wf-net that has a unique start place and a unique end place with one token in the start place (all other places are empty). all nodes lead from the start to the end place such that when the enactment is complete, only one token is in the unique end place, and all other places are empty.11 it should be noted that a wf-net specifies the dynamic behavior of a single case fig. 13. node data validation process. dao: decentralized autonomous organizations. fig. 14. workflow net example.72 https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 17 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems in isolation. this means that every piece of work is executed for a specific case, which is also called a workflow instance.67 formalized setup top-level figure 15 illustrates the procedural aspects involved in the formulation of a claim that encompasses multiple organizational entities. to assemble a medical claim, the  insurance provider procures healthcare data from diverse origins, such as patient records, healthcare provider systems, and systems utilized by healthcare specialists. data collectionand claimer definition internal processes involve interaction with stakeholders’ decentralized systems. we use different colors for internal processes to better visualize an interorganizational process. the patient’s internal process is shown in red, the healthcare provider in brown, and the healthcare professional in green. our cpn model is based on the bpmn processes as defined earlier in this article. the whole insurance provider claimer definition process is derived from figure 10. the cpn layers that define processes encapsulating the internal claimer definition of business logic are based on the bpmn processes. modeling internal claimer definition processes enables conflict occurrence and simulation when evaluating the cpn model. the mapping between bpmn diagrams and cpn model layers of patient-, fig. 15. the cpn model’s external layer defines the interorganizational process. cpn: colored petri nets. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.27618 (page number not for citation purpose) kormiltsyn et al. healthcare professional-, and healthcare provider claimer definition processes is shown in table 2. the claim preparation process starts from a unique start place with two tokens describing independent process identifiers. the model design supports several parallel process executions when providing process identifiers for each place. all transitions performed by insurance provider are marked with blue color and start with prefix ip_. subsequently, the initiation of the ip_assign patient data specification transition is instigated, thereby commencing the internal process of the patient. the patient’s internal process has two outputs—blood pressure measurement and claimer proposal. the same workflow exists for both healthcare providers and healthcare professionals. the claimer definition is based on the internal rules described in figure 6. when all three internal processes are executed, the transition collect claimers is triggered with three inputs defining the claimer proposed by each stakeholder. the find claimer consensus transition processes all three proposed claimers and selects one based on the consensus algorithm. before a claim can be prepared, another process runs in parallel with the claimer definition—collected blood pressure measurement validation. this process verifies the potential compromise of data and subsequently resolves any discrepancies that may arise in the event of data divergence. after consensus algorithms agree with the claimer and blood pressure measurements, the prepare claim transition takes place. interorganizational stakeholders’ internal workflows processing integrated ehrs and phrs to depict the workflows of different stakeholders, we employ substitution transitions that entail subnets that elaborate on the activities linked to a transition. the subnet that is linked with a transition is commonly denoted as a subpage within academic discourse. the utilization of the cpn formalism enables the hierarchical arrangement of subpages to an indefinite extent, thereby facilitating the representation of system descriptions at diverse levels of intricacy.73 in order to enhance the collaborative scenario we further augment it by incorporating a simulation utilizing cpn. additionally, we incorporate the conflict scenario into the quantitatively simulatable model. to organize the overall cpn model,2 we divide it into multiple subpages, as shown in table 3. figure 16 depicts a screenshot derived from cpn tools, which presents the hierarchical arrangement of subpages within the design model. these subpages, serving 2. https://goo.by/joqj8 as reusable components, contribute to enhancing the comprehensibility of the intricate model. within this framework, the principal page, referred to as “external,” assumes the role of delineating the interorganizational protocol for the preparation of an insurance claim. the internal procedures of different stakeholders, namely, the patient internal process, hospital internal process, and healthcare professional internal process, are encompassed within separate subpages. conflict resolution is carried out at a higher level of the process, specifically within the subpages dedicated to insurance claim data validation and the attainment of consensus among claimants. cpn model evaluation we assess our model using two different approaches. first, we evaluate the original model through simulation in cpn tools, ensuring that all initial tokens lead to the unique final state of the model. given the complexity of the provided cpn model, we conduct a state-space analysis for each subpage individually. if the page incorporates any of the subpages, we imitate its output. this imitation involves substituting the actual execution of the subpage with a single element that generates constant data. by doing so, we maintain the integrity of the main page flow while reducing the complexity of the state-space analysis. the predetermined values are established based on the potential outcomes of the subpage. throughout the state-space analysis, we compute and present all reachable states and state changes of the cpn model as a directed graph. the graph presents states as nodes and occurring events as arcs. the main goal of the state-space analysis is to describe the system’s behavior and check that there are no deadlocks, a given state is always reachable, and the given service is always delivered.71 the report on state-space provides an account of both home and liveness properties. the first ones pertain to a specific home marking that is accessible from any reachable marking. in our scenario, each process associated with a subpage will ultimately reach its terminal state. on the other hand, the liveness properties delineate markings without active binding elements. a marking without activity can be both a dead marking table 2. internal claimer definition process mapping from bpmn diagrams to cpn model layers. bpmn process cpn layer patient-claimer internal decision process (figure 8) patient internal process healthcare-provider claimer internal decision process (figure 9) healthcare provider internal process healthcare professional claimer internal decision process (figure 10) healthcare professional internal process bpmn: business process model and notation; cpn: colored petri nets. https://doi.org/10.30953/bhty.v6.276 https://goo.by/joqj8 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 19 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems and a home marking simultaneously, as any marking can be accessed from itself via a trivial occurrence sequence of zero length. following this, the state space report delineates live transitions. in an academic context, a transition is deemed live when it is perpetually feasible to identify a sequence of occurrences that include the transition from any attainable marking. the state-space report provides an account of inactive transitions. a transition is classified as inactive if it is either enabled or unattainable. these transitions delineate the functionality of a model that can never be executed.74 all reports analyzing the state space are based on each subpage found within the presented cpn model, as indicated by the corresponding tables provided later. the initial files of the state-space analysis report can be accessed online.3 according to the findings presented in table 4, it is evident that loops are inherent in the process of data collection. the data repository contains information about various processes, and the data collection process continues to retrieve data until it locates information associated with the ongoing process. all subpages in our process do not contain any dead and live transitions, indicating the absence of unused components. notably, the state of all subpages aligns with that of home and dead markings. proof-of-concept prototype implementation for the running case this study introduces the implementation of a prototype for the e-health data-sharing process75 developed in scope of ref. 76. in our specific context, we propose the utilization of polygon77 smart contracts (scs) for the insurance provider system. at the same time, ethereum scs are recommended for the patient, hospital, and healthcare professional systems. the polygon network is built on a high-throughput blockchain architecture, where each checkpoint selects a group of block producers to achieve consensus. the validation of blocks is conducted through a pos layer, which also periodically updates the ethereum mainnet with the proofs provided by the block producers. to enhance scalability and enable interoperability between different blockchain-based systems, we employ polkadot,78 which facilitates secure and trust-free communication among specialized blockchains. 3. https://goo.by/qaisc table 3. subpages in the cpn model’s hierarchy. subpage meaning patient internal process patient data collectionand claimer definition processes hospital internal process general practitioner data collectionand claimer definition processes healthcare professional internal process healthcare professional data collectionand claimer definition processes data tampering data tampering process that takes place during the data collection collect data data collection process define patient claimer patient claimer definition process define healthcare professional claimer healthcare professional claimer definition process define hospital claimer hospital claimer definition process insurance claims data validation process of validating all collected data from different stakeholders data validation process of validating blood pressure measurement by several nodes node validation blood pressure measurement validation process performed by a single node find claimer consensus final claimer consensus process cpn: colored petri nets. fig. 16. cpn model page hierarchy. cpn: colored petri nets. https://doi.org/10.30953/bhty.v6.276 https://goo.by/qaisc citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.27620 (page number not for citation purpose) kormiltsyn et al. in the decentralized web environment facilitated by the polkadot foundational layer, users exercise authority over their data. this prototype comprises three primary blockchain-based elements. specifically, it encompasses two distinct applications for inputting medical records, namely, those of the patient and the doctor. additionally, it incorporates an application that executes the interorganizational procedure involving the insurance provider alongside a dao smart contract that undertakes data comparison in the event of conflicts and provides reliable data. the poc prototype under consideration focuses on the scenario where the patient and doctor input blood pressure measurements. figure 17 presents a screenshot of the patient’s application interface, explicitly showcasing the input of blood pressure measurements. this application is integrated with the metamask wallet, enabling the sharing of entered data through a smart contract. notably, the application incorporates deploying a smart contract on the ethereum blockchain. figure 18 visually represents a conflict that arises while collecting data. the depicted scenario exemplifies a discrepancy between the blood pressure measurements recorded by the patient and those documented by the doctor. in such instances, the data are transmitted to a dao, which assumes the responsibility of validating the data and resolving any potential conflicts. the interorganizational process is implemented with the polkadot parachain that enables cross-blockchain communication. we implement an application-specific blockchain-based module with the substrate framework.79 the insurance provider’s application runs as a substrate backend local node. discussions of research implications on similar works this research proposes that blockchain enables autonomous conflict resolution transparently without a single point of trust. also, blockchain and smart contract technologies support personalized e-health services that include several stakeholders while ensuring the individual’s ownership of healthcare data. the rise of the m2x economy and non-human agents in the interorganizational processes require new authentication methods based on the multifactor challenge set mechanism. such an approach enables new interorganizational processes in situations with a lack of trust between stakeholders. evaluation with cpn shows that such a process can be feasible with the example of decentralized e-health insurance. still, we do not have empirical in vivo proof that this is table 4. state-space analysis results for cpn model subpages. subpage loops home marking dead marking dead transitions live transitions patient internal process no yes yes no no healthcare professional internal process no yes yes no no hospital internal process no yes yes no no define patient claimer no yes yes no no define healthcare professional claimer no yes yes no no define hospital claimer no yes yes no no find claimer consensus no yes yes no no collect data yes yes yes no no data tampering no no yes no no data validation no yes yes no no node validation no yes yes no no external layer no yes yes no no prepare claim no yes yes no no cpn: colored petri nets. fig. 17. screenshot of a patient’s application. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 21 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems possible; instead, this paper yields the in vitro feasibility proof. the possibility of autonomous conflict resolution in decentralized e-health enables the valuable usage of the person’s health data across different industries in a trustable and transparent way. finally, the implementation of the poc prototype shows that the running case can be implemented with the current state-of-the-art decentralized technologies. our research is based on work by narendra and colleagues,11 where the authors propose conflict resolution with negotiation depending on the conflict type. this study provides the framework for autonomous participants united in virtual enterprises (ve) that proposes the layered structure presenting different business logic contexts. research shows that conflicts occur on the interorganizational, external layer. our paper adapts the approach defined in ref. 11 to the e-health domain. the healthcare use case confirms that conflicts occur in the interorganizational collaboration layer because different stakeholders can have e-health data that differ from each other. also, the business decisions of each stakeholder can differ from others as all participants have their internal processes. stahnke and colleagues80 state that blockchain technology enables the enforcement of interorganizational workflows. to establish reliable workflows acceptable to all stakeholders involved in interorganizational processes, it is necessary to design and validate these workflows using cpn before converting them into smart contracts. while cpn is employed to validate interorganizational processes, it is important to acknowledge the requirement for legally relevant smart contracts and the necessary support. a study by park and colleagues81 suggests incorporating enterprise resource planning (erp) systems into the business process simulation model to utilize real-life data in the cpn-designed simulation process. park and van der aalst81 aim to overcome the complexity of erp systems by implementing a framework that allows to integrate them into the simulation processes. however, we differ from this approach as we refrain from introducing real-life components into the simulation due to the sensitive nature of e-health data. additionally, we assume an unlimited number of stakeholders and their internal systems participating in interorganizational e-health processes. consequently, the integration of individual systems does not yield any additional value. in their work, jadav and colleagues82 focused on using ai to discover wearable attacks and share healthcare data with a public blockchain. the authors propose the usage of blockchain technology for data immutability. in our research, we also state that blockchain technology enables e-health data immutability, but we do not focus on ai usage to discover data tampering. still, the interorganizational process design proposed in this paper allows for integrating non-human actors such as ai agents. the deepblockshield framework proposed by kim and kim83 aims to solve medical data leakage issues with blockchain technology. the corresponding solution proposes to store data on a blockchain while providing access to special agents. in our research, we assume that medical data can be stored not only on a blockchain and propose the mfssia framework to establish safe collaboration between different stakeholders when sharing the e-health data. a recent study by abbas and colleagues84 proposed a framework for secure sharing and accessing data from wearable devices, utilizing blockchain technology to ensure data transmission security and management between interconnected nodes. the authors have assessed the effectiveness of their research outcomes in terms of accuracy, precision ratio, average trust value, and response time. in our research, we employed the formal cpn tools to evaluate the design process and ensure no design issues. furthermore, our designed process emphasizes resolving data processing conflicts in addition to addressing security concerns. this article primarily focuses on the technical aspects of blockchain implementation in healthcare data fig. 18. insurance provider’s data collection screenshot. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.27622 (page number not for citation purpose) kormiltsyn et al. management, specifically in integrating personal and ehrs. given this technical orientation, this study does not directly involve human subjects or collected data where race or ethnicity would be relevant factors. in cases like these, where the research is centered on technology development rather than on human subjects, collecting race or ethnicity data is not applicable. our study is more concerned with the systemic and technological challenges and solutions in healthcare data integration rather than with end-users’ demographic characteristics. however, in the broader deployment context, such factors influence the implementation of healthcare technology and its societal impacts. conclusions in this paper, we research automatic conflict resolution in decentralized e-healthcare systems with blockchain technology. the latter enables autonomous and transparent interorganizational processes and a trustful conflict-resolution mechanism without involving a central authority. our proposed approach is based on several scientific methods, such as dsr, cpn modeling, and frameworks, such as t-dm, esourcing, and mfssia. we use t-dm for a blockchain-based system-requirement definition to lay the foundation for the system’s architecture design, token economy defining on-chain transaction sets, and dynamic protocol development. also, we map t-dm-defined functional goals where conflicts occur to the bpmn process notations. finally, we evaluate our research results with cpn as it validates conflict-resolution concepts defined with t-dm in the running process. our evaluation includes a poc prototype implementation of the running case. with both the cpn’s and prototype’s poc evaluation, we ensure that research can be used in real-time processes. we propose to use a dao as an automatic conflict-resolver when processing and mapping personal e-health data into interorganizational processes. the requirements for automatic conflict resolution are the creation of both phr and ehr data by several stakeholders in the decentralized environment. such stakeholders shall be onboarded and authenticated with mffsia to agree on the e-health data sharing and usage conflict-resolution techniques used by a dao. the e-health data from different sources shall be merged before its usage. after defining the requirement to the e-health data collection and processing, we propose two types of conflicts—internal business rule and data difference conflicts. finally, we propose that in case of a data-difference conflict, the decentralized system rechecks the data by several nodes and then decides which data are correct. there are several limitations inherent in our research. first, we need to comprehensively evaluate the integrated mfssia in the context of interorganizational data-sharing processes. additionally, this study has not thoroughly defined the specific challenges faced in implementing e-health systems. furthermore, the concept of a token economy, which involves the sharing of community income between content producers and service users who contribute value, is beyond the scope of this paper. consequently, the aspects of the token economy and transaction costs are not addressed in our research. preserving user data privacy is of utmost importance, as failure to do so can have legal implications. however, this study does not explore the legal aspects related to privacy protection in user data. therefore, the acceptance and implementation of the proposed techniques are contingent upon the legal jurisdiction of the country and the hospital’s compliance with relevant laws and regulations. we work on the e-health-specific challenge-set rules for mfssia. after implementing a poc prototype, we plan to collaborate with healthcare providers to test our research results with real use cases. also, future work is related to solving the challenges associated with the heterogeneity of the socioadministrative environment. in the proposed design, we define agreements between stakeholders with immutable smart contracts. the future work is related to overcoming this challenge with e-health smart-contract lifecycle development that enables the adoption of the changes in real-life agreements to the ones defined by smart contracts. finally, interoperability of e-healthcare data is one of the biggest challenges in e-healthcare. using common standards, such as snomed ct, hl7, loinc, etc., aims to solve this issue. at the same time, as we consider the interorganizational process to be flexible and to support an unlimited number of stakeholders, there are challenges related to data privacy and interoperability. we assume that both humanand non-human participants in the m2x context must authenticate with the mfssia framework to access such processes. as mfssia uses challenge sets and responses-based identity authentication, the supported e-health data standards can be a part of challenge sets that will be developed in the future. future work also includes adopting ai agents that can be utilized in different interorganizational process phases, such as mfssia authentication, e-health data collection, and conflict resolution, with more to come. funding statement no funding. financial and non-financial relationship and activities dr. norta is a bhty editorial board member—no other disclosures to report. https://doi.org/10.30953/bhty.v6.276 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 23 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems contributors all authors contributed to this paper. aleklsandr kormiltsyn wrote the 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https://doi.org/10.1109/tcss.2021.3099475 http://mlwiki.org http://mlwiki.org/index.php/workflow_nets https://doi.org/10.1007/s10009-007-0038-x https://doi.org/10.1007/s10009-007-0038-x https://github.com/sanamnisarmalik/hprivacyconflictresolutionbyblockchain https://github.com/sanamnisarmalik/hprivacyconflictresolutionbyblockchain https://polygon.technology https://www.polkadot.network/ http://substrate.io https://substrate.io/vision/substrate-and-polkadot/ https://substrate.io/vision/substrate-and-polkadot/ https://doi.org/10.3390/math11030637 https://doi.org/10.3390/math9091069 https://doi.org/10.3390/math9091069 http://creativecommons.org/ licenses/by-nc/4.0 http://creativecommons.org/ licenses/by-nc/4.0 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.27626 (page number not for citation purpose) kormiltsyn et al. appendix 3-tuple n: antuple is a finite sequence or ordered list of numbers or, more generally, mathematical objects, which are called the elements of the tuple. a 3-tuple is called a triple (or triplet). the number n can be any non-negative integer. agent-oriented modeling (aom): used in organization and information system modeling for providing intentional descriptions of processes as a network of relationships among actors. as such, they capture and represent goals, dependencies, intentions, beliefs, alternatives, etc. algorithmic decision systems (ads): the delegation of decision-making and implementation to machines. bipartite graph: a graph where the vertices can be divided into two disjoint sets such that all edges connect a vertex in one set to a vertex in another set. business process model and notation (bpmn): a graphical representation for specifying business processes in a business process model. colored petri net (cpn)69 model: backward-compatible extension of the mathematical concept of petri nets. colored petri nets (cpns): extend the vocabulary of ordinary petri nets and add features that make them suitable for modeling large systems. copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, and enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0. decentralized autonomous organizations (daos): an entity in which all members participate in decision-making because there is no central authority. article 7 in the charter of fundamental rights of the european union: as defined in the charter of fundamental rights of the european union,13 article 7 is the right of any individual to respect their private and family life, home, and correspondence. deepblockshield: a model that implements secure sharing of clinical data. it adopts a two-way user verification and asynchronous information provision methodology to enhance the security of clinical data. design science research cycles: the process that includes six steps: problem identification and motivation, objectives for a solution, design and development, evaluation, and communication. design-science research (dsr): research that invents a new purposeful artifact to address a generalized type of problem and evaluates its utility for solving problems of that type. electronic health (ehr): a patient’s data created by healthcare professionals and stored digitally. enterprise resource planning (erp): a type of software that organizations use to manage their day-to-day activities and streamline business processes. erp systems integrate various functions across different departments, such as finance, human resources, procurement, manufacturing, supply chain management, and more, into a single unified platform. ethereum mainnet: the primary public ethereum production blockchain, where actual-value transactions occur on the distributed ledger. ethereum uses a proof-of-stake (pos) where validation is based not on the resources spent on mathematical problem-solving but on a node’s reputation. european data protection board (edpb): european union independent body with juridical personality whose purpose is to ensure consistent application of the general data protection regulation and to promote cooperation among the eu’s data protection authorities. graph model for conflict resolution (gmcr): a flexible tool for use in strategic management within a competitive environment. health level seven international (hl7): a clinical result reporting standard that is now ubiquitous in healthcare systems around the world. hidden markov models (hmm): sequence models. that is, given a sequence of inputs, such as words, an hmm will compute a sequence of outputs of the same length. an hmm model is a graph where nodes are probability distributions over labels and edges, giving the probability of transitioning from one node to the other. internet of things (iot): the collective network of connected devices and the technology that facilitates communication between devices and the cloud, as well as between the devices themselves. https://doi.org/10.30953/bhty.v6.276 http://creativecommons.org/licenses/by-nc/4.0 citation: blockchain in healthcare today 2023, 6: 276 https://doi.org/10.30953/bhty.v6.276 27 (page number not for citation purpose) privacy-conflict resolution for records in blockchain-based systems logical observation identifiers names and codes (loinc®): clinical terminology that is important for laboratory test orders and results and is one of a suite of designated standards for use in u.s. federal government systems for the electronic exchange of clinical health information. merkle tree or hash tree: ensures that the transactions stored on a blockchain are correlated through mathematical hashes. metamask wallet: software cryptocurrency wallet used to interact with the ethereum blockchain. multi-agent systems (mas): a computerized system composed of multiple interacting intelligent agents. multiagent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. multifactor challenge-set self-sovereign identity authentication (mfssia): enables cross-blockchain interoperability by utilizing blockchain oracles. parachains: blockchains connected to the relay chain of polkadot or kusama. they are application-specific data structures that validate transactions using the relay chain, an underlying structure that supports secure communication between all connected blockchains, also known as parachains. personal health record (phr): an individual’s electronic health-related information. personal health token (pht): a utility token for the decentralized person-centric e-health system. polkadot: enables cross-blockchain transfers of any type of data or asset, not just tokens. connecting to polkadot gives the ability to interoperate with a wide variety of blockchains in the polkadot network. proof-of-concept (poc): also known as proof of principle, it is a realization of a certain method or idea in order to demonstrate its feasibility or a demonstration in principle with the aim of verifying that some concept or theory has practical potential. a proof of concept is usually small and may or may not be complete. proof-of-work (pow) consensus algorithm: a decentralized consensus mechanism that requires network members to expend effort in solving an encrypted hexadecimal number. proof of work is also called mining, in reference to receiving a reward for work done. snomed ct or snomed: a systematically organized computer-processable collection of medical terms providing codes, terms, synonyms, and definitions used in clinical documentation and reporting. software development lifecycle (sdlc): the cost-effective and time-efficient process that development teams use to design and build high-quality software. the goal of sdlc is to minimize project risks through forward planning so that software meets customer expectations during production and beyond. “soulbound” tokens (sbt): a type of token that can only be owned and transferred by a specific address. this means that once a soulbound token is created and assigned to an address, it cannot be transferred or owned by any other address. spanish data protection authority (aepd): an independent agency of the government of spain that oversees the compliance with the legal provisions on the protection of personal data. standard contractual clauses (scc): documentation by the ec; this update is a significant step in ensuring robust and up-to-date data protection measures in cross-border data transfers. test data management (tdm): the process for providing controlled data access to modern teams throughout the software development lifecycle (sdlc). utilityand non-transferable “soulbound” tokens (sbt): also called “a non-transferrable token,” it is a type of nft that cannot be transferred or sold to another wallet. these types of tokens are often used to represent credentials, affiliations, achievements, or memberships. wf-nets: a formalization for describing process models in parallel and distributed systems. https://doi.org/10.30953/bhty.v6.276 1 (page number not for citation purpose) original research secure and reliable fog-enabled architecture using blockchain with functional biased elliptic curve cryptography algorithm for healthcare services charu awasthi, phd student1 ; satya prakash awasthi, phd2; and prashant kumar mishra, phd3 1research scholar department of computer engineering poornima university, jaipur, india; 2associate professor, department of computer engineering, poornima university, jaipur, india; 3associate professor, department of computer science and engineering, pranveer singh institute of technology, kanpur, india corresponding author: charu awasthi, email: charuawasthi@gmail.com doi: https://doi.org/10.30953/bhty.v7.347 keywords: blockchain, fb-ecc, fog computing, functional biased elliptic curve cryptography algorithm, galactic bee colony optimization algorithm, gbcoa, healthcare services abstract fog computing (fc) is an emerging technology that extends the capability and efficiency of cloud computing networks by acting as a bridge among the cloud and the device. fog devices can process an enormous volume of information locally, are transportable, and can be deployed on a variety of systems. because of its realtime processing and event reactions, it is ideal for healthcare. with such a wide range of characteristics, new security and privacy concerns arise. due to the safe transmission, arrival, and access, as well as the availability of medical devices, security creates new issues in the area of healthcare. as an outcome, fc necessitates a unique approach to security and privacy metrics, as opposed to standard cloud computing methods. hence, this article suggests an effective blockchain depending on secure healthcare services in fc. here, the fog nodes gather the information from the medical sensor device and the data are validated using smart contracts in the blockchain network. we propose a functional biased elliptic curve cryptography algorithm to encrypt the data. the optimization is performed using the galactic bee colony optimization algorithm to enhance the procedure of encryption. the performance of the suggested methodology is assessed and contrasted with the traditional techniques. it is proved that the combination of fc with blockchain has increased the security of data transmission in healthcare services. submitted: august 7, 2024; accepted: october 4, 2024; published: december 31, 2024 recent breakthroughs in electronic communication have altered the internet-of-things (iot) with the creation of small appliances that utilize and control the gathering and sharing of information. these permit the creation of tiny, cost-efficient, and less powerful multifunctional sensing systems possessing the capability to observe and convey different data in numerous areas, including transportation, healthcare, and industry.1 the healthcare iot provides several advantages, including data transfer in real-time mode and the capability to control the physiological status of the patients for varied durations. equipment, including glucose meters, electroencephalography, electromyography wearable devices, etc., permits health providers to gather a patient’s health data locally and create a decision depending on the health of the patient’s information. clinics have been implementing the iot for several years, and now they have healthcare iot appliances in patient care rooms and their systems. however, clinical agencies, clinics, and corporations do not address the protection threat of healthcare iot that is linked to a local area network or a wide area network. the iot devices are readily hijacked, and this can lead to various concerns owing to weak validation and encoding techniques. blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-5903-2459 mailto:charuawasthi@gmail.com https://doi.org/10.30953/bhty.v7.347 citation: blockchain in healthcare today 2024, 7: 347 https://doi.org/10.30953/bhty.v7.3472 (page number not for citation purpose) c. awasthi et al. therefore, blockchain is launched for safe and trustworthy transfer in healthcare iot. figure 1 demonstrates the framework of fog computing (fc). the development of iot systems, especially in the healthcare industry, is generating massive quantities of information, which are transported and saved on the cloud. due to the need for real-time information processing and storage, handling such large amounts of cloudbased information creates a bottleneck. the protection of information in the cloud is also a significant issue.2 the fc idea was an attempt to solve the issue. fog computing is a cloud computing system extension. accompanying the cloud’s functioning is fog’s primary role. for example, fog delivers computational resources to devices that are nearer to the edge of the network. the typical iot cloud framework has problems with scalability and dependability, but fc fixes such problems. because fog nodes operate at the edge of the network and are more geographically dispersed, as shown in figure 1, they improve information protection and precision, as well as minimize delay, which is critical for applications such as medical information. the total bandwidth to the cloud is also minimized, resulting in improved service quality. healthcare iot device detection, validation, and verification in a decentralized context may be solved by integrating fc with blockchain.3 to address this, we present a fog-enabled architecture using blockchain with a fc, functional biased-elliptic curve cryptography (fb-ecc) algorithm for healthcare services. related work this article proposes a fb-ecc algorithm for data encryption, with optimization accomplished by the galactic bee colony optimization algorithm (gbcoa). this algorithm is compared with various algorithms proposed in different articles for its performance analysis. table 1 presents top line observations from researcher regarding related observations of fog-enabled architecture. the related literature summaries suggest that integration of fog architecture with blockchain utilizing capabilities of iot devices is an interesting domain if we can incorporate a suitable algorithm for proper working. ngabo et al.4 stated that the primary goal of their work is to develop protection mechanisms against medical data mining attacks created by the sensing layer and information storage in the cloud database of the iot. a public-permission blockchain protection process that uses ecc digital signatures to assist a dispersed ledger database (server) to give immutable protection and transfer clarity as well as to protect patient information tampering at the iot’s fog layer. baniata and kertesz5 presented a thorough literature analysis and categorization of the fc-block chain (fcbc) combination—the current state of the art of the fc-bc combination. the authors discuss and organize the relevant work based on the publishing year and area, as well as the algorithms employed, bc functions, and bc position in the fc architectural design. investigations, evaluations, and future difficulties for the bc-fc combination are presented in detail by the author. tariq et al.6 attempted to address the issues of future digital infrastructure protection at a time when it is still under development. the functionality-dependent fog framework is established with the arrival of the architecture that generates large amounts of information. also discussed are the need for additional protection of fog-enabled iot devices, as are fc protection challenges and large information confidentiality related to fog-enabled iot. then, consideration of the complementary fig. 1. fog computing architecture. https://doi.org/10.30953/bhty.v7.347 citation: blockchain in healthcare today 2024, 7: 347 https://doi.org/10.30953/bhty.v7.347 3 (page number not for citation purpose) fog-enabled architecture using blockchain interdependencies between blockchain and fc, as well as their role in addressing a wide range of protection concerns in iot, are discussed. as a consequence, this study offers a taxonomy of the kinds of attacks on fog-dependent iot systems, com pares the most recent contributions to the area in terms of their protection service, and makes recommenda tions for future research. banerjee et al.7 enhanced the user experience and service resiliency in the event of an emergency; fc techniques have been utilized to link iot with real-time computation at edge networks. fog edge computing, with its dispersed design and proximity to end-users, may deliver quicker reaction times and higher quality services for iot use. fc, iot, and machine learning are included in every part of the paradigm provided by the researchers to improve the quality of healthcare. blockchain technology is used to assure architecture protection. fernández et al.8 implemented a system that adds iot capabilities to the commercial continuous glucose monitor (cgm) to enable remote surveillance of patients and, therefore, notify them of potentially hazardous circumstances. to gather cgm blood glucose measurements, cellphones are used to send measurements to a distant cloud or scattered nodes in the fog. also included is a decentralized storage system that collects, processes, and saves the acquired information to share accurate, trusted, and cyber-secure information with medical scientists, clinicians, and caregivers. glucocoin was created as an incentive scheme for individuals to provide fresh information to the system, as well as digital money. using a blockchain capable of executing smart contracts, this system may automate cgm sensor purchases or compensate users who provide their information to enable the system function. muthanna et al.9 proposed a framework for software-defined networking (sdn) to regulate and manage an edge computation layer of fog nodes and provide great availability and dependability for delayed iot applications. openflow switches with resource constraints are used in the sdn network, which has dispersed controllers. trustworthy decentralization may be achieved with the usage of the blockchain. openflow switches will be assigned computational processing duties depending on their present workload through an information-offloading technique. a traffic model has been suggested for the network as a whole. the algorithm is tested using simulation and a testbed. srivastava et al.10 discussed fc, blockchain, and the iot in healthcare. unlike cloud computing, which operates among cloud and end-user devices known as iot appliances, fc extends cloud computing’s capacity to execute functions such as processing, saving, and interaction across the internet. it offers superior information storage facilities with real-time access, reduced delay, greater responsiveness, better fault tolerance, and a protected and concealed context. fog, access, information interface, application, and protection layers are all fragmented into five levels in the iot system. the authors highlighted blockchain technology and consensus mechanisms to improve information protection in the healthcare context. yánez et al.11 suggested a new context-aware approach for on-chain information allocation in iot-blockchain networks. additionally, they create a data controller using fuzzy logic, which estimates a request’s roa value using several context characteristics, such as the quality and quantity of the information and the networks it is being sent over. the mechanism’s design and implementation also led to the refining of two popular iot-blockchain table 1. observations by researchers in the field of fog-enabled architecture source top line observation ngabo et al.4 the primary goal is to develop protection mechanisms against medical data mining attacks created by the sensing layer and information storage in the cloud database of the iot. tariq and colleagues6 address issues related to future digital infrastructure protection. banerjee et al.7 enhanced user experience and service resiliency in the event of an emergency. fernández et al.8 implemented a system that adds iot capabilities to the commercial cgm to enable remote surveillance of patients. muthanna et al.9 proposed a framework for sdn to regulate and manage an edge computation layer of fog nodes. srivastava et al.10 discussed fc, blockchain, and the iot in healthcare yánez et al.11 suggested a new context-aware approach for on-chain information allocation in iot-blockchain networks. kumari et al.12 provided an examination of the functions of fog and cloud computing and the iot in providing continuous context-aware services to end users. pareek et al.13 observed that iot links many gadgets globally. hanumantharaju et al.14 states that iot might help patients and healthcare providers keep in touch and offer their community clear, value-dependent care. mayer et al.15 proposed a fc architectural paradigm that integrates blockchain, fog computing, and the iot for the healthcare area. fc: fog computing; cgm: continuous glucose monitoring; iot: internet of things; sdn: software-defined networking. https://doi.org/10.30953/bhty.v7.347 citation: blockchain in healthcare today 2024, 7: 347 https://doi.org/10.30953/bhty.v7.3474 (page number not for citation purpose) c. awasthi et al. architectural features. the data allocation method is instantiated in the blockchain-dependent cloud and fog architectures and evaluated using fog bus to show the efficacy of our approach. using real-world healthcare uses, they also compare our method to current decision-making processes. kumari et al.12 provided an examination of the functions of fog and cloud computing and the iot in providing continuous context-aware services to end users when and where they are needed. for real-time information gathering, processing, and transfer, they suggest a threelayer patient-driven healthcare framework. it provides end users with information on the usage of fog devices and gateway in the healthcare 4.0 ecosystem for present and future uses. pareek et al.13 mentioned that the iot links many gadgets throughout the globe. to relieve the strain on healthcare systems, iot-dependent technologies may help decrease healthcare expenses, as well as boost computing and speed of processing. in the iot, greater and more sophisticated healthcare information sets need the use of cloud computing. delay, bandwidth usage, real-time reaction latency, security, and confidentiality are just a few of the problems that come with integrating iot with the cloud. when it comes to cloud computing, many concerns and challenges must be addressed before any of the iotfog-based system model designs can be evaluated. hanumantharaju et al.,14 mentioned that the role of the iot might help patients and healthcare providers keep in touch and offer their community clear, value-dependent care by making it simpler for both to remain in contact. fc may serve as the foundation for using iot in healthcare. the experts discussed healthcare 4.0. researchers will explore how fc taxonomy might be the best answer to healthcare 4.0 in terms of information gathering and evaluation, protection and confidentiality, and e-healthcare services. mayer et al.15 proposed a fc architectural paradigm that integrates blockchain, fc, and the iot for the healthcare area. for the most part, the fc architecture and its differential approaches to overcoming iot restrictions are the most significant contributions. the related literature review suggests that integration of fog architecture with blockchain utilizing capabilities of iot devices is an interesting domain if we can incorporate a suitable algorithm for proper working. this article proposes a fb-ecc algorithm for data encryption, with optimization accomplished by the gbcoa. this algorithm is compared with various algorithms proposed in different articles for its performance analysis. proposed method an effective blockchain-dependent protected healthcare service in fc is explained briefly in this section. the fog node gathers the information from the medical sensor devices and the data are validated using smart contracts in the blockchain network. to encrypt the data, the fb-ecc algorithm is proposed. to optimize the encryption process, the gbcoa is implemented. the performance of the suggested methodology is assessed and compared with the traditional approach. figure 2 shows the flow illustration of the implemented techniques. four levels may be detected in this infrastructure: the iot layer, the fog with blockchain layer, the cloud layer, and the data analysis layer. patients’ health information is acquired utilizing medical sensor devices. with wired or wireless accessible media, including zigbee and wi-fi, every iot medical equipment may be linked to a single fog node. fog nodes impose preset protection standards to control connected iot devices and services, as well as serve as an intermediary between the cloud and the blockchain, allowing authorization index for information queries. data validation using smart contract although the word was used previously in the context of protocols among strangers on the internet, smart contracts are examples of contracts implemented on the ethereum blockchain. a smart contract has the following rules: 1. negotiate the agreement’s conditions 2. validate the agreement automatically 3. implement the agreed conditions a smart contract is made up of many functionalities that may be accessed from outside of the blockchain or through other smart contracts. the use of blockchain in conjunction with smart contract technology eliminates the need for transactional parties to rely on a centralized system. every linked participant in the network has a replica of the smart contracts since they are kept on the blockchain. when initiated by an allowed or agreed-upon event, a smart contract may perform the agreed-upon stored procedure. every contract transfer, as well as the whole audit trail of activities, are saved in chronological order for future access. any party attempting to alter a contract or transaction on the blockchain will be detected and prevented by all other participants. the system continues to work even if one of the parties crashes, with no loss of information or integrity. as a result, a huge, safe, logical computer system is created without the dangers, expenses, or trust difficulties associated with a centralized paradigm. block verification using stellar consensus protocols the stellar consensus protocol is a decentralized consensus protocol in which nodes in a network do not have https://doi.org/10.30953/bhty.v7.347 citation: blockchain in healthcare today 2024, 7: 347 https://doi.org/10.30953/bhty.v7.347 5 (page number not for citation purpose) fog-enabled architecture using blockchain trustworthiness in all nodes in the network and may instead pick which nodes they trust.16 the idea of a “quorum slice,” which was initially established by this protocol, refers to a collection of nodes that trust one another. a “quorum” is a group of nodes large enough to establish a consensus, while a “quorum slice” is a subset of a quorum that persuades one or more nodes to agree. every node that gets these values will check the block for a single value among them, resulting in a single value being used to validate the block. nodes begin checking the block on whether or not to accept or abort the values chosen in the prior stage throughout this stage. if a group of nodes cannot agree, the value is transferred to a greater block for authentication. data encryption using functional biased elliptic curve cryptography algorithm the fb-ecc is a well-known public key cryptography technology that reliably and safely keeps the confidentiality and secret of encoded medical information. identical keys are used for encryption and decryption (table 2). the fb-ecc is a common public key encryption method that uses distinct key pairs for encryption and decryption procedures, such as the randomized creation of public and private keys. public-key cryptography techniques, such as fb-ecc, are also integrated into this technology. authorization and clarity of new transactions depend on the dispersed agreement (greater than 50%) between its users, which gives this technique an edge over public-key cryptography. because only the fb-ecc secret key can return the actual information, the medical information that is concealed cannot be retrieved by any unauthorized individual. in asymmetric key cryptography, the fb-ecc technique plays a crucial role in conducting public key cryptography. furthermore, a numerical expression is created utilizing the defined base point, curve, and the highest limit of a prime number function, and encryption is done by using the following fb-ecc equation: k2 = l3 + bl + c (1) fig. 2. flow illustration of the implemented techniques. fb-eec: functionally biased elliptic curve cryptograph; fc-bc: fog computing-block chain; gbcoa: galactic bee colony optimization algorithm, iot: internet of things. https://doi.org/10.30953/bhty.v7.347 citation: blockchain in healthcare today 2024, 7: 347 https://doi.org/10.30953/bhty.v7.3476 (page number not for citation purpose) c. awasthi et al. the integers are indicated by the b and c. however, the encryption process’s overall strength is determined by the production of a key depending on every cryptographic algorithm. the initial process is to make the public key that will be used to encrypt the information, which is usually received from the receiver. the second process is to generate a private key, enabling decryption of the original information on the recipient’s side. w is the curve’s starting point, and a is the chosen random integer within the range of 1– (m – 1): s = a x w (2) the public key is represented as s, whereas the private key is signified as a. encryption is a method of transforming actual information into ciphertext information, and it is used to increase protection. the fb-ecc is the most often used technique in cloud security to provide protection depending on the complexities of issues. the encryption procedure’s strength is determined by the key generation process, which may give a better solution for information by supporting more confidentiality in the transmission of secret keys between various communication entities. the input original information di and the private key k are supplied as inputs in this encryption procedure, and the generating function (go) creates the public key (pk). as a consequence, the cipher cs are created using the 4-bit random numbers rm and go. the input data di is then encoded using the curve’s base point w, followed by the generation of the public key pk and the random number rm. galactic bee colony optimization algorithm the gbcoa simulates the motion of stars, galaxies, and super galaxies to find feasible alternatives in a given search space. like stars in galaxies, communicate with one another. the agent is divided into two levels by the galactic bee colony. the stars are shown on the first level, while galaxies are represented on the second level. except for the starting population of the second level, which is drawn from the best solutions of the first  level, every level has its search mechanism. at every stage, several search techniques may be used. in all stages, the researchers opted to employ the bee colony optimization method. so, at the first level, every subpopulation uses bco to find the optimal answer, and then sends it to the higher level to build super bees. super bees are utilized as the starting population in a new bco run to find the optimum solution. the bcoa multilayered strike is represented in (3): s s q m: 1, 2, ,q p p∈ = … b s b best s:p p p p( )∈ =  g bpp m 1 = = (3) the first subpopulation of n solutions is generated randomly in the original galactic bee optimization technique. sq p denotes the jth solution of the ith subpopulation. sp denotes the ith subpopulation. bp (best(sp)) indicates the great solution of the subpopulation sp. set g denotes superpopulation that comprises of the best solutions comes from subpopulations. the better solutions gotten from each subpopulation in stage 1 are used as the first population of stage 2. stage 2 is run l2 times then great outcome identified in stage 2 agreed as the last solution of the epoch. the total algorithm is run epoch count times then great outcomes identified so far in epochs agreed as the last outcome of the algorithm. cloud database a centralized health record server communicates with a medical record repository or database under this paradigm. the patient owns the information, which includes sensitive personal information. the patient’s medical record, which is often contained in such documents, may also include biometric data, physical, psychological and mental health issues, personal history, allergies, medicines used, medical conditions, prior medical therapies, and illnesses, among other things. financial data, including table 2. a functional biased elliptic curve cryptography algorithm input: input data (di), private key (k). output: encrypted data (ed). 1: randomly generate the public key (pk); 2: pk= a * go/* the function of generation go depending on the curve equation, go is extracted from the mapping function. 3: generate the ciphers cs as cs ← rm * go; 4: the encrypted data (ed) is created as ed ← (sm * pa) + (dp, w);/* w /* w denotes the base point on the curve. 5: the encrypted data (ed) is uploaded to the public cloud environment note: see text for greater context. https://doi.org/10.30953/bhty.v7.347 citation: blockchain in healthcare today 2024, 7: 347 https://doi.org/10.30953/bhty.v7.347 7 (page number not for citation purpose) fog-enabled architecture using blockchain bank account, credit and debit card numbers, as well as the patient’s identity, may be included in the medical records.17 the protection of the security and confidentiality of electronic health records is regarded as a core element of health information management. the major goal of the health data system is to guarantee that information is accessible when it is required and that it is not improperly utilized, disclosed, acquired, changed, or destroyed while being saved or sent. the security privacy standards work together to ensure suitable controls and safeguards. the patient identification number is used to identify the medical record in the medical record store or database. under the ids stated, the patient’s healthcare information is personally identifiable. the patient’s data might be utilized alone or in conjunction with additional data. the patient information is stored in databases on the virtual computer in the private cloud. for information management, the private cloud architecture includes computation, storage, and network services. while delivering health information to the cloud, the encryption and hash operations are done according to the private cloud security framework. the actual health record information is kept in one database, while the key necessary for encryption is saved in another. as a result, an attacker’s access to critical patient information stored in the cloud electronic health record database is restricted. as a result, the suggested framework safeguards the patient’s sensitive data by ensuring information confidentiality and integrity. as a consequence, healthcare users may decrypt data and access crucial information from anywhere and at any duration. performance analysis the proposed model’s performance assessment criteria for safe storage of encrypted medical information in a fc framework employing blockchain and a functional biased ecc algorithm are described. the key generation time (kgt), encryption time (et), decryption time (dt), and level of security are used to assess the protected storage section of this suggested algorithm system. moreover, the relevant formulae for estimating the various time are given in equations (4), (5), and (6). encryption time the encryption time is described as the time taken to encrypt the information in milliseconds. it is calculated as follow: encryption time = end time – starting time kgt = itt + et (4) where itt represents the information transferring time, while et stands for encryption time. the et computed here is the time it took the information to encode the original information and transform it to encrypted information. where endt denotes the end time and startt indicates the starting duration of the encryption procedure. et = endt – startt (5) figure 3 displays the suggested technique’s encryption time utilizing the functional biased ecc method. researchers discovered that encryption time improved as the number of bits in the key became larger. our suggested architecture, on the other hand, takes much less time to encrypt than traditional approaches like  advanced encryption standard (aes), data encryption standard (des), and rivest-shamir-adleman (rsa). decryption time the time necessary to decode the encrypted information is referred to as decryption time, and it is calculated as follows: decryption time = end time – starting time here, the dt (decryption time) is computed as the amount of duration it takes the information used to decode the encrypted information in milliseconds, and it is calculated using equation (6). dt = endt – startt (6) figure 4 depicts the suggested framework’s decryption times versus traditional ways decryption duration raises with increasing key size due to the introduction of disruptive information in the cloud server and various fig. 3. key generation time analysis. aes: advanced encryption standard, des: data encryption standard, ecc: elliptic curve cryptography, rsa: rivest-shamir-adleman. https://doi.org/10.30953/bhty.v7.347 citation: blockchain in healthcare today 2024, 7: 347 https://doi.org/10.30953/bhty.v7.3478 (page number not for citation purpose) c. awasthi et al. keywords. when compared to traditional approaches such as aes, des, and rsa, it has been shown that our suggested methodology takes less duration to decode even with larger keys. key generation time from figure 5 it can be observed that compared to traditional approaches such as aes, des, and rsa, the suggested secured storage functional biased elliptic curve encryption algorithm consumes less time. security level figure 6 compares the proposed functional biassed ecc to conventional secure storage algorithms like des, rsa, and aes in terms of security level. in comparison to traditional methodologies, the presented alternatives offer a higher degree of security. figures 3 to 6 illustrate the comparisons between various algorithms such as aes, rsa, des with proposed method fb ecc on various parameters such as: i) encryption time ii) decryption time iii) key generation time iv) security level the overall analysis of algorithms shows that it gives much better results than traditional algorithms on all of the four discussed parameters. conclusion to protectively store patient medical information in fog-enabled cloud databases, a secure medical information storage model is designed and included in this system. medical information is gathered from numerous patients who support the e-healthcare gadgets in this system. the fb-ecc technique is used to deploy a private server that requires decryption and encryption for computing. the functionally fb-ecc method is used in this research to execute the encryption, decryption, and key generation procedures on protected storage architecture. when compared to existing approaches, the suggested techniques outperform them in terms of security, encryption, decryption, and kgt. the suggested encryption algorithm, fb-ecc, has a security level of 98.64%. it has been shown that combining fc with blockchain has improved the security of information transfer in healthcare. because only the functionally biased ecc secret key can return the actual information, the medical information that is concealed cannot be retrieved by any unauthorized individual. future studies in this area might include the development of a new cryptographic algorithm, which is an upgraded-level suggested encryption approach of fb-ecc with a higher degree of security.18 funding no funding was used in the preparation of this article. fig. 4. decryption time analysis. aes: advanced encryption standard, des: data encryption standard, ecc: elliptic curve cryptography, rsa: rivest-shamir-adleman. fig. 5. key generation time analysis. aes: advanced encryption standard, des: data encryption standard, ecc: elliptic curve cryptography, rsa: rivest-shamir-adleman. fig. 6. security level analysis. aes: advanced encryption standard, des: data encryption standard, ecc: elliptic curve cryptography, rsa: rivest-shamir-adleman. https://doi.org/10.30953/bhty.v7.347 citation: blockchain in healthcare today 2024, 7: 347 https://doi.org/10.30953/bhty.v7.347 9 (page number not for citation purpose) fog-enabled architecture using blockchain conflicts of interest there are no conflicts of interest. contributors the authors are responsible for development of this article. data availability statement (das), data sharing, reproducibility, and data repositories data are not available. application of ai-generated text or related technology these was no use of ai. references 1. bouachir o, aloqaily m, tseng l, boukerche a. blockchain and fog computing for cyberphysical systems: the case of smart industry. computer. 2020 sep;53(9):36–45. https://doi. org/10.1109/mc.2020.2996212 2. 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https://doi.org/10.1109/access.2020.2999213 https://doi.org/10.3390/s19081788 https://www.researchgate.net/publication/340896382_a_secure_iot-fog_enabled_smart_decision_making_system_using_machine_learning_for_intensive_care_unit https://www.researchgate.net/publication/340896382_a_secure_iot-fog_enabled_smart_decision_making_system_using_machine_learning_for_intensive_care_unit https://www.researchgate.net/publication/340896382_a_secure_iot-fog_enabled_smart_decision_making_system_using_machine_learning_for_intensive_care_unit https://doi.org/10.3390/s19153319 https://doi.org/10.3390/s19153319 https://doi.org/10.3390/jsan8010015 https://doi.org/10.3390/jsan8010015 https://doi.org/10.1007/978-3-030-46197-3_22 https://doi.org/10.1109/jiot.2020.2972776 https://doi.org/10.1016/j.compeleceng.2018.08.015 https://doi.org/10.1016/j.compeleceng.2018.08.015 https://doi.org/10.1109/access.2021.3109822 https://doi.org/10.1109/access.2021.3109822 https://doi.org/10.3233/jifs-191490 https://doi.org/10.1109/access.2017.2757844 https://doi.org/10.1109/access.2017.2757844 https://doi.org/10.32604/cmc.2022.020614 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1blockchain in healthcare today issn 2573-8240 proof of concept/pilots/methodologies securing the chain of custody and integrity of data in a global north–south partnership to monitor the quality of essential medicines kathleen hayes1 ; natalie meyers, ma, mlis2 ; christopher sweet, phd3 ; ayenew ashenef, ms4 ; tim johann, phd5 ; marya lieberman, phd1 ; and david kochalko, mba, ma, mpp6 1department of chemistry and biochemistry, university of notre dame, indiana, usa; 2lucy family institute for data & society, university of notre dame, indiana, usa; 3center for research computing, university of notre dame, indiana, usa; 4department of pharmaceutical chemistry, school of pharmacy, college of health sciences, addis ababa university, ethiopia; 5department of chemistry, roanoke college, salem, virginia, usa; 6artifacts, cambridge, massachusetts, usa corresponding author: kathleen hayes, email: khayes5@nd.edu keywords: analytical chemistry, blockchain, ledger, pharmaceutical, quality of medicine, supply chain abstract substandard and falsified (sf) pharmaceuticals account for an estimated 10% of the pharmaceutical supply chain in lowand middle-income countries (lmics), where a lack of regulatory and laboratory resources limits the ability to conduct effective post-market surveillance and allows sf products to penetrate the supply chain. the distributed pharmaceutical analysis laboratory (dpal) was established in 2014 to expand testing of pharmaceutical dosage forms sourced from lmics; dpal is an alliance of academic institutions throughout the united states and abroad that provides high-quality, validated chemical analysis of pharmaceutical dosage forms sourced from partners in lmics. results from analysis are reported to relevant regulatory agencies and are used to inform purchasing decisions made by in-country stakeholders. as the dpal program has expanded to testing more than 1,000 pharmaceutical dosage forms annually, challenges have surfaced regarding data management and sample tracking. here, we describe a pilot project between dpal and artifacts that applies the blockchain to organize and manage key data generated during the dpal workflow, including a sample’s progress through the workflow, its physical location, provenance of metadata, and lab reputability. recording time and date stamps with these data will create a permanent and verifiable chain of custody for samples. this secure, distributed ledger will be linked to an easy-to-use dashboard, allowing stakeholders to view results and experimental details for each sample in real time and verify the integrity of dpal analysis data. introducing this blockchain-based system as a pilot will allow us to test the technology with real users analyzing real samples. feedback from users will be recorded and necessary adjustments will be made to the system before the implementation of blockchain across all dpal sites. anticipated benefits of implementing the blockchain technology for managing dpal data include efficient management for routing work, increasing throughput, creating a chain of custody for samples and their data in alignment with the distributed nature of dpal, and using the analysis results to detect patterns of quality within and across brands of products and develop enhanced sampling techniques and best practices. received: december 16, 2021; revised: december 21, 2021; accepted: february 21, 2022; published: march 21, 2022 an estimated 10% of pharmaceutical products in lowand middle-income countries (lmics) are substandard or falsified (sf). substandard drugs are authorized medical products that do not meet their quality standards and/or specifications, while falsified drugs are medical products that are deliberately misrepresented in their identity, composition, or source.1 sf drugs are responsible for adverse human health outcomes https://orcid.org/0000-0003-1217-0050 https://orcid.org/0000-0001-6441-6716 https://orcid.org/0000-0001-8214-7177 https://orcid.org/0000-0003-2505-899x https://orcid.org/0000-0003-2212-9684 https://orcid.org/0000-0003-3968-8044 https://orcid.org/0000-0002-3331-2751 mailto:khayes5@nd.edu citation: blockchain in healthcare today 2022, 5: 230 http://dx.doi.org/10.30953/bhty.v5.2302 kathleen hayes et al. including failure to treat and prevent illness, leading to increased morbidity and mortality, as well as decreased trust in medical care systems.1,2 one of the reasons that sf drugs are so prevalent in some lmics is a lack of widespread, post-market and pre-market quality testing of pharmaceutical dosage forms.3 as of 2017, 26 of the 54 drug regulatory agencies in africa did not have a pharmaceutical quality control lab, and 40 did not conduct regular post-market surveillance activities.4 most pharmacopeia assays for pharmaceutical testing rely on high-performance liquid chromatography (hplc), which can be used to measure the amount of the active pharmaceutical ingredient (api) in a given dosage form – a key indicator of medicine quality. however, hplc is a scarce resource in lmics, as it is prohibitively expensive, requiring highly trained personnel, high-cost instrumentation, reagents, and consumables, as well as resources for maintenance and repairs. the distributed pharmaceutical analysis lab (dpal) was established in 2014 to leverage the testing capacity of colleges and universities in the united states and abroad to assess the quality of drugs that are collected in lmics.5 most colleges and universities have the capability to carry out hplc analysis, which is the gold standard for pharmaceutical analysis. once participants have carried out a series of careful experiments to demonstrate system suitability – showing that their hplc system works for the analysis of a desired pharmaceutical product– that dpal site can measure the amount of api in a given dosage form and compare it with the accepted amount of api required to qualify a dosage form as good quality. while this api content assay is only one component of quality testing, it is a key indicator of quality problems that can cause adverse patient outcomes. dpal allows more dosage forms collected in lmics to be tested for quality than would otherwise be analyzed through post-market surveillance. dosage forms undergo two types of sample analysis throughout the dpal workflow. in addition to the hplc-based sample analysis of dpal, dosage forms are screened in-country at collection sites using a paper analytical device (pad: a cost-effective tool for field screening of pharmaceutical dosage forms in low-resource settings.) the pad is a field-friendly device that is used to screen drug dosage forms to identify suspicious products, prior to hplc analysis.6 samples that are flagged as suspicious via pad screening can then be prioritized for hplc analysis. throughout the past 7 years that dpal has existed, over 1,000 dosage forms have been analyzed and 168 sf drugs have been detected through the program, including falsified acetaminophen, adulterated amoxicillin clavulanate and doxycycline, and substandard losartan.5 this number of detected sf drugs is in alignment with estimates that 10% of drugs in lmics are sf. the analysis results collected through dpal are communicated to the stakeholders including the pertinent regulatory agencies and partner organizations in the country where the samples were collected and can be used to inform purchasing options. throughout the workflow of dpal, a large amount of data are generated, stored, and shared; the physical location of each sample, its progression through the analysis workflow, results of the analysis, system suitability information, requests for samples, and alerts that samples are on their way to a location. all these data are currently housed on a myriad of platforms, including open science framework (osf), excel, dropbox (a file-hosting service), email, external hard drives, and personal computers. in addition, changes are made to existing data by several users, with no way to log or track the changes. the lack of streamlined data management threatens the security of the data, which means that the dpal program does not operate as efficiently as possible. to mitigate the challenges associated with the data management in dpal, the artifacts team is developing a blockchain database for managing dpal data, which allows the system to be secured and show provenance of data. founded by a team experienced delivering solutions widely used for scientific research, artifacts had blended the creation of cloud-based information platforms tethered to trusted distributed ledger technology. employing web 3 technologies, artifacts offers provenance security services for researchers across all disciplines and partners with universities, publishers, and other organizations seeking solutions for providing timely and verifiable access to information with scientific, societal, and commercial value.7–9 blockchain has been adapted for numerous applications in science and health care that include managing research evidence, collection of informed consent for clinical trials, and confirming patient identity, with benefits for building a secure platform for managing and storing data.10–12 incorporating blockchain in the technology stack for these and other applications introduces an enhanced layer of trust and verifiability, both of which are essential with use cases involving multiple participant contributors engaged in geographically distributed processes. the key benefits of integrating blockchain into healthcare applications include decentralized management, an immutable record of actions and data, robustness and availability, and enhanced security and privacy.13,14 applying blockchain to mitigate dpal’s current shortcomings is expected to enable the testing and sample handling workflow to operate more efficiently. actions performed are recorded locally but are not visible to other participants. controlled handling of samples and the provenance of results obtained cannot be verified. this approach is unable to scale and process the volume of samples required to maintain active monitoring and reporting of drug quality. the use of an interactive platform by all contributors, operating 24/7 http://dx.doi.org/10.30953/bhty.v5.230 citation: blockchain in healthcare today 2022, 5: 230 http://dx.doi.org/10.30953/bhty.v5.230 3 securing the chain of custody and integrity of data in a global north–south partnership to monitor the quality of essential medicines that records all actions and scientific metadata to a blockchain ledger, is expected to speed handling, reporting, verification and management activities among multiple parties working from multiple geographic and organizational locations. benefits include streamlining all data, results, requests, and physical sample location information into one platform, having record of provenance so we know when and by whom changes were made to data, and ensuring that updates can only be made by authorized personnel. we describe, here, plans for a blockchain system to manage dpal data, which uses a subset of dpal participants as a model for the larger system. the blockchain-based system is being designed by an artifacts team. the dpal participants included in the pilot are the in-country coordinator from addis ababa university (addis ababa, ethiopia), the dpal manager from the university of notre dame (nd) (notre dame, indiana, usa), and the dpal site coordinator and analyst(s) at roanoke college (salem, virginia, usa). after this pilot, the use of blockchain will be expanded to all dpal participants, including other collaborators in lmics and other colleges in the us and abroad. dpal the distributed pharmaceutical analysis lab leverages the testing capacity of academic institutions in the us and abroad, including lmic institutions to test drugs sourced from lmics. the major players in the dpal workflow include the coordinators at each collection site, the dpal manager at nd, the site coordinator and analysts at each dpal site, and stakeholders, such as pharmaceutical regulatory agencies. workflow the process of dpal occurs in multiple countries and multiple locations, with each location contributing different data to the overall database, as outlined in figure 1. 1. collection: the dpal process begins in-country, where overt or covert shoppers visit local pharmacies and other places that sell medicines to purchase finished pharmaceutical dosage forms of drugs, such as amoxicillin, azithromycin, and ceftriaxone. the drugs are screened with a pad designed to identify suspicious products, but this test must be backed up with confirmatory analysis by hplc.6,15 2. arrival at notre dame: the in-country collaborators retain a portion of the pills in each package for their own analysis. the remaining pills are sent to the university of notre dame, indiana, usa for intake where they are given unique identification codes, inspected, and prepared for distribution. 3. distribution: prior to receiving samples for analysis, academic institutions must prove their ability to analyze a specific api. at nd, packages of pills are subdivided so some pills can be sent to participating dpal locations for analysis; nd retains a portion of the pills for further testing. 4. analysis: samples are analyzed via hplc at dpal locations, and the results are sent back to nd. some samples are also analyzed in-house at nd or in the lmic sites. 5. reporting: results of analysis are sent back to nd and are compiled into a formal report that is sent to in-country stakeholders. when necessary, international sf medicines monitoring schemes including the who will be notified. data generated from workflow at each step of the dpal workflow, data are generated, and the existing metadata are updated. the data involved in each step are outlined below: 1. collection: during collection, each dosage form is assigned a temporary identification number and is fig. 1. the dpal (distributed pharmaceutical analysis lab) process occurs in multiple countries and locations. each location contributes different data to the overall database, as illustrated here. hplc: high-performance liquid chromatography; lmics: low-and middle-income countries; nd: university of notre dame. http://dx.doi.org/10.30953/bhty.v5.230 citation: blockchain in healthcare today 2022, 5: 230 http://dx.doi.org/10.30953/bhty.v5.2304 kathleen hayes et al. attributed information on api, drug class, brand, manufacturer, country of origin, batch/lot number, api content, manufacturing date, and expiry date, as well as the overt or covert shopper’s name and town and location where the purchase was made. historically, these data have been collected in a single spreadsheet of metadata. pitfalls to this current arrangement include the manual entry of metadata creating typos, most often in lot/batch number, and the accumulation of multiple versions of the spreadsheets. 2. arrival at notre dame: an alert that samples are on their way to nd is sent from in-country to nd via email and the metadata spreadsheet is shared via dropbox. once the physical samples arrive at nd, they are assigned a new (and permanent) identification code, which is updated in the metadata spreadsheet and physically affixed to the sample. challenges that can arise at this step include discovering missing or unaccounted for samples and recompiling shipping lists and lists of metadata for customs clearance. 3. distribution: before receiving samples for analysis, participating dpal locations must prove that their instrument is capable of accurately determining api concentration through a process called “system suitability,” which is a set of experiments based off of united states pharmacopeia <1225> “validation of compendial procedures” method that determines the instrument and method precision, accuracy, range, and other capabilities.16 spreadsheet templates for system suitability are available to dpal locations through osf. these results are also shared back to the university of notre dame through osf. when a dpal school is ready to analyze samples, they send an email to nd requesting a specific number of samples. alternatively, nd will reach out to dpal schools via email to see who has the capacity for more samples. from there, the nd dpal coordinator will select dosage forms awaiting analysis and, in the case of dosage forms containing multiple pills, will split two pills from the dosage form and send those in a baggie to the requesting dpal site. nd must retain a portion of the sample in case it requires further testing. the only sample information included in this shipment to dpal schools is the unique identification code of the sample, api, and api content, which are physically affixed to the sample baggie. a shipping letter is included in the package, which lists unique identification codes of the samples as well as year and country of purchase of the samples. this shipping letter is historically the only record of the physical location of each sample. when a different dpal school requests samples, a manual search of shipping letters is required to determine which samples are still at nd and awaiting analysis. 4. analysis: similar to system suitability, sample analysis spreadsheet templates are available on osf for download and use by dpal schools. once analysis is completed, the finalized sample analysis spreadsheets, along with supplementary information including pdfs of the chromatographic results, are uploaded to osf where they can be accessed by the dpal coordinator. the results of these analyses and the spreadsheets themselves are then input into the existing metadata spreadsheet. this spreadsheet is only accessible to the dpal manager. in-country stakeholders are not able to track the progress of a sample through analysis in real time, but must send requests for information about their samples via email or dropbox. unfortunately, this makes the system vulnerable, as errors can be introduced during data transfer from the sample analysis spreadsheet to the metadata spreadsheet, when results are manually input into the spreadsheets. 5. reporting: the results of each sample’s analysis as compiled in the metadata spreadsheet are individually input into a manually typed report, which is shared with in-country stakeholders and regulatory agencies. a lack of automatic report generation or dashboard capabilities creates inefficiencies and workflows for sharing that do not scale well. current limitations it is crucial to be able to detect sf pharmaceuticals, as sf pharmaceuticals that go unnoticed can endanger health, prolong illness, cause mortality, promote drug resistance, and cause distrust in the medical system.1,2 while dpal is not a certified pharmaceutical analysis laboratory and data produced cannot be used in a legal capacity to certify a medicine’s quality, the data generated through dpal are used to report suspicious samples to regulatory agencies so they can conduct the compendial analysis that is needed to take action, such as recalling a product or banning a manufacturer from selling his or her products in the country. results of the dpal sample analysis are transmitted directly to stakeholders so they can make more informed decisions on purchasing certain brands and manufacturers. as indicated above, dpal data are created, managed, and accessed by multiple people in multiple locations with various editing permissions. a current limitation of the program is that the data generated through dpal are stored in multiple types of file formats, including spreadsheet, pdf, and word document, and are stored and shared on multiple different platforms, including osf, dropbox, email, personal computer, and external storage drive. in addition, changes are made to data by multiple users, and the existing methods of data management do not offer a way to track or log these changes. by combining all aspects http://dx.doi.org/10.30953/bhty.v5.230 citation: blockchain in healthcare today 2022, 5: 230 http://dx.doi.org/10.30953/bhty.v5.230 5 securing the chain of custody and integrity of data in a global north–south partnership to monitor the quality of essential medicines of data management registered into a single blockchain ledger, this pilot project has potential to improve efficiency and throughput of sample analysis by streamlining data management into a single, secured platform. blockchain technology blockchain is a distributed ledger shared across multiple locations that permanently records each datum entered with time and date stamps. it is attractive for its immutability, thus ensuring provenance and verifiability of data.13,14 while notably applied to cryptocurrencies and non-fungible tokens (nfts), blockchain is also emerging as a solution for data management in medical applications.17–21 blockchain technology has also been implemented to address challenges associated with the pharmaceutical supply chain, with a prominent goal being to combat sf medicines by tracking medicines as they traverse the global supply chain, allowing participants to identify the presence of sf products by greater visibility of rogue transactions.22–28 blockchain is a good fit for managing dpal data, as the dpal process is distributed geographically throughout north america, africa, and europe. blockchain offers solutions to dpal’s current weaknesses, including the distribution of important data and communications across multiple file types and platforms, and a lack of data provenance when new information is added to the original metadata. by introducing blockchain as a pilot project, we will have the benefit of testing the ledger with real samples and real users, recording feedback regularly to implement into the final form of the database before launching with all 30 schools and multiple in-county locations. the incorporation of blockchain provides a unique and flexible technology well-suited to the dpal use case. several significant fundamental advantages that the blockchain will contribute include the following: • creating a verifiable and immutable thread of all actions and data obtained associated with samples as they advance through the multi-step and multi-location process. • reliance on a trusted network of research institutions operating a ledger distributed among many countries and regions globally. • proof-of-authority method of confirming the legitimacy of all recorded data and activity, which operates with minimal overhead or energy consumption. • seamless interoperability with a workflow platform for managing data encryption and addressing on-chain and off-chain storage requirements. • ability to scale efficiently as sample volume and metadata generated escalate with the addition of laboratories and locations. for the pilot, we selected three members of the current dpal community which in microcosm together will allow us to test and confirm suitability of the system for handling samples from beginning-to-end. we chose to partner with artifacts to create this application to tap their experience and accelerate the timeline for confirming viability and moving forward with a broader deployment. integration of blockchain into dpal the blockchain-based system designed by artifacts will be implemented in january 2022 with dpal participants at addis ababa university, the university of notre dame, and roanoke college. regular feedback will be solicited from users and incorporated into further development of the ledger-based application for future implementation across all of dpal. workflow the workflow of a sample that is registered onto the blockchain ledger, beginning with sample creation at the time of purchase of the dosage form, and including workflows for sample shipment, analysis, and reporting of results, as well as the sample management dashboard, is outlined, as shown in figure 2. the integration of blockchain into the dpal process is described further in this section. pharmaceutical samples are acquired at collection sites such as the lab in addis ababa, screened at the point of collection, and distributed to multiple testing laboratories. sample inventory and stage in workflow are monitored by the ledger. instrumental analysis methods such as hplc are curated at the university of notre dame, and quality control measures such as systems suitability tests and pharmaceutical assay data and results are recorded at multiple lab sites, such as at roanoke college. data analysis and regulatory reporting are facilitated by cross-site information sharing. at each stage, the ledger records who has custody of each sample, what tests have been performed, and what the results were. 1. collection: after a pharmaceutical dosage form is purchased, a sample is created for it in the blockchain ledger. once this sample is created, it will be assigned a unique identification code, and a label will be generated that can be affixed to the physical sample. in addition, all sample information that would have previously been manually input into the metadata spreadsheet for that sample will now be input directly into the blockchain database. this will include identification of the overt or covert shopper who purchased the sample. typographical errors will be minimized using dropdown menu selections for all sample information, including brand, manufacturer, batch/lot number and more. in addition, the sample creator will be able to add new fields to each drop down menu – as there are thousands of possible drug manufacturers, brands, and batch/ http://dx.doi.org/10.30953/bhty.v5.230 citation: blockchain in healthcare today 2022, 5: 230 http://dx.doi.org/10.30953/bhty.v5.2306 kathleen hayes et al. lot numbers that could be purchased. the sample and its information will be visible to the users: in-country lab manager, nd dpal manager, and the relevant regulatory agencies. in addition, a record of when and by whom these changes were made will be recorded on the blockchain, creating an immutable history. 2. arrival at notre dame: samples can be selected and grouped together as a package to be shipped at the university of notre dame. this establishes a record of the physical location of each sample. when the samples arrive at notre dame, they no longer need to be given new identifiers, while updates to blockchain record physical receipt – simplifying the intake process. 3. distribution: now, requests for samples by dpal schools will take place entirely on the blockchain-based system and recorded in the database, rather than through other channels like email, again streamlining where dpal business takes place. records of system suitability that prove a dpal location’s ability to analyze a certain api will be housed and accessible to the dpal manager on the blockchain. at nd, the samples that are being sent to a dpal site will be split, both physically and on the blockchain, so that each sample has a new, unique identifier but will be traceable back to the original “parent” sample and its pertinent metadata. again, multiple samples will be selected fig. 2. swim lane diagram detailing the sample analysis workflow through the blockchain ledger. hplc: high performance liquid chromatography; pad: paper analytical device. http://dx.doi.org/10.30953/bhty.v5.230 citation: blockchain in healthcare today 2022, 5: 230 http://dx.doi.org/10.30953/bhty.v5.230 7 securing the chain of custody and integrity of data in a global north–south partnership to monitor the quality of essential medicines and grouped together as a package to be shipped to the dpal site. now, all information regarding the sample location, history, and metadata will be housed in the blockchain using a workflow application that streamlines the distribution of samples. 4. analysis: results of analysis will be directly uploaded to the blockchain, including relevant attachments, including sample analysis spreadsheets and chromatographs of each sample. dpal analysts will have permissions to upload their results, dpal site coordinators, the dpal manager, and in-country collaborators will be able to view where each sample is in its workflow as well as results of analysis in real time. 5. reporting: results of analysis will be easily viewed by all relevant stakeholders on a dashboard of the blockchain database, including individual sample results and big picture information such as how many samples have been analyzed or are awaiting analysis, and trends in brand/manufacturer. users as mentioned, there are many different users in dpal who should have different rights to access and edit certain data within the dpal data set. these users, their geographic location, responsibilities, and permissions on the blockchain ledger are described in table 1. overall benefits of the blockchain pilot blockchain offers clear benefits to dpal data management compared with existing methods of data management. first, all data will be housed on a single platform, rather than being dispersed through dropbox, osf, email, external hard drive, and personal computer. this includes information about physical sample location, analysis results, and analysis procedural details. in addition, every piece of data will have date and time stamps, securing provenance of these data. this distributed ledger will be linked to an easy-to-use dashboard, so stakeholders will be able to view sample information in real time. by combining all this information on a user-friendly platform, it will be easier to detect patterns of quality problems within and across different brands of products, so that regulators and purchasers can deal with problem brands proactively. this will also aid in developing enhanced statistical sampling techniques and best practices for routing work so that we can increase throughput, save cost for regulators, detect problem products faster, and impact fewer patients. limitations of the blockchain pilot the pilot will connect the workflows of only one lab and one role within each lab. potential operational challenges to overcome with the proposed pilot include (1) confirming skill requirements for users, (2) education and training of users, (3) achieving understanding and adoption of a ledger-based information system, (4) ability of personnel to capture and record all actions and metadata without disruption in a continuous chain of custody, (5) maintaining updated lists of persons authorized to access the ledger and enter data at different sites, and (6) challenges in the scalability and sustainability of the proposed system. in addition, the pilot will retain a number of functional limitations, including the following: • the pilot feature set will be small and will not be sufficient for broader deployment. a number of capabilities table 1. distributed pharmaceutical analysis laboratory (dpal) users and their locations, responsibilities, and blockchain permissions title location responsibilities permissions dpal manager university of notre dame, indiana, usa overseeing all aspects of dpal sample intake sample distribution to dpal sites communication with dpal sites and in-county coordinator should be able to view and edit all aspects of blockchain in-country coordinator addis ababa university, ethiopia purchasing pharmaceutical dosage forms, creating samples, and distributing samples from in-country to notre dame communication with dpal manager should be able to create samples and view status of each sample through the workflow and results of analysis dpal site coordinator roanoke college, virginia, usa overseeing site analyst and sample analysis communication with dpal manager should be able to request samples for analysis dpal site analyst roanoke college, virginia, usa system suitability sample analysis should be able to upload results of analysis http://dx.doi.org/10.30953/bhty.v5.230 citation: blockchain in healthcare today 2022, 5: 230 http://dx.doi.org/10.30953/bhty.v5.2308 kathleen hayes et al. are deemed to be out of scope because they require more developmental efforts than can be completed in the time frame allotted to this project. some of these include establishing interoperability with an android mobile application created by dpal for enhanced data capture, providing a protocol specification interface to expand the types of drugs tested and analyzed with this system, and blinding access to data and identities based on roles and clearance definitions. • the pilot is expected to provide a valuable learning experience for both dpal and artifacts that will surface new requirements and will guide the design and development of new functionality for broader deployment. • the pilot design is scaled to support a limited number of users and locations. broader deployment of this system will require the ability to operate at scale to support data capture from multiple nations and regional locations within different countries, multiple further testing laboratories required to perform confirmatory analyses, many users working simultaneously across multiple time zones, and a significant increase in the types of drugs processed and frequency/volume of drugs screened. conclusions dpal aims to aid in the detection of sf pharmaceutical products in lmics using pharmaceutical analysis capabilities at academic institutions throughout the us and abroad. the data generated through dpal regarding dosage form quality are used to inform in-country stakeholders when making purchasing options and in reporting suspicious samples to regulatory agencies for compendial analysis. the use of blockchain technology for the management of dpal data has the potential to increase the efficiency and throughput of dpal, so that stakeholders can have results of post-market surveillance of pharmaceutical products in lmics faster. the major intended benefits of using the blockchain include increasing efficiency by streamlining all data and communications into a single platform, securing provenance of data, and having an easy-to-use dashboard connected to the distributed ledger so stakeholders can view results of analysis in close to real time. the initial pilot of this blockchain technology will include a subset of dpal participants, including the dpal manager at the university of notre dame (indiana, usa), the in-country dpal coordinator at addis ababa university (addis ababa, ethiopia), and the dpal site coordinator and dpal analyst(s) at roanoke college (virginia, usa). feedback from the pilot study will be regularly recorded and implemented into the widespread application of blockchain for all dpal sites abroad and in the usa. conflicts of interest one of the authors is employed by a for-profit entity, artifacts.ai (artifacts of research, inc.), that provides the platform utilized in the pilot described in this article. artifacts provides a complementary service for the bhty journal, which allows the authors published in bhty to link supporting evidence with their manuscripts and receive citations to that evidence. artifacts intends to apply the learnings from this pilot to determine the viability of developing a commercial solution. funding this research work was funded by the following sources: • ethiopia ministry of innovation and technology (mint, https://mint.gov.et/) grant “developing, validating and adopting simple mobile technologies in drug quality evaluation and counterfeit detection” • nsf cmmi 1842369 “eager: isn. unraveling illicit supply chains with a citizen science approach” (https://www.nsf.gov/awardsearch/showaward?awd_ id=1842369) • walther cancer foundation grant #0178.01 “developing paper analytical device for detection of counterfeit chemotherapy (chemo-pad) in kenya” https://www.walther.org/grants/ • pfizer foundation, inc. (eindiana: 13-6083839) antimicrobial resistance project moi teaching and referral hospital grant 419 (gr000419). “implementing a model of improved care for infectious diseases and antibiotic stewardship across multiple levels of the health system in western kenya” https://www.pfizer.com/purpose/responsibility/healthcare-access/global-health-grants • 2019 blockchain initiative award (internal und funding—https://research.nd.edu/news/ notre-dame-blockchain initiative-announces-seed-grant-recipients/) • roanoke college and roanoke college department of chemistry. the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. contributors kathleen hayes contributed to conceptualization, project administration, original draft preparation, reviewing, and editing of the article. natalie meyers and david kochalko were involved in conceptualization, original draft preparation, and reviewing and editing of the manuscript. christopher sweet also contributed to conceptualization. ayenew ashenef and tim johann contributed to conceptualization, review and editing of the manuscript. marya lieberman was involved in conceptualization, http://dx.doi.org/10.30953/bhty.v5.230 http://artifacts.ai https://mint.gov.et/ https://www.nsf.gov/awardsearch/showaward?awd_id=1842369 https://www.nsf.gov/awardsearch/showaward?awd_id=1842369 https://www.walther.org/grants/ https://www.pfizer.com/purpose/responsibility/healthcare-access/global-health-grants https://www.pfizer.com/purpose/responsibility/healthcare-access/global-health-grants https://research.nd.edu/news/ notre-dame-blockchain-initiative-announces-seed-grant-recipients/ https://research.nd.edu/news/ notre-dame-blockchain-initiative-announces-seed-grant-recipients/ citation: blockchain in healthcare today 2022, 5: 230 http://dx.doi.org/10.30953/bhty.v5.230 9 securing the chain of custody and integrity of data in a global north–south partnership to monitor the quality of essential medicines supervision, original draft preparation, and review and editing of the manuscript. acknowledgments the authors thank kristina davis for her contributions in designing figure 2. references 1. world health organization. who global surveillance and monitoring system for substandard and falsified medical products. available from: https://apps.who.int/iris/bitstream/handle /10665/326708/9789241513425-eng.pdf ?sequence=1&isallowed=y [cited 13 december 2021]. 2. world health organization. a study on the public health and socioeconomic impact of substandard and falsified medical products. available from: https://www.who.int/medicines/regulation/ssffc/publications/sestudy-executive-summary-en.pdf [cited 13 december 2021]. 3. roth l, bempong d, babigumira jb, et al. expanding global access to essential medicines: investment priorities for sustainably strengthening medical product regulatory systems. global health. 2018;14(1):1–2. https://doi.org/10.1186/s12992 018-0421-2 4. ndomondo-sigonda m, miot j, naidoo s, dodoo a, kaale e. medicines regulation in africa: current state and opportunities. pharmaceut med. 2017;31(6):383–97. https://doi.org/10.1007/ s40290-017-0210-x 5. bliese sl, berta m, lieberman m. involving students in the distributed pharmaceutical analysis laboratory: a citizen-science project to evaluate global medicine quality. j chem educ. 2020 oct 26;97(11):3976–83. https://doi.org/10.1021/acs. jchemed.0c00904 6. weaver aa, reiser h, barstis t, et al. paper analytical devices for fast field screening of beta lactam antibiotics and antituberculosis pharmaceuticals. anal chem. 2013 jul 2;85(13):6453–60. https://doi.org/10.1021/ac400989p 7. kochalko d, morris c, rollins j. applying blockchain solutions to address research reproducibility and enable scientometric analysis. insti 2018 conference proceedings. centre for science and technology studies (cwts); 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https://doi.org/10.1186/s12916-019-1296-7 24. sylim p, liu f, marcelo a, fontelo p. blockchain technology for detecting falsified and substandard drugs in distribution: pharmaceutical supply chain intervention. jmir res protoc. 2018;7(9):e10163. https://doi.org/10.2196/10163 25. clauson k, breeden ea, davidson c, mackey tk. leveraging blockchain technology to enhance supply chain management in healthcare: an exploration of challenges and opportunities in the health supply chain. blockchain healthcare today. 2018;1. https://doi.org/10.30953/bhty.v1.20 26. tseng jh, liao yc, chong b, liao sw. governance on the drug supply chain via gcoin blockchain. int j environ res public health. 2018;15(6):1055. https://doi.org/10.3390/ijerph15061055 27. vruddhula s. application of on-dose identification and blockchain to prevent drug counterfeiting. pathog glob health. 2018;112(4):161. https://doi.org/10.1080/20477724.2 018.1503268 28. mackey tk, nayyar g. a review of existing and emerging digital 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2017;16(5):587–602. https://doi.org/10.1 080/14740338.2017.1313227 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0. http://dx.doi.org/10.30953/bhty.v5.230 https://doi.org/10.1080/14740338.2017.1313227 https://doi.org/10.1080/14740338.2017.1313227 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 narrative/systematic review/meta-analysis emerging trends in cybersecurity: a holistic view on current threats, assessing solutions, and pioneering new frontiers taskeen zaid, phd1 and suman garai, mba2* 1associate professor it, jain (deemed to be university), bengaluru, karnataka, india; 2kalinga institute of industrial technology, bhubaneshwar, odisha, india *corresponding author: suman garai, email: mr.sumangarai.3122@gmail.com doi: https://doi.org/10.30953/bhty.v7.302 keywords: comparative analysis, cyber defense, cybersecurity, digital threat landscape, innovative framework, safeguard information abstract in an era dominated by digital advancements, cybersecurity plays a pivotal role in safeguarding information and systems from evolving threats. the escalating sophistication of cyber threats necessitates a critical examination of the efficacy of contemporary defenses. recognizing the limitations and gaps in current solutions, this research introduces a pioneering framework aimed at fortifying cyber defenses. motivated by a comprehensive exploration of research articles, surveys, online media, and practical studies, this study scrutinizes the intricacies of cyber threats and assesses the strengths and weaknesses of existing solutions. the proposed frameworks emerge from a meticulous feasibility and practicality study, leveraging insights garnered from diverse online sources. the “how” encompasses a comparative analysis, evaluating the novel framework against established solutions to delineate their respective merits and shortcomings. the impetus behind this research lies in offering valuable insights to researchers, practitioners, and policymakers grappling with the multifaceted challenges of cybersecurity. by navigating through the complexities of existing solutions and introducing innovative frameworks, this paper aims to guide efforts in bolstering cyber defenses. ultimately, this research envisions a continuous cycle of improvement and evolution in the realm of cybersecurity as stakeholders collectively strive to adapt to the ever-changing digital threat landscape. submitted: february 24, 2024; accepted: april 19, 2024; published: april 30, 2024 in this article, the authors seek to comprehensively investigate the contemporary cyber threat landscape, scrutinize existing security solutions, and propose novel frameworks for improvement. the primary objectives encompass thoroughly examining the prevailing threat landscape, analyzing current security threats, evaluating existing solutions, and pinpointing their inherent limitations. the research introduces two innovative solutions targeting specific domains within cybersecurity and conducts detailed comparative analyses against established security measures, elucidating their respective strengths and weaknesses. the methodologies employed include an extensive literature review, feasibility and practicality studies, and a comparative analysis, laying a robust foundation for generating insights, practical improvements, and identifying future research directions. historical perspective in the bustling digital age, our lives seamlessly intertwine with the invisible threads of the internet. we bank online, share thoughts on social media, and entrust our secrets to cloud storage. but lurking beneath this convenience lies a shadow world of digital threats, where malicious actors seek to exploit vulnerabilities and compromise our precious data. this realm, known as cybersecurity, has evolved from the realms of spies and codebreakers to a critical battleground for individuals, businesses, and nations alike. understanding its journey—from early https://orcid.org/0000-0002-1716-9262 https://orcid.org/0009-0009-9483-7939 mailto:mr.sumangarai.3122@gmail.com https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.3022 (page number not for citation purpose) t. zaidi and s. garai encryption efforts to the sophisticated attack landscapes of today—is crucial for navigating this ever-changing terrain. the seeds of cybersecurity were sown amidst the chaos of world war ii. in a desperate attempt to secure military communications, nations like germany deployed advanced encryption machines like the enigma, creating complex ciphers that baffled allied intelligence for years. the story of cracking enigma, spearheaded by a brilliant team at bletchley park, is a testament to the ingenuity and determination that underpin the field. even before the world embraced computers, cryptography served as the first line of defense against adversaries seeking to steal secrets and disrupt operations.1 following the digital revolution, the focus shifted from physical codes to safeguarding computer systems and networks. the early days were marked by isolated incidents like the morris worm attack of 1988, but as the internet’s reach expanded, so did the sophistication and frequency of cyber threats. hackers, motivated by mischief, espionage, or financial gain, exploited vulnerabilities in operating systems, websites, and user behavior. viruses, worms, and malware proliferated, targeting critical infrastructure, businesses, and even individuals. the rise of cybercrime syndicates added a layer of organized malice, fueling attacks like data breaches and identity theft.2 as these digital adversaries evolved, so did the arsenal of cybersecurity defenders. antivirus software, firewalls, and intrusion detection systems became essential tools for network defense. governments scrambled to establish cyber security agencies and formulate policies. international cooperation became vital, leading to treaties and agreements aimed at combatting cybercrime and promoting responsible online behavior. today, cybersecurity is a multi-billion-dollar industry, employing an army of skilled professionals from diverse backgrounds: ethical hackers, network security engineers, malware analysts, and incident response specialists.3 however, the arms race continues. hackers constantly innovate, exploiting emerging technologies like artificial intelligence (ai) and blockchain to launch novel attacks. ransomware, phishing scams, and supply chain attacks are just a few examples of the evolving threatscape. the stakes are higher than ever: critical infrastructure, healthcare systems, and even democratic processes are potential targets. as we hurtle towards an increasingly interconnected future, the need for robust cybersecurity measures has never been greater.4 the journey of cybersecurity is a testament to human ingenuity and the constant struggle between offense and defense. from the clandestine world of wartime codebreaking to the complex digital battlefields of today, the story highlights the importance of awareness, vigilance, and collaboration in safeguarding our digital lives. as we navigate the ever-evolving cyber landscape, understanding its history and the challenge of the present empowers us to build a more secure and resilient future for all. digital security risks in recent times the severity and quantity of cybersecurity threats have significantly increased in recent years, leading to substantial financial losses and harm to the reputation of many businesses. regrettably, several real-life examples ( figure 1) demonstrate the seriousness of these threats. supply chain attacks, a sophisticated form of cyber warfare, involve compromising third-party suppliers to gain unauthorized access to a target’s systems. this method allows attackers to exploit the trust established between fig. 1. a survey report stating the increase in different kinds of cyber threats since covid-19. sql: structured query language https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.302 3 (page number not for citation purpose) emerging trends in cybersecurity organizations and their suppliers. the 2020 solarwinds attack is a stark illustration of this strategy, where russian hackers utilized vulnerabilities in solarwinds’ update process to infiltrate thousands of organizations’ networks.5 exploiting vulnerabilities in internet of things (iot) devices is another facet of cyber threats. the 2016 mirai botnet attack on dyn is a notable example where compromised iot devices were used to launch a massive distributed denial of service (ddos) attack, which overwhelms any server or network with a flood of traffic, causing significant website outages across the eastern united states, and resulted in an estimated $110 million in damages.6 advanced persistent threats (apts) involve unauthorized users gaining prolonged undetected access to systems. in 2015, chinese hackers executed a large-scale apt against the u.s. office of personnel management (opm), compromising the personal data of over 21 million individuals.7 apts are characterized by their stealthy nature, often driven by nation-state actors with the intention of stealing sensitive data or perpetrating other attacks. the opm breach highlighted the significant challenges organizations face in detecting and mitigating apts. ransomware attacks encrypt a victim’s data, demanding payment for the decryption key. the 2017 wannacry attack affected over 300,000 computers globally, exploiting a vulnerability in microsoft windows.8 another notable incident was the 2021 colonial pipeline attack, where a ransomware attack disrupted fuel supplies across the eastern united states, emphasizing the critical role that cybersecurity plays in protecting essential infrastructure.9 social engineering is a tactic employed by cybercriminals to manipulate individuals into revealing sensitive information or taking actions that compromise security. in 2023, mgm resorts fell victim to a sophisticated social engineering attack, where hackers impersonated a legitimate vendor to gain access and steal unreleased movie scripts, confidential financial documents, and employee information.10 adding a layer of technological sophistication to social engineering are deepfakes, hyper-realistic manipulated videos or audio recordings. these “synthetic media” tools pose a growing threat, enabling attackers to impersonate executives, spread misinformation, or conduct sophisticated blackmail schemes. a 2020 study by the rand corporation warned of deepfakes’ potential use in disrupting elections, manipulating financial markets, and eroding public trust.11 the 2023 deepfake case involving actress rashmika mandanna showcases the evolving threat posed by this technology, causing distress and reputational damage.12 account takeovers are on the rise, affecting both individuals and large organizations. in 2019, capital one experienced a major account takeover attack where a hacker gained access to the personal data of millions of users. the consequences were severe, with the hacker able to access social security numbers, credit scores, and bank account numbers, leading to an $80 million fine for capital one.13 credential theft is a common tactic used by attackers to gain access to sensitive information or systems. the 2018 marriott international data breach exposed the personal information of approximately 500 million guests, as the hacker stole login credentials from a third-party vendor. marriott faced a $123 million fine consequently.14 malicious insiders, individuals with authorized access to an organization’s systems, can pose a significant threat when they misuse that access. in 2019, a former tesla employee was charged with stealing confidential information and intellectual property from the company’s system. the employee had access to the company’s systems and copied more than 300,000 files to his personal account.15 the 2023 leak of classified pentagon documents to a video game chat group underscores the insidious nature of insider threats too.16 the stuxnet worm is a notable example of a zero-day attack, where an attacker exploits a previously unknown vulnerability in software.17 it targeted industrial control systems, exploiting several zero-day vulnerabilities in windows and siemens software to modify programmable logic controllers and potentially cause physical damage. the attack is believed to have been carried out by a nation-state actor and has had significant implications for the development of cyber weapons and the use of zeroday exploits in warfare. these examples show the devastating financial and reputational consequences that cybersecurity attacks can have. companies may face legal sanctions, loss of customers, and significant damage to their brand reputation. additionally, society as a whole may suffer from the loss of sensitive information, disruptions to critical infrastructure, and an increased risk of identity theft and fraud. in light of these risks, organizations must take cybersecurity seriously and invest in robust security measures to safeguard their systems and data. current measures for protection against cyber threats in the dynamic arena of cybersecurity, staying ahead of ever-evolving threats is paramount. to achieve this, organizations must harness cutting-edge solutions. this exploration delves into six key advancements, unraveling their functionalities, benefits, and real-world applications. one pivotal advancement is the integration of ai and machine learning (ml) as digital sentinels (table 1). their prowess lies in real-time analysis of vast data streams, deciphering network traffic, user behavior, and system logs. this enables them to learn from existing vulnerabilities and predict future attack patterns. ai and ml act https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.3024 (page number not for citation purpose) t. zaidi and s. garai as superhuman surveillance systems, identifying subtle anomalies in network activity, unusual login attempts, and suspicious file modifications in real time. this facilitates reduced response times, enabling organizations to prevent data breaches by detecting threats early. moreover, these technologies excel at identifying zero-day attacks, providing a crucial layer of defense against novel threats. the beauty of ai-powered incident response lies in swift action. these systems can automatically isolate infected systems, remediate vulnerabilities, and even collect evidence and generate reports. this not only prevents the spread of threats but also streamlines the analysis process, providing valuable insights for enhancing future defenses.18 traditional security approaches, akin to a “castle-andmoat,” are becoming obsolete in today’s interconnected world. zero trust architecture (zta) offers a paradigm shift through micro-segmentation, access control, continuous authentication, and authorization. zta envisions dividing a network into mini fortresses, each housing specific data or applications. implementing least-privilege access prevents unauthorized lateral movement within the network (figure 2). dynamic trust verification ensures that trust is continuously verified throughout a session, adapting to the evolving risk landscape. zta employs a multi-factor security system on “steroids.” beyond passwords, it leverages factors such as fingerprints, biometric scans, or one-time codes for user verification. risk-based authentication considers contextual factors like user location and device type, adjusting authentication requirements based on the assessed risk.20 beyond cryptocurrencies, blockchain’s distributed, tamper-proof ledger offers unique advantages for cybersecurity, including secure data provenance, tamper-proofing, secure identity management, and decentralized fig. 2. setting up a zta solution for enterprises. zta: zero trust architecture.21 table 1. the options for machine learning use in the cybersecurity space19 use case description vulnerability management provides recommended vulnerability prioritization based on criticality for it and security teams. static file analysis enables threat prevention by predicting file maliciousness based on a file’s features. behavioral analysis analyzes adversary behavior at runtime to model and predict attack patterns across the cyber kill chain. static & behavioral hybrid analysis composes static file analysis and behavioral analysis to provide advanced threat detection. anomaly detection identifies anomalies in data to inform risk scoring and to direct threat investigations. forensic analysis runs counterintelligence to analyze attack progression and identify system vulnerabilities sandbox malware analysis analyzes code samples in isolated, safe environments to identify and classify malicious behavior, as well as map them to known adversaries. it: information technology. https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.302 5 (page number not for citation purpose) emerging trends in cybersecurity identity. blockchain employs cryptographic hashing and a distributed ledger to secure data blocks. each block is secured with a unique digital fingerprint, making any alterations immediately detectable. the distributed nature of the ledger across a network makes tampering virtually impossible. blockchain introduces decentralized identity (did) and verifiable credentials (vcs). the did allows users to control their identity data, eliminating single points of failure. vcs enable users to issue and share credentials without relying on intermediaries, reducing the risk of fraud (figure 3).23 deception technologies create a false digital reality, employing honeytraps, honeypots, threat emulation, and simulation to mislead and disable cybercriminals. honeytraps and honeypots act as digital lures and traps. honeytraps resemble actual systems, tricking attackers into wasting time. honeypots capture attacker tactics and techniques, providing valuable intelligence for security teams. threat emulation and simulation replicate actual attack vectors, allowing organizations to test their defenses and identify vulnerabilities. these simulations reveal weaknesses in existing security controls, aiding in prioritizing patching vulnerabilities and strengthening defenses (figure 4).25 robust legal frameworks are pivotal for risk mitigation and accountability. these legal measures, working in tandem with technological advancements, offer a comprehensive defense against malicious actors. the general data protection regulation, cybersecurity information sharing act (ccpa), and other regional laws set data security standards, elevating industry-wide cybersecurity. critical infrastructure protection mandates specific security controls to safeguard vital systems from cyber threats. the cisa encourages private-public collaboration and rapid response through shared threat intelligence.26 global agreements, like the budapest convention, foster collaborative efforts against cybercrime. laws like the computer fraud and abuse act in the u.s. criminalize various cyber-related offenses, serving as a legal deterrent against fig. 3. generalized concept of blockchain protecting assets.22 fig. 4. a pac-man-styled example of the deception concept.24 https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.3026 (page number not for citation purpose) t. zaidi and s. garai malicious activity. regulations like the eu cybersecurity act hold organizations liable for data breaches under certain circumstances, incentivizing robust security practices and promoting accountability.27 initiatives like the uk’s regulatory sandbox allow testing of emerging cybersecurity technologies in controlled environments, accelerating development and fostering innovation in response to new threats. regular review and updates of laws and frameworks are crucial to keep pace with the evolving threat landscape. open dialogues between policymakers, security experts, and industry stakeholders ensure frameworks remain relevant. behavioral biometrics adds a new dimension to security by recognizing users based on unique characteristics such as keystroke dynamics, mouse movements, and login habits. behavioral biometrics continuously monitors user activity, including keystroke dynamics, mouse movements, and login habits. this creates a digital guard that watches every move, enhancing security by recognizing deviations from established user profiles. this form of biometrics adjusts defenses based on the user’s risk profile. high-risk scenarios trigger additional biometric verification steps, while lower-risk activities remain streamlined, providing a user-friendly experience. behavioral biometrics also aids in fraud detection by identifying unusual changes in user behavior (figure 5).29 while these six advancements mark significant progress in cybersecurity, the landscape continues to evolve. technologies like quantum computing, secure multi-party computation (smpc), and homomorphic encryption hold promise for further strengthening defenses. however, for a resilient and secure digital environment, a holistic cybersecurity strategy is imperative. this involves combining advanced technology, traditional security practices, and fostering global collaboration to combat the evolving threat landscape. recognizing legal landscape variations across regions is vital for the effectiveness of such a strategy. analysis of current cyber resilience measures against real-world threats an in-depth examination of defensive strategies against multifaceted digital threats reveals a complex interplay of advanced technologies and comprehensive frameworks. this intricate dance involves a symbiotic relationship between ai, ml, zta, blockchain, legal frameworks, and robust cybersecurity practices, forming a multifaceted defense mechanism. both ai and ml operate as vigilant sentinels, harnessing extensive data analyses to identify compromised components and detect suspicious iot activities. this seamlessly complements robust cybersecurity frameworks that establish industry best practices. the zta further fortifies security by constraining lateral movement, even following a potential infiltration. the utilization of blockchain, an immutable ledger, ensures provenance tracking of components, addressing authenticity concerns in the supply chain. incorporating guidance from recognized cybersecurity frameworks, such as the national institute of standards and technology’s cybersecurity framework, enhances the secure implementation of blockchain. it is essential to acknowledge the potential for ai bias, underscoring the importance of ethical considerations and diverse training datasets. additionally, while zta complexity requires specialized expertise, the application of cybersecurity frameworks as implementation blueprints can mitigate challenges. addressing scalability limitations in current blockchain implementations demands collaborative efforts and regulatory clarity. moving to the realm of ddos attacks, ai and ml play a crucial role in swiftly responding to anomalous traffic patterns, mitigating their impact. coordinated incident fig. 5. types of uniqueness in one’s behavioral pattern.28 https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.302 7 (page number not for citation purpose) emerging trends in cybersecurity response plans, as outlined in established cybersecurity frameworks, ensure minimal downtime. specialized ddos mitigation services act as a bulwark against digital onslaughts, with legal frameworks holding service providers accountable for breaches. zta, through identity verification and access restrictions, reinforces defenses against ddos attacks originating from compromised accounts. however, it is essential to recognize that ai-based ddos mitigation systems may inadvertently disrupt legitimate traffic, causing service disruptions. the costs associated with specialized ddos mitigation services present financial barriers, especially for smaller organizations. coordination challenges during incident response, influenced by communication delays and jurisdictional complexities, underscore the complexities of ddos defense. in countering apts, ai and ml function as vigilant analysts, discerning subtle anomalies indicative of potential threats. legal frameworks facilitate threat intelligence sharing, fostering a collective defense against known apt tactics. zta, through restricted access and continuous identity verification, hinders apt movement and data exfiltration. the presence of data breach notification laws incentivizes swift disclosure, mitigating apt-related damage. however, ai-based apt detection systems may generate false positives, necessitating resource-intensive investigations. the sophisticated techniques employed by apts to obfuscate their activity underscore the limitations of advanced monitoring tools. privacy concerns and the necessity for trust may hinder the collaborative sharing of sensitive threat intelligence. shifting focus to social engineering, security awareness training equips individuals to recognize and resist manipulation, supported by cybersecurity frameworks guiding effective program implementation. multi-factor authentication adds an additional layer of security, with legal frameworks incentivizing its adoption. fostering an environment of open communication encourages early detection of social engineering schemes. however, the effectiveness of security awareness training may vary among employees, particularly those with limited technical knowledge. the constant evolution of attackers necessitates ongoing adaptation of strategies to counter new techniques. establishing a culture of open communication can be challenging, especially in hierarchical organizations. behavioral biometrics come into play when monitoring user behavior to expose potential unauthorized access attempts. strong passwords and multi-factor authentication provide a robust defense guided by cybersecurity frameworks. data loss prevention minimizes the risk of sensitive data exfiltration, with data breach notification laws incentivizing prompt responses. nonetheless, privacy concerns may arise in the collection of employee biometric data, and user fatigue with multi-factor authentication may impact consistent adoption. the implementation and maintenance of effective data loss prevention (dlp) solutions present financial challenges. when safeguarding intellectual property, data encryption ensures confidentiality, with legal frameworks penalizing inadequate protection of sensitive data. digital rights management controls access, preventing unauthorized copying and distribution. incident response planning, as outlined in cybersecurity frameworks, facilitates swift action in the event of suspected intellectual property theft. however, secure management of encryption keys is paramount to ensuring encryption effectiveness. interoperability challenges among different drm systems may hinder content distribution. the speed of incident response to intellectual property theft incidents necessitates extensive coordination and legal considerations. in enhancing zero-day monitoring, ai and ml continuously scan for anomalies, addressing zero-day attacks before widespread dissemination. deception technologies, such as honeypots and decoys, reveal zero-day exploits, with legal frameworks offering protection. threat modeling, facilitated by structured methodologies, enables proactive mitigation measures. however, ai explainability challenges may lead to false positives or oversight of threats. legal considerations in the deployment of deception technologies underscore potential disruptions and require careful planning. expertise gaps in threat modeling methodologies present challenges in effective planning for and mitigation of zero-day threats. addressing insider threats involves monitoring unusual user behavior through behavioral biometrics and conducting regular access reviews, as guided by cybersecurity frameworks. anonymous reporting mechanisms empower employees to report suspicious activity without fear of retaliation. balancing security needs with employee privacy concerns is crucial in monitoring employee activity. the efficiency of regular access reviews may be compromised by the resource-intensive nature of the process. despite legal protections, fear of retaliation may hinder timely reporting of insider threats. in the context of reducing ransomware attacks, data backups ensure swift restoration, mitigating the impact of such attacks. prompt vulnerability management reduces the attack surface, with legal frameworks incentivizing vulnerability disclosure. security awareness training educates employees about ransomware risks and phishing tactics. however, ransomware attacks may target backups, resulting in data loss even after primary system restoration. patching vulnerabilities promptly can be challenging, particularly in complex it environments. frequent security awareness training sessions may contribute to employee fatigue. social engineering attacks can be mitigated through social media monitoring, guided by data privacy https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.3028 (page number not for citation purpose) t. zaidi and s. garai regulations. phishing awareness campaigns and multi factor authentication reduce the success rate of such attacks. legal frameworks incentivize strong authentication practices. however, monitoring employee social media activity raises privacy concerns and requires transparent policies. developing effective phishing simulations can be resource-intensive. while effective, multi-factor authentication systems introduce potential vulnerabilities. data classification prioritizes and labels sensitive data, as mandated by data protection regulations. data access control limits access, minimizing the risk of unauthorized exfiltration. the dlp tools detect and prevent unauthorized data transfer, guided by cybersecurity frameworks. however, overly granular data classification may increase operational costs and hinder legitimate data access. implementing robust access control systems requires expertise in identity management and authorization. the dlp tools may generate false positives, necessitating careful management. continuous user monitoring, implemented through technical and organizational measures, aids in the prevention of unauthorized account takeovers. advanced analytics tools detect suspicious patterns in user logins. strong authentication practices, including multi-factor authentication and strong password policies, significantly reduce the risk of successful account takeovers. legal frameworks can incentivize organizations to adopt and maintain strong authentication practices. however, continuous user monitoring systems’ implementation and maintenance can be expensive, particularly for organizations with large user bases. a high volume of suspicious activity alerts may overwhelm security teams, leading to alert fatigue and potential oversight of genuine threats. convincing users to consistently adopt and utilize strong authentication methods poses a challenge, particularly among non-technical users. in navigating the intricate landscape of cybersecurity, it is imperative to recognize both the strengths and limitations of various strategies. the collaborative integration of ai, ml, zta, blockchain, legal frameworks, and comprehensive cybersecurity practices contributes to a multi-layered defense against the diverse and evolving threats that cast shadows on the digital realm. as technologies advance and threats evolve, a holistic approach encompassing technological innovations, ethical considerations, regulatory compliance, and continuous improvement remains critical in safeguarding the integrity, confidentiality, and availability of digital assets. proposed solutions for safeguarding digital integrity we’ve covered various solutions focused on preventing cybersecurity threats, acknowledging that there’s no one-size-fits-all solution. now, let’s shift our focus to a different angle of the issue. how can we minimize the dissemination of leaked confidential information? what measures can be implemented to make data breaches less rewarding, discouraging potential attackers from initiating such actions? let’s explore solutions centered around these questions. concept of deleakification embracing the philosophy that a proactive approach is key to effective defense, i’ve developed a concept that aligns with this principle. before delving into the details, let’s familiarize ourselves with some essential terms that will prove beneficial in our exploration. to begin, hex data, or hexadecimal data, is a representation of information in a base-16 numerical system. this serves as a fundamental format for encoding binary data, commonly employed in programming and computer science. content-based fingerprinting is a technique that examines a file’s visual or audio content to craft a unique fingerprint—a kind of digital “hash” that captures the media’s essence without requiring playback. algorithms extract features like colors, textures, shapes, or audio frequencies to compose this fingerprint. importantly, this method remains effective in identifying original content, even if the file format undergoes changes or compression.30 perceptual hashing shifts the focus to how humans perceive content. despite noise, compression artifacts, or editing, perceptual hashing remains robust in uniquely identifying media.31 moving on, a worm is a type of malicious software capable of independent replication and spreading across networks and systems. worms exploit vulnerabilities, presenting a significant threat to the security of interconnected environments. similarly, trojan malware disguises itself as legitimate software, deceiving users into installing it. once infiltrated, it enables unauthorized access and can compromise sensitive information or facilitate other malicious activities. furthermore, a logic bomb is a piece of code intentionally inserted into a software system to execute harmful actions when specific conditions are met. these conditions can be triggered by various events, potentially causing disruption or damage to the system. in the realm of recent vulnerabilities, it’s crucial to highlight the critical webp image vulnerability (cve-20234863).32 this flaw allowed attackers to execute malicious code through crafted .webp files, affecting numerous applications due to the widespread use of the libwebp library for handling such images. immediate software updates are essential to address this vulnerability and ensure protection. shifting our focus to built-in safeguards, operating system (os) inbuilt virus scanners and search indexers are integral tools designed to detect and neutralize viruses and malware. these utilities actively monitor and identify potential threats, contributing significantly to the overall security of the system. https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.302 9 (page number not for citation purpose) emerging trends in cybersecurity additionally, a content delivery network (cdn) serves as a distributed system of servers collaborating to deliver web content efficiently based on users’ geographical locations. beyond enhancing website performance, cdns provide an added layer of security against specific cyber threats. understanding these terminologies is essential for grasping the nuances of the concept that centers around a proactive defense strategy. now, let’s delve into the specifics of how each of these elements contributes to building my robust cybersecurity approach. the process involves taking the desired media or text files for “unleaking.” these files are then processed through software like hex dump or vim to obtain hex data for text files and unique identifier information for media files, leveraging content-based fingerprinting and perceptual hashing methodologies. subsequently, a worm code is developed, designed to spread through the internet and connect to cdns to download trojans (figure 6). once the worm is active on a device, it retrieves the trojan package, and the trojan, in turn, downloads the metadata (hex data or unique identifiers) previously generated and dispersed across various public or private cdns. the next step involves the trojan pairing with the system’s inbuilt virus scanners or search indexes, initiating a scan to find matches for the stored metadata. in cases where the system lacks a scanner for root/ admin access, the worm can download its own from preloaded ones in the cdn. utilizing a trojan horse strategy, the worm may deceive users into granting admin privileges by presenting itself as a system file. returning to the scanning process, if a match is detected, the worm acts as a logic bomb, corrupting files by replacing the original data with garbage data. this method aims to remove the leaked information without causing any additional destructive actions. in situations where cdn access is unavailable, the worm script can be attached to a file and sent to unsuspecting users, reminiscent of techniques like word macros or exploiting vulnerabilities like .webp. once executed, the worm undertakes its tasks. additionally, the worm is programmed to detect the presence of any existing trojans to prevent system overload and potential user detection. this comprehensive approach ensures a strategic and nuanced method for addressing leaked information. web3 data privacy model the transition from web 2.0 to web 3.0 marks a shift from centralized to decentralized systems. web 3.0’s core principle is decentralization, fundamentally transforming how data and applications are handled. this shift enhances privacy by reducing reliance on central authorities, allowing individuals to have greater control over their personal information. in web 3.0, technologies like blockchain, decentralized identifiers, and zero-knowledge proofs play key roles in fostering a more private, secure, and user-centric digital environment. now, moving into the intricacies of this model, it leverages contemporary concepts currently in development. before delving into the operational details of the model, it’s crucial to familiarize ourselves with the associated terminology. the dids are a foundational element of web 3.0, playing a vital role in providing users with a decentralized mechanism to create and manage unique identities online. users, through dids, gain the autonomy to establish and control their digital personas independently, thereby enhancing privacy. an example illustrating this is the capability of dids to enable individuals to create and manage online identities without relying on a central authority, aligning with the overarching theme of enhancing user privacy in the digital realm.33 fig. 6. flowchart diagram explanation of the working mechanism. https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.30210 (page number not for citation purpose) t. zaidi and s. garai vcs constitute a pivotal aspect of web 3.0, facilitating the issuance and presentation of tamper-proof, digitally verifiable credentials. in practical terms, individuals can share digitally signed credentials, such as diplomas, without divulging unnecessary personal information. this exemplifies the role of vcs in bolstering privacy and security, providing a tangible illustration of how they empower users in the digital landscape.34 zero-knowledge proofs (zkps) stand out as a crucial cryptographic technique within the web 3.0 paradigm, enabling parties to prove the authenticity of information without disclosing the actual data. the zkps contribute significantly to privacy by verifying information without revealing underlying details. an example that illustrates this concept is when zkps allow someone to prove knowledge of a secret without disclosing the secret itself, thereby ensuring privacy in digital transactions.35 federated learning (fl) transforms the landscape of ml in web 3.0 by facilitating collaborative model training across decentralized devices. an example that showcases fl’s privacy-conscious approach is its ability to enable mobile devices to collaboratively train a predictive model without exchanging raw data. this preserves user privacy while harnessing aggregated knowledge for the benefit of the entire system.36 smpc plays a critical role in web 3.0, enabling secure computation across multiple parties without exposing individual inputs. an illustrative example is when smpc allows multiple parties to jointly compute a result without revealing their individual inputs. this functionality proves valuable for confidential data analysis, highlighting its significance in safeguarding privacy.37 personal data stores (pds) empower individuals to manage their personal data securely within a private repository. for instance, pds enables users to control access to their stored information, reinforcing user control over their digital identity and enhancing privacy in the management of personal data.38 blockchain-based data storage (bbds) is a revolutionary concept in web 3.0, decentralizing information storage across a network of nodes. this transparent and tamper-resistant approach ensures data integrity and minimizes the risk of unauthorized alterations. an example illustrating this concept is how blockchain stores data, making it resistant to tampering and ensuring transparent, secure, and privacy-enhanced data storage.39 trusted execution environments (tees) contribute significantly to web 3.0 by providing secure spaces on devices for processing sensitive information. tees in action are their ability to safeguard encryption keys and protect user privacy by ensuring certain processes occur in a trusted and protected space on a device.40 currently, the terminologies may not be entirely clear (figure 7). let’s gain a comprehensive understanding of all these concepts by utilizing the model and exploring an example that demonstrates the functionality of each model. in this futuristic voting scenario powered by web 3.0 technologies, individuals experience a transformative and privacy-focused electoral process. each voter is equipped with a did stored on their mobile device, granting them ownership and control of their digital identity independent of a central authority. instead of traditional physical identification, voters issue secure vcs through their dids directly from government databases, proving eligibility without compromising personal details and thereby enhancing privacy. to further ensure privacy, the election authority employs zkps to verify voter eligibility without accessing individual records and confirm eligibility without exposing specific details. collaborative predictive modeling is achieved through fl, where ml models are trained on encrypted voter data stored securely on individual devices. this not only enhances predictive accuracy but also maintains privacy throughout the process. the integrity of the election results is safeguarded by leveraging smpc during result calculations. election officials and independent auditors collaboratively analyze voting data without directly sharing sensitive information, thereby preserving the confidentiality of individual votes. voters retain control over their voting history and preferences through pds, accessible by the election commission only with explicit voter consent through dids and vcs, minimizing data exposure and empowering users to manage their information securely (figure 8). transparent and tamper-resistant storage is achieved through bbds, where the election results and voting records are securely stored on a permissioned blockchain. this ensures the integrity of the electoral process while restricting access to authorized entities. additionally, fig. 7. web3 data privacy model (w3dpm). https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.302 11 (page number not for citation purpose) emerging trends in cybersecurity tees contribute an extra layer of security by isolating sensitive calculations, such as fraud detection and result verification, within trusted enclaves on voters’ devices. this comprehensive example demonstrates how web 3.0 principles revolutionize real-world applications. it offers a secure, transparent, and privacy-centric voting experience in which individuals maintain control over their identities and personal data throughout the entire electoral process. comparative and critical analysis as mentioned earlier, my cybersecurity solutions and approach deviate significantly from mainstream practices in the field. consequently, a direct comparison with existing methodologies poses distinct challenges. shifting our attention to the concept of deleakification, this concept proves to be controversial and potentially hazardous, embodying both advantages and drawbacks. a well-designed virus, integral to the deleakification concept, holds the potential to efficiently scan and remove targeted data, outpacing manual or traditional methods—especially beneficial when addressing extensive datasets. this advantage becomes particularly crucial in time-sensitive situations where the swift mitigation of leaked information is paramount. an additional benefit lies in the virus’s capability to access and remove data from infected systems that might prove challenging to reach through conventional means, such as offline devices or concealed storage locations. while advantageous in specific scenarios, concerns naturally arise regarding unintended consequences and potential privacy violations. the vision of a self-replicating virus within this concept introduces the prospect of automating the data removal process, reducing reliance on human intervention and potentially minimizing the risk of human error. however, this automation raises valid concerns related to controllability and the potential for unintended spread or damage. it is imperative to acknowledge that the practical implementation of such a virus would face considerable challenges. achieving precise targeting to remove leaked data without impacting legitimate online content proves to be an intricate task. given the quick and uncontrollable spread of viruses, there is a substantial risk of impacting unrelated data, potentially causing collateral damage. complicating matters further, leaked data often exists in fragmented forms across multiple sites and platforms. the virus would require an exceptionally sophisticated design to locate and remove all instances of the leaked information, presenting a practical impossibility in most cases. additionally, the underlying technology of such a virus, while designed for ethical purposes within deleakification, could be susceptible to misuse for malicious objectives. this raises a concerning precedent for potential future cyberattacks, underscoring significant ethical and security concerns. web 3.0 data privacy solutions bring a paradigm shift in how individuals manage their identities, and among these, dids play a crucial role. by allowing individuals to own and manage their identities, dids reduce the influence of central authorities, thereby minimizing data vulnerabilities. however, the complexity of managing dids and vcs could impede widespread adoption, especially among non-technical users. the need for continuous refinement in standards and interoperability is paramount to ensure seamless collaboration across diverse platforms. the vcs offer a secure way to share specific data attributes, mitigating the risks of data manipulation and identity theft. yet, integrating vcs across various sectors demands widespread adoption and consistent formats to facilitate seamless verification and utilization. ethical implementation is crucial to prevent the potential discriminatory use of vcs. the zkps provide an innovative solution by proving information possession without disclosing details, reducing data exposure. however, implementing and understanding fig. 8. voting scenario example. fl: federated learning; tee: trusted execution environments; zkp: zero-knowledge proofs. https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.30212 (page number not for citation purpose) t. zaidi and s. garai zkps pose challenges for both developers and users, demanding technical expertise. the computational cost of complex zkps could impact processing resources, necessitating careful consideration during integration. the fl minimizes data sharing by training models on local devices, enhancing privacy. however, the aggregation and management of decentralized data may result in slower processes. robust security protocols are essential to ensure data security across diverse devices and networks, and well-designed incentives are crucial to encourage user participation. the smpc enables joint data analysis without revealing individual contributions, fostering secure collaboration. yet, the computational expense of complex protocols and challenges in scaling for large datasets necessitate powerful hardware. effective implementation requires specialized technical knowledge and expertise. the pdss empower individuals to own and manage their data, reducing vulnerability to centralized breaches. however, consistent data formats and access protocols are vital for seamless sharing. robust backup and recovery mechanisms are essential to avoid data loss, and user education is key for widespread adoption. the bbds ensures data immutability and transparency, but challenges in scaling for large volumes and environmental concerns with some consensus mechanisms persist. privacy-preserving techniques are crucial to balance the benefits of transparency with user privacy. the tees provide secure enclaves for sensitive computations, enhancing data security. however, limited availability on all devices and potential execution overhead require consideration. continuous research is essential to address potential vulnerabilities and ensure robust security. overall, the concept of deleakification & web 3.0 data privacy solutions offers great potential for empowering individuals with greater control over their data and ensuring privacy in the digital world. however, each technology comes with its own set of advantages and disadvantages, and their successful implementation requires careful consideration of these factors as well as collaboration across various stakeholders to address existing challenges and ensure ethical and responsible development. conclusion and future scope the current landscape of unleakification and web3 data privacy models faces limitations, with these technologies still in their early stages. however, this nascent stage provides a significant opportunity for improvement, making them more viable, user-friendly, and widely adoptable, ultimately enhancing their stability. challenges such as technical complexity, user adoption, and scalability need to be addressed, but the opportunities presented by decentralization and user-owned data hold promise for a more secure and user-centric future. in the realm of ai and cybersecurity, the increased attack surfaces resulting from ai integration are concerning. frameworks and regulations, like iso 42001 & european ai laws, are steps in the right direction for responsible development and robust security. while emerging solutions like quantum cryptography hold promise, vigilance against potential threats, especially from artificial general intelligence (agi), is crucial. beyond technical considerations, addressing the social and ethical implications of ai and data privacy is crucial. open discussions about data ownership, algorithmic bias, and ai-driven manipulation are necessary for responsible development. emphasizing human-ai collaboration, viewing ai as a tool for empowerment rather than a replacement, can contribute to ethical and beneficial development. as the ai revolution progresses, it brings both advancements and challenges. the widespread implementation of ai in various sectors expands the attack surface, posing challenges for cybersecurity professionals who must elevate their game. despite existing frameworks, there is still much to protect. the computational power of ai could render currently secure technologies obsolete. while emerging solutions like quantum cryptography show promise, potential threats from artificial general intelligence underscore the need for cautious research, robust security measures, and ethical considerations.41 continued research, collaboration, and careful consideration of challenges and ethical implications can help harness the potential of these technologies for a secure, equitable, and enriching future. funding no funding was provided for the development of this article. financial and non-financial relationships and activities none reported by the authors. contributors all authors of this research paper have directly participated in the planning, execution, or analysis of this study. all authors of this paper have read and approved the final version submitted. data availability statement (das), data sharing, reproducibility, and data repositories original data were not used in the development of the article. application of ai-generated text or related technology ai and related technologies were not used in the preparation of this article. https://doi.org/10.30953/bhty.v7.302 citation: blockchain in healthcare today 2024, 7: 302 https://doi.org/10.30953/bhty.v7.302 13 (page number not for 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[cited 2024 jan 14]. available from: https://www.weforum.org/publications/realizing-the-potent ial-ofblockchain/ 40. lee d, kohlbrenner d, shinde s, asanovi k, song d. keystone: an open framework for architecting trusted execution environments. in proceedings of the fifteenth european conference on computer systems, in eurosys ’20. new york, ny: association for computing machinery, 2020; p. 1–16. 41. kaur r, gabrijelič d, klobučar t. artificial intelligence for cybersecurity: literature review and future research directions. inf fusion. 2023;97:101804. https://doi.org/10.1016/j.inffus.2023.101804 copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their deriva tive works on different terms, provided the original work is properly cited, and the use is non-commercial. see http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.302 https://info.cs.st-andrews.ac.uk/student-handbook/files/project-library/cs4796/gf45-final_report.pdf https://info.cs.st-andrews.ac.uk/student-handbook/files/project-library/cs4796/gf45-final_report.pdf https://doi.org/10.48550/arxiv.2201.09384 https://doi.org/10.48550/arxiv.2201.09384 https://ssrn.com/abstract=4617953 https://ssrn.com/abstract=4617953 https://doi.org/10.2139/ssrn.4617953 https://www.weforum.org/publications/realizing-the-potential-of-blockchain/ https://www.weforum.org/publications/realizing-the-potential-of-blockchain/ https://doi.org/10.1016/j.inffus.2023.101804 http://creativecommons. org/licenses/by-nc/4.0 http://creativecommons. org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research blockchain applications in the pharmaceutical industry mark gaynor, phd1, kathleen gillespie, phd1, allison roe1 , erica crannage, phd2 and j.e. tuttle-newhall, md3 1college of social justice and public health, saint louis university, st. louis, missouri, usa; 2associate professor, pharmacy practice, university of health sciences and pharmacy, st. louis, missouri, usa; 3department chair of surgery, east carolina university, greenville, north carolina, usa corresponding author: mark gaynor, email: mark.gaynor@slu.edu doi: https://doi.org/10.30953/bhty.v7.298 keywords: 4d framework, blockchain applications, blockchain technology, clinical trials, health records, inventory systems, pharmaceutical industry, prescription misuse and abuse abstract methods: we utilized a 4d framework using ease of implementation, novelty, necessity, and fit of the overall industry to examine the adoption of blockchain technology in the pharmaceutical industry. based on the 2d framework of difficulty and novelty as driving factors for the development of foundational technologies in the world of business by iansiti and lakhani, each application was ranked and scored for the best potential implementation. the potential applications proposed in this paper can be grouped into two main categories. the first category, management, includes best-use cases, such as health records, clinical trials, and inventory systems. the second category, monitoring, highlights cases, such as pharmaceutical products, preventing counterfeits, optimizing supply chains, and addressing prescription misuse and abuse. results: each application was ranked by the four metrics in the framework, giving the greatest weight to necessity and ease of implementation. using the highlighted methodology earlier, the applications for best implementation include prescription drug misuse and abuse prevention, prevention of counterfeits, clinical trial outcomes, and smart contracts. conclusion: blockchain technology offers a new and promising solution to the pharmaceutical industry’s needs. to promote the most appropriate use, each application of blockchain technology must fit within the framework of necessity, ease of implementation, familiarity amongst stakeholders, and fit of the overall industry. by using the extended framework proposed by iansiti and lakhani, we show how blockchain, in all these domains, shows promise to improve pharmaceutical industry performance. received: december 12, 2023; accepted: march 12, 2024; published; april 30, 2024 in recent years, blockchain technology has made great strides in diverse industries, but it has fallen behind within the pharmaceutical industry. the pharmaceutical industry is complex and would benefit greatly from the distributed database and emphasis on information privacy promoted by blockchain technology. based on the 2d framework of difficulty and novelty as driving factors for the development of foundational technologies in the world of business by iansiti and lakhani,1 this paper identifies the potential best application for blockchain technology in the united states pharmaceutical industry by identifying current trends, companies exploring the possibilities of blockchain technology, and industry concerns with opportunities for improvement. as society becomes more familiar with the revolution of blockchain technologies in transaction processing, record management, surveillance, and data management, blockchain will likely be implemented at greater rates into a diverse set of industries, including healthcare. this paper evaluates and discusses the ways that blockchain technology can be implemented in the pharmaceutical industry and transform multifaceted problems. since the invention of the cyber currency, bitcoin, blockchain technology has been implemented across blockchain in healthcare today issn 2573-8240 https://orcid.org/0009-0008-8141-6273 https://orcid.org/0000-0001-8509-9609 mailto:mark.gaynor@slu.edu https://doi.org/10.30953/bhty.v7.298 citation: blockchain in healthcare today 2024, 7: 298 https://doi.org/10.30953/bhty.v7.2982 (page number not for citation purpose) mark gaynor et al. many industries to solve a plethora of problems. blockchain technology links blocks that store transactions to one another in a distributed ledger. the use of a distributed ledger in a blockchain is crucial due to its unique security features. distributed ledgers are “a type of database that (are) shared, replicated, and synchronized among the members of a network. the distribution ledger records the transactions, such as the exchange of assets or data, among the participants in the network.”.2 in a private ledger within a block, access is limited to authorized members, while in a public ledger, data are independently verified, and transaction participants can remain anonymous.3 a public ledger does not require membership, while a private blockchain requires contributions of a ledger to be approved by an organization to confirm the transaction is allowed. in any given network, participants interact to view, store, and exchange information. the ledger of any blockchain is permanently recorded as an incorruptible set of data.3 figure 1 illustrates a simple blockchain. each block holds a piece of data (e.g. a transaction), a hash of the preceding block, and a hash for the data within the block.4 the dashed lines represent the region that each block hash covers. every block (except the root block) is linked to the previous block and each subsequent block in the secure chain. if any alterations to the data were to occur, the hash of the changed block and each hash following the chain will also be altered.5 the blockchain can be viewed as a distributed ledger that is a permanently recorded set of data that is incorruptible.3 individuals in each blockchain network interact to store, exchange, and view information. the data are confirmed and then validated as transaction blocks are linked and chained from the beginning of the chain to the most current block.2 with each transaction, the blockchain becomes increasingly difficult to alter, as each block must be verified by all users in the ledger.4 additionally, a blockchain network will conduct automatic self-checks that decrease corruptibility and maximize the overall transparency among stakeholders in a blockchain.6 these two concepts work together to uphold the overall integrity of a blockchain. blockchain technology does not depend on a centralized authority. instead, each record is accessible to all members of a blockchain and can be easily verified. however, due to security needs in health care, these blocks could operate as semipublic – using permission rights to verify data before it permanently joins a blockchain.4 this approach allows restricted access. simultaneously, an audit trail accompanies each transaction to verify and authenticate it. each of these records will have a corresponding timestamp and cryptographic signature.2 if a set of data were to have a private key, it would act as a password allowing specific individuals the ability to access data within a contained transaction.7 in a public key system, a user is traced by their address on the blockchain to prove original ownership. blockchain uses both a public and private key model to ensure that the stored data are not only incorruptible but also traceable to a source while maintaining anonymity.4 a cryptographic hash acts as the digital signature to authenticate each block of data in a blockchain.2 hashing complements the use of both private and public keys by authenticating that the information in a transaction has remained unaltered. together, these blockchain functions support the elimination of centralized intermediaries in establishing trust.7 the elimination of a centralized authority fosters trust and facilitates more efficient data and information transfer.6 problem overview within the pharmaceutical industry in 2023, the united states encompassed over 43% of the total global pharmaceutical industry market share.8 this is expected to continue increasing with a predicted annual growth rate of 5.96% between the years 2024 and 2028.9 due to this, the united states will continue to grapple with complex issues related to the management of high-value pharmaceutical products. within the united states, pharmaceuticals account for a growing share of the healthcare economy. retail prescription fig. 1. visual representation of a simple blockchain. comprised of a root block, a hash of the preceding block, and a hash of the data in the block. reproduced from the author, gaynor et al.5 b(1) etc.: abbreviation for “block.” https://doi.org/10.30953/bhty.v7.298 citation: blockchain in healthcare today 2024, 7: 298 https://doi.org/10.30953/bhty.v7.298 3 (page number not for citation purpose) blockchain in the pharmaceutical industry expenditures accounted for over $300 billion of the $2.3  trillion spent on healthcare in 2021 or 9% of the total retail market.10 the pharmaceutical industry encompasses several key internal stakeholders, including pharmaceutical manufacturers, pharmaceutical wholesalers, health systems, pharmacies, and individual patients with prescription needs (see appendix for greater detail). most prescription drugs are sold by the manufacturer to a wholesaler. the wholesaler then sells the drug to pharmaceutical benefits management companies, health systems, group purchasing arrangements, and retail pharmacy companies. prices within this market change frequently, and there is a complex practice of rebates, discounts, and chargebacks that can occur from the point of the original sale to the wholesaler until after the drug is dispensed. thus, multiple organizations require secure access to financial transactions over time. different organizations have different abilities to make requests to alter those transactions and then to approve or deny them. the pharmaceutical industry also has several external stakeholders that include the general public, government entities that oversee and regulate the industry, and accreditation and trade organizations—all of which add increased regulation and verification pressure on the pharma sector. current challenges include the ability to monitor pharmaceuticals throughout the supply chain, protection against fraud, the ability to follow a streamlined research and development process (including clinical trials), and lack of prevention for misuse and abuse of addictive substances. by implementing blockchain technology, the pharmaceutical industry can improve population health outcomes and provide better transparency among stakeholders. applications in the pharmaceutical industry blockchain technology can be implemented to create more efficient, secure, and transparent systematic approaches within the pharmaceutical industry. by prioritizing applications that meet these needs, it is easier to create areas of possible application. these can be categorized into two main categories: monitoring and management. numerous companies and organizations are utilizing blockchain technology to revolutionize the pharmaceutical industry. many of these current applications use blockchain technology to provide real-time tracking and data transparency within the pharmaceutical industry, increasing patient safety, understanding, and overall health outcomes. these applications will be expanded upon below within the outlined hierarchical structure. monitoring the pharmaceutical industry’s ability to monitor goods and products is essential. many stakeholders are involved in the delivery of one specific product to any individual consumer. however, given the industry’s multifaceted nature, there is a deficiency in authenticating products and preventing prescription misuse. blockchain technology’s inherent lack of central governance can enhance visibility, authentication, and information flow, ultimately improving patient care in the context of pharmaceutical needs. the successful implementation of blockchain technology occurs when it integrates seamlessly with existing technology systems within the pharmaceutical industry. fig. 2. blockchain functioning across the pharmaceutical industry is divided into two categories: (1) monitoring and (2) management. these categories reflect implementation strategies using different components and functions. however, when combined, they work together to support best practices and a potential framework for blockchain technology in the pharmaceutical industry. https://doi.org/10.30953/bhty.v7.298 citation: blockchain in healthcare today 2024, 7: 298 https://doi.org/10.30953/bhty.v7.2984 (page number not for citation purpose) mark gaynor et al. further analysis of potential applications for monitoring pharmaceutical products includes: 1. to avoid counterfeit products, the ability to track and trace pharmaceutical products is essential. mediledger uses blockchain as a verification system across the pharmaceutical industry. the network forms groups among stakeholders to expedite the ability to verify the authenticity of a medication or product in a sale, or more specifically, a return.11 verifying drugs as authentic before being resold after a return can take up to 48 hours. however, mediledger significantly shortens this process using a blockchain network and barcode scanners.12 this process uses serial numbers for verification. the network first launched in 2019 and is working to reduce the complications in the sale and transfer of products. today, mediledger partners with some of the biggest names in the pharmaceutical industry including bayer, mckesson, pfizer, etc. 2. the pharmaceutical supply chain presents unique challenges, given the high value and specific storage conditions required for some products. the use of smart contracts is one way that blockchain technology can be used to promote proper supply chain monitoring. smart contracts automate the tracking of products throughout a supply chain. sensors are attached to products to record key information, such as environmental factors (humidity or temperature) and shipping errors (being dropped or lost). the parties involved in each transaction can set guidelines for shipping conditions that must be met. by doing so, they build trust through error reduction and decreased risk of manipulated conditions.13 smart contracts automatically track, generate notifications for updates, and create automated payments when all conditions are met. this process not only alleviates the burden of ensuring proper shipping conditions but also creates better interparty relationships. 3. many prescriptions have intense or adverse effects when combined inappropriately, known as contraindications. public–private partnerships, like pharmaledger, leverage blockchain technology to address this concern by providing consumers with more information and eliminating the need for printed  warnings, information, and instructions. pharmaledger collaborated with its stakeholders to create a secure electronic product information (epi) solution that instantly allows anyone with a smartphone to scan medical packaging and receive information regarding the use of a product.14 in real-time, the epi system can continuously update information from a manufacturer, preventing the continuous spread of out-of-date information in the epi.15 4. the misuse and abuse of prescription medications, particularly high-value or addictive ones, pose significant concerns. historically, it has been difficult to track and monitor these cases across providers, health systems, and states. by operating on a blockchain, all health providers can access essential medical information, including prior prescribed medications. using permission rights, the blockchain will track in realtime who is accessing and viewing all records of an individual while performing regular audits to ensure patient data safety in the case of unauthorized access. blockchain technologies offer a new way to grant healthcare providers the necessary information to fig. 3. potential opportunities for blockchain technology monitoring in the pharmaceutical industry, specifically as it relates to the prevention of counterfeits, supply chain function, prescription misuse, and warnings. flow diagram with permission from the author, gaynor.4 https://doi.org/10.30953/bhty.v7.298 citation: blockchain in healthcare today 2024, 7: 298 https://doi.org/10.30953/bhty.v7.298 5 (page number not for citation purpose) blockchain in the pharmaceutical industry prevent over prescription and misuse of pharmaceuticals. companies such as healthchain can help curb this problem by using blockchain technology to provide healthcare professionals with more efficient, interoperable data on an individual’s prescription history.16 once a patient’s prescription is entered into the blockchain, it can be easily verified and will remain a permanent record throughout time. healthchain strives to provide better patient outcomes through safer prescription practices. management the healthcare industry, including pharmaceutical makers, generates an enormous amount of data. personal health information includes a wide variety of data sources, such as electronic health records, wearable devices, health apps, etc. protecting these data is essential as much of it includes high-value personal health information. the distributed ledger and cryptographic hashing features of a blockchain allow for better data governance. the future of blockchain technology implementation within pharmaceutical industry management includes the following applications: 1. in the united states, the rate of prescription recalls remains high. according to the united states food and drug administration (fda), in 2023 alone, there were over 1,500 recalled products in medical devices, biologics, and drugs.17 the current system that is used to issue recalls to patients is tedious and cannot ensure the rate at which messages are received. the implementation of blockchain technology can effectively track the distribution of medications and identify patients impacted by the recall through an indisputable record system, enabling targeted alerts to only those individuals who received the recalled product. this application can also be used to prevent the distribution or use of expired medications. 2. efficiently managing the distribution of all pharmaceutical goods throughout the supply chain can be complex. not only is the location of the product in shipping important but so is locating the product in the scenario of supply shortages. the decentralization of blockchain promotes the ability for multiple actors to form a network displaying the status of drug supplies at pharmacies. this collaboration can efficiently improve patient outcomes. in 2019, wakemed health and indiana university health piloted a program in collaboration with good shepard pharmacy, called remedichain. remedichain focused on product tracking to address inventory shortages of high-value goods.18 additionally, they propose using this technology across networks in cases of shortages, emergencies, and negotiations.18 currently, they accept donated medications and match them to patients who are in immediate need. in real time, they can verify products and produce urgent sales with the hopes of relieving  pharmaceutical waste. since implementation, remedichain estimates they have prevented $17 million worth of pharmaceutical product waste.18 3. each prescription issued to an individual comes with a record and transaction trail of personal health information. protecting personal health information efficiently and effectively is a priority; however, current privacy protection practices result in information silos. blockchain technology allows for personal health information to be stored within a network, granting healthcare providers wider access to a patient’s essential health information while securely storing it through cryptographic signatures. patientory, a mobile app utilizing blockchain technology, is designed to grant patients the right to port and share their medical information to improve communication, store data, and even improve payment systems. 4. the clinical trial company, triall, is working to address some of their top concerns by decentralizing clinical trial information and data. to date, they have been used as the primary source for data governance in over 7,000 clinical trials.19 the use of blockchain technologies in clinical trials can promote greater participant monitoring, data management, and documentation management.19 this is particularly important in clinical trial research, which has a large influx of patient information and data. paperless clinical trials can reduce the overall cost and prevent stalls from ineffective paper management.20 additionally, the blockchain allows greater access for stakeholders to evaluate outcomes and needs. 5. blockchain technologies in genomic sequencing are offering a unique new way for individuals to take ownership and data governance of their health information into their own hands. companies such as nebula and encrypgen use blockchain technology along with cyber currency to allow individuals to gain autonomy over their genetic information. these companies provide patients with information regarding their genomic sequences and allow them to release it directly to pharmaceutical and clinical trial leaders as they deem appropriate.21 unlike ever before, this application removes intermediary parties left in control of entire genomic sequences and the sale of individuals’ personal health information. blockchain technology provides a new cryptographically secure way to store, share, and manage data across the pharmaceutical industry. similarly, blockchain technology offers a unique capability in managing inventory and supplies across hospital and healthcare systems by https://doi.org/10.30953/bhty.v7.298 citation: blockchain in healthcare today 2024, 7: 298 https://doi.org/10.30953/bhty.v7.2986 (page number not for citation purpose) mark gaynor et al. decentralizing management and enhancing accessibility. figure 4 portrays a hierarchy for possible applications of blockchain technology within the management of both data and inventory in the pharmaceutical industry. technology selection algorithm our selection algorithm for technology in the pharmaceutical industry is based on the framework presented by iansiti and lakhani.1 their framework presents the essential components of ease of implementation and industry familiarity. this framework was extended by gaynor to include necessity.4 in this article, we extend our framework to include fit-for-purpose.22 this extended framework enables us to rank applications of blockchain technology in the pharmaceutical industry based on essential metrics and to select the applications most likely to be adopted with this emerging technology. figure 5 illustrates nine potential applications for blockchain technology in the pharmaceutical industry ranked by their overall score. applications with the highest scores are the most likely to be successfully implemented. each of the nine applications was ranked on ease, familiarity, fit, and necessity. after this ranking, their scores were summed and given an official ranking. selection algorithm applications each of the nine applications was ranked on a scale of 1–5 in the four metrics: ease, familiarity, fit, and necessity, to select applications that can best be implemented in the pharmaceutical industry. figure 6 presents this model. low performers in any given category received a grade of one (furthest left), and high performers received a grade of five (furthest right). any score of three or below can be immediately removed from consideration of possible best applications. the four metrics, ease, familiarity, fit, and necessity, each play a different and essential role in determining how applications could function in the pharmaceutical industry. fig. 4. systematic analysis of management functions of blockchain technology. management functions can be defined as inventory management or data management. reproduced with permission from the author, gaynor et al.4 fig. 5. selection of algorithms for the application of blockchain trichology in the pharmaceutical industry. the graph depicts the total score of each application of the nine applications based on the decision matrix of ease, familiarity, fit, and necessity. https://doi.org/10.30953/bhty.v7.298 citation: blockchain in healthcare today 2024, 7: 298 https://doi.org/10.30953/bhty.v7.298 7 (page number not for citation purpose) blockchain in the pharmaceutical industry 1. ease of implementation relates to the simplicity and convenience of executing an application or task. this often relates to the question of what requires the least amount of effort or resources. this is the second most important metric in considering the best application for adoption. an application that ranks the highest in this metric is the prevention of counterfeit products, while the lowest performer is personal health information due to the complexities of interoperability in health data. 2. while important, familiarity holds less weight than ease and necessity. familiarity relates to the expectations and readiness to adopt a new product. applications ranking high in familiarity include clinical trial outcomes due to their importance and relevance in the pharmaceutical industry, and the lowest-ranking application includes prescription interactions. 3. fit is important as it relates to the overall alignment with strategic goals and needs of the pharmaceutical industry. smart contracts rank high in overall fit as they enable multiple parties in the pharmaceutical industry to build trusting relations for high-value pharmaceutical goods throughout their transportation. the  lowest performer in fit is personal health information, as this is not of utmost importance to stakeholders. 4. necessity relates to the importance of an application. the necessity metric is the most important attribute in considering potential applications. without genuine need, the success of implementation is likely to be minimal. a high performer in necessity is prescription abuse and misuse, while a low performer is generic vs. brand name availability. pharmacists, insurers, and other stakeholders are invested in preventing patient prescription misuse while caring far less about the availability or use of generic vs. brand-name products. the four categories for best implementation are (1) prescription drug misuse and abuse prevention, (2) prevention of counterfeits, (3) clinical trial outcomes, and (4) smart contracts. these four categories ranked the overall highest in creating innovative solutions for current pharmaceutical industry needs. 1. as seen in recent years, prescription drug misuse and abuse prevention for addictive prescription drugs, such as opiates, has become concerningly more prevalent. the dire need for a new system to track and prevent fig. 6. the best applications for blockchain technology are shown by individualizing scores in the decision matrix of ease, familiarity, fit, and necessity. https://doi.org/10.30953/bhty.v7.298 citation: blockchain in healthcare today 2024, 7: 298 https://doi.org/10.30953/bhty.v7.2988 (page number not for citation purpose) mark gaynor et al. misuse of these drugs creates an opportunity for the success of new technology. blockchain technology can create an indisputable record and transaction history of any individual’s prescription history, creating a safer prescribing practice. prescription drug misuse and abuse prevention ranks particularly high in the metrics of familiarity and necessity. 2. the prevention of counterfeit drugs ranks highest in terms of ease of implementation. this can be attributed to applications, such as mediledger, which already exist and have key pharmaceutical stakeholders engaged. applications such as this are beneficial as they reduce the burden of the verification process for pharmaceutical companies and members throughout their sale and transfer. 3. clinical trial outcomes are essential to the pharmaceutical industry’s function. with a high level of innovation and an influx of health data in clinical trial information, data tracking and reporting can become burdensome and lack transparency. clinical trial outcomes rank highest in familiarity due to the ongoing number of clinical trials in the united states. 4. smart contracts can allow for the monitoring and supply chain management of high-value goods. in the pharmaceutical industry, this can be particularly useful in ensuring shipping conditions (e.g. temperature and orientation) are met to deliver quality products. once products meet these conditions, automated payments are issued. smart contracts have proven to be beneficial in other high-value industries. for these reasons, smart contracts rank particularly high in the necessity and overall fit of the industry. limitations potential limitations of this analysis include the inability to perfectly predict technology implementation and outcomes. technology has been notoriously unpredictable for decades. however, these potential limitations are addressed by emphasizing the importance of necessary applications. without a genuine need, the likelihood of achieving favorable outcomes is diminished. while this analysis can identify those areas of the pharmaceutical industry where blockchain technology may be most appropriate, the adoption of blockchain is not certain. the structure of the pharmaceutical industry in the u.s. will limit the adoption of some applications. most of the organizations involved are for-profit enterprises; all are interested in improving efficiency and minimizing costs where appropriate. many of the applications identified above have benefits that will accrue to parties outside of the pharmaceutical industry. such benefits are referred to by several terms. they can be called external benefits or spillover effects, or the product may be said to have public goods aspects. one example of this is the idea of network neutrality, which defines a free and open architectural principle where data are treated equally on a network.23–26 if the industry must bear all the costs of creating and maintaining the network but is not able to reap all of the benefits (by, for example, charging user fees), it is unlikely that network neutrality and the use of blockchain will be voluntarily adopted. prescription drug misuse and abuse prevention is another example where spillover effects matter. the primary benefits of reducing or preventing prescription drug misuse and abuse are the additional years of life gained by prevented overdoses and the reduced medical expenditures to treat the abuse. these benefits accrue to many people and organizations, primarily outside of the pharmaceutical industry. a for-profit pharmaceutical firm will have little economic incentive, beyond the threat of lawsuits, to develop and use programs that reduce misuse and abuse because they cannot recoup their costs. to promote the adoption of these programs, pharmaceutical firms need an incentive. the incentive can be financial (e.g. subsidies, grants, and tax benefits) or legal (e.g. legislation and regulation).27 conclusion blockchain technology offers a new and promising solution to the pharmaceutical industry’s needs. the distributed database prioritizes privacy in the verification and authentication process. the implementation of blockchain technology must fit within a framework that supports necessity, ease of implementation, familiarity amongst stakeholders, and fit within the overall industry. based on these four metrics, the hierarchical structure outlined throughout this paper suggests that applications that best fit these include prescription drug misuse and abuse prevention, prevention of counterfeits, clinical trial outcomes, and smart contracts. future research in blockchain technology includes further exploring the economic impact and adoption, ethical and legal considerations for data ownership and privacy, and ai integration for potential data analysis from the blockchain. funding this research is not funded by any organization or government. financial and non-financial relationships and activities the authors have no conflicts of interest or financial relationships to disclose. contributors all authors contributed to this paper. dr. mark gaynor provided formatting, text context on blockchain technology and technology selection algorithms, and proofreading. dr. kathleen gillespie provided text on economic https://doi.org/10.30953/bhty.v7.298 citation: blockchain in healthcare today 2024, 7: 298 https://doi.org/10.30953/bhty.v7.298 9 (page number not for citation purpose) blockchain in the pharmaceutical industry evaluation and proofreading. allison roe constructed the context of the text along with figures. dr. erica crannage provided text and proofreading on the pharmaceutical industry. dr. j.e. tuttle-newhall provided a proofreading of the paper and healthcare industry context. application of ai-generated text or related technology after completing the manuscript, the authors used chatgpt3.5 as a corrective suggestion and proofreading tool. some of these suggestions were incorporated into the article. acknowledgments this paper was partially proofread and edited by chatgpt. references 1. iansiti m, lakhani, kr. the truth about blockchain [internet]. the harvard business review. 2017. 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https://doi.org/10.1080/10826084.2022.2026969 https://doi.org/10.1080/10826084.2022.2026969 http://creativecommons.org/licenses/by-nc/4.0 citation: blockchain in healthcare today 2024, 7: 298 https://doi.org/10.30953/bhty.v7.29810 (page number not for citation purpose) mark gaynor et al. appendix there are several companies breaking into the space of blockchain solutions within the united states pharmaceutical industry. this market is new and evolving, with relatively high volatility. the following list is not comprehensive of all companies within the blockchain and pharmaceutical realm but instead is a representation of the innovative technologies that exist on the market today. 1. embleema is a clinical research platform that utilizes blockchain technology to expedite the regulatory review process. they have several key factors in their technologies, including participant recruiting, secure and safe data sharing, audit trail of research data, evidence, and the united states food and drug administration (fda) engagement for approval. today, embleema is engaged with a variety of stakeholders, including academic medical centers, governmental agencies, biomedical technical companies, research institutes, hospitals, and universities. 2. healthchain is founded on creating a connection between providers, payers, and patients. they have several different applications that can be tailored to specific needs. by creating an integrated and interoperable system, healthchain could be utilized in the pharmaceutical industry to provide real-time tracking of a patient’s prescription history. 3. ledgerdomain is focused on drug quality and security act (dscsa) compliance. through their portal systems, pharmacies can quickly trace and verify products. ledgerdomain does this by creating authorized trading partners (atp) to drive the supply chain forward. this creates interoperable, data-driven, and enhanced security. 4. mediledger uses blockchain as a verification system across the pharmaceutical industry. their technology was born out of a collaboration with chronicled and has powered accurate, private, and decentralized transactions between manufacturers, group purchasing organizations, and wholesalers in the pharmaceutical supply chain. mediledger is engaged with pharmaceutical companies, such as pfizer, mckesson, cardinal health, etc. 5. nebula genomics offers full genomic sequencing directly to patients. nebula genomics is working to utilize a blockchain network to eliminate key concerns of cost, regulatory matters, and privacy. ultimately, this will provide users with full control over their health information and genomic sequence provided by nebula genomics. 6. pharmaledger’s blockchain technology has three primary product lines: product trust, decentralized trials, and supply chain. each of these solutions offers different value. for example, in the product trust space, pharmaledger collaborated with stakeholders to create a secure electronic product information (epi) solution that allows for instant product information after scanning product packaging via smartphone. 7. remedichain utilizes blockchain technology to repurpose unused and unopened prescription medications for those who may not otherwise be able to afford them. it has created a blockchain database that can authenticate and redistribute high-value goods. 8. solulab uses blockchain technology to create a decentralized tracking system on the lifecycle of pharmaceutical products. this includes the sourcing of raw materials, manufacturing processes, distribution, and ultimately reaching the end consumer. by leveraging blockchain, solulab helps pharmaceutical companies ensure the authenticity of their products, prevent counterfeiting, and streamline regulatory compliance. 9. triall is a clinical trial platform that works to streamline the full clinical trial lifecycle from study-design, execution, and post-study evaluation. to date, they have been used as the primary source for data governance in over 8,000 clinical trials. https://doi.org/10.30953/bhty.v7.298 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 editorial/discussion predictions for 2025: artificial intelligence in modern drug development, quantum-proof encryption, and health data monetization ingrid vasiliu-feltes, md, emba1,2 , jennifer hinkel, msc, chw, frsa3 and olga kubassova, phd4 1institute for science, entrepreneurship and investments, and faculty, university of miami, miami, fl, usa; 2softhread, catonsville, md, usa; 3the data economics company, los angeles, ca, usa; 4image analysis group, london, united kingdom doi: https://doi.org/10.30953/bhty.v7.361 corresponding author : ingrid vasiliu-feltes, email: ivfeltes@miami.edu keywords: ai, artificial technology, drug development, health data monetization, healthcare, healthcare finance, quantum-proof encryption abstract we are witnessing an unprecedented convergence of scientific discoveries, technology innovations, exponential adoption of technology, and remarkable population demographic shifts towards a digitally native society. the nobel prizes in medicine, chemistry, and physics awarded this year further validate the profound impact of technology on healthcare and life sciences. for 2025, designated by the united nations as the year of quantum technology, we envision further technology-driven innovations in all domains, triggering the transition to a novel health ecosystem. the role of artificial technology in modern drug development, the demand for quantum-proof encryption, and the opportunities of blockchain in health data monetization are all trends that can be disruptive for pharma, healthcare, and healthcare finance. received: october 15, 2024; accepted: november 13, 2024; published: december 16, 2024 ingrid vasiliu-feltes, md, emba in 2025, blockchain technology will be uniquely positioned to become a fundamental component of healthcare’s digital infrastructure, with the integration of quantum-proof encryption becoming imperative due to rapid advancements in quantum computing and quantum simulation. blockchain’s decentralized and immutable ledger capabilities will play a crucial role in safeguarding sensitive health data, especially as the risk of quantum computing’s ability to break traditional cryptographic methods increases. quantum-proof encryption, such as lattice-based cryptography or post-quantum algorithms, will be essential to secure healthcare data exchanges. integrating blockchain with data mesh and data fabric architectures could further augment the scalability and flexibility of healthcare data ecosystems. data mesh decentralizes data ownership by enabling different healthcare entities (e.g., payers, hospitals, or research institutions) to manage and maintain their data autonomously. this ensures that data are stored at the source, enhancing both security and accessibility. data fabric provides an overarching data integration layer that enables seamless, real-time access to this distributed data, bridging various healthcare systems and platforms. the combination of blockchain, data mesh, and data fabric could create a robust decentralized architecture that optimizes interoperability and ensures data consistency across the healthcare ecosystem. decentralized identity management powered by blockchain will grant patients greater control over their health data. this patient-centric approach can restore trust, increase transparency, and enforce accountability. with the rapid development of quantum computing, the cryptographic protocols underpinning traditional blockchain solutions must be fortified. the ability of blockchain to adopt quantum-resistant cryptography, such as lattice-based cryptography or hash-based signatures, will be https://orcid.org/0000-0001-7276-354x https://orcid.org/0000-0002-8461-7037 https://doi.org/10.30953/bhty.v7.361 mailto:ivfeltes@miami.edu 2 (page number not for citation purpose) ingrid vasiliu-feltes et al. citation: blockchain in healthcare today 2024, 7: 361 https://doi.org/10.30953/bhty.v7.361 crucial in ensuring the security and longevity of healthcare data. these advanced cryptographic techniques are designed to withstand the immense computational power of quantum computers, ensuring optimal privacy and security. jennifer hinkel, msc, chw, frsa in the next 1–2 years, patients will own their health data in the form of data assets that they will be able to monetize more directly with researchers and life sciences companies through decentralized markets. patients will also have access to “digital twins” of themselves, allowing them to simulate the outcome or impact of various interventions and health behaviors. across the healthcare system, payments and financing will increasingly align with the value of outcomes delivered. more data will be discovered by translational teams, passed through clinical development and real-world evidence, and then returned to translational, powering new developments or optimizations of pharmaceutical assets. olga kubassova, phd the year 2024 was challenging for biotechnology companies, with many folding their operations due to a lack of funding and the market being dominated by opportunistic mergers and acquisitions that combine two or more companies into one entity.1 we finally see the performance and capabilities of the groundbreaking advancements (e.g., large-scale artificial intelligence (ai) models such as large language models, blockchain-powered security, quantum computing, and effective use of cloud platforms to support global data management of decentralized trial infrastructure) significantly impact the life sciences industry. for years, ai promised to revolutionize drug discovery by automating laborious tasks. in 2024, the first data on the success of ai-discovered drug candidates were published to show that the success rate of ai-discovered drug candidates is doubled compared to the non-ai discovered molecules.2,3 the success here is defined as the probability of a molecule to succeed across all clinical phases end-to-end. we are observing that the new drug categories, like metabolic modulation via glp-1s/gips (glucagon-like peptide-1 agonists/gastric inhibitory polypeptides), have shown financial potential of innovation at scale.4 assuming that ai can be used to either increase productivity of drug discovery or reduce the costs of development, it will make discovery precise and focus development efforts on retrieving early go-no-go decisions that will ultimately reduce the costs and risk of clinical research and development. generally, it is recommended to view ai through the lens of business capabilities rather than available technologies. this leads us to use industry-acceptable technologies to deliver expected results with added improvements in quality of automation. however, the use of modern data management infrastructure, as well as deploying technology powers from other industries, can deliver beyond expectations. data interoperability or the ability of different systems and applications to access, exchange, and utilize data seamlessly, will impact drug development at scale. data interoperability has emerged as a critical component of modern clinical trial operations. to unlock the full potential of pharmaceutical data assets and the ability to utilize modern technologies in clinical trials, organizations will adopt comprehensive strategies that address these obstacles. looking into 2025, we predict that ai will power the change in mindset in clinical drug development and clinical operations, with greater reliance on technological platforms. modern data interoperability approaches and use of surrogate endpoints and companion biomarkers in clinical trials will finally be part of a circular data flow and the creation of company-wide knowledge base systems. while ai remains a powerful tool for reshaping data value in real-world evidence studies, engagement with treating physicians, and most importantly, patients, we will witness more pharma companies using ai to engage with patients and physicians, offering them tools and systems to assess drug impact and predict if a patient is the right candidate for a particular treatment. this will dictate that the government set new standards for privacy, collection, and retention of data. once adopted, these predictive tools will become the norm in personalized medicine and a key differentiator for companies that want to prove the value of their drugs to secure nation-wide or state-wide adoption of their therapies. the rise of ai-driven biotech companies and powerful collaborations between pharma and tech players will change risk profiles and investment strategies into pharmaceutical research and development. conclusion these trends illustrate the exceptional dual power of scientific and technological advancements, with the potential to redefine, recalibrate, and reconfigure the global healthcare and life sciences industry, as well as the economy and society. the year 2025 can mark the onset of a new era, where deep tech such as blockchain, ai, quantum, satellite internet, and 6g will lead the development of novel healthcare standards, as well as revised healthcare best practices, new healthcare delivery models, new healthcare financing instruments, and the evolution of a secure, trustworthy, precision healthcare ecosystem. https://doi.org/10.30953/bhty.v7.361 3 (page number not for citation purpose) ai in drug development citation: blockchain in healthcare today 2024, 7: 361 https://doi.org/10.30953/bhty.v7.361 funding none. financial and non-financial relationships and activities jennifer hinkel is co-editor-in-chief, bhty editorial board. ingrid vasiliu-feltes is a member of the bhty editorial board. contributors the authors contributed their sections, development, and approval of the final content of this article. data availability statement (das), data sharing, reproducibility, and data repositories none were used in the development of this article. application of ai-generated text or related technology none were used in the development of this article. references 1. bookbinder, m. (2017, september 29). the intelligent trial: ai comes to clinical trials. [cited 2018 apr 11]. available from: http://www.clinicalinformaticsnews.com/2017/09/29/the intelligent-trial-ai-comes-to-clinical-trials.aspx 2. brynjolfsson, e, mcafee, a. (2018, february 01). the business of artificial intelligence. [cited 2018 apr 12]. available from: https://hbr.org/cover-story/2017/07/the-business-of-artificial intelligence 3. ronanki, r, davenport, th. artificial intelligence for the real world. harvard business review. 2018;108–116. 4. collins l, costello ra. glucagon-like peptide-1 receptor agonists. [updated 2024 feb 29]. in: statpearls [internet]. treasure island (fl): statpearls publishing; [cited 2018 apr 12]. available from: https://www.ncbi.nlm.nih.gov/books/ nbk551568/ copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see http://creativecommons. org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.361 http://www.clinicalinformaticsnews.com/2017/09/29/the-intelligent-trial-ai-comes-to-clinical-trials.aspx http://www.clinicalinformaticsnews.com/2017/09/29/the-intelligent-trial-ai-comes-to-clinical-trials.aspx https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence https://hbr.org/cover-story/2017/07/the-business-of-artificial-intelligence https://www.ncbi.nlm.nih.gov/books/nbk551568/ https://www.ncbi.nlm.nih.gov/books/nbk551568/ http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research hyperledger fabric-powered digital identity scheme: transforming cia—triad security in iomt integrated healthcare eco-system sanjay jena, phd scholar1 ; ram chandra barik, phd2 and saroj padhan, phd3 1department of computer science and engineering, c. v. raman global university, odisha, india; 2associate professor, department of computer science and engineering, c. v. raman global university, odisha, india; 3associate professor, department of electrical engineering, parala maharaja engineering college, odisha, india corresponding author: sanjay jena: sanjayjena51@gmail.com doi: https://doi.org/10.30953/bhty.v8.411 keywords: availability, biomedical, blockchain, confidentiality, healthcare, integrity abstract this study underscores blockchain technology’s potential to tackle critical healthcare challenges, including data security, interoperability, and collaboration, delivering a scalable and efficient framework that enhances patient trust, operational efficiency, and compliance with global data protection standards. the advent of blockchain technology has radically altered centralized data management to adopt decentralized distributed systems with their inherent features—such as transparency, immutability, and security—that offer a promising answer for the challenges faced by modern healthcare systems. the authors introduce a smart healthcare solution for a secured digital identity of a patient by maintaining its confidentiality, integrity, and availability (cia-triad). it customizes an open-source hyperledger fabric-based framework for developing and utilizing the healthcare ecosystem as per the requirements of maintaining the digital identity of a patient. in addition, it uses fabrics’ key components, such as privacy-preserving channels, endorsing peers, anchor peers, orderer nodes, and a secure consensus for an efficient collaboration among stakeholders that ensures data integrity and confidentiality. the decentralized storage feature allows secure secure hash algorithm 256-bit (256-bit) during digital signature generation and verification algorithms across the network, and its one-way cryptographic function feature adds an advantage to maintain digital identity encryption both on-chain and off-chain during sharing and storing. this acts as a resistance to different cyberattacks as a record of the common vulnerability scoring system scorecard. the efficiency of the proposed operational model tested in a closed experimental network gets a more balanced output than that of a test network, which may be chosen for an adoption. plain language summary modern healthcare systems are widely dependent on sensors to monitor patients, collect vital data, and share information between hospitals, doctors, and insurance providers. with this modern approach it has become more efficient for seamless movement of patients as well as their data; however, it raises concerns about privacy, data security, and trust. to address these challenges, a secured digital identity system can be used. every patient, doctor, and device get a unique digital id, ensuring that only authorized users can access sensitive health data. this approach relies on the cia principles: confidentiality—only authorized entities can read patient data; integrity—the data cannot be altered without detection. availability—data are accessible when needed. by integrating blockchain technology through hyperledger, healthcare systems can store records in a decentralized and tamper-proof ledger. this ensures that every transaction, such as a medical report update or an insurance claim, is securely recorded and verifiable. submitted: june 6, 2025; accepted: august 26, 2025; published: september 3, 2025 blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0001-8285-8008 https://orcid.org/0000-0002-2803-5868 https://orcid.org/0009-0008-6227-5663 mailto:sanjayjena51@gmail.com https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v8.4112 (page number not for citation purpose) sanjay jena et al. the transformation of traditional healthcare management to a smart ecosystem demands a secure and reliable framework for managing sensitive patient information. blockchain technology, with its decentralized framework hyperledger fabric, offers this opportunity for establishing an authenticated and controlled data exchange among the stakeholders.1 it also embeds with it the core principles of confidentiality, integrity, and availability (cia triad). confidentiality is preserved through its cryptographic access controls, private data channels, and encrypted storage mechanisms. integrity is safeguarded by the feature of immutability and consensus-driven transaction validation. availability is achieved through its distributed architecture that ensures continuous service without any network failure. at the heart of this transformation lie internet of medical things (iomt) devices that collect, monitor, and transmit this sensitive information and are uniquely authenticated and authorized.2,3 this integration of blockchain technology with iomt devices enhances a security system and develops a responsive healthcare ecosystem where patient trust is intrinsic to digital transformation.4 although blockchain applications in healthcare have been explored extensively, most approaches concentrate on developing only a test network from the fabric framework that limits peer nodes and would focus solely on electronic health records (ehr) or internet of medical things (iomt) separately. this research combines digital identity management with permissioned blockchain to protect continuous iomt data streams while fully integrating the principles of the confidentiality, integrity, and availability (cia triad). the absence of an integrated, scalable, and realtime framework of iomt in blockchain-enabled smart hospitals represents a critical gap that has been found for study. to address this gap, this study is guided by three research questions: 1) how can hyperledger fabric be used to develop a smart hospital network while managing digital identities of patients? 2) what blockchain-enabled strategies will ensure confidentiality, integrity, and availability for real-time iomt data exchange by complying with healthcare security regulations? 3) what architectural framework can integrate blockchain, digital identity, iomt, and the cia triad into a scalable and interoperable smart hospital model? the structure of this article is outlined as follows: aan extensive literature review of twenty-three selected articles. detailing of the technical and procedural methodology, incorporating various diagrams, flowcharts, equations, and code screenshots to illustrate the proposed model. examination of the challenges and results, supported by cited references to validate the model’s credibility. finally, there is a summary of findings and key takeaways. system under study what follows is a summary of the sources used in a descriptive format as well as in tabulated form for the readers, making the section easier to comprehend. the survey starts with the benefits of digital identification in the rapidly modernizing healthcare industry; it reveals that there are numerous online articles, journals, and conferences available. out of those, some sixteen articles have been selected for this literature review. yousef and colleagues5 integrate iomt and blockchain for secure, tamper-proof, and traceable healthcare applications. an online post6 by siddharth gandhi presents a threepoint rationale, the first of which is regarding exclusive ownership of the patient’s healthcare data. whereas the availability of a clear image of the patient’s healthcare is discussed in the second point, and error-free treatment and safety in healthcare are referred to in the third point. the deployment of digital identification will result in the demise of the current username and password system. natarajan and colleagues7 demonstrate how decentralization enhances traceability, security, and transparency, which are critical in medical logistics. internet of things (iot) technology may help with these tasks, as is detailed in the aforementioned review paper.8 with the installation of iot sensors, also known as body sensing devices, in the patients, the system would function as a save our souls (sos) signal in the event of an emergency.9 it is also feasible to diagnose patients remotely, which is a benefit for isolated rural locations. whether it was monitoring temperature or wheelchair management, monitoring asthma, or monitoring glucose levels, everything was recorded and saved in the fabric chain to simplify treatment that could be simplified.10 this was done to make it easier to provide care. implementation of iot, which uses a lot of body-sensing devices, can be the solution to physical visits and save time, with a simultaneous advantage for the doctor to attend to more patients at the same time.11 the current healthcare system continues to use the conventional mode of physically visiting a doctor and diagnosing the diseases before any treatment procedure starts, and when it comes to some critical situations, the patient needs to visit the doctor regularly.12,13 agbo and mahmoud15,16 provided a table that summarizes the key differences between the various blockchain frameworks. as a result, hyperledger is superior to all competing frameworks in terms of performance, scalability, latency, and security. the results of the comparison show that fabric can significantly impact any industry that chooses to embrace it because the framework’s contents are freely accessible on github. to fully realize the technology’s potential, the linux foundation had, of course, https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v5.xxx 3 (page number not for citation purpose) hyperledger fabric-powered digital identity scheme released the code as open source. the open-source code facility of hyperledger provides several benefits. first, the openness facility of hyperledger fabric refers to its open-source nature that provides accessibility, flexibility, and community-driven development. the developers can view, modify, and contribute to the hyperledger fabric’s full source code on github, which lets them participate in a transparent development process with community development for their specific enterprise needs.17 next, related to cooperation and creativity, hyperledger fabric, as an open-source enterprise blockchain framework, fosters cooperation and creativity through its community-driven development, modular design, and open collaboration. these facilities allow developers, academics, and businesses to all benefit from working together, thanks to open source. individuals are invited to introduce changes to the project or suggest improvements.18 regarding personalization and flexibility, hyperledger fabric is designed to provide high levels of personalization and flexibility, making it an ideal enterprise blockchain solution. unlike rigid, public blockchains, hyperledger fabric allows organizations to customize their networks, consensus mechanisms, and smart contracts to fit their unique business needs. the open-source nature makes it easy for businesses to customize it to their requirements. they are adaptable and can introduce changes to the code, implement new features, and combine them with other systems.19 next, lessened vendor involvement is one of the key advantages of hyperledger fabric, and organizations can pick service providers or create their expertise, reducing vendor lock-in and providing greater support, customization, and deployment alternatives.20 this facility guarantees data integrity, privacy, security, and trustworthiness for businesses. its robust security controls, consensus mechanisms, identity management, and regulatory compliance features are the trusted solutions to develop a system with a secured network architecture. the broad use of peer review of the code enhances the likelihood that any potential security flaws will be found and fixed promptly.21 finally, to permit affordability, the high licensing costs that are often associated with proprietary software solutions are not required when using open-source software. as a result of being able to exploit the open-source code of hyperledger fabric without having to spend major up-front fees, businesses will be able to manage their resources more effectively.22 now, after covering digital identity and the application of the iot in the healthcare sector, the next consideration is to move on to healthcare data management utilizing an immutable decentralized database system, which is something that can only be made feasible by a blockchain platform built on top of the hyperledger architecture.23,24,25 as a result, several publications have been discovered that could explain the aforementioned aspects. concerned about maintaining patients’ confidentiality while maintaining the integrity of their medical data, several nations have begun to implement electronic healthcare systems. the decentralized technology offers a mechanism for storing and distributing e-health data remotely, making it useful for remote healthcare.26 the articles reviewed here were chosen because they discussed different ways of looking at perspectives. a comprehensive evaluation of the references used in this section is presented in table 1 for clarity and a better understanding of their relevance and credibility. technical and procedural approach procedural design the article presents a new idea of application to blockchain technology cascading with the hyperledger fabric project of the linux foundation for developing a production network in the field of healthcare. the pieces of the architecture are used to describe the making model’s fundamental transaction process. the concise diagram in figure 1 describes the hierarchical model of different nodes in the architecture, where it provides an in-depth idea to the reader’s mind via distributed technology so that it can be adopted by any real-world place to get upgraded and establish a secure milieu by the cia plan that would eliminate current challenges experienced by the organizations and the users (patients). this will be an overall network configuration that is described in the article. moving forward in the section, it goes much deeper into the model, with several more figures to put a clear picture in the minds of the readers. the network contains the setting up of certificate authorities (cas) and membership service providers (msps), roles of ordering nodes, endorsing peers, anchor peers, committing peers. by setting up the contents of the network, it is possible to establish a decentralized network using the hyperledger fabric, which consists of several nodes that can interact with one another. the blockchain stores the chaincode, the ledger data, and the transactions that are executed over it. in addition to this, it will manage the identities by using msps. identity and membership, which enable permission and access control for many kinds of activities, are the most important components for making use of the fabric. the members of a network may be identified from inside the network by their distinct digital fingerprints, which are known as their identities. these kinds of identities may be preserved using hyperledger via the use of certificates, which can include digital certificates in the x.509 standard, which is very similar to a secure https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v8.4114 (page number not for citation purpose) sanjay jena et al. socket layer. certificates, including public and private key pairs, may be found in the aforementioned github repository, labelled fabric-ca-client, fabric-ca-server, or peer. these certificates provide the information needed to decrypt both the private and public keys. the permits granted to the actor are based on the actions that are carried out by the actor and are determined by the certificate. these certificates in the hyperledger are managed by the msp, and they adhere to a specific standard called x.509. this level of security to the identity makes blockchain technology desirable for many organizations. hyperledger fabric uses team members who are legally separate entities to join the blockchain network. figure 2 uses an example of a hospital named allied care experts (ace) hospitals as a member organization, where more than one branch acts as the nodes. each of the host nodes table 1. comprehensive description of the references used in this literature review. journal/year/reference/publisher methodology used in article comments results in engineering, 20255, elsevier gm-sso is integrated to enhance authentication security in a lightweight encryption of data protection. decentralized storage strengthens privacy preservation, optimizing healthcare blockchain applications for scalability and reliability. et healthworld. 20236, the economic times highlighted the value of digital identities in healthcare future healthcare will rely heavily on digital identification verification. results in engineering. 20257, elsevier ethereum blockchain and smart contracts enhance security, traceability, and decentralization in cord blood procurement, improving transparency, efficiency, and accountability in healthcare supply chains. it presents an innovative approach to transforming cord blood procurement in healthcare supply chains using ethereum blockchain and smart contracts. journal of healthcare engineering. 20217, hindawi various healthcare iot devices with their architecture have been discussed. several challenges and limitations like standardization, power consumption, self-configuration, data privacy, and the security and environmental impact of hiot devices, need to be addressed. journal of clinical orthopedics and trauma. 202010, elsevier orthopedic patients in pandemics benefit from iomt. security and interoperability are key issues, and orthopedic iomt requires deeper research. ieee access. 2020,11 ieee bakmp-iomt was tested using the popular avispa program to show its resistance to various assaults. decentralization helps protect iomt communication from different threats. journal of information security and applications. 2020,12 elsevier the author tested the system’s performance and settings using hyperledger caliper. blockchain technology improves health record management. ieee access. 2020,13 ieee aims to check blockchain technology’s applicability in patient data and identity management with ehr and phr implementations. distributed ledger technology is a potential solution for patient data management and self-sovereignty. internet technology letters. 2019,14 wiley compared bitcoin, ethereum, and hyperledger fabric for healthcare. hyperledger fabric has greater healthcare application development abilities. conference ieee explore. 2022,15 ieee ethereum vs. hyperledger fabric success rate, average latency, throughput, and resource usage. ethereum is a public blockchain, therefore all data is public, whereas hyperledger fabric is for private use cases. ieee transactions on network science and engineering. 2024,18 ieee ethereum and hyperledger have been combined to develop a hybrid model by using sqlite. they also integrate iomt devices in their model. hyperledger fabric is a permissioned decentralized framework. it is not a centralized architecture. ieee transactions on industrial informatics. 2020,19 ieee an architecture based on blockchain technology by using hyperledger framework has been designed. the authors had designed the architecture on the test network. ieee transactions on emerging topics in computing. 2019,20 ieee a 360 degree review of the applications, benefits, challenges and future roadmap. authors had done a deep review on the applications of blockchain in healthcare sector. ieee access. 2022,21 ieee developed a patient data exchange model using hyperledger platform utilized the advantages of blockchain technology for patient information sharing. conference ieee explore. 2023,25 ieee discussed hyperledger fabric’s healthcare data management process. hyperledger fabric is distributed, reliable, and immutable, it is able to use for sharing a patient’s medical history. ieee journal of biomedical and health informatics. 2022,26 ieee proxy re-encryption on semi-trusted cloud storage allows for trackable, anonymous, aloof healthcare data storage and exchange through decentralized consortium blockchain. the report has addressed all technical issues and in an efficient system via simulation and analytical theory. avispa: automated validation of internet security protocols and applications; bakmp-iomt: blockchain-based authentication and key management scheme for the internet of medical things; ehr: electronic health records; gm-sso: genetically modified salp swarm optimization; hiot: health internet of things; ieee: institute of electrical and electronics engineers; iot: internet of things; phr: professional in human resources; sqlite: structured query language of lightweight nature.   https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v5.xxx 5 (page number not for citation purpose) hyperledger fabric-powered digital identity scheme fig. 1. hierarchical model of a smart hospital using hyperledger fabri. fig. 2. smart healthcare ecosystem using hyperledger fabric. https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v8.4116 (page number not for citation purpose) sanjay jena et al. provided by each of the member entities shares one or more distributed ledgers. these nodes are used to manage the ledger status inside the organization and to submit transactions. all the data generated by the ordering service are recorded in the ledgers. it records all the events in chronological order and organizes them in a timeline. users can access the information, and any request may be fulfilled for an authentic individual. the orderer node creation is one of the initialization steps followed during the deployment and is responsible for the creation of new blocks for the transaction by forming an ordering service, which may implement centralized or distributed protocols. the central orderer node acts as a primary coordinator for the network, managing critical consensus tasks across branches. its ordering service acts as a central hub where patients and peers may exchange diagnostic reports via iot and prescribe treatment for speedy recovery. each branch has its own local orderer node used for local transactions and ensuring data availability even if the central orderer is temporarily unreachable. it also uses cryptographic services with software or hardware cryptographic service providers on the platform and exposes cryptographic features such as encryption, decryption, keypair creation, private key message digest, and many other things. this orderer is one of the several binaries that have been cloned from the github link of the hyperledger community repository using the client url command during the setup of prerequisites and installation of fabric and fabric samples. raft is used for cluster formation, through which the orderer will be able to communicate with any other organization or peer. it requires the genesis block for initialization and the runtime properties. the ledger data for the blocks is written to the file system by the orderer during execution. according to the settings, the orderer is given the location of the ledger data. the orderer.yaml file is configured, and before that, the orderer binary is initialized using the genesis block. the peer node is the physical body in any hyperledger framework implemented. there might be many peer nodes in any organization. as in the model, the organization is in a healthcare center, so the peers are also placed. each peer may consist of several endorsing peers, one anchor peers, and several member peers. the transaction is processed once the endorsing peers validate the signature. hence, the peers are created to establish a network structure. to effectively endorse a transaction, these peers must adhere to the endorsement rules in place. when deploying transactions, certain rules and regulations must be followed to ensure both security and performance. these policies can be found in the configuration transaction generator  (i.e., configtxgen) binary. organizational participants in a channel are the legal owners of the peers they use. they are the nodes that house the ledgers, link to the ordering service and other peers, and run the smart contracts. as the fundamental parts of the network, these peers perform a crucial role. like the orderer node, the peer node contains a core. yaml file. as seen from the github link, the core.yaml has multiple sections, such as the peer section, which contains networking, msp, and storage paths; the ledger section, which contains the state database in couchdb; and the chaincode section, which contains the logs. a specific naming scheme is followed by creating specific folders with paths for the new peers created. ledger and channel data are distributed in a scalable manner using the gossip protocol used by peers. using this gossip messaging protocol, each peer may exchange ledger data with several other peers in real-time. there may be only one anchor peer in a peer group. the anchor peer is the only peer that can communicate with another anchor peer group. in a similar vein, the endorser peers may take the form of one or more individuals and can communicate both ways with the patients. patients are the end users who utilize the software development kit to propose a transaction to the endorsing peer. then only the endorsing peers will validate the transaction by simulating the chaincodeid and txpayload with a copy of the ledger. the member peers become part of the peer group with limited credentials, as they can only communicate with other peers through the anchor peers. the patients are the client nodes that use software development kits made by golang to make any transaction by broadcasting a message that can only be received and validated by the endorsing peers. all the transactions that are started in the channel are kept private, meaning that only the members of the channel can access them. figure 2 shows the organogram that might be followed by hospital management, where the three types of peers were given charge distribution as per the policy made in the chain code. you can establish a decentralized network using the hyperledger fabric, which consists of several nodes that can interact with one another. in addition, the system keeps track of users’ identities by employing an msp. the blockchain contains the chain code, the ledger data, and the transactions that are executed across it. nodes in a blockchain network are referred to as peers inside the network. when it is talked about the nodes in this context, it is about the computers that are responsible for executing the apps that make up the blockchain. each peer can store a copy of the ledger and any smart contracts. it is possible to create new peers in hyperledger, start them, stop them, reconfigure them, and remove them. they make available a collection of application programming interfaces (apis), which make it possible for administrators and applications to communicate with hyperledger services such as ledgers and chaincode. https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v5.xxx 7 (page number not for citation purpose) hyperledger fabric-powered digital identity scheme advantageously, a peer may host more than one ledger and chaincode since this provides for greater flexibility in the system. like the staff nurse-1, she can be part of the orthopedic channel as well as the surgery channel. the staff nurse-1 who is running a computer is a node that stores the information on ledgers for both channels, and it will also have both the orthopedic chain and surgery chain. although this article speaks to the broader use of blockchain in hyperledger fabric in the healthcare industry, its primary concern is with the digital identity of a patient. it is safe to assume that in today’s world, having an identity is one of the most fundamental needs for every living being to exist, and to demonstrate one’s identity, several organizations provide a wide variety of identification cards that have been vetted by recognized organizations or government entities. similarly, the safety of an individual’s identity, even though it is robust enough to be broken, continues to be the target of multiple attempts by unauthorized individuals in the hopes of achieving success in identity theft. additionally, there are instances in which the theft of an individual’s identity was successful, which raises the question of whether there is any other strategy or awareness that can keep an individual’s identity from being tampered with. as if some instances could be picked to talk about, if an accident takes place, then in this urgent circumstance, it becomes extremely tough to identify the patient until and unless his or her close family arrives to identify them. this is the case that implies that the second person who arrives to identify the patient or corpse becomes the first and main option. this choice has several drawbacks, such as the fact that the relative might be fake or that it could skip the patient’s true relative. a circumstance quite similar to this one occurred after the recent railway catastrophe in india, which sent shockwaves across the whole globe. when it comes to determining someone’s identification, it is a legitimate challenge that the administration of the hospital, as well as any government agency or other organization, must confront. similar cases of identifying identities occur in every sector of the area, and solutions must be found for them all. however, in this particular instance, the sector in question is the medical field, which is one in which identity is the primary concern. this is because the life of a human being is a precious commodity, and as such, its identity should be protected from being altered and made available to whom it belongs at the appropriate time. only by adhering to the cia scheme, which is proven and discussed in the latter part of this article, is it possible to keep one’s identity secure. in addition, this article demonstrates how the model is ready for production and adheres to the highest level for maintaining one’s confidentiality, integrity, and availability at the right time and to the right person. because it was stated earlier that this study is concerned with a patient’s digital identity, the suggested larger model has just the digital identity of a patient as its primary emphasis, and it has been explained in detail. the network topology of a smart hospital is laid out in figure 3. several bodies that are engaged in the process of diagnosis and treatment are all documented in the ledgers that are made of fabric. using iot in the network causes the process to become automated. in this automated process, the patient’s information and tracking are captured by iot wearable devices, which has the advantage of allowing for speedier treatment at the needed time. one patient could have multiple diseases, which could be treated at the same hospital organization or a different organization like (hospital laboratory, medico insurance, orthopedic dept., surgery dept., ear nose and throat dept. etc.). however, as described in figure 3, all the organizations are interlinked in the hyperledger chain, and and one patient is a node in several channels. additionally, the staff nurse who is the endorsing peer, as mentioned in the previous paragraph, could also be a node point in any other channel. therefore, it can be said that all the node points are attached in some fashion, depending on the range of permissions that each node point was granted by the organization’s orderer. hyperledger fabric is one of the platforms that blockchain uses, and it is being developed by open source. blockchain is a technology, and some of its applications have already achieved the height of popularity in the realm of the cryptocurrency world. despite this, hyperledger fabric is one of the platforms that blockchain uses. fabric is designed to be extremely modular and adaptable, making it suitable for applications in the banking, finance, insurance, healthcare, human resources, supply chain, and even digital music distribution sectors. and here the healthcare industry has been selected to receive an enterprise-grade, permissioned distributed ledger technology platform. this platform delivers many key differentiating capabilities compared to other popular distributed platforms, which may be best in some areas. however, the fabric is taking advantage of and overcoming the disadvantages of those decentralized platforms. the core workflow of the deployed hyperledger fabric for the production network of the hospital organization is displayed in figure 4. in it, the transactional mechanics that occur during a patient’s enrollment or registration at the appropriate hospital department are laid out in detail. for patients already registered, they may use a software development kit to have their registration identification recognized in the application by supplying their biometrics using body sensing equipment to identify themselves, or they can use a software development kit (sdk) to become registered by filling out the needed data of the sdk themselves. https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v8.4118 (page number not for citation purpose) sanjay jena et al. a transaction proposal is generated when an application written in one of the supported sdks (node, java, or python) makes use of one of the apis. to read from or write to the ledger, the proposal asks for the execution of a chaincode function. the sdk acts as a bridge, transforming the patient’s cryptographic credentials into a one-of-a-kind signature for the transaction proposal and packaging it in the correct architectural format. this workflow, explained further below, begins with the initiation of the first message a patient sends to the endorser peer node, which then follows the endorsement policy specified by the orderer node for making a transaction in the channel. the patient will initially broadcast the message, which is received by the endorsing peer, who acts as the assistant nurse and may simulate the chaincodeid and txpayload from the message. here, the format of the message may be <propose, tx, [anchor]>, where tx is mandatory and anchor is optional. again, tx=<cliendid, txpayload, timestamp, clientsig> and the entire message format is explained in the readme file at the included github link. the endorsing peer checks that the transaction proposal is correctly formatted and that the signature is legitimate by using this method. the transaction results, which include a response value, read set, and write set, are produced when the endorsing peers provide the transaction proposal inputs as arguments to the function of the called chaincode. this function is then performed against the existing database. one thing to keep in mind is that the ledger is not undergoing any modifications at this time. this is subsequently forwarded along as a ‘proposal response,’ which is examined by the ordering service after being received by the orderer node. the ordering service takes in all the transactions from the various channels in the network, sorts them in order of their occurrence in time, and then creates blocks of transactions for each channel. these blocks of transactions are then sent to all the peers in the relevant channel. and then the ledger is eventually updated across all the nodes. in all, the endorsing peer sends the transaction-endorsed messages to the patient, which will then be sent to the orderer peer, who will assign the patient to the required channel with a particular endorsing peer and anchor peer. in a broader sense, it can be said that finally, the patient is allotted to a particular peer node, which is where the therapy process will proceed. now, if the question arises as to how digital identity is incorporated into the overall procedure, the answer is fig. 3. hyperledger fabric network topology for smart healthcare solution. https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v5.xxx 9 (page number not for citation purpose) hyperledger fabric-powered digital identity scheme straightforward: hyperledger is a massive chain of blocks that incorporates a number of organizations into it. alternatively, it could say that it is an interconnection of all the organizations, in which case various sets of policies are in place for the nodes to act upon, and a single node may have varying roles in various channels. policies are also maintained by the orderer of the organization. patients will invariably have linked themselves, either directly or indirectly, to the chain of networks, which will have individually given them an identity as well as caused them to be stored and recognized with ease. tools and methods used the hyperledger fabric framework is provided by the linux foundation in the open-source market to make research and innovations with much more developmental work. it requires becoming acquainted with the key concepts of hyperledger fabric and then installing the prerequisites depending on the platform (here it is for windows: docker, wsl2, vs code, git to run the software). thereafter, it is needed to clone the hyperledger fabric with the git repository, or else you can download the fabric binaries from git to the system and deploy it by using the curl command. it could also be downloaded and opened up with vs code directly in the windows platform. all these were tested on a test network first of fabric samples and then implemented on the fabric main to make a production network. the network is started by executing the network.sh command; also, vagrant can be used. fig. 4. process flow of transaction lifecycle in hyperledger fabric for smart healthcare. ca: certificate authority. https://doi.org/10.30953/bhty.v8.411 http://network.sh citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v8.41110 (page number not for citation purpose) sanjay jena et al. thereafter, it is needed to set up the network by creating channels, peers, orgs, orderer, patient nodes and policies for the nodes, and the cas are set up to create the msps. a screenshot of a patient identity structure is shown in figure 4. similarly, figure 5 shows the screenshot of peer  nodes created using ain’t markup language (yaml). the policies assigned for different peer nodes in the model are displayed in a screenshot of figure 6. these dictate who can endorse transactions, access data, manage the network, and make administrative changes. the database is couchdb, while leveldb can potentially be utilized similarly. golang is used for chaincode development. all these need to be done by a network administrator. the strong network creation is very much required to defend against the vulnerabilities that are played by the network administrator in a very efficiently. the hardware used for the implementation of the fabric kit is as follows: dell amd ryzen-5 hexa core processor-5515 with fig. 6. a piece of code for patient identity structure in the go language. figure 6a shows the screenshot of peer nodes created using ain’t markup language (yaml). the policies assigned for different peer nodes in the model are displayed in a screenshot of figure 6b. fig. 5. a screenshot of a patient identity structure. https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v5.xxx 11 (page number not for citation purpose) hyperledger fabric-powered digital identity scheme 16 gb of ram and 4 gb of nvdia rtx-3050/120hz discrete graphics. crypto hash techniques used to guarantee the security, confidentiality, and reliability of sensitive medical data, the article’s cryptographic hash algorithms are essential for an increasingly linked healthcare setting. these methods form the basis for establishing a secure communication between iot devices by maintaining data integrity and producing tamper-proof digital identities. healthcare systems can create distinct digital fingerprints for patient records and iot device data by utilizing cryptographic hash functions like sha-256 and sha-3 as well as lightweight substitutes like blake2, guaranteeing that any alteration is identifiable. while hashbased digital signatures safeguard the secrecy and legitimacy of data transferred across networks, blockchain hash chaining further ensures the integrity of medical records. cryptographic hash generator, h(x) is a single direction mathematical function that transforms an input x with an arbitrary length into an output with a specific length  h. this result is known as the hash value or digest. x is the unique biometric data of a patient, which is hashed to create a secured digital identity h. a hash function is formally defined as: h: {0,1}* → {0,1}n (1) where: • {0,1}*denotes an input arbitrary length. • {0,1}n denotes a fixed-length output of n bits. the function h(x) must satisfy the following properties: 1. for same input x the function always produces same hash value h. 2. given h, finding x such that h(x) = h is computationally impossible. 3. it is computationally impossible to obtain two separate inputs, x1 and x2, such that h(x1) = h(x2)  4. a small change in x significantly affects h. cryptographic hash functions are generated and used to link blocks of transactions or data (such as patient records). this ensures data integrity because tampering with any block changes the hash values of subsequent blocks, disclosing the tampering. the hash of the most recent block h(b1) is obtained as follows: h(bi) = h(di ||h(bi–1)||ti (2) • di denotes recorded iot sensor data. • || denotes concatenation of values. • h(bi–1) is the hash of previous block. • ti denotes timestamp. digital signatures use cryptographic hash functions and asymmetric encryption to assure data secrecy and authenticity, which legitimates patient data or medical reports that are transferred across devices. this ensures confidentiality and authenticity of digital signatures in an iot enabled healthcare system. mathematically, the digital signature of a message m is created as follows: step 1: compute hash of the message h = h(m) step 2: encrypt hash by using the sender’s private key kpriv where: σ =epriv (h) (3) and σ is the signature that is sent alongside the message. step 3: verification of signature by decrypting it using sender’s public key kpuv, if h1 = h2 by, h1 = dpuv (σ) (4) h2 = dpuv (σ) (5) merkle trees are used in blockchain-enabled healthcare systems to efficiently validate big datasets, such as patient information, while keeping the full dataset private. the structure of a merkle tree is as follows: step 1: the hashes h1, h2,……..hn of each individual piece of data are calculated. hi = h(di) f or i = 1, 2, … .n (6) step 2: parent nodes are formed by concatenating pairwise hashes and hashing them once more. hij = h(hi||hj) (7) step 3: this procedure keeps going until the merkle root is obtained. sha-256 is a key technique in blockchain-based systems that maintains the security and integrity of critical patient data. it is a one-way cryptographic hash function that outputs a constant 256-bit (32-byte) value regardless of input size. this means that the original input cannot be deduced from the output, and it is collision-resistant, ensuring that no two distinct inputs yield the same hash value. each block in blockchain-based healthcare systems includes a timestamp, patient data, and a reference to the preceding block’s hash. https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v8.41112 (page number not for citation purpose) sanjay jena et al. mathematically: h = sha − 256 (x) (8) where: • x denotes the input message. • h denotes the fixed output of 256-bit hash. and, h(bi) = sha – 256 (di||ti|| h(bi−1)) (9) where: • h(bi) denotes the hash of current block i. • di denotes the data in a block. • ti denotes the timestamp of block. • h(bi−1) denotes the hash of previous block. • || denotes the concatenation operator. algorithm for digital identity encryption algorithm 1 & algorithm 2 are used for digital signature generation and verification that ensures data authenticity, integrity, and confidentiality in secure systems. after creating a digital signature, the sender first uses a cryptographic hash function, sha-256 to hash the original message into a fixed-length digest, which is then encrypted using their private key. the original message gets forwarded to the recipient along with this signature. to verify the message, the recipient computes the hash of the received message after decrypting the signature with the sender’s public key. the validity of the signature is next verified by comparing the two hashes; if they match, the message’s authenticity and integrity are confirmed. result analysis security challenges hyperledger fabric has numerous safeguards to ensure a network operates in accordance with the cia scheme. immutability and decentralization of data are both preserved in the hyperledger fabric ledger, which is a distributed network of peer nodes.27 many businesses are adapting other popular blockchain platforms for enterprise use right now, but hyperledger fabric is still ahead of the pack because it was built specifically with permissioned distributed ledger technology to deliver key differentiating capabilities over other popular distributed technologies and also the open-source availability of codes allows many developers to apply their intellectual abilities in making fabric bug-free28,29 it is the first distributed ledger platform to allow smart contracts/chaincode to be written in general-purpose programming languages like java, go, and node.js, providing a highly modular and editable architecture that promotes innovation, versatility, and efficiency across a wide variety of industry use cases. and also, no cryptocurrency is needed to incentivize mining or power the execution of smart contracts. despite hyperledger fabric’s versatility and a number of useful high-level capabilities, certain organizations might have difficulties during implementation and use.30 identification and authentication it is the crucial part of hyperledger fabric to establish a robust identity management system. any failure in managing the identity may lead to unauthorized access with malicious activities. risks in chaincodes the chaincodes written in go, node.js may have some vulnerabilities that can be exploited. so, organization need to have through code reviews, security audits, and testing to identify the vulnerabilities. information security the private channels which carry sensitive data within the network of authorized nodes. but this could be a challenging factor for the organization, so it is needed to carefully implement encryption techniques, write policies to protect the data from unauthorized disclosure. protecting dispersed networks as hyperledger rests on distributed network technology, hence the organizations should implement advanced encryption algorithms, firewalls, intrusion detection algorithm 1. digital signature generation. for, m = message and kpriv = private key compute the hash of the message h ← h(m) for, h = hash and h(m) = cryptographic hash function encrypt the hash with the sender’s private key σ ← encrypt(kpriv, h) return digital signature σ end algorithm 2. digital signature verification. for, m = message and kpub = public key decrypt the digital signature with public key h’ ← decrypt (kpub, σ) compute the hash of the message received h’’ ← h(m’) compare the hashes if h’ = h’’ return valid digital signature (σ) return invalid digital signature (σ) elseif end https://doi.org/10.30953/bhty.v8.411 http://node.js http://node.js citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v5.xxx 13 (page number not for citation purpose) hyperledger fabric-powered digital identity scheme systems to protect against network level attacks using unauthorized access. consensus-based attacks when many parties have agreed upon a transaction, it is stored in a distributed ledger, which is a shared database among various nodes. the consensus algorithm used in hyperledger fabric can have malicious actors that attempt to manipulate the order of content of transactions. and another point would be the nodes struggle for control during the consensus process; the branching event could happen. resistance to cyber attacks the distributed ledger technology provides certain mechanisms and best practices that contribute to the resistance against different cyberattacks, including brute force attacks, distributed denial of service attacks (ddos), denial of service attacks (dos) and 51% attack. the organization should maintain best security practices, conduct regular security audits, and stay updated with the latest security patches to get a secured hyperledger network. the main risk to security that must be fixed to acquire the trust of hyperledger members and new clients. it is very much necessary to understand the attacks to defend them. here is the analysis of how the newer technology mitigates these types of attacks. the references31,32,33 give a broader idea to understand and make strategic plans to prepare resistance methodologies for the possible security issues. similarly, in regard to34 i.e., table 2, four vulnerabilities have been discovered to date. brute force attacks the attacker might use the brute force method on a website that requires a user id and password. it may use any automated program to make a hit-and-trial method of several password combinations to find the correct one. or, as the article describes about biometric identification methods, the attacker may collect several biometric identification samples and apply them in a brute force method to collect patient health information. keeping in view these types of attacking techniques, there are several features and mechanisms that help resist the brute force attacks. some of them, such as the robust access control and authentication mechanism ensures that only authorized entities can access the network. by applying the best cryptographic techniques for encryption of data, an extra layer of protection makes it extremely difficult to decrypt it without encryption, such that even if the attacker intercepts the encrypted data, it becomes more difficult for attackers to make brute force attacks possible. dos attacks a dos attack is a type of attack that prohibits the legitimate users from reaching a network, host, or other pieces of the architecture. it often targets banks, credit card gateways, and other financial institutions to temporarily disrupt the host. it is always a key source of worry in any cybersecurity infrastructure. due to a dos attack, a load is created on the web server, making it overload with a large number of request packets. the persona of an endorsing peer is known to all members of a channel, opens the door for dos attacks. so, maintaining the anonymity of the endorsing peers can be the prevention against the attack. ddos attacks the ddos attacks are a significant variant of the dos threat with many categories that affect the network bandwidth, ram and cpu resources and slow the processing power. the solution can be a strong network and policymaking. it can be a risk via profiling load.it might not be direct, but could one of them be on port 6060, which has been resolved in the upgraded versions. so, it is needed to use the updated sdk of fabric. 51% attack it can also be termed a majority attack, as the attacker owns more than half the power of decision-making in the blockchain network. hyperledger fabric is a private, permissioned network, so the risk of a 51% attack is very low. however, it cannot be disregarded because a scenario might emerge if the network is not properly configured. the network can be designed in such a way that the consortium group, rather than being part of the internal members of the organization, can be a group of other organizations also, and any transaction would need approval of all the participants rather than a few selected table 2. discovered vulnerabilities of hyperledger fabric, recorded in cvss scorecard.34 cve id affected product name type of attack cvss score epss score cve-2022-45196 hyperledger fabric denial of service 7.5 0.05% cve-2022-36023 hyperledger fabric input validation 7.0 0.12% cve-2022-31121 hyperledger fabric input validation 7.5 0.15% cve-2022-3756 hyperledger fabric security vulnerability 7.5 0.08% cve: common vulnerabilities and exposures, cvss: common vulnerability scoring system, epss: exploit prediction scoring system, id: identification. https://doi.org/10.30953/bhty.v8.411 http://load.it citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v8.41114 (page number not for citation purpose) sanjay jena et al. members. this differs from the traditional centralized network where the administrator has the full permission that may become malicious at any time. empirical findings the efficiency of the model is compared with the cited references in table 3, which results in a highly efficient model with unique architecture that can be implemented for operations. which are then analyzed through resource utilization of various nodes are evaluated using hyperledger caliper of the model, which has been displayed in table 4. organizations can better deal with fabric’s challenges by following secure network design, which includes things like implementing the cia scheme, performing regular security audits and testing, and using the most recent security patches and updates provided by the hyperledger community. to further strengthen the safety of their hyperledger fabric deployments, businesses may also seek the advice of security professionals and consultants versed in blockchain and distributed ledger technology. thus, a networked cia architecture is essential. hyperledger fabric has the following important safety measures. immutability any decentralized ledger is known for its immutability. and this ensures data integrity, trust, and security within the network. once a transaction is recorded on blockchain, then it cannot be altered, deleted, or tampered with. this safety measure of data integrity, table 3. comparison of efficiency of the cited references. reference consensus mechanism performance indicators scalability privacy & security latency throughput remarks cyran5 poa identity verification speed, level of security moderate strong high low highly suitable and lightweight design with enhanced security and authentication. natarajan, et al.7 pos, pow authentication time, access control, cryptographic strength moderate strong moderate high the study is for application in healthcare supply chains and is scalable for streamlining medical asset logistics. tanwar, et al.12 pow data privacy, processing efficiency limited strong high low despite having high security, huge networks suffer from performance overhead. rehman, et al.18 hybrid (pos + consortium consensus) resource efficiency, data processing speed high strong low high recommended for iomt and making effective use of resources. barbaria, et al.21 raft data sharing speed, network reliability moderate strong moderate moderate robust security but limited scalability for large-scale healthcare systems. gohar23 pbft semantic data interoperability, access speed high moderate low high strong semantic interoperability but potential privacy issues. saranya and murugan25 raft transaction speed, data integrity high strong low high high efficiency with strong data integrity, best suited for private networks. proposed pbft confidentiality, integrity, availability high strong moderate high it is developed for an operational model rather than a test network. pbft: practical byzantine fault tolerance, poa: proof of authority, pos: proof of stake, pow: proof of work, raft: crash fault tolerant consensus. table 4. performance indicators across different parameters of the tested model. type avg. name avg. memory cpu usage traffic in traffic out disc write docker admin@orderer.com 59.3 mb 2.11% 3.4 mb 16.0 mb 4.8 mb docker peer0.acehospital.com 274.4 mb 6.55% 4.5 mb 440.9 kb 6.3 mb docker admin@acesurgery.com 206.4 mb 6.53% 4.8 mb 435.7 kb 6.2 mb docker user1@acesurgery.com 223.9 mb 5.98% 3.2 mb 480.3 kb 6.7 mb docker admin@aceortho.com 207.4 mb 7.33% 5.2 mb 432.5 kb 5.3 mb docker admin@medicinsurance.com 274.4 mb 12.3% 4.6 mb 430.0kb 6.8 mb cpu: central processing unit, kb: kilobyte, mb: megabyte. https://doi.org/10.30953/bhty.v8.411 mailto:admin@orderer.com http://peer0.acehospital.com mailto:admin@acesurgery.com mailto:user1@acesurgery.com mailto:admin@aceortho.com mailto:admin@medicinsurance.com citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v5.xxx 15 (page number not for citation purpose) hyperledger fabric-powered digital identity scheme security, and trustworthiness that makes fabric an ideal choice for enterprise applications. so it can be said that all the transactions are guaranteed tamperproof with a tracking facility. data isolation it is the ability to restrict access to certain data so that only authorized participants in the network can view or interact with it. data stored or exchanged on the blockchain platform is isolated using advanced encryption algorithms. this is a key feature in permissioned blockchain networks, where organizations may need to maintain privacy and confidentiality within a shared ecosystem. this proves the confidentiality feature of fabric. permissioned network hyperledger maintains a permissioned network where all the nodes are identified and authenticated. each participant has a role-based permission (peer, orderer, client) where the access control lists (acls) define who can read, write, and execute transactions. only authorized participants can join and transact on the channel, ensuring that the network is secured and protected from unauthorized access. identity management the fabric supports various identity frameworks including x.509 certificates, msps, and cas, which ensure that only trusted participants can engage in transactions on the network. access control it is a mechanism that defines who can perform specific actions within the block network. a fine grade access control mechanism is maintained, that enforces access policies for network resources. there are also acls configured to regulate channels, chaincodes, and any other specific data within the ledger. this proves the authorization policy. endorsement policies the endorsement policies determine the required number of endorsements from specific participants to validate transactions, which proves the validation and integrity of the network. an endorsing peer simulates the transaction and signs the result, and if it complies with the policy, it is sent back to the client. consensus mechanisms consensus algorithms like practical byzantine fault tolerance (pbft) and raft were used. the consensus mechanism is offered by hyperledger fabric allows to choose the most suitable consensus algorithm for a specific requirement. efficient chaincode operation in hyperledger fabric, chaincode is a smart contract that specifies business logic for carrying out transactions. organizations can develop and deploy their own chaincodes in the channels that can run in a secure and isolated execution environment with controlled access to resources preventing unauthorized access. conclusion the use of hyperledger fabric-based architecture to manage patient identification in smart healthcare systems demonstrates blockchain’s capability to transform data security, interoperability, and stakeholder engagement. using fabric’s modular architecture, the solution ensures confidentiality, real-time access, and decentralized storage, enabling both healthcare providers and patients. through detailed testing and debugging, the system displays scalability and efficiency, addressing critical difficulties in healthcare data management. this work highlights blockchain’s disruptive potential by improving trust, operational effectiveness, and compliance with global data protection regulations, opening the path for a more secure and linked healthcare ecosystem. funding none of the authors of this article has received by any funding for doing any experimentation work. conflicts of interest the authors declare that they have no conflict of interest. author contributions sanjay kumar jena: writing original draft, investigation, conceptualization, formal analysis. ram chandra barik: project administration, visualization, supervision. saroj padhan: data curation, methodology, validation. data availability statement (das), data sharing, reproducibility, and data repositories data may be made available on request. no ai-generated text was used. application of ai-generated text or related technology no ai-generated text was used. acknowledgments dr. jenna would like to express my gratitude to my primary supervisor, who guided me through this paper. i  would also like to thank our madam, head of the department of computer science and engineering at c. v. raman global university, bhubaneswar, for her extensive guidance. thanks are also due to the library staff and research participants who provided invaluable https://doi.org/10.30953/bhty.v8.411 citation: blockchain in healthcare today 2025, 8: 411 https://doi.org/10.30953/bhty.v8.41116 (page number not for citation purpose) sanjay jena et al. 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34. cvedetails.com. cve security vulnerability database. security vulnerabilities, exploits, references and more [internet]. 2025. available from: https://www.cvedetails.com copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see http:// creativecommons.org/licenses/by-nc/4.0. the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.411 https://blockchainhealthcaretoday.com/index.php/journal/article/view/278 https://blockchainhealthcaretoday.com/index.php/journal/article/view/278 https://health.economictimes.indiatimes.com/news/health-it/the-role-of-digital-identity-in-modernising-healthcare/99391030 https://health.economictimes.indiatimes.com/news/health-it/the-role-of-digital-identity-in-modernising-healthcare/99391030 https://health.economictimes.indiatimes.com/news/health-it/the-role-of-digital-identity-in-modernising-healthcare/99391030 https://www.tevora.com https://www.cvedetails.com http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) discussion highlights from advancing the business of health with blockchain and frontier tech at conv2x symposium 2023 tory cenaj, ba founder, publisher, and curator of blockchain in healthcare today and conv2x, stamford, connecticut, usa corresponding author: tory cenaj; email: t.cenaj@partnersindigitalhealth.com keywords: blockchain, blockchain in healthcare, business applications in healthcare, conv2x, desci community, digital twin, healthcare technology, web3 abstract many people in the healthcare industry mistake the turbulent cryptocurrency market for a technology that offers significant benefits to healthcare administration, including improved interoperability, revenue recapture, and enhanced security and patient safety. recently, these subjects were explored during the converge2xcelerate (conv2x) 2023 symposium held at loyola university in new orleans, louisiana, usa. who benefits most from this discussion – service providers, those seeking to transform outdated business models, or both? the mention of blockchain technology often discourages conversation, causing those who have sound success solutions to approach the topic from alternate perspectives. in the blockchain in healthcare platform approaches special issue, volume 7, issue 1, 2024, we share valuable insights based on specific use cases to provide healthcare executives with a nuanced understanding. several open-access recordings are available, providing a glimpse into the wealth of knowledge and insights shared to advance the business of health with blockchain technology. recognizing the need for a paradigm shift, we explore topical subjects and cases with various thought leaders in the field. submitted: 27 october 2023; accepted: 26 january 2024; published: 31 january 2024 the maryville experience burstiq ceo frank ricotta and president brian jackson (conv2x platinum sponsors) discussed market forces, collaborations with higher education institutions, and their partnership with maryville university to establish a transparent, reliable data ecosystem that incorporates data, artificial intelligence (ai), blockchain, and analytics. upon closer examination, maryville university’s innovative approach offers a revolutionary model that dismantles silos in higher education and aligns with gartner’s view1 on transitioning data-enabled businesses to where information technology (it) plays a supportive rather than a controlling role. in this context, blockchain emerges as a crucial catalyst for establishing reliable data objects and promoting transparent communication in scientific research. in contrast to many other universities, maryville operates without an it department. the cost for a university to maintain an it department varies widely depending on the size and needs of the institution. however, it typically involves significant expenses related to staff salaries, hardware and software purchases, maintenance, and upgrades. furthermore, maryville’s approach allows for cost savings due to the collective technologies that revolve around person-centric data (figure 1). this involves creating data and communities that showcase diplomas, credentials, and verified skills throughout various career paths. it is an approach that is particularly useful in the healthcare workforce and extends to undergraduate, graduate, and alumni communities. the traditional role of the chief information officer (cio) shifts towards being a “conductor” who integrates information and creates seamless experiences at the data level. this novel approach allows businesses to transform into human users who take responsibility for their own data management. it is a concept that aligns with gartner’s idea of transitioning data-enabled businesses, data access, data usage, and data governance to where it serves as an enabler instead of a controller. students now hold their “superpower” in the form of their own data – known as a digital twin. blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0003-1206-5277 mailto:t.cenaj@partnersindigitalhealth.com citation: blockchain in healthcare today 2024, 7: 288 https://doi.org/10.30953/bhty.v7.2882 (page number not for citation purpose) tory cenaj blockchain technology plays a crucial role in this approach by creating dependable data objects (active metadata) that are treated as digital assets with clear ownership and permissions, featuring three dimensions in one product. emphasis is on connecting data and constructing data ecosystems. in some health systems, the monetary value of this approach is estimated to deliver savings of up to $9 million in operating costs. expert predictions suggest that graph models will emerge as powerful tools in the market over the next few years (figure 2). the desci community at conv2x, jelani clarke, executive lead at desciworld, and ray dogum, manager at vibe bio, discussed the decentralized science (desci) community and incentivization systems within science. they focused on the quest for fairness and equity in scientific endeavors, with these innovators working towards ensuring that everyone involved in scientific research benefits from the incentives directed towards sharing knowledge across diverse decentralized landscapes. perspective moderator jason cross, chief strategy officer at rymedi, addressed bridging desci with legacy health innovation, as most healthcare organizations run through centralized organizations and comply with laws using traditional liabilities and compliance standards to get things done. some of the most interesting current design projects employ decentralized autonomous organizations (daos) and intellectual property non-fungible tokens (ip nfts). according to dr. cross, there is a need for a business model that supports the monetizable value that feeds into compliant ecosystems. “we need to consider whether platform providers will bridge centralized health innovation institutions with decentralized activities.” compliance issues require attention to ensure that findings are actionable for regulators and replicable from the perspective of scientific credibility. managing rights in desci follow a wide variety of approaches. these include intellectual property rights for what is created, patient data fig. 1. organizational chart of a learner-centric university. adm: application data management; bi: business intelligence; crm: customer relationship management; lms: learning management systems, mkt: marketing; sa: system administrator; sis: server intelligent storage. reproduce with permission. fig. 2. evolving consumer and regulatory pressure. reproduced with permission. https://doi.org/10.30953/bhty.v7.288 citation: blockchain in healthcare today 2024, 7: 288 https://doi.org/10.30953/bhty.v7.288 3 (page number not for citation purpose) highlights conv2x symposium 2023 rights, and downstream monetization rights of patients in the value streams created from their data or biological samples via material transfer agreement rights management. each of these factors must be considered and planned. additional real-life cautionary development considerations included: 1. interacting with legacy health innovation institutions, where all parties need to know about litigation (aka who to sue). 2. phone number(s) to call for customer support. 3. negotiation, organizational, and business scale issues; for example, how does a dao with 10,000 patients negotiate with a health system over patient cut and data monetization versus negotiating on behalf of 10 million patients? 4. standards that enable bargaining power to democratize visions for the future. 5. liability issues are related to referring individuals, as no current legal infrastructure makes it safe for individuals coming into or from a dao. when you try to merge that with traditional legacy pharma or legacy health innovation, there is no corporate structure or employment structure. in addition, there is no structure to preserve or keep the data and movement in a specific format or between any more open guardrails. many questions must be answered. these topics and discussions will, no doubt, continue. navigating enterprise business decisions in his informative keynote, sathya krishnasamy, former senior director at elevance health, discussed the global phenomenon of blockchain technology (figure 3). he emphasized its potential to improve the efficiency of any supply chain by automating the center rather than the periphery – whether it involves people, data, numbers, or money. he commended the erc 20/erc 721 token standards for enabling interoperability, which allows numerous decentralized applications (dapps) to communicate and self-organize. mr. krishnasamy highlighted the self-organizing capabilities of the blockchain developer community, which can collectively solve problems. in addition, he emphasized the convergence of blockchain with enterprise and emerging technologies. mr. krishnasamy challenged the idea that everyone will be on a single blockchain. instead, he proposed a multi-chain world’s natural evolution, urging enterprises to prepare for blockchain interoperability. during his keynote, mr. krishnasamy identified opportunities for healthcare as part of the next level of blockchain advancement. he emphasized the core theme of consumer ownership of data as the focus of the web3 era. mr. krishnasamy highlighted the potential to drive healthcare consumer engagement by understanding backend costs for specific processes and leveraging them. he explored data quality and the removal of inefficiencies through counter-party sensitive data aggregation with on/ off-chain designs. in addition, he delved into the potential for business decision convergence and distinguished between multilateral and unilateral decisions in reference implementations and use cases. mr. krishnasamy noted full provenance and incremental data exposure/ zero-knowledge proof (zkp) as mechanisms to induce further efficiencies. for a deeper dive into this topic, a systematic review is published in blockchain in healthcare today platform fig. 3. keynote speaker sathya krishnasamy discussed the global phenomenon of blockchain technology, emphasizing its potential to improve the efficiency of any supply chain by automating the center rather than the periphery. https://doi.org/10.30953/bhty.v7.288 citation: blockchain in healthcare today 2024, 7: 288 https://doi.org/10.30953/bhty.v7.2884 (page number not for citation purpose) tory cenaj approaches in healthcare journal by sathya krishnasamy and available to read open access at doi: https:// doi.org/10.30953/bhty.v6.280. blockchain: new architecture for internet and human data robert rachford is vice president of biostatistics and statistical programming at propharma, a mid-sized contract research organization (cro). during his presentation, mr. rachford shared insights into the regulatory challenges that service providers face when implementing blockchain solutions (figure 4). he highlighted success stories and emphasized risk mitigation, real-world evidence, and digital signatures as critical factors in navigating risk-based organizations. multijurisdictional clinical trials have gained market acceptance without explicitly mentioning blockchain. however, regulatory gaps exist, particularly in transferring digital records and the need to regulate current procedural terminology (cpt) codes. mr. rachford envisions a future where the adoption of blockchain in the healthcare industry is complemented by regulatory guidance. personalized health narratives and future technologies during the conference, brigette piniewski, the author of “wealthcare,” presented a thought-provoking perspective on the intersection of blockchain and personalized health narratives (figure 5). dr. piniewski emphasized the importance of educational programs to bridge the gap between increasing blockchain-based solutions and investor understanding. she envisions a future where individuals will define what is researchable and leverage data aggregation for personalized insights that foretell future patient journeys and outcomes, potentially avoiding unattractive journeys. the discussion explored potential outcomes decades into the future and drew parallels with successful models in the cannabis, sleep, and high-performance athletics arenas. dr. piniewski advocated for creating data aggregation platforms similar to strava, enabling individuals to glean insights into various aspects of their health. holographic teleportation and remote healthcare fernando de la peña, ceo of aexa, introduced groundbreaking holographic teleportation technology for remote healthcare. the technology, demonstrated by nasa flight surgeon josef schmid, showcased the potential for real-time, immersive patient care. dr. de la peña emphasized the transformative potential of holographic technology, envisioning its widespread adoption within the next 3–5 years. he positioned aexa’s holoconnect as a tool that bridges the gap between academic and consumer use, making remote healthcare accessible to all. aexa is the company that successfully followed dr. josef schmid from nasa mission control to the space station, in october 2021, and performed multiple holographic teleportations, pushing patient care technology to the next level in a challenging environment, traveling at 17,500 miles per hour. seeing a patient in volumetric form is analogous to 3d printing with many “slices” and options for making videos or photographs. with teleportation, it’s possible to create 10,000 slices of video, producing a realistic 3d image of a person. aexa was invited to conv2x because they promote low-cost access for everyone. the application can be downloaded for only $9 per month, using equipment that people already own – a smartphone – making this agnostic technology accessible to anyone and anywhere, representing the next frontier in healthcare. a historic first during conv2x, in a historic first on earth, josef schmid, iii, nasa flight surgeon and major general (ret), was projected from russia to the stage in new orleans, louisiana, usa. the photo below (figure 6) shows the test conducted on september 21st, where he appeared at the podium. the holoconnect color technology from aexa aerospace is available on the android store and is encrypted, ensuring that aexa does not monitor conversations. fig. 4. robert rachford shared insights into the regulatory challenges that service providers face when implementing blockchain solutions. https://doi.org/10.30953/bhty.v7.288 https://doi.org/10.30953/bhty.v6.280 https://doi.org/10.30953/bhty.v6.280 citation: blockchain in healthcare today 2024, 7: 288 https://doi.org/10.30953/bhty.v7.288 5 (page number not for citation purpose) highlights conv2x symposium 2023 four types of communication tools are available, and color protection creates volume for project applications. dr. de la peña believes this field will enable tools everyone will use in three to five years. currently, both users need to have an ios device. no other application is required; just download it, and you’re ready to go. it’s similar to star trek’s technology but even better. streaming audio transcription pilot to enhance accessibility for the hearing impaired and non-english speaking audience, conv2x was extremely pleased to pilot a new 140 multilingual streaming audio transcription app created by dan scarfe, ceo at xrai glass. the app, demonstrated during the symposium, offers seamless transcription of discussions, further enriching the accessibility and reach of the event. conv2x thoroughly appreciated his generosity in providing the hardware to pilot at the event. the discussion recaps for writing this article were derived with the assistance of an english transcription app on september 21, 2023. to view the entire conv2x blockchain in healthcare 2023 agenda and speakers, visit https://conv2xsymposium.com. conclusions the conv2x event brings together world-leading innovators and researchers to converge ideas, foster collaboration, and push the boundaries of what is possible at the intersection of healthcare, platform, and blockchain technology. the conference dispels misconceptions, presents innovative applications, and provides a panoramic view of the diverse facets of blockchain technology in healthcare. recognizing the need for a paradigm shift, dr. anjum khurshid, chief data scientist and lead informaticist for sentinel operations center at harvard pilgrim health care, emphasized the significance of conferences like conv2x, which focuses on research, technical, and business applications of blockchain technology, bringing practitioners, developers, and researchers together. robert ratchford, vice president at propharma, praised the quality of attendees and speakers at conv2x 2023, describing it as “the highest among blockchain conferences focused on industry adoption” while also calling for the overcoming of outdated perceptions and fostering trust in a technology capable of transforming business operations. the convergence of thought leaders, industry experts, and innovators at conv2x highlights the transformation occurring in healthcare delivery and business operations. the discussions at the event left attendees with a profound sense of pragmatic possibilities and current market applications – leveraging blockchain to advance healthcare. these visionaries are driving us to the crossroads of technology and healthcare for the benefit of all. we look forward to an expansive conv2x 2024 encompassing blockchain and emerging tech in healthcare and life sciences. conv2x 2024 is scheduled to take place in september in boston, massachusetts, usa. funding no funding was provided for the preparation of this article. financial and non-financial relationships each contributor is affiliated with the organization listed in the article. contributors the author is the founder, publisher, and curator of blockchain in healthcare today and conv2x. the discussion recaps for this article were derived with the fig. 5. brigette piniewski, the author of “wealthcare,” shared her perspective on the intersection of blockchain and personalized health narratives. fig. 6. in a historic first on earth, josef schmid, iii, nasa flight surgeon and major general (ret), was projected from russia to the stage in new orleans, louisiana, usa. https://doi.org/10.30953/bhty.v7.288 https://conv2xsymposium.com citation: blockchain in healthcare today 2024, 7: 288 https://doi.org/10.30953/bhty.v7.2886 (page number not for citation purpose) tory cenaj assistance of the xrai glass app transcription in english on september 21, 2023. acknowledgments thanks to the speakers for lending their expertise and to the judges of the ignition pitch competition. thanks to sponsors burstiq, discover, and mediledger for making the event possible. thanks to participants around the globe who attended in person and virtually. thanks to conv2x advisory committee members. reference 1. gartner. building an edge computing strategy. [cited 2023 nov  11]. available from: https://www.equinix.com/resources/ analyst-reports/gartner-edge-computing-strategy copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/ licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.288 https://www.equinix.com/resources/analyst-reports/gartner-edge-computing-strategy https://www.equinix.com/resources/analyst-reports/gartner-edge-computing-strategy http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) narrative, systematic review, meta-analysis harnessing blockchain to transform healthcare data management: a comprehensive research agenda horst treiblmaier, phd1 , abderahman rejeb, phd2 , mike gault, phd3 , anjum khurshid, phd4, alex norta, phd5 , jim poteet, bs6 and suresh sivagnanam7 1full professor, school of international management, modul university vienna, vienna, austria; 2faculty of business economics, széchenyi istván university, győr, hungary; 3guardtime, lausanne, vaud, switzerland; 4sentinel program and faculty, department of population medicine, harvard pilgrim health care institute and harvard medical school, boston, massachusetts, usa; 5dymaxion oü, tallinn university, tallinn, estonia; 6technology and operations leader, oracle cerner, overland park, kansas, usa; 7healthcare and higher education, talents squared limited, london, uk corresponding author: horst treiblmaier, email: horst.treiblmaier@modul.ac.at doi: https://doi.org/10.30953/bhty.v7.301 keywords: blockchain, distributed ledger, healthcare, healthcare data, panel discussion, research agenda abstract properly managing healthcare data is a complex endeavor that must balance the requirements and interests of many stakeholders. in this paper, we present the findings from a panel discussion with healthcare professionals and academics, who elaborate on the current situation in healthcare data management as well as the future role that blockchain could play in this sector. based on the findings of this panel, we structure the research field of healthcare data management and provide numerous avenues for future research. the outcome is a framework that highlights the important role of healthcare data and puts them into context. from a patient’s perspective, we specifically elaborate on trust and privacy as well as the expected benefits. additionally, four important data aspects are identified: integrity, security, interoperability, and, finally, sharing and transfer. we also outline the importance of current problems and derive several relevant and timely research questions that build the foundation of a research agenda for blockchain-driven innovation in healthcare data management. in summary, the framework will inform practitioners of blockchain’s potential in healthcare and structure the area for researchers, who are called upon to investigate the respective topics in greater detail. received: february 20, 2024; accepted: march 20, 2024; published: april 30, 2024 in recent years, the healthcare industry has undergone a profound development triggered by digital transformation and accelerated by the covid-19 pandemic. technologies, such as the internet of things (iot), artificial intelligence (ai), and blockchain, as well as the interplay among them, have caught the attention of academics and practitioners.1 in this article, we focus on blockchain and its impact on healthcare data management. as pointed out in a comprehensive literature review by dionisio et al.,2 two types of blockchain applications must be differentiated: patient based and entity based. examples of the former include facilitating access to patient data for authorized entities and managing a patient’s prescription history while maintaining data privacy and security. from an entity viewpoint, it is especially the accuracy of data management that can help to avoid patient misidentification and the duplication of medical records, as well as ensure the provenance of data sources. the relevance of blockchain for managing secure and sharable electronic medical records was highlighted several years ago,3 but despite numerous promising applications and creative use cases, many barriers remain to be overcome to exploit the technology’s potential fully and to develop solutions, which simultaneously consider the privacy of the patients while allowing medical facilities to access accurate, complete, and timely data they need for providing high-quality services. in a recent meta-analysis, krishnasamy and gopalakrishnan.4 sum up the current blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-0755-5223 https://orcid.org/0000-0002-2817-5377 https://orcid.org/0000-0002-8946-0622 https://orcid.org/0000-0003-0593-8244 mailto:horst.treiblmaier@modul.ac.at https://doi.org/10.30953/bhty.v7.301 citation: blockchain in healthcare today 2024, 7: 301 https://doi.org/10.30953/bhty.v7.3012 (page number not for citation purpose) horst treiblmaier et al. situation: “…even though the technology is still maturing, this effort demonstrates and underscores the need for healthcare enterprises to take a broader and bolder look into the blockchain and allied emerging technologies.” in this article, we follow this call for action by scrutinizing the important role of healthcare data management and how blockchain can contribute to improving existing systems. we aim to compile and structure promising research avenues that can serve as starting points for future research projects and summarize and highlight the most important topics for practitioners. literature review in this era, blockchain technology stands as a seminal innovation that offers efficiency in operational and regulatory verification as well as visibility across numerous sectors of the economy.5 a blockchain operates as a decentralized, ever-expanding series of records, referred to as “blocks,” which are interconnected in a sequence through a process that requires consensus across a number of peers. each block in the chain contains numerous transactions, a cryptographic hash of its predecessor, and a timestamp.6 any block data alteration can trigger a domino effect, potentially disrupting the entire chain. upon processing data, every computer in the network synchronizes simultaneously and forges a permanent and unalterable digital record. the blockchain system also sets the rules for who can add new blocks and the requirements for doing so. blockchain’s distinguishing features include its ability to share data and transactions on an unchangeable peer-to-peer network and enhance transparency and security. initially prominent in cryptocurrency and financial transactions, blockchain has been embraced by numerous other sectors, such as tourism, manufacturing, logistics, smart cities, and transportation, all of which capitalize on its robust security and privacy features.7–9 it is important to point out that blockchain is not a monolithic concept but rather a bundle of cleverly combined technologies that yield desired features such as immutability and decentralization. important building blocks include key ideas such as linked timestamping, public key cryptography, and smart contracts10 and enable properties such as flexibility, opaqueness, performance, policy, practicality, and security.11 as a consequence, the emerging platforms can be conceptually quite different and either be open to anyone (“public”) or restricted to a group of entities (“private,” “consortium”). similarly, different ways exist to reach consensus in such a peer-to-peer network. the most well-known mechanism is proof-of-work, in which energy is used to determine who is eligible to add a new block to the chain. however, alternative mechanisms exist, such as proof-of-stake, in which validators are selected based on the quantity of holdings in a specific coin or cryptocurrency.12 in the remainder of this article, the authors abstract from a specific implementation and use the term blockchain to denote a system that yields the desired properties mentioned above. blockchain has the potential to revolutionize healthcare management through how data are managed, shared, and protected.13,14 blockchain technology can be used to address core issues such as data fragmentation, interoperability challenges, security vulnerabilities, and high operational costs, thereby setting new standards in healthcare data management. the impact of blockchain extends beyond operational efficiencies to fundamentally enhance patient care and safety. regarding patient care, blockchain can be applied to ensure accurate and complete healthcare data and enable healthcare providers to make more informed decisions.15,16 it can also help facilitate a more comprehensive view of a patient’s medical history, including past allergies, medication history, former treatments, and allow for more personalized, coordinated, and effective care.17 this is particularly crucial in case of emergencies where immediate access to patient history can be life saving. blockchain-stored data’s secure and uniform nature opens new avenues for medical research. for instance, researchers can access vast amounts of anonymized patient data and ensure robust data sets for technical trials and studies, which can accelerate the development of new treatments and drugs, ultimately benefiting patient care. as blockchain allows traceability and auditability of each data transaction, patients can track the use of their data and grant or revoke access to their health records, which ensures increased privacy and autonomy. this empowerment and the patient-centric approach not only enhances trust in the healthcare system but also encourages patients to be more engaged in health management and willing to share their data for clinical trials and medical research.18 as the focus on data privacy and security intensifies, healthcare providers are also navigating stringent regulatory requirements. blockchain’s inherent features, including data immutability and automatic audit trails, aid in meeting these compliance standards.13 moreover, blockchain reduces the risk of data tampering and helps mitigate fraud, which constitutes a significant concern in healthcare billing and insurance claims.19 in light of the problem of drug fraud, the widespread issue of counterfeit medications can be effectively addressed by blockchain’s improved drug traceability,20 ensuring the authenticity of pharmaceutical products throughout their supply chain, from production to customer delivery. the immutable and timestamped transactions in blockchain make it extremely challenging for counterfeit drugs to penetrate the legitimate supply chain. blockchain can enhance the reliability and accuracy of data related to clinical trials and precision medicine. https://doi.org/10.30953/bhty.v7.301 citation: blockchain in healthcare today 2024, 7: 301 https://doi.org/10.30953/bhty.v7.301 3 (page number not for citation purpose) harnessing blockchain to transform healthcare data management this is particularly significant in ensuring the integrity of clinical trial data and improving analytics. for precision medicine, the role of blockchain in securely managing genomic sequences empowers individuals to control their genetic data.21 as a result, this contributes to proactive treatment strategies for genetically inherited diseases, which is a major advancement in personalized healthcare. in emergencies, blockchain can address the need for consistent access to patient data. its use of smart contracts and cryptographic keys provides seamless and secure management of data access, thereby reducing errors and speeding up the process of data collection in critical situations. the cybersecurity risks telehealth systems face— including data breaches, unauthorized access, and susceptibility—can potentially be overcome with the adoption of blockchain because the technology helps improve security and privacy, though its integration may increase costs, especially in remote areas.22 blockchain technology can also establish a reliable and tamper-proof patient identity management system needed in today’s physical and virtual healthcare delivery environment.23 the advancements of blockchain in terms of data transparency and sharing efficiency are also key in detecting claims and enhancing the precision of health insurance coverage. this is due to the potential of the technology to automate transactions and record agreements through smart contracts, which minimize the need for third-party involvement and streamline administrative processes. finally, the adoption of blockchain in billing processes, particularly in insurance claims, ensures data storage and faster processing of transactions, resulting in lower operational costs. overall, the emergence of blockchain plays a crucial role in elevating the efficiency, security, and trustworthiness of a wide range of healthcare processes, fostering the development of a more cohesive and patient-focused healthcare system. however, the direction in which this development goes is not necessarily predetermined, and there exists a huge potential to create blockchain-based systems that are more streamlined, effective, and, most importantly, consider the interests of patients. research questions the healthcare sector faces substantial and multifaceted problems. to produce viable solutions, it is crucial that important stakeholders collaborate and create solutions that are in the best interest of the patients. the common denominator of most of the pending issues is patient data, which is at the core of every healthcare system. compared to other industry sectors, they can be considered more sensitive to privacy violations and must be accurate and up-to-date. in this article, we therefore pose the following research questions: • what are the existing problems of healthcare data management? • who are the main stakeholders in healthcare data management? • how can patients benefit from the potential of blockchain technology? • how can blockchain help store, process, and transfer data? • what are the most important future topics related to blockchain innovation in healthcare data management? methodology in june 2023, a panel of experts convened at the conv2x conference (https://conv2xsymposium.com/) for a thought-provoking discussion on how blockchain and decentralized technologies might reshape the future of healthcare data storage, sharing, privacy, and access. moderated by professor horst treiblmaier of modul university vienna, the cross-disciplinary group of panelists brought diverse perspectives spanning healthcare, technology, research, and public policy. the panel consisted of experts in blockchain and healthcare from the industry and academia. mike gault (g), the founder and ceo of guardtime, in 2008 deployed the first blockchain in healthcare in estonia, which is still used today. anjum khurshid (dr. khurshid), chief data scientist at harvard pilgrim health care institute and faculty member of harvard medical school, has helped to build health information exchanges to connect electronic health records (ehrs) and clinical decision support systems within ehrs, as well as a blockchain-based identify management platform called medilinker.24 alex norta (dr. norta), researcher and entrepreneur, founded dymaxion, a company that focuses on multi-factor self-sovereign identity authentication. jim poteet (mr. poteet) from oracle cerner has 25 years of experience in the healthcare industry and owns a patent on clinical data exchange. suresh sivagnanam, entrepreneur, investor, director and chairman in healthcare and higher education, previously founded vdoc, an alternative worldwide healthcare platform that offers telemedicine services and now develops aider, a unified global healthcare solution integrating telemedicine and primary healthcare services. the 1-hour conversation was fully transcribed. the analysis followed established practices of qualitative content analysis and included a coding process in which relevant portions of the interview text were clustered into categories. this resulted in the creation of a category system that identifies the core topics related to healthcare data management and illustrates how they are related to each other. the analysis was done in an inductive and iterative manner such that the emerging categories were consistently refined up to the point where an agreement among all the researchers was reached.25 finally, the results were sent https://doi.org/10.30953/bhty.v7.301 https://conv2xsymposium.com/ citation: blockchain in healthcare today 2024, 7: 301 https://doi.org/10.30953/bhty.v7.3014 (page number not for citation purpose) horst treiblmaier et al. back to all the participating experts for further refinement and final confirmation. results in the following sections, we begin by sketching existing problems of the healthcare system, which provides a starting point for a deep dive into how these issues can be tackled with blockchain technology. after briefly mentioning pending issues and acknowledging the multitude of stakeholders in this complex environment, we then focus on patients’ problems and the potential gains they might experience from blockchain-based solutions. this is followed by an in-depth discussion of several important data properties that deserve further attention. we end this section with an outlook on what the future might bring and a comprehensive research framework that highlights several important topics that need to be addressed through the development of practical applications or rigorous academic research. current problems to start, the panelists identified key challenges that plague the current healthcare system. some of those problems are caused by population demographics in relation to active healthcare professionals, expressed in statements such as “an aging population globally, too few healthcare professionals coming through the network” (mr. sivagnanam). despite the relevance of the problem and the urgency of the situation, conflicting interests or general negligence impede the development of viable solutions: “it is very difficult to get collaboration going between different government departments, in particular healthcare” (dr. gault). the whole situation is complicated by the fact that “[current systems] are working in legal and regulatory frameworks that were probably developed 15 or 20 years ago” (dr. khurshid). taking a global perspective, the situation becomes more complicated due to the counterfeiting of medication: “there are a lot of developing countries where two-thirds of the medications are counterfeit” (dr. khurshid).in recent years, technology has evolved substantially, opening novel ways for the storage, processing, and retrieval of data. in parallel, general awareness has increased regarding the value of personal data and their worthiness of protection. legislatures have recognized this development and passed more stringent laws that address the needs of the individual and increase the responsibilities and liabilities of those who collect and store sensitive information. situations must be avoided in which information is easily accessible to non-authorized individuals, as illustrated by (dr. norta): “the nurse she was a bit curious and without actually being permitted to do so, peeped into the health records.” however, given the speed of the current transformation, the response of governments is expected to lag: “they will be reactive and they will be behind by 10 to 15 years” (dr. khurshid). even countries that are very advanced in applying technology in the healthcare sector might not exploit the full potential of the technology: “the estonian id card does not suffice, and there are many other examples in other countries working the same” (dr. gault). the same holds especially true for large economies: “healthcare in the us market is very slow to change from a technology perspective” (mr. poteet). additionally, patients’ increasing awareness of their vulnerability through data misuse must be taken into account. such misuse can be aggravated through the sharing of sensitive information on centrally managed systems: “to create another shared database and getting people to upload data into a centralized system is never going to happen. it will never succeed because nobody wants to change their behavior or share their data in a way that they cannot control” (dr. gault). the covid-19 pandemic has also triggered an important change in consumer behavior, such that an increasing number of individuals suffering from an ailment try to find health-related information online, which might cause problems: “trying to understand what it means leads to wrong conclusions about the risk or the kinds of treatments that may be available” (dr. khurshid). in this regard, a major success factor will be to produce technical solutions and to advise the general public about the advantages that blockchain technology may offer: “it is not the cost of the technology, it is the cost of educating everyone in the population how to use it” (dr. gault). to wrap up, the role of information in the healthcare industry is key: “it is much more complicated than in many other industries in terms of how the information is used by consumers as well as by those who are providing that information” (dr. norta). stakeholders and the legal environment the healthcare industry is complex and composed of numerous stakeholders, the roles and relevance of whom differ from country to country. in this article, we position the patients in the center and take a data-centric perspective. however, it must be taken into account that the general environment in this industry has evolved over numerous decades, and many stakeholders have a vested interest in actively shaping current developments. healthcare providers consist of professionals directly involved in patient care (e.g., doctors, nurses, therapists), and they are the ones who actually deliver medical services. these services are regularly provided in healthcare institutions (e.g., hospitals and clinics) that are responsible for staffing, general infrastructure, and the coordination of all activities. the professionals are frequently organized in associations that advocate for their interests and establish industry standards. the interest of the patients is safeguarded by advocacy groups that strive to influence policy, raise awareness about pending https://doi.org/10.30953/bhty.v7.301 citation: blockchain in healthcare today 2024, 7: 301 https://doi.org/10.30953/bhty.v7.301 5 (page number not for citation purpose) harnessing blockchain to transform healthcare data management topics and, occasionally, also foster research.pharmaceutical and medical device companies drive research and innovation and play an important role in the distribution of pharmaceutical products. additionally, research and teaching activities can be carried out by specialized technology and research entities as well as educational institutions. in case innovation is triggered by technology, as is the case with blockchain, startups can play an important role as early adopters of innovation and in the development of innovative solutions that can potentially disrupt existing business models: “there is currently no way […] to have a super system that, from a legal point of view, allows us to effectively share data with public sector organizations in the us or the uk. it has to be a disruptor” (mr. sivagnanam). the legal/regulatory framework is provided by governments and the associated regulatory bodies, which set the policies and procedures for healthcare delivery and may also be responsible for funding, research, and the implementation of health initiatives. however, “most of the legal frameworks for blockchain are a reaction to what is happening in financial markets rather than what their potential is for social services and for health” (dr. khurshid). when it comes to personal health information, protection laws are especially strict. for example, in the european union, the general data protection regulation (gdpr), which is not specific to healthcare, has important implications for the handling and processing of healthcare data. it emphasizes the protection of personal data and reinforces individuals’ rights regarding their data. furthermore, it mandates that organizations obtain consent prior to the collection and processing of personal data and to prove that they are compliant with the gdpr. in the us, the health insurance portability and accountability act (hipaa) covers several topics related to healthcare data with the goal of protecting sensitive information from being disclosed without the consent of the patient. in the uk, it is the goal of the health security agency not only to protect individuals from health threats but also to control the use of personal information.26 patients at the core of our framework are the patients and their personal healthcare data. patients have specific expectations regarding the services they receive from healthcare providers. however, it is important in this context to focus on the quality and the handling of their sensitive data. in this regard, blockchain is one of those technologies that bear the potential to improve the existing healthcare system and benefit patients in numerous ways. one of the core topics for patients is trust, which pertains to trust in organizations to handle their data in a proper manner but also trust that these data can be used in the interest of the patient in the most efficient and effective manner. closely related to trust is the perception of privacy, which can be assured by keeping personal information confidential. ultimately, to trigger a sustainable change, it must be clear to patients how they can potentially benefit from a blockchain-based solution. trust and privacy trust in healthcare systems is usually twofold: “not only in data, but it is also in terms of who is providing those services” (dr. khurshid). trust is closely related to the topic of authentication to be able to certify the source of the data and those allowed to use or access it. those providing services to patients, such as doctors, also need to trust the data available to them in order to make the best clinical decisions in the interest of their patients. in this regard, current developments can foreshadow the emergence of a machine-to-everything economy in which “humans have wearables and directly engage with systems” (dr. norta). to achieve a desirable level of trust, it is crucial to be transparent and to communicate the benefits that blockchain can bring for patients. the immutability of information on the blockchain provides protection against fraud and tampering with patient data. specifically, “it is important to understand what we are actually trying to achieve in terms of trust with the end user” (mr. sivagnanam). in summary, blockchain-based healthcare systems must possess the “ability to verify and search patient records and get a verifiable result without destroying privacy” (dr. gault). this can potentially be achieved by giving control over their data back to the patients via “a mpi (master patient index) that i control as a patient. and i just have pointers to all the systems that have my data” (mr. poteet). expected benefits as is the case with any new technology, the introduction of blockchain-based systems can create expectations among patients regarding the functionality and the changes they can bring about. obviously, blockchain will be applied as a backend technology while keeping the front end simple and user-friendly, but the implications of these changes must still be communicated to patients. apart from empowering the system users, it is also important that patients actually experience the feeling of being in control: “patients want to be able to authorize a transfer of data of some type” (mr. sivagnanam). to facilitate controlled data sharing, blockchain can build on the concept of tokenization: “they can download the token, and they can decide who they want to share that information with” (dr. gault). from a patient’s perspective, using tokens can offer the https://doi.org/10.30953/bhty.v7.301 citation: blockchain in healthcare today 2024, 7: 301 https://doi.org/10.30953/bhty.v7.3016 (page number not for citation purpose) horst treiblmaier et al. advantage of being able to easily access important data all over the world: “and then i will […] say, here is my patient record, take all the details that you want from it […] which means wherever i travel the world, i’ll give any doctor access to that record” (mr. sivagnanam). apart from issues pertaining to sensitive data, this might also open opportunities to reduce costs, which ultimately can result in savings for healthcare institutions and patients: “here is a tremendous opportunity to reduce the cost to the providers and to the payers in this use case” (mr. poteet). one of the core features of blockchain is decentralization, which refers to the elimination of intermediaries. originally devised for the financial industry, the same concept might be equally relevant for the healthcare industry, and this creates opportunities for cost savings on the patient side as well as for healthcare service providers: “there is a whole middle industry in the u.s. market that monetizes gathering these data. and so there is a tremendous opportunity to reduce the cost to the providers and to the payers in this use case” (mr. poteet). data when it comes to the relevance of data, the qualitative content analysis yielded four important categories in which blockchain can induce major changes: the integrity of the data such that unwanted alterations become impossible; the security level of data during storage and transactions; the creation of interoperable systems that facilitate the exchange of data and, finally, the sharing and transfer of highly sensitive data. it is important to point out that the first two categories are attributes of the data, the third is an attribute of the system, and the fourth refers to an activity. thus, the categories are not disjunct but rather intertwined. integrity data integrity pertains to the accuracy and consistency of data throughout the entire lifecycle. in addition, it guarantees that data have not been tampered with, which perfectly aligns with a core feature of blockchain: data immutability ensured by cryptographic means. this entails that data can be verified as soon as they are written on the blockchain, and trusted intermediaries are not needed for this task: “they have an audit trail, they have provenance, and you can verify them without having to trust all the parties in between that have been managing those records” (dr. gault). another important feature of blockchain is its ability to provide shared access to a group of authorized entities. this entails that several parties independently verify the quality of the data: “so that anyone can guarantee that the healthcare records are consistent” (dr. gault). security in a word, “security” refers to all measures that protect data from unauthorized access, alteration, disclosure, theft, or deletion. once again, this is where the properties of blockchain come in handy: “the opportunity for blockchain and healthcare has always been security” (dr.  gault). in this regard, a core issue is to combine data security with the authentication of the system users: “because you can’t have this fine-grained access management which you would get with multifactor challenge sets” (dr. norta). in combination, core constituents of blockchain, such as the immutability of the underlying ledger, the use of cryptography, sophisticated ways to reach consensus, as well as transparency and auditability, allow the designing of innovative systems that improve the security of sensitive patient data. interoperability interoperability refers to the ability of ehr systems to communicate and interact seamlessly with each other. in this regard, blockchain’s decentralized network structure facilitates the sharing of data across entities such as hospitals and research institutions, enhanced security makes the sharing of data easier, platforms can enforce uniform standards, and the elimination of intermediaries makes data exchange more efficient. consequently, the experts expect a major impact on the healthcare system, consisting of numerous stakeholders who require access to information and who might work on disparate systems, many of which might even be proprietary. “there is interoperability needed because there is never going to be one ehr in a country or a region, there is going to be multiple” (mr. poteet). apart from the need to coordinate and integrate these systems to improve patient healthcare, in some countries, there also exists a strong external pressure to increase collaboration in the sector: “there has been a federal policy that has forced some of these players to interoperate” (dr. khurshid). given that healthcare systems regularly have a strong connection to state-owned institutions, this includes the involvement of public organizations: “interoperability is the challenge, not just in healthcare, but it is the challenge in government” (dr. gault). this issue is also closely connected to the problem of integrating data such that needed information is immediately available: “the engagement to achieve on the fly personal healthcare records that get dynamically integrated into more static electronic healthcare records that hospitals manage” (dr. norta). sharing and transfer a blockchain-based system can fundamentally change the way healthcare data are handled. at the core of the problem is the need to share and transfer highly sensitive https://doi.org/10.30953/bhty.v7.301 citation: blockchain in healthcare today 2024, 7: 301 https://doi.org/10.30953/bhty.v7.301 7 (page number not for citation purpose) harnessing blockchain to transform healthcare data management data, which might not only be intercepted during transmission but, once shared, become available in numerous places: “how can we transfer that data on a global basis securely, authentically, in a way which effectively bypasses everything that is problematic at the moment?” (mr. sivagnanam). as a potential solution, rather than sending and sharing data, it becomes possible to grant access to a blockchain system with verifiable data: “you not sending packaged data across the wire. you are allowing that api exchange model where the data stay in the original system throughout the process” (mr. poteet). this goes along with granting additional rights to the patients, who can effectively authorize data access on a case-by-case basis: “but, at the same time, they decide who they want to share data with as opposed to loading it into a centralized database” (dr. gault). self-authorization also provides an opportunity to reduce transaction costs: “when you desire to share your data with someone, you have control to do that, and you have a mechanism, and the expense of moving data into a centralized database goes away” (mr. poteet). finally, a major factor related to patient data is legislation and the need to comply with it: “every country and every jurisdiction has its own way of how it deals with its patient records” (mr. sivagnanam). research framework and future topics figure 1 summarizes the core components of our framework, as outlined above, and puts them in context. decisive forces, which influence the development from the outside, are the existing healthcare industry, which drives fig 1. core constituents of healthcare data management: stakeholders, patients, and data. https://doi.org/10.30953/bhty.v7.301 citation: blockchain in healthcare today 2024, 7: 301 https://doi.org/10.30953/bhty.v7.3018 (page number not for citation purpose) horst treiblmaier et al. innovation but might also be skeptical when it comes to disruptive innovations that might threaten incumbents’ core interests. contrariwise, innovative startups do not have any legacy systems that might determine the ways in which they operate, and they can be among the first to implement blockchain-based solutions for healthcare data management. all actors operate within a legal and regulatory framework that determines the boundaries of the system. importantly, this framework can also be analyzed on a supranational level, which limits the strategic flexibility of individual countries and necessitates international cooperation to promote change. at the bottom of figure 1, blockchain is shown as a main driver for technological disruption. notwithstanding the fact that the technology, or rather the bundle of technologies, is multifaceted and under constant development, we postulate that the application of distributed ledgers opens up countless opportunities in the healthcare sector and can help to solve numerous pending problems. patients and their data make up the core of the framework. the patients are mainly concerned about the correct use of their highly sensitive data and need to trust the technology to enable further adoption. in addition, they have specific expectations and need to see concrete benefits in order to be willing to change the status quo. from a data perspective, it is crucial to ensure the integrity and security of the data and to develop solutions that foster interoperability and data sharing between existing and future solutions. to wrap up the discussion, the experts were asked to produce future healthcare data topics that deserve further investigation and that can potentially be supported by blockchain technology. in this regard, identity management was mentioned several times. as (dr. khurshid) points out, “being able to ensure identity is a very important role in healthcare in the future.” this is confirmed by (dr. norta), who also stresses the importance of achieving a self-sovereign identity authentication that is based on multiple factors. furthermore, it is also possible to capitalize on the experience gained from applying blockchain in different use cases, such as non-fungible tokens (nfts), which have previously been used to create communities. using the same practices, blockchain can be used to “assign nfts to serious products such as medicine” (dr. norta). as intriguing as the potentials of blockchain technology are, to be successful, integrated solutions must be developed that are “on top of the systems from the various vendors across the country that allow individuals to access their healthcare data. they also need to tie in financial healthcare data” (mr. poteet). finally, patients’ perceptions of what the technology can do for them or potential threats associated with it will ultimately determine its success: “the challenge is getting the technology right so that you don’t scare people off by privacy and putting healthcare records where they could be accessed by others” (dr. gault).table 1 summarizes the topics discussed by listing numerous important research questions, many of which can be answered using qualitative or quantitative approaches from the social sciences. “how” questions indicate that a design science approach might be adequate, with the goal of designing and developing systems that demonstrate the viability of a specific idea or approach. discussion previous academic literature has identified numerous areas in which blockchain technology can contribute to more effective and efficient healthcare management. the industry has already produced promising applications that allow for a glimpse into how the technology can add value in this important industry. in this article, we used qualitative content analysis to summarize the findings from an expert panel on blockchain transformation in healthcare data management. we started by discussing several pending problems and then presented an emerging framework that depicts existing players in the healthcare industry, startups as potential disruptors, and the legal/regulatory context. patients are at the center of our framework. on the one hand, patients expect a certain level of privacy regarding their sensitive data and also need to trust the general system. on the other hand, they have specific expectations of how innovative solutions can benefit them. from a data perspective, we identified integrity, security, interoperability, and sharing/transfer as important topics. we round up our analysis with important future topics that the experts identified and the derivation of relevant research questions for each respective topic. these questions are intended to inspire further investigation by practitioners as well as academics and should spark the design and development of applications that can tackle existing challenges and provide value. our study has several limitations. first, the expertise is specific to the experience and knowledge of the experts in the panel. while we ensured a broad practical and academic expertise of the participants, it is possible that a different group of experts from other geographical regions, healthcare specialties and technical backgrounds put forth additional topics. given that this was an exploratory venue, and we did not weigh the importance of the relative topics. however, this should not be a major issue, and we leave it to future studies to refine the framework that we generated here. second, we did not discuss in detail the ethical implications of increasing blockchain adoption in the healthcare https://doi.org/10.30953/bhty.v7.301 citation: blockchain in healthcare today 2024, 7: 301 https://doi.org/10.30953/bhty.v7.301 9 (page number not for citation purpose) harnessing blockchain to transform healthcare data management industry, especially when it comes to topics such as patient consent and data ownership. as far as future research is concerned, we encourage academics to use the questions that we raised as starting points for their own research and to dive deep into the respective topics, each of which deserves a thorough investigation. this especially pertains to the conduction of case studies, which illustrate the applicability of blockchain to remedy the problems that we identified. further empirical research might either quantify the importance of a specific problem and the extent to which blockchain can help or be used to create models based on survey data, which highlight important antecedents of blockchain adoption in the healthcare sector. as soon as table 1. research questions, with blockchain shown to be a main driver for technological disruption topic research questions* current challenges • given an aging population and a dearth of healthcare professionals, how can adequate healthcare provision be ensured? • how can collaboration between key stakeholders in the healthcare sector be promoted? • what is the current state of legislation/regulation, and in what ways does it foster or inhibit the introduction of blockchain technology? • how can the counterfeiting of medications be eliminated or reduced? • what is the current state of healthcare systems [in different countries] and in what way does it take into account patients’ requirements? • what is the current state of data privacy in healthcare systems? • what are the main factors that drive the adoption of healthcare systems from a patient’s perspective? • what is the awareness among patients regarding the use of personal information within the healthcare system? stakeholders and legal environment • how are the respective stakeholders in the healthcare system impacted by the introduction of blockchain-based solutions? • what changes in the current legal/regulatory framework are needed in case blockchain-based solutions are introduced? patients: trust and privacy • what are the perceptions among patients regarding the use of their personal data in healthcare systems? • how can a machine-to-everything economy in the healthcare space be designed that works in the best interest of patients? • how can healthcare systems be designed to balance the privacy needs of patients with the easy availability of information? patients: expected benefits • what is the level of subjective empowerment among patients pertaining to the use of their data in healthcare systems? • can tokenization facilitate the sharing of sensitive patient information? • what cost savings are possible in blockchain-based healthcare systems and how can the patients benefit from these savings? data: integrity • how can blockchain be applied to design and develop systems that offer data integrity? • who are the important stakeholders in patient healthcare data, and how can they access data in case of need, including for care coordination? data: security • which blockchain core properties impact the security of patient data? • how can systems be designed with a focus on healthcare data security? data: interoperability • what is the role of legislation and regulation when it comes to the interoperability of blockchain-based healthcare systems? • how can healthcare data systems be designed that communicate and interact seamlessly? data: sharing and transfer • can the transfer of sensitive patient data be replaced by allowing access to a blockchain-based system on a case-by-case basis? • how can blockchain-based systems be aligned with national rules and legislation when it comes to the sharing and transfer of patient data? future topics • how can patient identity be ensured? • can blockchain contribute to the design and development of self-sovereign identity in healthcare management? • how can nfts be applied to streamline existing applications (either patient related or medication related)? • what are patients’ perceptions of blockchain-based healthcare data systems? • what are patients’ expectations of blockchain-based healthcare data systems? “how” questions indicate that a design science approach might be adequate, with the goal of designing and developing systems that demonstrate the viability of a specific idea or approach. nfts: nonfungible data. https://doi.org/10.30953/bhty.v7.301 citation: blockchain in healthcare today 2024, 7: 301 https://doi.org/10.30953/bhty.v7.30110 (page number not for citation purpose) horst treiblmaier et al. there is a consensus on what the most important topics are and how blockchain can help to overcome pending problems, roadmaps for the implementation of blockchain, ideally based on practical evidence, are needed to guide practitioners and provide value for stakeholders. conclusions the future of blockchain in healthcare data management holds significant promise, offering potential solutions to various challenges within the industry. however, many use cases must still be validated, and there is also a need to consider that blockchain comprises a couple of technologies that are currently under development. it remains to be seen how they can be fruitfully applied to solve pending issues and which advantages they can offer over existing systems. given the relevance of healthcare data management, we explicitly encourage fruitful cooperation between the industry and academia to design, develop, and assess solutions that can benefit patients, which, at one point in time, means each and every one of us. funding none. financial and non-financial relationships and activities 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computing to blockchain. academic press; 2023, p. 389–404. https://doi.org/10.1016/b978-0-323-96146-2.00018-8 22. ahmad rw, salah k, jayaraman r, yaqoob i, ellahham s, omar m. the role of blockchain technology in telehealth and telemedicine. int j med inform. 2021;148:104399. https://doi. org/10.1016/j.ijmedinf.2021.104399 23. khurshid a, gadnis a. using blockchain to create transaction identity for persons experiencing homelessness in america: policy proposal. jmir res protoc. 2019;8:e10654. https://doi. org/10.2196/10654 24. khurshid a, holan c, cowley c, alexander j, harrell dt, usman m, et al. designing and testing a blockchain application for patient identity management in healthcare. jamia open. 2021;4:ooaa073. https://doi.org/10.1093/jamiaopen/ooaa073 25. mayring p. qualitative content analysis. forum. 2000;1(2). https://doi.org/10.17169/fqs-1.2.1089 26. mclaren m. understanding the current and future state of complex health data protection laws. pharmaceutical technology; 2023 [cited 2023 jan 29]. available from: https://www.pharmaceutical-technology.com/sponsored/understanding-the-current-andfuture-state-of-complex-health-data-protection-laws/ copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/ licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.301 https://doi.org/10.1016/j.jksuci.2022.12.016 https://doi.org/10.1016/b978-0-323-96146-2.00018-8 https://doi.org/10.1016/j.ijmedinf.2021.104399 https://doi.org/10.1016/j.ijmedinf.2021.104399 https://doi.org/10.2196/10654 https://doi.org/10.2196/10654 https://doi.org/10.1093/jamiaopen/ooaa073 https://doi.org/10.17169/fqs-1.2.1089 https://www.pharmaceutical-technology.com/sponsored/understanding-the-current-and-future-state-of-complex-health-data-protection-laws/ https://www.pharmaceutical-technology.com/sponsored/understanding-the-current-and-future-state-of-complex-health-data-protection-laws/ https://www.pharmaceutical-technology.com/sponsored/understanding-the-current-and-future-state-of-complex-health-data-protection-laws/ http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 original research optimization of health service utilization among elderly people with chronic diseases in rural ethnic minorities in northwest yunnan using graph neural networks jing zhang, md and haitao fan, md school of health and wellness, yunnan technology and business, university, kunming city, china corresponding author: jing zhang, email: yngs_1591249@hotmail.com doi: https://doi.org/10.30953/bhty.v8.436 keywords: ethnic minority areas, health service utilization, multimorbidity elderly, service path optimization abstract background: the demand for health services among elderly patients with chronic diseases in rural ethnic minority areas of northwest yunnan is increasing. yet, service utilization remains imbalanced. existing studies mainly focus on disease combinations, overlooking temporal and spatial variations in medical behavior. methods: this study applies graph neural networks to construct a heterogeneous graph integrating patients, medical institutions, and geographic units, modeling dynamic service paths to identify high-frequency and potentially lost-contact patients. using a heterogeneous graph attention network for feature embedding and a graph attention network classifier, the model captures behavioral similarity and service path patterns. geographic and social variables such as ethnicity, terrain, and road accessibility further enhance sensitivity to regional disparities. based on node centrality and path distribution, targeted service optimization strategies—such as mobile medical points and cross-regional collaboration nodes—are proposed for resource allocation. results: experimental results reveal marked spatial and structural disparities: diqing prefecture shows an accessibility index of 68 min versus 29 min in dali; multimorbidity (3+) groups have a 68.6% matching rate but a 1.138 utilization rate, indicating resource imbalance; and mountain unit g18’s coverage index is only 0.31. conclusion: the proposed model achieves a macro-f1 of 0.83, outperforming xgboost (0.76), effectively identifying high-risk groups, locating service bottlenecks, and supporting precise health resource optimization. submitted: july 31, 2025; accepted: november 4, 2025; published: december 15, 2025 as the growth of an aging population intensifies, chronic diseases—especially multimorbidity— have become the major health burden among the elderly. according to the report on the nutrition and chronic disease status of chinese residents (national health commission, 2020), coexisting chronic diseases not only increase health risks but also place greater pressure on the healthcare system.1,2 patients with multimorbidity often experience poor treatment continuity and fragmented service pathways, challenging the coordination of primary care. in rural china, where the elderly population is large and rapidly growing, limited resources, inconvenient transportation, and low service accessibility exacerbate service mismatches and health risks.3–6 northwest yunnan (figure 1)—comprising dali, lijiang, diqing, and nujiang—features complex terrain, ethnic diversity, and an underdeveloped healthcare system, leading to a high prevalence of chronic comorbidity. existing studies remain largely static and lack systematic modeling of service paths and behavioral heterogeneity.7,8 therefore, new approaches integrating behavioral pattern mining and spatial network analysis are urgently needed to identify high-risk groups, locate bottlenecks, and optimize health resource allocation for improved equity and efficiency. with the development of society, research on chronic disease comorbidity has increased, especially in the areas of epidemiological statistics, disease combination patterns, and factors affecting medical behavior.9,10 however, there are two significant limitations in the reported results of https://orcid.org/0009-0004-7819-3073 https://orcid.org/0009-0003-4277-9526 mailto:yngs_1591249@hotmail.com https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.4362 (page number not for citation purpose) jing zhang and haitao fan research. first, comorbidity analysis focuses on the types of elderly diseases themselves and often uses co-occurrence frequency or cluster analysis to divide comorbidity patterns.11,12 this ignores the characteristics of patients’ medical behavior in the time dimension, such as frequency of visits, intervals, referral trajectories, and other temporal factors. second, there is a lack of systematic modeling of social structural variables that affect service utilization. the spatial interaction relationship between geographical location, traffic accessibility, and medical institution networks is often simplified.13,14 this fails to explain the structural constraints behind service accessibility. therefore, when dealing with a geographically closed region like northwest yunnan, which has significant differences in ethnicity, traditional methods struggle to fully characterize the complex relationship between individuals and systems. most related studies are based on questionnaire surveys or health statistics, using traditional methods such as statistical regression models, multivariate analysis of variance, and structural equation models to analyze comorbidities and their relationship with health service utilization.15,16 these studies yield certain results in identifying the factors that influence service utilization. they are insufficient in dealing with complex system structures, such as dynamic, heterogeneous, and spatially interactive ones. in particular, conventional analysis methods struggle to identify potential high-risk elderly populations and optimize service paths, as they cannot achieve data-driven intelligent reasoning and prediction and fail to model the service path relationships between individuals and institutions at the graph structure level.17,18 at the same time, few studies have attempted to apply artificial intelligence methods, especially graph learning models, to address the path dependence of individual behavior, the coupling relationships of multidimensional variables, and the structural characteristics of spatial service networks. existing studies reveal that graph neural networks (gnns) can effectively process graph-structured data with the ability to depict complex relationships between nodes, dynamic paths, and multidimensional feature fusion. the networks demonstrated good performance in social network analysis, recommendation systems, traffic route planning, and other fields. in the field of health services, some scholars have attempted to utilize gnn for electronic health record analysis, disease transmission path modeling, and drug prediction,19,20 achieving certain research results. however, there remains a lack of systematic modeling frameworks based on gnn in the identification of chronic disease comorbidity service pathways and the optimization of service accessibility,21,22 especially in research on the health service network structure for rural elderly groups in ethnic minority areas. this makes it difficult to accurately identify and respond to relevant policy formulation and resource allocation, thereby exacerbating service disparities and structural health inequalities. in recent years, gnn applications in health informatics have expanded, demonstrating strong potential in electronic health record modeling, disease pathway inference, and drug interaction prediction. for instance, memory-enhanced and transformer-integrated gnns have been used for personalized medication recommendations and heart failure patient stratification. however, most fig. 1. northwest yunnan—comprising dali, lijiang, diqing, and nujiang. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.436 3 (page number not for citation purpose) health service utilization using gnn studies focus on clinical diagnosis or risk prediction, with limited exploration of primary health service utilization in resource-limited, geographically isolated minority regions. this study extends these methods by integrating patients, institutions, and geographic units into a unified heterogeneous graph, capturing both spatial interactions and institutional linkages, thereby addressing a key gap in evaluating healthcare accessibility and equity. in this article, the authors focus on healthcare service utilization behavior among elderly people with chronic diseases from ethnic minorities in rural areas of northwestern yunnan province and construct a modeling framework based on gnn.23 this study integrates patients, medical institutions, and geographical units to construct a heterogeneous graph network and defines edge connections based on medical behavior and spatial proximity. a heterogeneous graph attention network (han) is used to extract node embeddings to characterize the behavioral similarities and potential path preferences among patients.24,25 subsequently, a graph attention classifier is used to identify service utilization patterns and classify frequent patients and potential lost-to-follow-up patients.26 the model further integrates geographical and social variables such as ethnicity, topography, and transportation to improve the responsiveness to healthcare disparities in remote areas. based on the output results and graph structure indicators (such as node centrality and path connectivity), suggestions for precise allocation of regional health resources and dynamic optimization of service paths are proposed.27,28 the results reveal significant differences in the structure and space of medical services for ethnic minorities in northwestern yunnan. the topographic coefficient of the mountain unit g18 reached 1.8, but the coverage rate was only 0.31. the matching rate of multiple disease combinations was only 68.6%, but the utilization rate was as high at 1.138. the accessibility index of diqing prefecture was 68 min, which was much higher than that of dali at 29 min, reflecting the constraints of geographical and transportation factors on medical accessibility. in addition, the findings provide technical support and a decision-making basis for precision medical intervention and resource optimization in rural and ethnic minority areas29 and expanded the application space of graph learning in the field of public health. methods: health service optimization modeling path building a heterogeneous health service utilization graph network definition of node and edge types: to model health service utilization among elderly patients with chronic comorbidities in rural ethnic regions of northwest yunnan, a heterogeneous graph network was constructed with multiple entity types and relationships. nodes include patients (p), medical institutions (h), and geographic units (g). patient nodes represent elderly individuals with chronic comorbidities; institution nodes cover village clinics, township health centers, and county hospitals; and geographic units correspond to township divisions. each node carries multidimensional attributes such as demographics, health status, spatial coordinates, and accessibility indicators. edges are defined by inter-entity relationships: patient–institution (p–h) edges denote medical visits, with weights based on normalized visit frequency; institution– institution (h–h) edges reflect spatial distance and referral links, weighted via a gaussian kernel; and geographic unit–unit (g–g) edges indicate spatial adjacency. additionally, patient–geographic unit (p–g) and geographic unit–institution (g–h) edges capture residential affiliation and spatial service linkage, forming a comprehensive representation of the regional health service network. graph structure construction and data mapping process the data sources include three parts: outpatient and inpatient medical records from the past 3 years (health information system), spatial location and road data (gis), and administrative divisions and economic indicators (socioeconomic database). all data were standardized, had missing data filled in, and were linked by primary keys before being uniformly numbered. medical record data were aggregated into time series by patient, and diagnostic codes were retained for label generation. after the coordinates of medical institutions and geographic units were transformed, spatial distances were calculated using spherical cosine distance to construct spatial correlation edges. to avoid the skewed distribution of node and edge relationships in heterogeneous networks, an edge sampling strategy is applied to regulate the density of extremely high-frequency edges. the specific method involves performing log compression on the patient-institution edge according to the visit frequency, ensuring that high-frequency medical behavior does not dominate the model learning. the final form of the heterogeneous graph is g = (v, e, t_v, t_e), where v is the set of all nodes, e is the set of edges, and t_v and t_e represent the types of nodes and edges, respectively. to further enhance the network’s semantic expression ability, independent meta-path combinations are designed for different edge types during the graph construction process, such as p-h-p, p-h-h-p, and p-g-h-p. these meta-paths are used to capture crosstype behavioral dependencies and service associations in the subsequent heterogeneous gnn learning process.30,31 figure 2 illustrates the heterogeneous health service network and node degree distribution of elderly patients with https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.4364 (page number not for citation purpose) jing zhang and haitao fan chronic comorbidities in rural ethnic minority areas of northwest yunnan. the left panel maps patients, medical institutions, and geographic units by spatial coordinates, showing medical visit links (patient–hospital) and referral collaborations (hospital–hospital). frequent cross-unit visits reveal patient mobility and a mismatch between service supply and demand. the right panel shows node degree distribution, where most patients have degrees of 1–3, while several hospitals exhibit high degrees, indicating central roles, resource concentration, and potential service bottlenecks. this structure underpins subsequent gnn-based analyses for identifying high-risk groups and optimizing service pathways. the focus of this phase is to address the challenge of representing healthcare service behavior data in a multidimensional structure. by clarifying node types and the semantic categories of edges and combining multidimensional features such as time series, spatial distribution, and behavioral paths, the original unstructured healthcare service data are transformed into a graph structure suitable for deep learning modeling. embedded feature representation learning design of heterogeneous graph attention mechanism: after completing the construction of the heterogeneous graph structure, it is necessary to learn effective feature representations for various nodes in the graph to capture the high-order semantic correlations between patients in terms of service behaviors, geographical distribution, and institutional access paths.32,33 to adapt to heterogeneous graphs with multiple node types and diverse edge structures, this study selected the han for node embedding calculations. this method can process information heterogeneity at both the meta-path level and the node adjacency level through a multi-level attention mechanism, generating node representations that are sensitive to semantic relationships. the construction of the meta-path is based on the structure defined in the previous paper. the three types of composite paths, p-h-p, p-h-h-p, and p-g-h-p, are mainly selected, corresponding to the three types of connections: behavioral homogeneity, institutional shared access, and spatial similarity. at the meta-path level semantic fusion layer, an independent semantic attention mechanism is applied to calculate the importance weight for each type of meta-path. assuming that the embedding vector of a node vi in the graph under the meta-path type m is, hi m the semantic attention coefficient is calculated by the following formula: ∑ β µ µ ( ) ( )= ⋅ ⋅ ⋅ ⋅ q w q w exp tanh( ) exp tanh( ) m t m m m t m m ' ' ' (1) where ∑µ = =v h1m i v i m 1 , q is a trainable semantic vector, and wm is a semantic-specific transformation matrix. this mechanism weightedly aggregates node embeddings in different semantic spaces under multiple meta-paths to generate the final node representation for subsequent classification model input. figure 3 shows the distribution of different types of nodes in the embedding space after t-distributed stochastic neighbor embedding (t-sne) dimensionality reduction. the x and y axes represent the principal component fig. 2. heterogeneous health service network structure and node degree distribution characteristics. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.436 5 (page number not for citation purpose) health service utilization using gnn dimensions extracted by t-sne, used to preserve local structural features. the results show that the three types of nodes exhibit significant clustering, indicating that the model effectively captures structural and semantic relationships. among them, patient nodes show the most significant clustering, while the distribution of medical institutions and geographical units is more complex. these results validate the effectiveness of heterogeneous graph models in modeling relationships among multiple types of nodes, providing a semantic foundation for subsequent path modeling and attention allocation. node feature initialization and graph convolution design: initial node features were derived from integrated attribute data. patient nodes include age, gender, number of comorbidities, visit frequency in the past year, and duration of missed visits; medical institution nodes include level, service radius, bed count, and number of doctors34,35; geographic unit nodes incorporate road accessibility, ethnic composition, altitude, and per capita income. all features were z-score normalized and projected to a unified 128-dimensional space via a fully connected network. during gnn computation, the han model applies a structured node attention mechanism to aggregate neighborhood information, capturing fine-grained interactions between individuals and service pathways. for each layer, the attention weight between node i and its neighbor j under meta-path m is computed as follows: ∑ α ( ) ( )=                ∈ a wh wh a wh wh exp leakyrelu exp leakyrelu ij m t i m j m k n t i m k m i m (2) where ni m represents the adjacent set of node i under meta-path m, and a and w are trainable parameters. the multi-head attention mechanism is used to stabilize the training process, and the final node representation is obtained through multi-head aggregation. a two-layer han with 64-dimensional outputs per layer is used to enhance embedding representation and capture cross-layer dependencies. the exponential linear unit (elu) serves as the activation function, with a 0.5 dropout rate to prevent overfitting. each node thus obtains an embedding vector integrating meta-path semantics and neighborhood interactions, providing a structure-aware input for subsequent classification. patient service utilization classification model construction this study aims to implement equitable health interventions in ethnic minority areas. it incorporates the world health organization’s framework of social determinants of health and identifies ethnic identity, language barriers, cultural beliefs, and geographical marginalization as key drivers of structural inequality. particularly, in tibetan and lisu-populated areas like diqing and nujiang, the disconnect between traditional healthcare preferences and primary healthcare services often leads to institutional loss to follow-up. therefore, this model not only identifies potentially lost-to-follow-up individuals but also incorporates variables such as ethnic composition and the ethnic matching between village doctors and patients, ensuring that the classification results reflect the dimension of cultural accessibility. model structure and input design: after completing the graph structure construction and node embedding learning, it is necessary to apply a supervised learning mechanism to further identify the differences in patients’ behavioral patterns in the health service system. based on the patient node embedding vectors output by the han model in the previous stage, this study constructed a graph attention network (gat) classifier to complete the multi-classification recognition task of three types of service utilization behaviors: “frequent medical visitors,” “regular medical visitors,” and “potentially lost visits.” the anderson behavioral model serves as the theoretical basis for constructing service utilization labels, explaining influencing factors across three dimensions: predisposing, enabling, and need factors. sex, age, and education represent predisposing factors; insurance type, income, and accessibility index are enabling factors; and the number and severity of chronic diseases reflect health needs. these factors jointly define label logic and feature vectors, guiding the classifier in distinguishing “frequent medical visitors” from “potentially lost visits.” the model input is a patient node vector with an embedding dimension of 128, and the output is a category distribution vector of length 3, which is normalized using fig. 3. distribution of different types of nodes in the embedding space after t-sne dimensionality reduction. t-sne: t-distributed stochastic neighbor embedding. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.4366 (page number not for citation purpose) jing zhang and haitao fan the softmax function. in the gat structure, two layers of attention graph convolution are set; each layer uses eight parallel attention heads, and the output dimension of each head is 16. the elu activation function and dropout (with a rate set to 0.5) are used after the first layer to prevent overfitting. the second layer outputs the classification dimension, and the fully connected layer is used for decision prediction. the loss function in model training utilizes category-weighted cross-entropy to mitigate the training offset resulting from the insufficient number of samples in the “potentially lost visit” class. to further enhance the classifier’s ability to express the heterogeneity of service behaviors in the graph structure, the adjacency weight ijα in the gat model is calculated in the following form: ∑ α ( ) ( ) ( ) ( )=    ∈ a wh wh a wh wh exp leakyrelu exp leakyrelu ij t i j k n t i k i (3) where a is a learnable attention weight vector; w is a shared linear transformation matrix; hi and hj are node embedding representation. this mechanism dynamically adjusts the aggregation strength according to the similarity between adjacent nodes, thereby improving the model’s ability to identify overlapping groups of frequent medical treatment paths and marginal behaviors of lost patients. in this study, the dropout rate was set to 0.5, and the initial learning rate was set to 0.001, determined based on the performance of a grid search on the validation set. specifically, dropout values were tested within the range of {0.3, 0.5, 0.7}, and it was found that a value of 0.5 achieved the highest recall and most stable validation loss for the “potentially lost” category. a comparison of learning rates within the range of {0.01, 0.001, 0.0001} revealed that 0.001 achieved the best balance between convergence speed and generalization ability. label definition and supervised training mechanism: the supervision signals for service model classification are derived from historical medical behavior data. patients with an average annual visit frequency ≥ 6 and intervals ≤ 30 days are labeled as frequent visitors; those with regular but below-average annual visits as regular visitors; and those with no visits or gaps ≥ 12 months as potentially lost visitors. this labeling accounts for both visit density and long-term discontinuity to capture behavioral heterogeneity. to enhance model stability and generalization, class balance is achieved using elu oversampling, with an 8:2 training–validation split. the model is trained using adam (learning rate = 0.001, 200 epochs) with early stopping after 10 stagnant validation losses. performance is evaluated by macro-average f1 and recall to address class imbalance and ensure robust multi-class prediction. figure 4 shows the patient service classification model based on the gat. integration of geographic and socioeconomic variables health geography and spatial accessibility are not separate concepts in rural health service research but rather mutually supportive theoretical dimensions. health geography emphasizes the spatially nested relationship between disease distribution, medical services, and the social environment, while spatial accessibility quantifies the physical and institutional barriers to residents’ access to services. in frontier regions like yunnan in northwest china, with its complex terrain and concentrated ethnic populations, the two are highly coupled: plateau valleys not only prolong medical care but also reinforce institutional exclusion by restricting transportation networks. this study co-encodes road grade, elevation fluctuations, and administrative boundaries as edge weights and node attributes in a graph structure. based on the health geography perspective that “space is social structure,” this study elevates accessibility beyond a mere distance function to a composite indicator embedded with ethnic identity, resource density, and topographical constraints. spatial attribute feature injection mechanism to enhance the graph structure’s ability to represent service accessibility and regional behavioral heterogeneity, this study introduces multidimensional geographical and socioeconomic variables into the embedding and classification models to construct an extended heterogeneous graph. geographical variables include road accessibility (shortest travel time), terrain complexity, and spatial marginality (standardized shortest distance to township or county hospitals); social variables cover ethnic identity, household registration level (village, township, county), and regional medical resource density (number of beds, number of practicing physicians). variables are injected through a node-level attribute expansion mechanism, performing feature concatenation at both patient and geographical unit nodes to improve the model’s ability to perceive service disparities in remote ethnic minority areas. specifically, for the patient node, its original embedding representation vector ∈hi 128 is expanded to = ⊕h' h fi i i , where ∈fi 6 represents the six-dimensional auxiliary feature vector of the geographic unit corresponding to the patient, and the final node representation dimension is expanded to 134 dimensions. the auxiliary features are all normalized by min-max to avoid distribution bias interfering with attention-weight learning. to enhance the structural influence of spatial variables on the graph propagation process, a weighted adjacency adjustment mechanism is applied in the definition of edge connections. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.436 7 (page number not for citation purpose) health service utilization using gnn if there is a spatial connection between patient vi and his administrative unit , the edge weight is given as: β γ( ) ( )= − ⋅ ⋅w d sexpij ij j (4) where dij represents the spatial distance; β is the attenuation coefficient (set to 0.15); sj is the medical resource index of the region; and γ is the normalized mapping function used to suppress gradient instability caused by extreme resource distribution. in this way, the model not only relies on the graph structure itself during information dissemination but also dynamically adjusts the dissemination intensity to reflect the impact of spatial accessibility and uneven resource distribution. attribute-sensitive aggregation and edge region specialization modeling considering the structural differences and semantic heterogeneity between the applied geo-social features and the original embedded features, the classification model performs channel encoding on the input vector and applies a dual-channel perception layer. the first channel receives the structural embedding generated by han or gat, and the second channel specifically processes the injected geo-social features. the two channels are fused through the attention-gating mechanism, and the fusion weights are learned as follows: σ ( )= + +g w h w f bi i i1 2 (5) ( )= + − z g h g f1i i i i i (6) where w1 and w2 are linear transformation matrices; ⊙ is the hadamard product; σ is the sigmoid function; and the final output fusion vector z is used as input for subsequent classification tasks. while maintaining the dominant position of the main image information, this structure gives adjustable weights to spatial edge variables through a gating mechanism, which is suitable for identifying service variables with significant behavioral heterogeneity in areas such as ethnic minority settlements and complex plateau terrain. during the model training phase, regional grouping supervision indicators are set, and the performance of each regional model is guided by applying geographical stratification supervision loss. specifically, based on the original cross-entropy loss, a regularization term is added to minimize cross-regional prediction errors, thereby reducing the prediction performance gap between high-resource fig. 4. patient service classification model based on gat. gat: graph attention network; smot: synthetic minority oversampling technique. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.4368 (page number not for citation purpose) jing zhang and haitao fan and low-resource areas during the model training process. the following regularization loss function implements this mechanism: ∑= −∈l f1 f1geo g g g (7) where g is the set of all region groups, f1g is the validation set f1 value of the g-th region, and f1 is the mean f1 of all regions. the final total loss function is: λ= + ⋅l l ltotal ce geo (8) where λ is the weight factor, which is set to 0.1. this mechanism encourages the model to prioritize regional fairness while optimizing the overall situation, thereby improving prediction stability and explanatory power in areas with severe uneven medical resources. figure 5 illustrates the distribution of three patient types (high-frequency patients, regular patients, and potential lost contacts) across three geo-social characteristics: road accessibility, altitude, and economic level. the x-axis represents the corresponding characteristic value, and the y-axis is the sample frequency. overall, those who frequently seek medical treatment (red) have a higher mean road accessibility, indicating that convenient transportation may promote high-frequency medical treatment behavior. the road feasibility of potential lost patients is relatively dense at around 0.06, revealing that the disadvantaged groups in road traffic are prone to be out of the service system; the altitudes of the three types of patients are all distributed between 2,000 and 4,000, reflecting that the patients are at a higher altitude; in terms of economic level, the economic level of potential lost patients is relatively dense at around 0.06, which is lower than that of regular and frequent medical treatment seekers, revealing that economically disadvantaged groups are more likely to be out of the service system. service path optimization suggestion generation after completing the classification of high-frequency medical patients and potential lost follow-up groups, the health fig. 5. distribution of patients of different categories in three typical geographical and social characteristics. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.436 9 (page number not for citation purpose) health service utilization using gnn service utilization graph is further structurally analyzed to identify nodes and paths that play a key intermediary role or form structural bottlenecks in the service network and then provide structurally reasonable resource allocation suggestions. first, the centrality index of each type of node (patient, medical institution, and geographic unit) in the graph is calculated, including degree centrality, eigenvector centrality, and betweenness centrality. among them, betweenness centrality is used to measure the transit capacity of the node in the patient service path, expressed as: ∑ σ σ( ) ( ) = ≠ ≠c v v b s v t st st (9) where σst represents the number of shortest paths from node s to node t and σst (v) represents the number of paths passing through node v. based on this, the top 10% of medical institution nodes and geographic unit nodes in terms of centrality are extracted and marked as “core service nodes.” at the same time, the intermediate geographic units along the path between frequent medical patients and the core service nodes are selected, and their average path length and betweenness centrality in the entire graph are calculated to identify the “key service corridors” that form bottlenecks. at the edge connection level, the edge load index is applied to measure the congestion degree of the edge, as shown in formula (10): ∑ σ σ( ) ( ) = ∈l e e i j v ij ij , (10) this indicator measures the importance of the edge e in the shortest path to all nodes. the internal edge load distribution is calculated for the subgraph of the frequent medical treatment group and the subgraph of the potential lost visit group. by comparing their edge density and average path length, it is determined whether the service connection poses a risk of centralization or spatial fracture. the edges with excessively high load and the connections with path spans greater than the average value are marked to form a set of “weak service connections.” figure 6 illustrates the identification of key institutions and spatial path pressure analysis within the healthcare service network. the radar chart on the left characterizes five major healthcare institutions using degree centrality, eigenvector centrality, and betweenness centrality. the results show that hospitals-8 and -19 perform exceptionally well across multiple dimensions, exhibiting high connectivity and bridging capabilities. the heatmap on the right reflects the edge load between geographical units; darker colors represent higher service traffic. units 6 and 9 show a load exceeding 0.8, forming a significant bottleneck. overall, some institutions and paths experience high service pressure, providing a basis for regional collaborative optimization and intervention at key nodes. health service optimization modeling path the data for this study come from a special survey on the utilization of health services by the rural elderly conducted in 2023 in four autonomous prefectures (dali, lijiang, diqing, and nujiang) in northwestern yunnan province. the survey subjects were rural residents of ethnic minorities aged <60 years. the study focused on individuals with two or more chronic diseases (such as hypertension, diabetes, coronary heart disease [chd], stroke, and chronic obstructive pulmonary disease [copd]). data collection covered demographic characteristics, family economic status, health behaviors (smoking, drinking, exercise, nutrition, sleep, etc.), and medical service utilization fig. 6. analysis of key institution identification and spatial path pressure in optimizing medical service network. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.43610 (page number not for citation purpose) jing zhang and haitao fan characteristics (outpatient visits, hospitalizations, frequency of visits every 2 weeks, etc.). spatial coordinates of patients’ residences, administrative units, and the distribution of medical institutions were also integrated for graph structure construction and gnn model training. minors, non-rural residents, individuals with severe cognitive impairment, and those who refused to participate in the survey were excluded. the sample is regionally representative but does not fully cover the dulongjiang river basin in nujiang. to evaluate the model’s generalization ability, stratified 5-fold cross-validation was used, dividing the training and validation sets by geographical unit to ensure that data from the same township did not overlap, thus simulating cross-regional deployment. the results show that the macroscopic f1 standard deviation is ±0.021, indicating that the model is stable across different sub-regions. service accessibility index the service accessibility index is calculated using patients’ home and institution coordinates from medical records, corrected with high-resolution geographic data. based on 1:50,000 terrain and road grade maps, a weighted shortest path algorithm adjusts for elevation and road accessibility while removing abnormal points. average travel time for each patient’s visits over the past year is aggregated at the village level to map regional accessibility. higher scores indicate greater spatial barriers, typical in high-altitude and sparsely populated areas, reflecting uneven basic service distribution. figure 7 analyzes the access to medical services in the four prefectures of northwestern yunnan (dali, lijiang, diqing, and nujiang) from the perspective of spatial accessibility. the x-axis of the left figure is the village altitude (meters), and the y-axis is the accessibility index (unit: minutes). in diqing (approximately 3,600 ± 400 meters above sea level) and nujiang (with an average altitude of about 2,700 meters), the time it takes to receive medical services is significantly higher than in low-altitude areas, such as dali, indicating that patients in plateau areas face more pronounced transportation obstacles. the right figure is a box plot of medical accessibility in each state, with the y-axis representing access time. it can be seen that the average accessibility index of diqing is as high as 68 min, which is much higher than dali’s 29 min, and its internal differences are also greater. figure 8 quantitatively reveals the uneven impact of regional geographical differences on medical accessibility, providing intuitive evidence for understanding the problem of primary medical accessibility in border multiethnic areas. service utilization intensity ratio based on individual medical data, the frequency of service utilization by patients in a given year is counted by the level of medical institution, and health needs are quantified in combination with the number of illnesses and previous hospitalization records. a theoretical treatment interval is set for each type of disease combination (refer to the national primary care chronic disease management standards), and the expected service intensity is estimated by constructing a hierarchical linear model. the ratio of actual service intensity to the model expectation is then calculated as an indicator of the service utilization intensity ratio. those with fig. 7. spatial differences in medical accessibility among 50 administrative villages. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.436 11 (page number not for citation purpose) health service utilization using gnn significant deviations in the ratio are identified as potential service mismatch objects, particularly for individuals with complex comorbidities who receive insufficient medical treatment or those with mild conditions but frequent visits to high-level hospitals, providing an objective basis for identifying resource utilization deviations. figure 8 illustrates the utilization rates of different chronic disease combinations and elderly age groups in northwest yunnan, reflecting the service matching status of multimorbid older adults. overall, most combinations show slightly lower-than-expected visit frequencies, indicating insufficient service coverage. the “cataract + diabetes” group has the lowest utilization rate (0.645), suggesting poor attention to vision-related complications. in contrast, the “multimorbidity (>3+)” group shows a high rate (1.138), reflecting adequate service matching and a resource bias toward severe cases. the “copd + chd” combination (1.152) indicates mild overuse, likely due to overlapping respiratory–cardiovascular symptoms. by age, utilization rises to a peak of 1.22 among those aged 76 to 80 years but declines to 0.68 in those >86 years, implying reduced access due to mobility and care constraints. these findings highlight structural imbalances in regional resource allocation and the need for ageand disease-specific service optimization. service path rationality score each patient’s continuous medical records are mapped to a heterogeneous graph, and the service path is extracted. the average number of hops, the shortest path deviation, and the frequency of cross-administrative-level medical treatment are calculated. the spatial adjacency matrix and service level mapping table between medical institutions are used to evaluate the structural optimization of all service paths. if the path repeatedly crosses county lines, unnecessary level upgrades and frequent changes of institutions occur, and the rationality score is reduced. this method helps to identify irrational medical behavior caused by poor referral processes, information islands, or medical preferences. it is particularly important to evaluate the rationality of the path, especially in the context of high costs of cross-regional medical treatment in plateau areas. table 1 shows the rationality evaluation results of typical medical treatment paths. combined with the average number of hops, path deviation, frequency of cross-county medical treatment, unnecessary level upgrade, and other indicators, the rationality score is calculated and evaluated. it can be seen that the medical treatment path with path id p015 performs best, with an average number of hops of only 1.2, a shortest path deviation of 8.7%, no cross-county medical treatment, and no unnecessary level upgrade, resulting in a rationality score of 92.3, which is evaluated as “excellent.” in contrast, the p042 path has significant problems, with an average number of hops of 4.1, a deviation of 72.5% from the shortest path, six cross-county medical treatments, three unnecessary level upgrades, and a rationality score of only 41.2, which is a “high-risk” path, indicating that its path planning is seriously unreasonable; p102 performs above average, with a rationality score of 78.9, which is “good,” and there are significant differences in service accessibility and rationality between different paths. service frequency and health needs matching rate for elderly people with two or more chronic diseases, a disease combination-service frequency mapping model fig. 8. performance of service utilization intensity ratio in different disease combinations and age groups. chd: congested heart disease; ckd: chronic kidney disease; copd: chronic obstructive pulmonary disease. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.43612 (page number not for citation purpose) jing zhang and haitao fan is constructed. the model input includes the number of comorbidities, core disease combinations (such as diabetes and hypertension, copd and chd, etc.), disease duration, and previous hospitalizations and outputs the expected range of individual annual service frequencies. the actual number of visits is compared with this interval, and those with a higher number are classified as having “over-dependence.” in comparison, those with a lower number are considered to have a “potential loss to follow-up.” this indicator is further adjusted based on the individual’s demographic and social attributes to enhance the sensitivity of assessing matching service utilization with health status and guide targeted health interventions. table 2 presents the matching of different chronic disease combinations in terms of medical service utilization, measuring the rationality of services through indicators such as the number of patients, expected frequency of visits, actual matching rate, and utilization intensity classification ratio. overall, the service matching rate of most disease combinations remains at a high level. for example, the matching rate of the combination of “hypertension + hyperlipidemia” reaches 88.2%, indicating that the frequency of visits for most patients falls within a reasonable range [4.8, 6.0], and 80% of the patients in this classification ratio are classified as having “normal” utilization levels. in contrast, the matching rate of the “multi-disease combination (3+)” is only 68.6%, the lowest value, and the over-dependence rate is as high as 46%, highlighting the significant lack of service access for patients with multiple chronic diseases, which may be affected by functional impairment, transportation accessibility, or lack of service continuity. in addition, the matching rate for the “chronic kidney disease + diabetes” combination is also low, at only 72.4%, and the proportion of over-dependence is as high as 42%, suggesting that active management and service resource allocation should be strengthened in these high-risk groups. therefore, the data in table 2 reveal significant differences in the rationality of service utilization among different disease groups, providing an empirical basis for the formulation of personalized intervention strategies. adequacy of spatial resource coverage this indicator quantifies the spatial accessibility of medical resources within a specific geographical unit through buffer zone analysis. with each household as the center, three equidistant buffer zones of 5 km, 10 km, and 15 km are established to count the number of medical institutions and their service capacities (number of beds, staff size, annual outpatient volume, etc.). the slope and road table 2. matching of different chronic disease combinations in terms of medical service utilization disease combination number of patients expected range [l, u] matching rate (%) classification proportion (%)* hypertension + diabetes 142 [5.5, 7.2] 85.9 20 / 75 / 5 chd + stroke 87 [7.0, 9.0] 78.2 35 / 58 / 7 copd + asthma 63 [6.5, 8.0] 82.5 25 / 68 / 7 ckd + diabetes 58 [8.0, 10.5] 72.4 42 / 50 / 8 hypertension + hyperlipidemia 127 [4.8, 6.0] 88.2 15 / 80 / 5 peptic ulcer + diabetes 49 [5.2, 7.0] 83.7 22 / 72 / 6 cataract + diabetes 92 [3.5, 4.8] 79.3 12 / 65 / 23 copd + chd 54 [7.5, 9.5] 75.9 38 / 55 / 7 stroke + hypertension 103 [6.5, 8.8] 76.7 33 / 60 / 7 multimorbidity (3+) 118 [9.0, 11.5] 68.6 46 / 48 / 6 * indicates that the data has statistical significance. chd: chronic heart disease; copd: chronic obstructive pulmonary disease; ckd: chronic kidney disease. table 1. rationality evaluation results of typical medical treatment pathways pathway id avg. steps shortest path deviation (%) cross-county visits unnecessary level escalations rationality score* evaluation result p001 2.8 34.2 3 1 68.5 moderate p015 1.2 8.7 0 0 92.3 excellent p042 4.1 72.5 6 3 41.2 high risk p087 3.3 48.6 4 2 56.8 low p102 2.1 22.3 1 1 78.9 good * data are significant. id: identification. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.436 13 (page number not for citation purpose) health service utilization using gnn grade correction factors are applied to the mountain samples for weighted adjustment. finally, a per-population medical resource coverage index is formed. if areas with low resource coverage overlap with groups with high service demand or high terrain obstacles, they are identified as “high-risk medical areas.” providing spatial decision support for the construction of new medical sites or the implementation of mobile service vehicles. table 3 presents the coverage of medical resources across different geographical units and assesses their risk levels by combining population density, coverage index, and terrain correction coefficient. for example, g07 has a high population density (86.2 people/km²), a 5 km coverage index of 0.92, and a small terrain impact (coefficient 1.0) and is therefore rated as low risk. in contrast, g18 and g29 have low population densities (28.7 and 38.9, respectively), 5 km coverage indexes of only 0.31 and 0.42, respectively, and complex terrain (coefficients as high as 1.8 and 2.1, respectively), both of which are considered high-risk areas. overall, it reflects that resource coverage in mountainous or remote areas is insufficient and needs to be optimized. performance comparison with traditional models to systematically evaluate the effectiveness of the proposed hierarchical attention network-graph attention network (han-gat) heterogeneous gnn model in classifying the service utilization of elderly populations with chronic disease comorbidities in rural northwest yunnan, this paper selected five traditional models as baselines for comparative experiments. these models include logistic regression (lr), random forest (rf), xgboost, k-means clustering, and multilayer perceptron (mlp), covering the mainstream paradigms of statistical learning, ensemble methods, and unsupervised techniques. the data in table 4 reveal the significant performance advantages of the han-gat model. first, in terms of overall accuracy, han-gat achieves a macro-f1 score of 0.83, which is higher than that of the traditional model, xgboost (0.76). this is because its graph structure embedding mechanism dynamically captures patient-institution interactions (such as referral paths), breaking through the traditional method’s reliance on static attributes. although xgboost performs better in f1 for frequent medical visitors (0.79), han-gat further improves to 0.85, indicating that it is more sensitive to high-frequency behavior patterns. second, in terms of marginal group identification, the recall rate of potential lost contacts of han-gat is 0.89, much higher than lr (0.58) and mlp (0.61), proving that the model effectively integrated geographic social variables and solved the blind spot caused by ignoring temporal behavior of k-means (recall rate is only 0.52). in summary, data-driven hangat accurately locates high-risk areas in resource optimization and promotes intervention measures such as the establishment of mobile medical points. conclusion based on the health service utilization of elderly patients with chronic diseases in rural ethnic areas of northwest yunnan, this study constructs a heterogeneous graph integrating patients, medical institutions, table 3. medical resource coverage in different geographical units geographic unit population density (people/km²) 5 km coverage index 10 km coverage index 15 km coverage index terrain adjustment factor risk level g07 86.2 0.92 0.97 0.99 1 low risk g12 45.3 0.68 0.82 0.91 1.2 medium risk g18 28.7 0.31 0.47 0.63 1.8 high risk g23 62.1 0.58 0.76 0.85 1.5 high risk g29 38.9 0.42 0.61 0.78 2.1 high risk table 4. performance comparison of each model model macro-f1 frequent user f1 regular user f1 recall of potential lost contact logistic regression 0.65 0.71 0.68 0.58 random forest 0.72 0.75 0.74 0.63 xgboost 0.76 0.79 0.77 0.67 k-means clustering 0.61 0.68 0.64 0.52 mlp 0.74 0.78 0.76 0.61 han-gat (ours) 0.83 0.85 0.82 0.89 han-gat: hierarchical attention network-graph attention network; mlp: multilayer perceptron; xgboost: extreme gradient boosting. https://doi.org/10.30953/bhty.v8.436 citation: blockchain in healthcare today 2025, 8: 436 https://doi.org/10.30953/bhty.v8.43614 (page number not for citation purpose) jing zhang and haitao fan and geographic units. using a gnn, it models medical behavior paths, identifies frequent visitors and potentially lost visits, and proposes service optimization strategies based on node centrality and path rationality. results show that the gnn effectively captures behavioral structures and path heterogeneity, detects service mismatches, and enhances targeted resource allocation. by integrating spatial, ethnic, and health factors, the model demonstrates strong adaptability in complex contexts. limitations include a restricted data scope and partial reliance on retrospective self-reports. future work will expand sampling, incorporate real-time multi-source data, and improve model generalization to better support precise health service optimization in underdeveloped regions. funding project of scientific research fund of yunnan provincial department of education (2025j1328). contributors jing zhang conceptualized this work. haitao fan wrote the manuscript. jing zhang and haitao fan conducted the review of the first draft and response to reviewer comments. conflicts of interest the authors declare that they have no financial conflicts of interest. data availability statement (das), data sharing, reproducibility, and data repositories the data that support the findings of this study are available from the corresponding author upon reasonable request. application of ai-generated text or related technology none. references 1. quiñones ar, hwang j, heintzman j, huguet n, lucas ja, schmidt td, et al. 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review on iot and machine learning algorithms in e-healthcare. int j comput digit syst. 2024;16(1):279–94. https://doi.org/10.12785/ijcds/160122 copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.436 https://doi.org/10.1038/s41591-024-03164-7 https://doi.org/10.1038/s41591-024-03164-7 https://doi.org/10.1038/s43587-021-00082-y https://doi.org/10.1145/3696111 https://doi.org/10.1007/s10916-025-02140-z https://doi.org/10.1007/s10916-025-02140-z https://doi.org/10.1146/annurev-biodatasci-110123-041001 https://doi.org/10.1038/s41598-025-03332-w https://doi.org/10.1038/s41598-025-03332-w https://doi.org/10.1093/jamia/ocae182 https://doi.org/10.1093/jamia/ocae182 https://doi.org/10.1093/eurheartj/ehac758 https://doi.org/10.1007/s10729-023-09653-4 https://doi.org/10.1007/s10729-023-09653-4 https://doi.org/10.1016/j.jcin.2024.01.284 https://doi.org/10.32996/jcsts.2025.7.1.12 https://doi.org/10.1159/000517285 https://doi.org/10.1159/000517285 https://doi.org/10.1186/s12888-024-06299-6 https://doi.org/10.1186/s12888-024-06299-6 https://doi.org/10.1093/ijpp/riad025 https://doi.org/10.3928/00989134-20241210-01 https://doi.org/10.3928/00989134-20241210-01 https://doi.org/10.5114/ait.2021.108084 https://doi.org/10.31181/jopi31202538 https://doi.org/10.12785/ijcds/160122 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research impact of covid-19 on primary healthcare research: trends and suggestions for better services approaches via blockchain based applications muhammet damar, phd1,2  ; andrew david pinto, md msc2,3,4,5  ; fatih safa erenay, phd6  and omer aydin, phd6,7  1computer science department, faculty of science, dokuz eylul university, alsancak, i̇zmir, turkiye; 2upstream lab, map, li ka shing knowledge institute, unity health toronto, toronto, ontario, canada; 3department of family and community medicine, faculty of medicine, university of toronto, toronto, ontario, canada; 4department of family and community medicine, st. michael’s hospital, unity health toronto, toronto, ontario, canada; 5dalla lana school of public health, university of toronto, toronto, ontario, canada; 6management science and engineering, faculty of engineering, university of waterloo, waterloo, ontario, canada; 7electrical and electronics engineering, faculty of engineering, manisacelal bayar, university, manisa, turkiye corresponding author: omer aydin, email: omer.aydin@cbu.edu.tr doi: https://doi.org/10.30953/bhty.v8.400 keywords: bibliometric analysis, blockchain, covid-19, coronavirus, pandemic, primary healthcare, topic modeling abstract objective: the authors assessed how research in primary healthcare was affected by the covid-19 pandemic and identified the potential of blockchain technology to address pandemic-related challenges. methods: this quantitative bibliometric research study used machine learning techniques. a comprehensive analysis of all primary healthcare (phc) research was conducted using bibliometric data from the wos. we examined co-authorship, co-occurrences, citation and co-citation, thematic mapping, factorial, document, and latent dirichlet allocationtopic analyses. our main dataset was 1,885 articles produced by 9,185 researchers from 3,132 institutions in 113 countries. results: the most cited studies in the phc field during the pandemic related to telemedicine and remote consultation, along with clinical conditions such as mental health, diabetes, vaccinations, risks during pregnancy, and healthcare of the elderly. in addition, the impact of covid-19 on educational outcomes, changes to the organization of care, experiences and challenges to phc physicians and other health professionals, and the diversity of covid-19 symptoms were prominent. conclusions: the phc researchers adapted quickly to the pandemic and conducted multidisciplinary research that helped to mitigate the impact on individuals, health systems, and society. within this context, blockchain technology can be used to facilitate the security of health data, resource management (e.g., monitoring of the vaccine supply chain), and global collaboration toward pandemic control. by providing transparency, security, and efficiency in these areas, blockchain technology might lead to more effective pandemic preparedness and management in the future. plain language abstract the authors assessed how research in primary healthcare was affected by the covid-19 pandemic and identified the potential for blockchain technology to address pandemic-related challenges. the main dataset included 1,885 articles produced by 9,185 researchers from 3,132 institutions in 113 countries. the phc researchers adapted quickly to the pandemic and conducted multidisciplinary research that helped to mitigate the impact on individuals, health systems, and society. the results reveal that blockchain technology can facilitate the security of health data, resource management (e.g., monitoring of the vaccine supply chain), blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-3985-3073 https://orcid.org/0000-0003-1841-9347 https://orcid.org/0000-0002-3408-0366 https://orcid.org/0000-0002-7137-4881 mailto:omer.aydin@cbu.edu.tr https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.4002 (page number not for citation purpose) muhammet damar et al. and global collaboration toward pandemic control. by providing transparency, security, and efficiency in these areas, blockchain technology may lead to more effective pandemic preparedness and management in the future. submitted: april 28, 2025; accepted: june 4, 2015; published: august 1, 2025 the covid-19 pandemic resulted in over seven million deaths and profoundly affected health systems globally.1–3 primary healthcare (phc) practitioners played an important role in responding to the covid-19 pandemic. as the initial point of contact and the ‘front door’ to the healthcare system,4,5 phc providers supported key aspects of the pandemic response, including testing, vaccination, treatment of sars-cov-2, communication to the public, and adapting to maintain care for other acute illnesses and chronic diseases.6–8 bibliometric analysis methods are important tools for reviewing developments or prominent debates in a specific field over time.9,10 several bibliometric studies were conducted in the phc literature.11–14 exploring the impact of the covid-19 pandemic on phc research through a comprehensive bibliometric evaluation may provide insights to be better prepared for future pandemics. no previous study provides a comprehensive evaluation of the global impact of covid-19 on phc literature. using bibliometric tools, our objective was to provide a comprehensive evaluation of how covid-19 changes the phc research landscape. we sought to identify answers to the following issues. how has the covid-19 pandemic affected the phc literature? what are the most highly cited covid-19 articles in phc? which researchers, institutions, and in what countries conducted the most intensive research on covid-19 in phc. in addition, we sought to identify how blockchain technology might be used more efficiently during similar outbreaks in the future with. methodology data on articles published between january 1, 2020 and february 2, 2024, were obtained from nine different queries through the web of science (wos) core collection (appendix a). bibliometric data were first obtained as  excel files and plain text, and we used vos viewer and  the biblioshiny application in the bibliometrix r package. in our analyses, we focused on research articles and reviews as the information carried by these document types is more comprehensive and relevant to the research field. several types of content analyses were conducted to capture and reveal the impact of the covid-19 pandemic on phc research. the set of conducted content analysis and associated methods includes co-authorship analysis,15,16 co-occurrence analysis (network, overlay, and density analysis),16,17 citation and co-citation analysis,18,19 thematic map analysis, thematic evaluation analysis, and factorial analysis (word map, words by cluster, and topic dendrogram),19,20 document analysis (most frequent words, word cloud, treemap, word dynamics, trend topics),19,20 and latent dirichlet allocation.21,22 the search sites used to retrieve the data for analysis from the wos core collection, the research methodology, and other details are provided in appendix a. topic modeling is one of the most powerful techniques for data/text mining and hidden data discovery to identify relationships between data and text documents. among the various methods of topic modeling, latent dirichlet allocation is one of the most popular.23 results general view of covid-19 literature in primary healthcare research area the covid-19 pandemic was first reflected in the phc literature in 2020. our bibliometric analysis accessed a total of 2,606 publications of 12 different types. the phc research area, which has had critical importance during the pandemic outbreak period, ranked 74th among wos research areas with 2,606 documents. we limited our main dataset to 1,885 publications by considering only research articles and reviews. appendix b illustrates the distribution of publications according to relevant fields (covid19 or phc), publication years, publication types, and research areas. the top five wos research areas where covid-19 studies were conducted include general internal medicine (f:50,613, 9.72%), public environmental occupational health (f:50,162, 9.63%), immunology (f:23,126, 4.44%), infectious diseases (f:22,779, 4.37%), and multidisciplinary sciences (f:19,831, 3.81%). interestingly, the vast majority of the relevant publications (f:1,779 out of 1,885, %94.4%) were open-access. authors, institutions, and countries the relevant articles in the main database (f:1,885) were written by 9,185 researchers from 3,132 different institutions located in 113 different countries. the top 30 countries, the top 20 institutions, and the top 20 authors showing the most intense interest in covid-19 topics within the phc literature are listed in appendices c, d, and e, respectively. the threefield-plot (journals, authors, and keyword plus) analysis https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.400 3 (page number not for citation purpose) impact of covid-19 on primary healthcare research in appendix e shows the journals and subject headings of the researchers’ publications in general. considering the publications in journals in the social sciences citation index (ssci) and science citation index expanded (sci-expanded), the university of oxford (f:40, 5.42%), university of toronto (f:34, 4.61%), university of london (f:33, 4.47%) stand out and make significant contributions to the field. references, most cited documents and sources the covid-19 articles in the field of phc were published in 29 different journals (see appendix f). the most cited articles are shown in appendix g. the 1,885 relevant articles cited 4,449 different sources (journals, theses, books, etc.) and 42,295 references. the co-citation source analysis shows the network analysis of 374 sources according to the minimum 20 works, the eight clusters with which the journals are associated, and the density map of the journals (figure 1). the most cited study in the main database evaluates the impact of covid-19 on loneliness, mental health, and health service utilization with a cause-and-effect relationship for older adults.24 that study revealed that older patients with multimorbidity in primary care experienced worse psychosocial health and an increase in missed scheduled medical appointments for chronic disease care after the start of the covid-19 pandemic. the top cited studies in the phc field during the relevant period considered prominent issues related to telemedicine,3,25 remote consultation,26,27 mental health,24,28,29 diabetes,30 anxiety,28,31 vaccination,32,33 risks for pregnant and elderly people,28,34 the impact of covid-19 on education,35 organizational problems in phc,36,37,38 phc physicians’ and health professionals’ experiences and problems,4,25,39,40 covid-19 symptoms,41,42 migrants,43 refugees,44 covid-19 vaccine hesitancy.45 themes of the relevant studies based on keywords, titles, abstracts, and research areas perspectives the results of the co-occurrence analysis for researcher keywords are shown in figure 2. note that the keywords equivalent to covid-19 in phc and primary care were excluded from the analysis for clarity. in figure 3, the size of each word is proportional to the usage frequency of that word. the words belonging to the same clusters have the same color in figures 2a and c, while the thickness of the links between words highlights co-occurrence frequency, which implies the existence and magnitude of relationships between the keywords. in figure 2b, from dark blue to red, the topics that were intensively studied between 2020 and 2024 are shown. furthermore, figure 2d shows the thematic evolution performed on the researchers’ keywords. in figure 2a and c the words belonging to the same clusters share the same color, while in figure 2d, the thickness of the links between words highlights co-occurrence frequency, which implies the existence and magnitude of relationships between the keywords. in addition, figure 2d shows the thematic evolution performed on the researchers’ keywords. note that the keywords equivalent to covid-19 in phc and primary care were excluded from the analysis for clarity. (a) network analyses, (b) overlay analyses, (c) density analyses, and (d) thematic evolution. phc: primary healthcare. we also analyzed the relevant articles on covid-19 in phc literature using latent dirichlet allocation topic modeling to summarize and extract article themes. these distinct themes obtained from this analysis are depicted on six different word clouds in figure 3a–f. based on a topic dendrogram analysis performed on the abstracts of the articles to the main research areas, covid-19 articles in phc literature focus on eight main research areas (see appendix h, appendix i and appendix j). the related fields are general internal medicine (appendix ha), endocrinology metabolism (appendix hb), healthcare sciences services (appendix ic), health policy services (appendix id), public environmental occupational health (appendix id), orthopedics (appendix je), sport sciences (appendix jf), respiratory system (appendix jf). covid-19 articles in health policy services are also associated with the public environmental occupational health field (appendix id), and covid-19 articles in orthopedics are also associated with the sports sciences field (appendix je). discussion and conclusion during the pandemic and during the following years, covid-19-related issues have drawn significant interest in the phc literature, for example, nearly one out of every five phc studies published between 2021 and 2023 were related to covid-19. this study offers a comprehensive bibliometric assessment of articles using machine learning techniques on covid-19 in the phc research field, providing insights into the pandemic’s impact on phc research from a holistic perspective. upon review of the literature, this study represents the most comprehensive investigation into the impact of the pandemic on phc literature. in addition, the pandemic was beyond a regular health problem or disease. thus, its multidimensional impact has been studied by experts from different fields.46–48 however, existing studies have reviewed phc research related to covid-19 within specific subareas of the field.28,29,33,49 our bibliometric analyses revealed that such largescale pandemics affect a vast variety of subareas within the phc field as well as other fields of medicine. therefore, multidisciplinary approaches are often necessary for https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.4004 (page number not for citation purpose) muhammet damar et al. effective problem-solving for the control and management of such large-scale pandemics. comorbidity, multimorbidity, stress, telemedicine, remote consultation, mental health, diabetes, anxiety, vaccination, risks for pregnant and elderly, vulnerable population, impact of covid-19 on education, organizational problems in phc, experiences and problems of phc physicians and health workers, covid-19 symptoms, migrants, refugees, covid-19 vaccine hesitancy, and disease management were the other intensively studied topics worth mentioning. for instance, care for patients with multimorbidity emerged as an intensely questioned issue fig. 1. co-citation sources analyses for covid-19 studies in phc research area. phc: primary healthcare. https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.400 5 (page number not for citation purpose) impact of covid-19 on primary healthcare research fig. 2. co-occurrence analyses for covid-19 studies keywords in phc research area. phc: primary healthcare https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.4006 (page number not for citation purpose) muhammet damar et al. for phc services during the covid-19 pandemic given their vulnerability against covid-19.6,7 in addition, as the pandemic progressed, vulnerable groups were more susceptible to covid-19 infection and had worse health outcomes compared to others.6 refugees, migrants, the homeless population, children, pregnant women, elderly people, veterans, people with chronic diseases (e.g., diabetes, cancer, cardiovascular problems, neurological diseases, chronic respiratory diseases), and rural populations are other outstanding keywords for vulnerable populations. keeping an up-to-date database for patients with multimorbidity and vulnerable patients is vital to be better prepared for such pandemics in the future and minimize the adverse outcomes among these individuals. in our study, stress and anxiety emerged as very intensively studied topics in the phc field. this situation is reasonable considering the burden of the pandemic.24 mental health, anxiety, depression, stress, and burnout were among the keywords and themes captured by our topic modeling applications. these topics were discussed in the context of problems faced by doctors, nurses, and other health professionals, as well as by patients and medical students. quality of life, health equity, health service accessibility, social determinants of health, and gender differences were other topics discussed in relation to phc services. these topics were discussed in the phc literature in conjecture with disease management, triage, risk factors, and health management.50–53 the most important covid-19 measures for mitigating the pandemic propagation were social distancing3 and the use of masks, herd immunity, personnel protective equipment, quarantine, and the management and coordination of the outbreak in phc services.54 in addition to these, telemedicine digital technology has been a relevant and necessary technology for the covid-19 pandemic process.55,56 telemedicine has supported the delivery of health services when severe social-distancing and isolation measures were in place, especially for public health, disease prevention, and clinical practices.3,25 telemedicine was the most intensively studied useful technology in our study. a properly implemented telemedicine service can integrate with health services.56 nurses and other allied health professionals performing important tasks on the frontline are affected as much as family doctors, physicians, or medical students globally38,40,57–59 and the technology developed must be planned with the needs of all health stakeholders in mind. therefore, being a useful technology, especially considering the importance of social distance in the way the virus is transmitted, it has been used in the pandemic process as a critical technology for diagnosis, treatment, and monitoring, and even for the training of doctors and doctor candidates.26 this has also significantly impacted the educational processes of these professions.60,61 therefore, it can be suggested that education processes should be ready for such outbreaks. in general, patients have reported high satisfaction with telehealth in general practice during quarantine, and integrating telemedicine into the healthcare system as a natural component is considered beneficial for being better prepared for future pandemics.62 strengths and limitations of our study it can be said that the dataset analyzes publications in journals indexed by wos, but most of the relevant journals with phc are also indexed by scopus.63 in addition, many analyses were performed on the data obtained, and only part of them are presented here due to limitations in the number of figures, tables, and text. additional findings are presented as appendices, as they may be useful for researchers and strengthen the article. future studies for future studies it might be useful to discuss phc and the pandemic process with computer science, artificial intelligence, information systems, interdisciplinary applications, software engineering, medical informatics, robotics, respiratory systems, public administration, psychology, social, psychiatry, and economics. this contribution stems from the critical value and importance of the phc field in covid-19-like outbreaks. experiences gained from pandemics should not be lost, lessons should be learned and used to create new approaches for phc in the future. singapore is a successful example.64 contemporary technologies such as the metaverse play a significant role in the healthcare field.65 during the covid-19 pandemic, phc services have been virtualized, and the use of virtual technologies has become widespread.66 in particular, virtual visits in canada are considered an alternative fig. 3. latent dirichlet allocation topic modeling analysis (fields: abstracts and titles). https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.400 7 (page number not for citation purpose) impact of covid-19 on primary healthcare research healthcare service for those with chronic illnesses.67 for future pandemics, it is necessary to thoroughly investigate virtual healthcare services and reorganize phc to incorporate remote technologies. given the likelihood of encountering similar epidemics in the future due to global climate change, population growth, and deteriorating living conditions, the study emphasizes the importance of distance services (telehealth, distance education, remote consultation, etc.) in connecting patients, healthcare providers, and health services remotely during epidemic periods. it recommends that policymakers and governments integrate relevant systems into national health frameworks, prioritizing epidemic preparedness for future outbreaks and addressing integration-related challenges promptly. many countries faced challenges in accessing protective equipment, masks, and respiratory systems crucial for pandemic control. to prevent similar issues in future outbreaks, the results published here advise countries to develop strategic pandemic plans urgently and ensure sufficient stock and distribution of pandemic-fighting materials during covid-19-like crises. it also underscores the importance of prioritizing phc, particularly in densely populated countries such as india, indonesia, pakistan, and bangladesh. developing nations must utilize their limited resources more effectively, with phc offering significant potential in reducing health inequalities and providing critical health services during pandemics. for these countries, investing in phc services is deemed essential and strategic to ensure more equitable access to healthcare services for citizens. what blockchain technology can provide for future pandemics the covid-19 is transmitted to humans through zoonosis, which can occur again in the future.68 therefore, the global community should invest in potential technologies that can be helpful for future pandemics. blockchain is among such a critical technology and its applications such as hashlog, vechain, the public health blockchain consortium (phbc) platform, and hyperchain have been used during covid-19 pandemic for various purposes.69 blockchain is a technology that attracts attention with its decentralized, transparent, secure, and immutable data storage and transaction features.70 these features can facilitate pandemic control through early detection of outbreaks, accelerating drug delivery, and protecting user privacy during treatment.69 during pandemics, rapid access to accurate and reliable health data is vital. blockchain technology provides real-time information to all strategic partners and traceability in the disease control process, which can ensure secure and transparent management of health data.71 the blockchain technology can be used to globally track the spread of coronavirus infections by placing a blockchain network on citizens’ mobile devices.69 it also facilitates the use of international vaccination certificates and health status monitoring.72 the transparent structure of blockchain enables tracking of how health data are used at each step of care delivery. for example, during pandemics, efficient and proper production, transportation, and distribution of vaccines, therapeutic drugs, masks, and hygiene products is critical to combat the pandemic. various significant problems have been experienced in these processes during the covid-19 pandemic.73–76 with blockchain technology, the vaccine and drug production processes can be made transparent and secure and the processes can be monitored in real-time. in this way, definitive traceability of transported and stored vaccines and drugs can be achieved. this could also reduce the risk of counterfeiting in vaccines and other protective products. sharing of research and development data during the pandemic has also been critical in the drug development process and early pandemic response.77 in vaccine development processes, sharing data from different research organizations on the blockchain can enable faster research progress as a part of their pandemic preparedness plans. during future pandemics, citizens do not only need to be provided with the right medicine or equipment but also with the most accurate information. during the covid19 process, many problems have been experienced in social media and other platforms, rendering access to accurate information.78 the most appropriate way to combat this is to share information with citizens through social media communication channels where content can be monitored. khurshid79 suggested a nationally coordinated partnership (consisting of academia, researchers, the business world, and industry) to accelerate the adoption of blockchain and their national/international use in accurately disseminating information, which can prevent disinformation and manipulation, especially in social media. on a global scale, the need for pandemic research collaboration, and thus, secure data-sharing channels is even greater. global collaboration efforts require quick and secure transfer of large-scale data between different countries, organizations, and health authorities. at this point, blockchain technology is a real, applicable, and secure technology. therefore, it would be appropriate to carry out such processes and initiatives through and under the leadership of the world health organization. funding associate professor muhammet damar, phd and associate professor omer aydin, phd were funded by the scientific and technological research council of türkiye (tubitak) under the 2219 international postdoctoral research fellowship program for turkish citizens. https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.4008 (page number not for citation purpose) muhammet damar et al. conflicts of interest none contributors muhammet damar: conceptualization, methodology, software, validation, formal analysis, data curation, writing—original draft, writing—review and editing, visualization. andrew david pinto: conceptualization, validation, investigation, writing—review and editing, supervision. fatih safa erenay: conceptualization, investigation, writing—review and editing, supervision. ömer aydın: conceptualization, writing—review and editing, supervision. data availability statement (das), data sharing, reproducibility, and data repositories the data that support the findings of this study are available from the corresponding author upon reasonable request. also, we retrieve our bibliometric data from wos core collection database and this database is open for everyone. application of ai-generated text or related technology no ai tools were used for content creation in this manuscript (e.g., drafting, rewriting, or generating ideas). acknowledgments associate professor muhammet damar and associate professor omer aydin would like to thank the scientific and technological research council of türkiye (tubitak) for their support. associate professor muhammet damar would like to thank the upstream lab, map, li ka shing knowledge institute at the university of toronto for its excellent hospitality. the study was conducted by upstream lab project team members to evaluate the reflections of the covid19 pandemic in primary healthcare literature. the lab has a project evaluating the inequity created within the health system during the pandemic in canada. the research was carried out as a supportive but independent project from this project. references 1. worldometer. coronavirus toll update: cases & deaths by country [internet]. worldometer. 2024 [cited 2024 sep 21]. available from: https://www.worldometers.info/coronavirus/ 2. prado nm, rossi tr, chaves sc, barros sg, magno l, santos hl, et al. the international response of primary health care to covid-19: document analysis in selected countries. cad saude publica. 2020;36(12):e00183820. https://doi. org/10.1590/0102-311x00183820 3. vidal-alaball j, acosta-roja r, hernández np, luque us, morrison d, pérez sn, et al. telemedicine in the face of the covid-19 pandemic. 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non-commercial. see http://creativecommons.org/ licenses/by-nc/4.0. the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.400 https://doi.org/10.1111/hir.12465 https://doi.org/10.1111/hir.12465 https://doi.org/10.22454/fammed.2022.492688 https://doi.org/10.22454/fammed.2022.492688 https://doi.org/10.1111/jnu.12589 https://doi.org/10.46747/cfp.6904271 https://doi.org/10.46747/cfp.6904271 https://doi.org/10.4103/jfmpc.jfmpc_494_20 https://doi.org/10.1016/s0140-6736(20)30374-3 https://doi.org/10.1016/s0140-6736(20)30374-3 https://doi.org/10.57019/jmv.1132962 https://doi.org/10.46747/cfp.6904e78 https://doi.org/10.1007/s42600-020-00106-3 https://doi.org/10.1007/s42600-020-00106-3 https://doi.org/10.1109/mcse.2017.3421554 https://doi.org/10.1016/j.jceh.2020.04.019 https://doi.org/10.2196/32411 https://doi.org/10.2196/32411 https://doi.org/10.1111/emre.12449 https://doi.org/10.1080/01459740.2020.1746301 https://doi.org/10.1080/01459740.2020.1746301 https://doi.org/10.1016/s0140-6736(20)30644-9 https://doi.org/10.1016/s0140-6736(20)30644-9 https://doi.org/10.1016/j.jbusres.2022.01.037 https://doi.org/10.1016/j.jbusres.2022.01.037 https://doi.org/10.1016/j.vaccine.2021.08.101 https://doi.org/10.1016/j.vaccine.2021.08.101 https://doi.org/10.2147/rmhp.s284313 https://doi.org/10.2147/rmhp.s284313 https://doi.org/10.2196/20477 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.400 11 (page number not for citation purpose) impact of covid-19 on primary healthcare research appendix a. research methodology and study descriptions. phca; primary health care appendix b. three fields plot with authors, journals, and keywords for covid-19 studies in phc research area. (au: author, de: author keyword, phc: primary healthcare, so: source/journal/conference) https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.40012 (page number not for citation purpose) muhammet damar et al. appendix c. top 30 countries where studies of covid-19 were at phc research areas. (acpa: avarage citation per article hi: h-index phc: primary healthcare, tc: total cited) # countries tc hi acpa n % 1 india 1,592 17 2.91 547 29.01 2 usa 2,756 21 6.75 408 21.64 3 england 1,765 20 10.09 175 9.28 4 spain 703 12 5.49 128 6.79 5 canada 399 10 4.75 84 4.45 6 australia 540 9 6.84 79 4.19 7 saudi arabia 178 7 2.70 66 3.50 8 south africa 510 11 8.95 57 3.02 9 iran 224 8 4.48 50 2.65 10 germany 430 11 9.15 47 2.49 11 turkiye 299 10 8.54 35 1.85 12 netherlands 486 9 13.89 35 1.85 13 new zealand 270 6 8.71 31 1.64 14 belgium 237 6 8.78 27 1.43 15 france 146 6 5.41 27 1.43 16 scotland 346 8 13.31 26 1.37 17 china 419 8 17.46 24 1.27 18 egypt 82 5 3.73 22 1.16 19 poland 142 6 6.45 22 1.16 20 sweden 134 5 6.09 22 1.16 21 ireland 157 5 7.48 21 1.11 22 japan 33 3 1.65 20 1.06 23 denmark 56 4 2.95 19 1.00 24 italy 115 6 6.05 19 1.00 25 norway 47 4 2.76 17 0.90 26 israel 130 7 8.13 16 0.84 27 indonesia 55 4 3.67 15 0.79 28 pakistan 127 6 9.07 14 0.74 29 switzerland 113 6 8.07 14 0.74 30 wales 106 5 7.57 14 0.74 https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.400 13 (page number not for citation purpose) impact of covid-19 on primary healthcare research country collaborations (co-authorship country analyses) https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.40014 (page number not for citation purpose) muhammet damar et al. affiliations collaborations (co-authorship affiliations analyses). appendix d. top 20 institutes where covid-19 was studied at phc research areas. phc: primary healthcare rank institutes country hi tc acpa n % 1 university of london england 13 407 8.48 48 2.54 2 university of oxford england 13 635 13.51 47 2.49 3 all india institute of medical sciences (aiims) new delhi india 8 162 3.95 41 2.17 4 university of toronto canada 8 287 7.18 40 2.12 5 all india institute of medical sciences (aiims) rishikesh india 8 130 3.94 33 1.75 6 university of california system usa 7 241 8.31 29 1.53 7 post graduate institute of medical education research (pgimer) chandigarh india 6 136 4.86 28 1.48 8 university system of ohio usa 6 119 4.25 28 1.48 9 all india institute of medical sciences (aiims) jodhpur india 6 119 4.76 25 1.32 10 mayo clinic usa 8 183 7.32 25 1.32 11 government medical college india 4 40 1.82 22 1.16 12 university of colorado system usa 7 220 10.00 22 1.16 13 egyptian knowledge bank egypt 5 82 3.90 21 1.11 14 imperial college london england 7 324 15.43 21 1.11 15 headquarters of the catalan institute of health spain 7 309 14.71 21 1.11 16 all india institute of medical sciences (aiims) patna india 5 71 3.55 20 1.06 17 university of colorado anschutz medical campus usa 7 218 10.90 20 1.06 18 all india institutes of medical sciences india 4 38 2.11 18 0.95 19 king’s college london england 8 216 12.00 18 0.95 20 all india institute of medical sciences (aiims) raipur india 5 48 2.82 17 0.90 21 state university system of florida usa 6 112 6.59 17 0.90 22 stellenbosch university south africa 9 193 11.35 17 0.90 23 king george s medical university india 4 68 4.25 16 0.84 24 university of california los angeles (ucla) usa 6 194 12.13 16 0.84 25 harvard university usa 6 134 8.93 15 0.79 https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.400 15 (page number not for citation purpose) impact of covid-19 on primary healthcare research https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.40016 (page number not for citation purpose) muhammet damar et al. authors’ collaborations (co-authorship authors analyses). appendix e. top 20 authors who studied covid-19 at phc research areas f. phc: primary healthcare rank authors country institutions (from wos profile) tc acpa n % 1 kumar a india all india institute of medical sciences (aiims) new delhi 82 2.93 28 1.48 2 kumar r india vardhman mahavir medical college & safdarjung hospital 110 4.07 27 1.43 3 kumar s india all india institute of medical sciences (aiims) new delhi 78 3.12 25 1.32 4 singh s india sharda university 73 3.32 22 1.16 5 gupta a india tufts university 43 3.31 13 0.69 6 sharma a india department of dentistry | shkm, goverment medical college 21 2.10 10 0.53 7 sharma p india faculty of medicine, uttar pradesh university of medical sciences 34 3.40 10 0.53 8 wig n india all india institute of medical science 25 2.50 10 0.53 9 butler cc england university of oxford 100 11.11 9 0.47 10 croghan it usa mayo clinic 108 12.00 9 0.47 11 garg s india maulana azad medical college 19 2.11 9 0.47 12 nugent k usa texas tech university health sciences center 63 7.00 9 0.47 13 sharma n india post graduate institute of medical education & research, chandigarh 27 3.00 9 0.47 14 singh a india kgmc department of physical medicine orthopaedic 17 1.89 9 0.47 15 singh ak india all india institute of medical sciences (aiims) new delhi 51 5.67 9 0.47 16 goodyear-smith f new zealand university of auckland 81 10.13 9 0.47 17 pal r india college of nursing, aiims, bhubaneswar 64 8 8 0.42 18 serrano-cumplido a spain board of retired doctors 27 3.38 8 0.42 19 singh p india the george institute for global health 9 1.13 8 0.42 20 westfall jm usa robert graham center: policy studies in family medicine 71 8.88 8 0.42 https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.400 17 (page number not for citation purpose) impact of covid-19 on primary healthcare research https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.40018 (page number not for citation purpose) muhammet damar et al. appendix f. journals that are published most often on covid-19 at a phc research area rank journals research domain 5yif esci hi tc acpa n % 1 journal of family medicine and primary care primary healthcare yes 14 1,557 2.46 634 33.63 2 journal of primary care and community health primary healthcare yes 23 1,870 8.90 210 11.14 3 bmc primary care primary healthcare; medicine, general & internal no 7 233 2.31 101 5.35 4 journal of the american board of family medicine primary healthcare; medicine general & internal 3.024 no 14 695 6.95 100 5.30 5 family practice primary healthcare; medicine, general & internal 2.652 no 10 469 7.22 65 3.44 6 african journal of primary healthcare family medicine primary healthcare yes 10 424 6.84 62 3.28 7 bjgp open primary healthcare yes 10 269 4.34 62 3.28 8 primary care diabetes endocrinology & metabolism; primary healthcare 2.644 no 13 518 9.59 54 2.86 9 british journal of general practice primary healthcare medicine, general & internal 6.916 no 15 1,068 20.15 53 2.81 10 medicina de familia semergen primary healthcare yes 7 154 2.91 53 2.81 11 annals of family medicine primary healthcare; medicine, general & internal 6.697 no 11 454 9.27 49 2.59 12 atencion primaria primary healthcare; medicine, general & internal 1.982 no 9 346 7.36 47 2.49 13 bmc family practice primary healthcare; medicine, general & internal 3.301 no 15 818 20.45 40 2.12 14 primary healthcare research and development primary healthcare 1.947 no 6 121 303 40 2.12 15 australian journal of primary health healthcare sciences & services; health policy & services; primary healthcare; public, environmental & occupational health 1.764 no 4 112 3.20 35 1.85 16 family medicine primary healthcare; medicine, general & internal 1.957 no 5 78 2.23 35 1.85 17 education for primary care primary healthcare yes 7 158 4.79 33 1.75 18 family medicine and primary care review primary healthcare yes 3 51 1.59 32 1.69 19 european journal of general practice primary healthcare; medicine, general & internal 5.654 no 6 318 10.60 30 1.59 20 family medicine and community health primary healthcare yes 7 190 7.31 26 1.37 21 journal of primary healthcare primary healthcare yes 4 71 2.96 24 1.27 22 canadian family physician primary healthcare; medicine, general & internal 3.899 no 5 70 3.50 20 1.06 23 physician and sportsmedicine primary healthcare; orthopedics; sport sciences 2.883 no 8 182 9.58 19 1.00 24 american family physician primary healthcare; medicine, general & internal 7.361 no 4 40 3.33 12 0.63 25 korean journal of family medicine primary healthcare yes 2 16 1.33 12 0.63 26 npj primary care respiratory medicine primary healthcare; respiratory system 3.706 no 4 53 4.82 11 0.58 27 scandinavian journal of primary healthcare healthcare sciences & services; primary healthcare; medicine, general & internal 3.227 no 2 2 2.00 11 0.58 28 primary care primary healthcare medicine, general & internal 3.602 no 3 130 16.25 8 0.42 29 journal of family practice primary healthcare medicine; general & internal 0.837 no 1 1 0.14 7 0.37 *5yif: five year journal impact factor, esci: emerging sources citation index, hi:h-index, tc: times cited, apcd: average citation per articles, n:  record count. https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.400 19 (page number not for citation purpose) impact of covid-19 on primary healthcare research appendix g. top 30 most cited articles on covid-19 in phc research area: phc: primary healthcare rank title journal authors year acpy times cited 1 impact of covid-19 on loneliness, mental health, and health service utilisation: a prospective cohort study of older adults with multimorbidity in primary care british journal of general practice wong, sys; zhang, dx; (…); mercer, sw 2020 38.80 194 2 implementation of remote consulting in uk primary care following the covid-19 pandemic: a mixedmethods longitudinal study british journal of general practice murphy, m; scott, lj; (…); horwood, j 2021 48.25 193 3 lessons on the covid-19 pandemic, for and by primary care professionals worldwide european journal of general practice rawaf, s; allen, ln; (…); van weel, c 2020 35.40 177 4 telemedicine in the face of the covid-19 pandemic atencion primaria vidal-alaball, j; acostaroja, r; (…); segui, fl 2020 34.00 170 5 implementation and usefulness of telemedicine during the covid-19 pandemic: a scoping review journal of primary care and community health hincapie, ma; gallego, jc; (…); escobar, mf 2020 29.00 145 6 telehealth consultations in general practice during a pandemic lockdown: survey and interviews on patient experiences and preferences bmc family practice imlach, f; mckinlay, e; (…); mcbride-henry, k 2020 26.20 131 7 redesigning primary care to address the covid19 pandemic in the midst of the pandemic annals of family medicine krist, ah; devoe, je; (…); jones, sm 2020 25.40 127 8 post-acute and long-covid-19 symptoms in patients with mild diseases: a systematic review family practice van kessel, sam; hartman, tco; (…); van jaarsveld, chm 2022 41.67 125 9 a qualitative study of primary care physicians’ experiences with telemedicine during covid-19 journal of the american board of family medicine gomez, t; anaya, yb; (…); tarn, dm 2021 29.25 117 10 a mixed-methods pilot study of perinatal risk and resilience during covid-19 journal of primary care and community health farewell, cv; jewell, j; (…); leiferman, ja 2020 22.20 111 11 the impact of covid-19 on chronic care according to providers: a qualitative study among primary care practices in belgium bmc family practice danhieux, k; buffel, v; (…); van olmen, j 2020 19.20 96 12 mental health burden of the covid-19 outbreak in germany: predictors of mental health impairment journal of primary care and community health bauerle, a; steinbach, j; (…); skoda, em 2020 18.00 90 13 clinicopathological characteristics of 8697 patients with covid-19 in china: a meta-analysis family medicine and community health zhu, jy; zhong, zm; (…); zhao, cl 2020 17.20 86 14 the effects of the face mask on the skin underneath: a prospective survey during the covid-19 pandemic journal of primary care and community health techasatian, l; lebsing, s; (…); kosalaraksa, p 2020 15.40 77 15 impact of covid-19 on migrants’ access to primary care and implications for vaccine roll-out: a national qualitative study british journal of general practice knights, f; carter, j; (…); hargreaves, s 2021 18.25 73 16 primary care in the time of covid-19: monitoring the effect of the pandemic and the lockdown measures on 34 quality of care indicators calculated for 288 primary care practices covering about 6 million people in catalonia bmc family practice coma, e; mora, n; (…); medina, m 2020 14.60 73 17 reorganisation of primary care for older adults during covid-19: a cross-sectional database study in the uk british journal of general practice joy, m; mcgagh, d; (…); de lusignan, s 2020 14.40 72 18 the state of telehealth before and after the covid-19 pandemic primary care shaver, j 2022 23.67 71 19 spiritual care—‘a deeper immunity’—a response to covid-19 pandemic african journal of primary healthcare & family medicine roman, nv; mthembu, tg and hoosen, m 2020 13.40 67 (continued) https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.40020 (page number not for citation purpose) muhammet damar et al. appendix g. (continued) rank title journal authors year acpy times cited 20 the effect of isolation on athletes’ mental health during the covid-19 pandemic physician and sports medicine senisik, s; denerel, n; (…); tunc, s 2021 12.40 62 21 a multidisciplinary nhs covid-19 service to manage post-covid-19 syndrome in the community journal of primary care and community health parkin, a; davison, j; (…); sivan, m 2021 15.25 61 22 the effectiveness of teleconsultations in primary care: systematic review family practice de albornoz, sc; sia, kl and harris, a 2022 20.00 60 23 impact of lockdown covid-19 on metabolic control in type 2 diabetes mellitus and healthy people primary care diabetes karatas, s; yesim, t and beysel, s 2021 15.00 60 24 sociodemographic determinants of covid-19 vaccine hesitancy, fear of infection, and protection self-efficacy journal of primary care and community health mcelfish, pa; willis, de; (…); selig, jp 2021 14.50 58 25 recommendations for the recognition, diagnosis, and management of long covid: a delphi study british journal of general practice nurek, m; rayner, c; (…); delaney, bc 2021 14.25 57 26 transformation of primary care during the covid-19 pandemic: experiences of healthcare professionals in eight european countries british journal of general practice wanat, m; hoste, m; (…); tonkin-crine, s 2021 13.75 55 27 physician burnout in primary care during the covid-19 pandemic: a cross-sectional study in portugal journal of primary care and community health baptista, s; teixeira, a; (…); duarte, i 2021 12.50 50 28 what should primary care look like after the covid-19 pandemic? australian journal of primary health duckett, s 2020 10.00 50 29 telehealth challenges during covid-19 as reported by primary healthcare physicians in quebec and massachusetts bmc family practice breton, m; sullivan, ee; (…); mcalearney, as 2021 12.25 49 30 insulin resistance in covid-19 and diabetes primary care diabetes govender, n; khaliq, op; (…); naicker, t 2021 12.25 49 acpy: average citations per year, tc: times cited. https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.400 21 (page number not for citation purpose) impact of covid-19 on primary healthcare research appendix h. conceptual structure topic dendrogram factorial analysis (field: abstract) part 1 https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.40022 (page number not for citation purpose) muhammet damar et al. appendix i. conceptual structure topic dendrogram factorial analysis (field: abstract) part 2 https://doi.org/10.30953/bhty.v8.400 citation: blockchain in healthcare today 2025, 8: 400 https://doi.org/10.30953/bhty.v8.400 23 (page number not for citation purpose) impact of covid-19 on primary healthcare research appendix j. conceptual structure topic dendrogram factorial analysis (field: abstract) part 3 https://doi.org/10.30953/bhty.v8.400 1 (page number not for citation purpose) original research a blockchain-based framework with zero-knowledge proof incorporated for safeguarded sharing of genomic data through health record systems nandini krishappa, m.tech, phd  , girisha gowdra shivappa, phd  , sharon zachariah, b.tech  ,thanushree b.tech  , kavyashree i. pattan, m.tech, phd  , arpita paria, m.tech , savitha hiremath, phd  , and revathi vaithiyanathan, phd  department of computer science and engineering, dayananda sagar university, bengaluru south, karnataka, india corresponding author: nandini k, email: nandini-cse@dsu.edu.in doi: https://doi.org/10.30953/bhty.v8.419 keywords: blockchain, genomic data sharing, homomorphic encryption, smart contracts, zero-knowledge proofs abstract genomic data sharing remains a core problem in precision medicine because genomic data are highly sensitive and unchangeable. in this article, we propose a blockchain-based framework that utilizes zero-knowledge proofs (zkps), smart contracts, and off-chain storage to facilitate secure, privacy-preserving data sharing within health record systems. we implemented and evaluated a proof-of-concept prototype in python on a simulated genomic dataset. the prototype uses a hybrid storage system where metadata is retained on a blockchain and encrypted data are placed in an emulated interplanetary file system (ipfs). rule-based access is controlled using smart contracts, while privacy and security are achieved using zkps with interactive schnorr protocol and elliptic curve cryptography (ecc). empirical analysis using real-time testing over 100 iterations reported an average zero-knowledge proof with blockchain (zkpb) query latency of 5.83 ms with a 90.00% accuracy, smart contract latency of under 0.01 ms with 90.00% accuracy, blockchain query time of 0.01 ms with 90.00% accuracy, and ecc latency of 8.72 ms with 90.00% accuracy. these empirical findings validate the effectiveness and privacy guarantees of the framework, which can be utilized in healthcare research, clinical genomics, and personalized medicine workflows. plain language summary in the age of precision medicine, genomic data are becoming central to powering customized diagnosis and therapy. however, its permanent and sensitive nature raises concerns over privacy, misuse, and unauthorized exploitation. legacy centralized architecture remains vulnerable to breaches, thus necessitating more resilient alternatives. recent advances have turned towards blockchain for its decentralization and permanence but remain incomplete in terms of scalability and privacy. new research also combines federated learning, smart contracts, and consent mechanisms, but few attempt to adequately address the complexity of genomic data privacy, actual-world scalability, or data protection regulations compliance. we present secure chain, a decentralized, privacy-enhancing infrastructure for genomic data sharing with security. by drawing on blockchain, zero-knowledge proofs (zkps), off-chain storage (e.g. ipfs), and homomorphic encryption, the system provides confidentiality, verifiability, and scalability. the goal here is to compare this hybrid architecture’s performance on parameters such as security, computational cost, and query response time with full compliance with law (health insurance portability and accountability act [hipaa] and general data protection regulation [gdpr]). by comparative outputs, the framework shall prove that combining zkps and blockchain provides an optimal trade-off between privacy and efficiency in making secure chain a feasible, practical solution for safe, regulation-compliant genomic data exchange. submitted: june 28, 2025; accepted: june 28, 2025; published: november 29, 2025. blockchain in healthcare today issn 2573-8240 https://orcid.org/0009-0006-5743-3845 https://orcid.org/0009-0008-1313-3239 https://orcid.org/0009-0006-7037-3998 https://orcid.org/0009-0005-2494-8140 https://orcid.org/0009-0002-3292-9327 https://orcid.org/0009-0001-7322-3075 https://orcid.org/0000-0002-1150-8358 https://orcid.org/0000-0003-4574-9384 mailto:nandini-cse@dsu.edu.in https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.4192 (page number not for citation purpose) nandini k et al. in the context of precision medicine, genomic data are valued for improving diagnosis, tailoring treatment, and disease prediction. its high sensitivity, durability, and uniqueness, however, trigger fundamental privacy, data spillover, and ethics misuse concerns. genomic data made public or misused can irreversibly impact identity theft, genetic discrimination, and violation of consent in the hands of the wrong actors. these vulnerabilities point to the necessity for a privacy-protecting and secure paradigm to manage and share genomic information securely.1 blockchain centralized genomic databases, while widely used, are prone to cyberattacks and unauthorized entry due to single-point failures. blockchain technology introduces decentralization, immutability, and tamper-proof auditing and thus is an ideal choice for secure data sharing.2 nonetheless, blockchain alone is not feasible due to the sheer volume of genomic data—approximately 3 billion base pairs per genome. it is impossible to store such data on-chain, which means a hybrid model where blockchain stores metadata and off-chain infrastructure like interplanetary file system (ipfs) is utilized for the storage of big data. genomic information needs to be highly protected, as genetic information is highly intimate and unique to the person and can reveal private information such as ancestry, disease risk, and biological family relationships. the information, once leaked, can lead to egregious privacy violations, employment or insurance discrimination, and identity theft. genomic information is also permanent; it cannot be deleted once leaked, so it is a lifelong risk factor. hence, secure storage is essential to maintain people’s rights, provide assurance in genetic research and medical treatment, and satisfy the data protection ethics and legislation.3,4 zero knowledge proof with blockchain (zkpb) blockchain and zero-knowledge proofs (zkps) are to be used in conjunction with each other because they complement one another’s weaknesses. blockchains offer security and transparency but lack privacy and scalability, and zkps offer a way of proving information to be true without revealing the underlying data. by merging blockchain and zkps, the systems can get trusted and secure confirmation of computations or transactions while keeping sensitive data hidden and only having much less data stored and processed on-chain. this blend results in more efficient, scalable, and privacy-assuring decentralized applications, which is critical for real-world adoption.5,6 it is important to combine zkps and blockchain for the storage of genomic data because it will enable secure, tamper-evident storage of sensitive genetic information without compromising individual privacy. genomic data are very personal and valuable, and uploading them onto an open blockchain risks exposing sensitive data. the integration of zkps enables the proof that genomic data meet certain requirements without revealing the underlying sequence. this ensures that usage of and access to genomic data is traceable and compliant with data protection laws, using blockchain immutability and decentralization to deter tampering or abuse of data.7,8 to further ensure secure data exchange and user anonymity, advanced cryptographic techniques are integrated. homomorphic encryption facilitates computations on ciphertext itself, ensuring privacy during analysis. multiparty computation facilitates joint analysis without disclosing individual datasets. these technologies form the basis of a privacy-aware, scalable, and regulation-compliant genomic data-sharing model. background secure management and sharing of genomic data have been the focus of increasing research attention, particularly with blockchain technologies. however, table 1 provides an analysis of existing work and limitations for privacy protection, usability, scalability, and adaptability to genomic data specifically. a blockchain-based genomic data infrastructure using hyperledger and bittorrent integration helps enable decentralized storage and tracking of ownership. the system demonstrated scalability up to 400 transactions per second and emphasized equal gains for the stakeholders.9 the solution lacked inherent mechanisms for data privacy and integrity, which is critical in genomic data management, particularly when there was no fixed dataset for validation. in a different strategy, integrated federated learning with blockchain was used to enable secure medical artificial intelligence (ai) diagnosis. their system attained a classification accuracy of 92.86%, a latency of 43.52 ms, and cyberattack resistance (87%). although promising, the use case was restricted to image-based medical data and thus not applicable for genomic datasets with varying privacy and computation demands.10 the secure consent model utilized blockchain and smart contracts to dynamically manage consent in sharing genomic data, specifically for precision oncology datasets. although the system accommodated decentralized access control and dynamic consent updates, it required much user involvement, which may limit its use on larger or less technologically advanced groups.11 similarly, a theoretical model combined blockchain, smart contracts, and a de-identification process to facilitate genomic data privacy and traceability. nevertheless, the model is yet to be tested and not validated for performance in real or hypothetical genomic scenarios, hence better suited for future pilot studies rather than instant https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.419 3 (page number not for citation purpose) blockchain safeguarded sharing of genomic data application. the genesy model proposed a hybrid blockchain architecture that combined on-chain and off-chain data management in order to ensure privacy as well as secure data sharing. while the system puts emphasis on users’ ownership and control over genomic data, it is not yet proven and has not been operationalized or implemented within existing healthcare systems, which renders it unusable, and it also created a permissioned blockchain platform alongside ipfs to ensure confidentiality of private health records for telemedicine applications in another paper.12 though the platform provides support for fault tolerance and low latency, its complexity of setup is rather high, making it less viable for small-scale or resource-poor healthcare environments. additionally, the absence of a predefined dataset hinders reproducibility. a blockchain-based architecture for consent management employed hybrid cryptography to facilitate dynamic consent for sharing genomic data. although the model was scalable and privacy-oriented, it did not incorporate machine learning, making it less applicable for prediction analysis in personalized medicine.6 it also explored using federated learning with blockchain to enable secure, distributed collaboration in healthcare data without revealing raw data. while suited for collaborative analytics, methodology was not genome data-specific, limiting its application in the immediate context in dna-based studies and precision medicine.13,14,15 another study introduced a searchable encryption architecture based on ciphertext-policy attribute-based encryption and blockchain to secure electronic medical records.16 while the system provided fine-grained access and encrypted search, it was not designed for the high-dimensional characteristics of genomic data; thus, important alterations were required. lastly, the proposed blockchain-based shared data space utilized attribute-based access control to manage access rights in scientific data sharing. the model was audit-friendly and tamper-evident but did not address the specific legal and ethical demands of handling genomic data.17 together, these works demonstrate impressive progress toward blockchain systems in healthcare with great achievement but also highlight a few limitations—for example, lack of genomic focus, no inherent privacy-preserving mechanisms like zkps, or the need for usable and scalable designs tested using actual genomic data. this provokes the development of our new system: a blockchain system complemented by zkps and off-chain genomics data storage that attempts to push past such limitations using a realistic, privacy-oriented, and scalable strategy. abac: attribute-based access control; ai: artificial intelligence; cp-abe: ciphertext-policy attribute-based encryption; db: database; de-id: de-identification; emrs: electronic medical records; fl: federated learning ipfs: interplanetary file system. one of the core limitations across the discussed literature is a lack of genomic-specific testing and practical deployment. while theoretically robust ones such as amazon biobank9 genesy12 and secureconsent12 are promoting blockchain-influenced architectures for genomic data table 1. comparative analysis of the present work paper core focus strengths limitations genomic data specificity amazon bio bank9 blockchain infrastructure for genomic db scalability (400 tps), benefit-sharing ignores data privacy & integrity p genomic-focused federated learning in medical diagnostics10 blockchain + fl for ai diagnostics high accuracy, low latency, cyber-attack defense limited to image data, not genomics ï not genomic secure consent11 consent management on blockchain dynamic, real-time consent high user interaction overhead p genomic-focused gene data management de-id18 de-id + blockchain smart contracts data traceability, privacy not validated/tested p genomic-focused genesy model12 hybrid blockchain for fair data use ownership & control, privacy no operational testing p genomic-focused telemedicine platform13 blockchain + ipfs for remote care fault tolerance, low latency high complexity, no dataset ï not genomic decentralized consent model14 consent system w/ hybrid crypto dynamic & privacy-compliant no ai or analytics capability p genomic-focused fl + blockchain in healthcare19 secure decentralized learning preserves privacy, collaborative not tailored to genomics ï not genomic cp-abe for emrs16 searchable encrypted medical data fine-grained access, searchable encryption genomic adaptation needed ï not genomic research data sharing17 abac-based blockchain system tamper-evident, reproducible not tailored to genomic compliance ï not genomic https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.4194 (page number not for citation purpose) nandini k et al. sharing, they are yet to be validated in practice-based health care settings and lack firm deployment rates. substitutes such as the blockchain-based solution with the de-identifying scheme and genesy are best pilot-ready or theoretical, and interoperability, scalability, and suitability to be deployed on top of current clinical workflows are questionable. also, performance metrics— where known—are largely confined to non-genomic uses like medical imaging and have little bearing on genomics and precision medicine. security versus usability is a refrain that still holds. secure consent, for instance, offers high-fidelity, dynamic control at the cost of enormously high user interaction overhead, potentially rendering it not admissible to large populations or low-tech environments. in addition, these are largely directed towards general health information and not towards the privacy, ethics, and regulatory idiosyncrasies of genomic data. most of the proposed models lack ai or machine learning components, which are becoming increasingly important in predictive analytics and data-driven discovery across genomic studies. this puts their capability to support future next-generation ai-based genomic diagnostics and research at a disadvantage. in contrast, our proposed architecture addresses only genomic data sharing. in doing so, it incorporates blockchain, zkps, ipfs-based off-chain storage, and rule-based smart contract management. while this architecture directly addresses privacy, verifiability, and control of data specifically, it does not yet incorporate ai/ ml (machine learning) analytics, nor has it been tested through a deployed prototype or large-scale simulated environment. these features are identified as future work constraints and are left on hold awaiting further development. the system’s current evaluation is based on architectural analysis and theoretical performance estimates according to current cryptographic protocols and blockchain operation. thus, this book presents an implementation model for the future with a modular extensibility approach and compliance with standards for safeguarding genomic information. zero-knowledge proofs on blockchain network the method proposed here is the combination of three advanced cryptography systems, such as blockchain, smart contracts, and zkp to guarantee secure, privacy-enabling, and transparent coexistence in the governance of genomic data. in this mechanism operation section, implementation logic and the mathematical models that enable security, data integrity, and performance enhancement for each process are discussed. this research proposes a synergistic integration of blockchain, smart contracts, elliptic curve cryptography (ecc), and zkps in order to establish an open, privacy-assured infrastructure for genomics data sharing. though the existing models with zkps alone, smart contracts alone, or ecc alone meet independent security needs, they do not suffer from a lack of transparency, scalability, and user-level privacy at the same time. our solution allows information access to be secure via cryptographic proof without violating any confidentiality of underlying genomic data; blockchain provides auditability with tamper evidence and fine-grained access control with smart contracts. off-chain storage using ipfs addresses the problem of scalability and provides lightweight encryption using ecc. all these put together address the void of privacy-trust—bringing it to be deployable on real-world applications in healthcare and biomedical research where anonymity of patients, verifiability, and regulation compliance must be assured. the zkps address any other solution proposed for maintaining data confidential in its entirety by utilizing verification procedures. in contrast to federated learning that remains model training on local data or pre-searchable encryptions whose patterns are bound to leak during search, zkps are beneficial as they can prove data ownership without revealing even half of the data. figure 1 is a step-by-step procedure of a secure genomic data-sharing blockchain system that utilizes zkps and smart contracts to ensure data integrity and privacy. the procedure begins once we receive input of the genomic data, using the sha-256 hashing algorithm it is immediately converted into a secure and an irreversible fixed format. instead of storing the real genomic data on the blockchain network, only the generated hash can be recorded on the blockchain. this ensures that the original data remain private and tamper-proof, recorded on the blockchain. the real data will be stored on the ipfs network. if the user requests to access the genomic data, then the smart contract is invoked to manage and secure future access to the genomic data. on receiving an access request for the genomic data by a user, identification of the user and permission verification are done by the smart contract. for privacy, it employs a zkp system whereby the user can prove identity and authorization without revealing sensitive data. it grants access upon successful authentication and denies access upon failure. optimized efficiency methods are used to optimize the signed queries. the final operation is secure recovery or verification of the genomic information, closing a strong, open, and privacy-preserving data access infrastructure well-suited to sensitive biomedical use. the zkp offers a robust answer to genomic data privacy protection and safe sharing (figure 1). as zkp are subject to an agreed time, they are most appropriate in dealing with massive, confidential genomic data. the strongest point about zkps is that, instead of disclosing raw genomic data, they can offer mathematically verifiable data integrity statements or information without revealing the data. the capability is especially useful when the case demands user https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.419 5 (page number not for citation purpose) blockchain safeguarded sharing of genomic data anonymity and adherence to rigorous data privacy laws. the zkp also permits secure authentication and access controls to be performed without sacrificing user anonymity. this incorporates zkp, blockchain, and smart contract protocols to enable decentralized, auditable, and tamper-evident system development. the complementarity of cryptography, anonymity, and agility provides a synergic relationship such that zkp is the optimal solution to enable sharing of genomic data and maintain individual privacy as the largest concern but yet enable compliance and trust to be upheld. a zkp system is defined by a pair (p,v) of prover and verifier and satisfies three properties: step 1: completeness: equation (1), if the statement is true and both parties are following the protocol, the verifier will accept the proof. if x ∈ l, then pr[v(x)=1]=1 (1) step 2: soundness: equation (2), if the statement is false, no cheating prover can convince the verifier. if x ∈ l, then pr[v(x)=1] ≤ ε (2) step 3: zero-knowledge: equation (3), the proof reveals nothing beyond the truth of the statement. there exists a simulator s such that the verifier’s view can be simulated without knowing the witness: viewv(x, π) ≈s(x) (3) blockchain-based genomic data protection in order to protect and make genomic data unalterable, we employ a genomics sequence blockchain based on python for storing and managing genomic sequences. a genomic sequence, is represented as in algorithm 1. it ensures data are immutable and resists tampering and allows complete traceability of genomic data throughout the chain. consensus mechanism consensus is a basic mechanism of blockchain technology by which all the entire nodes in a network can agree to a shared public copy of the ledger in the absence of central control. it is necessary for verification of the transactions, checking data consistency, and preventing attacks such as double-spending or fraud. by first getting the participants to agree before presenting new information, consensus algorithms such as proof of work, proof of stake (pos), or practical byzantine fault tolerance create trust in a decentralized system because they make the blockchain stable, secure, and tamper-proof. blockchain’s consensus mechanism needs to give all the users of a network the shared presumption that transactions are legitimate in the absence of a single governing authority so that data integrity and trust in a distributed system are established. for genomic data storage, privacy and accuracy being greater issues, consensus will give data input integrity and tamper protection. the zkp takes this even further and allows a party to demonstrate correctness of computation or access of genomic information without ever revealing the underlying actual data itself, and hence ensures privacy to individuals. all these technologies are extremely complementary and together enable secure, verifiable, privacy-preserving genomic data sharing and analysis to become functional between many stakeholders. the proof-of-work protocol employs hard cryptographical challenges and consumes much computational effort and power, causing latency and costly processing, which is inefficient and specifically not well-fitted for large genomic transaction volumes. off-chain and on-chain on-chain and off-chain genomic information storage on blockchain and ipfs on-chain storage refers to storing genomic data directly within the blockchain. this guarantees immutability and high integrity of data but is typically not practicable for large genomic datasets because of storage constraints and algorithm 1. create a local ledger of genomic data protection input : genomic data(gi) for block i. output: local ledger block i (nbi). step1: encode the genomic data(gi) and add to the new block(nbi). step2: hi � find hash of nbi by combining gi and hi-1 step3: add to the nbi hi = hash of the current block gi = encoded genomic for block i using cryptographic protocol hi−1 = hash of the previous block ti = timestamp step 4: local ledger fig. 1. blockchain-based genomic data access flowchart. https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.4196 (page number not for citation purpose) nandini k et al. expense. rather, off-chain storage is more practical in dealing with enormous genomic datasets. here, the native genomic data are kept externally on decentralized storage such as ipfs, and only metadata, cryptographic hashes, or access control records are kept on-chain. in this hybrid model, the scalability of ipfs and the security and auditability of blockchain are utilized. on the other hand, it selects block validators by pos, minimizing computation time and providing less energy consumption. secure chain applies pos to reduce delays and computational burden. algorithm 2 provides the uploading of the genomic data on a decentralized network. zkpb in genomic data privacy zkpb preserve privacy by making it possible to validate certain genetic characteristics or computations without divulging the underlying genomic information. used in off-chain storage of genomics, zkpb can make it possible to verify that a certain computation performed on the genomic data is valid without the disclosure of the full sequence. the information stays safely stored on ipfs, with the blockchain storing the proof and making it tamper-proof. this architecture enables secure, privacy-preserving genomic data sharing and computing, which makes it well-suited for research and healthcare use cases where data sensitivity is critical. the schnorr protocol is an interactive zkp of knowledge enabling a prover to make a verifier believe that they possess a secret (such as a private key) without divulging the same. for group properties, schnorr protocol in interactive zkps includes front and backend communication between the prover and verifier to acknowledge knowledge without disclosing the information, and zk-snark and zk-stark are utilized to remove the interaction necessity. the proof produces evidence that can be checked by anyone at any time, making them much more effective for use that demands a fast verification process. access control smart contract to manage access to genomic data and the terms that govern access, the system incorporates smart contracts in python that simulate blockchain-native access controls. they are rule-based, autonomous programs that apply predetermined rules of access to anyone accessing information. on access, a smart contract is triggered to verify the user, validate consent rules, and securely record the access event on the blockchain. this leads to a decentralized and autonomous one, reduces the reliance on single point failure, and increases openness. the smart contracts are defined as python classes, with interoperability and flexibility in the simulated blockchain. figure 2 illustrates a hybrid blockchain-based architecture designed to enable privacy-preserving and secure sharing of genomic data. the process begins with the registration of the user and providing their genomic data through a smart contract, which acts as a bridge between the user and the system. once uploaded, the genomic data are encoded and saved off-chain within ipfs—an off-chain storage system that reduces the blockchain load and allows for effective storage of large data files. meanwhile, the metadata (e.g. data hash, access rights, and ownership) are accessed by the smart contract and stored on-chain in a decentralized blockchain network, ensuring transparency, immutability, and traceability. off-chain data storage and on-chain metadata are enhancing data privacy as well as system scalability with the possibility of still maintaining a verifiable connection between the user and his/her genomic data. this new structure ensures that genomics data that is sensitive is not openly revealed on the blockchain, thus maintaining the user’s confidentiality but having access to the most secure elements of blockchain. figure 3 illustrates the safe genomic data access procedure via a blockchain system combined with zkp for privacy protection. the procedure begins with user login and the smart contract authentication of their credentials by a zkp layer, ensuring user legitimacy without exposing sensitive data. if the user is not legitimate, access is rejected. for an authentic user, the smart contract verifies their on-chain authorities in a decentralized blockchain network with security metadata only. in rights permitted by authority, the smart contract authorizes access to the actual genomic data kept off-chain in ipfs. data access takes place in a secure chain: access metadata in blockchain, followed by access to encoded genomic data in ipfs. ultimately, the authenticated data are delivered by the smart contract to the user. this design guarantees data confidentiality, high-fidelity access control, and integrity and preserves data privacy using zkp and low-cost storage with off-chain ipfs. they enable user registry and roles and enforce permission levels based on context-aware attributes like user identity, use purpose, or time interval. logging all access as an immutable transaction, the system guarantees auditability and accountability in the high-stakes domains algorithm 2. upload the genomic data on a decentralized network input: new block (nbi) and genomic data (gi) output: add node to blockchain network step1: egi � encrypt the gi data using ecc and add in ipfs, take the transaction details of the ipfs-configured file. step 2: add egi to the new block nbi. step 3: add nbi to the blockchain network after mining using the poc and pos consensus mechanism. ipfs: interplanetary file system; poc: proof of concept; pos: proof of stake. https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.419 7 (page number not for citation purpose) blockchain safeguarded sharing of genomic data of genomic research and personalized medicine. this type of exercise ensures that only specially authorized staff accesses specific datasets and that everything is logged for recovery or verification at some point in the future to ensure compliance. the combination of zkps and blockchain technology holds great promise in the areas of privacy, security, and scalability. the combination also has some challenges that must be addressed to ensure that the blockchain system works as expected. the zkps require a lot of computational power to generate proofs and verify them, which slows down the transactions and increases their costs. hence, we have leveraged improvements in cryptographic techniques and hardware optimization protocols that will make zkps execute more quickly and be less resource-intensive. let prover interactive zkp on the basis of persuade verifier that they have x, with the property that y=gx without revealing x. g is a cyclic prime order of group q. g is a generator of g and x ∈ zp is the secret key and y is a public key. algorithm 3 supports fine-grained, rule-based access control and facilitates auditability and transparency of access permissions. apart from guaranteeing genomic data privacy, the system also utilizes zkp, where clients can demonstrate the fact of knowledge or possession of genomic sequences without showing the information. in our method, the genome sequence is safely stored in place as a cryptographic hash, and individuals may establish their rights of access or ownership of information through cryptographic proof, which is generated based on ecc. the process preserves genomic data in secret and never leaks in the course of verification processes, and it resolves one of the greatest issues of genomic data privacy. the zkps are especially useful where anonymity to the user and compliance with regulation are of greatest concern. involvement of a patient in a genomic study can be ensured to be admissible or a history of consent without rendering it revealed personal health information. in ecc, it is ensured that such protocols are rendered computationally secure and trustworthy even with large genomic databases. the zkp on the blockchain environment ensures an environment of trustworthy, privacy-protected algorithm 3. rule-based access control smart contract step1: if useri ∈ authorizedj then granted > accessij else denied where authorizedj = set of authorized users of dataset j fig. 2. hybrid blockchain architecture for genomic data storage using ipfs (interplanetary file system). https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.4198 (page number not for citation purpose) nandini k et al. channels by which information can be securely validated and accessed without encroaching on user anonymity. smart contracts, zkp, and blockchain combined provide a safe, transparent, and privacy-protected environment for the management of genomic information that is secure. verification and key generation 1. public key generation: p = k · g where: k = private key (random integer) g = generator point on elliptic curve p = public key 2. verification: sha256 (gtest) = sha256 (goriginal) k · g = p guarantees confidentiality of data during validation or authentication. it also avoids exposure of genomic data in verification operations. the zkp proves to the verifier that a statement is true without revealing any information beyond the validity of the statement itself. the zkp breaks down into layers; to evaluate the effectiveness and efficiency of our combined cryptographic system, we create and compare key performance indicators through graphical plots. each graph showcases comparative strengths within the four cryptographic levels: blockchain, smart contracts, ecc hash, and zkpb. query time comparison the response time of the query is being compared in milliseconds with four technologies: zkpb, smart contracts, ecc hash, and blockchain. zkpb performs the best, as they run in constant time without traveling or directly accessing data. this makes zkpb extremely efficient for real-time verification use cases. smart contracts, however, have a medium response time because they need to run identity checking and conditional access logic, which fig. 3. privacy-preserving genomic data access with blockchain and zero-knowledge proofs. https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.419 9 (page number not for citation purpose) blockchain safeguarded sharing of genomic data consumes time. blockchain, as very secure, takes the longest response time, as serially trawling through blocks to look for relevant data is required. zkpb provides virtually instant verification and is well-suited to where speed matters, whereas blockchain immutability comes at a latency cost due to sequential lookup. security level comparison the metric employed here is a composite security score, rated qualitatively on three security fundamentals: integrity, confidentiality, and resistance to tampering. zkpb are the highest on privacy since they demonstrate knowledge without exposing underlying data, thus giving full privacy. blockchain is highest on data integrity through the use of blockchain immutability to avoid any tampering or unauthorized tampering. smart contracts are very useful to apply conditional access control and allow rulebased permissions, in addition to ensuring only approved actions are carried out. each of the three technologies handles one aspect of security, and this explains the effectiveness of the integration of the three in a single system to ensure end-to-end security. computational complexity the measurement is relevant to computational complexity for the purpose of big-o notation. zkpb are o(1), having constant-time crypto verification that is computationally lightweight even with increasing data size. this is partially because they are ecc, which is secure and efficient. blockchain transactions are o(n), where n is the number of blocks, because finding the ledger involves sequential block searches. smart contracts are of complexity o(m), where m is user or access control list size, because contract logic must verify each entry for permission. zkpb offer constant-time complexity, leading to improved performance, while blockchain and smart contracts scale linearly, according to data volume and the size of the list of users, respectively. elliptic curve visualization in zkp this diagram charts ecc points, highlighting the correlation between public and private keys used in zkp protocols. there are two sets of points in the scatter plot: figure 4, prover keys are marked by blue points, which are computed based on private key values, and verifier keys are marked by red points, computed using their respective public values. the curve is employed graphically to demonstrate the asymmetry and non-reversibility of ecc operations; that is, although it is computationally easy to calculate a public key from a private key, its reverse cannot be performed, the source of which is cryptographic security. this mapping helps the zkp process by demonstrating how knowing a private key (or genomic data hash) can be demonstrated by a prover without revealing it. figure 5 presents a clear and intuitive illustration of the mathematically sound nature of ecc and serves to illustrate the zkp proof verification flow, enhancing the point that the private keys are never disclosed, not even during proof generation or verification. prototype implementation and evaluation we designed a poc implementation in python to test the proposed blockchain-based design for secure genomic data sharing. our poc implementation includes an on-premise blockchain ledger with proof-of-stake consensus, rule-based smart contracts, ecc for hybrid encryption, and zkps on interactive schnorr protocols. fig. 4. elliptic curve cryptographic (ecc) points in zkp protocols. https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.41910 (page number not for citation purpose) nandini k et al. genomic data were stored off-chain in a replicated ipfs and only metadata such as content hashes and ownership recorded on-chain. the data set consisted of 10 program-generated synthetic dna strands of length 1,000 each, which were parsed using biopython to make sure no patient data were utilized. to verify system functionality and reliability, we performed 100 iterative access tests for each component with 10% failure modes including unauthorized access, invalid proof, and simulated decryption keys. performance metrics included query response time in milliseconds, correctness of verification, and qualitative security decision, measured using high-precision timing. the prototype was implemented and results are reported as means over 100 runs, showing the effectiveness and usability of the presented framework to facilitate secure sharing of genomic data. results and discussion the system’s security, performance, and computation speed were measured via four comparative charts that test the vital attribute of techniques used: ecc hashing, blockchain, smart contracts, and zkpb comparisons aid in making a decision of merit and trade-offs on each component included in our synthetic genomic data protection system. security level comparison figure 6, a bar chart, depicts a composite security score (1–10 qualitative scale) for each technology. zkps attained the highest (9/10) since they can prove without revealing underlying information, yielding unmatched confidentiality. blockchain ranked second (8/10) due to its tamper-evident, unmodifiable ledger, which is critical for data integrity. smart contracts were given (7/10) for efficient rule-based access control, where sensitive genomic information is accessible only to permitted users. ecc hash functions were given a rating of 6/10, providing cryptographic integrity but weak privacy when used alone. verification accuracy a sample of verification accuracy of 100 iterations is shown in figure 7. all solutions such as zkpb, smart contracts, blockchain, and ecc achieved 90.00% accuracy, which shows good performance with 10% failure cases due to invalid proofs, unauthorized access, wrong keys on the our synthetic data set. zkpb accuracy arises from mathematically valid schnorr proofs, while accuracy in smart contracts and blockchain arises from valid access control and block searching. ecc correctness was slightly impacted by fundamental mismatches in failure cases, fig. 5. elliptic curve visualization for zkpb. fig. 6. comparison of security levels of cryptography methods. zkpb: zero-knowledge proof blockchain; ecc: elliptic curve cryptography. https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.419 11 (page number not for citation purpose) blockchain safeguarded sharing of genomic data which aligns with the safe key management observation cited in the original analysis. computational complexity figure 8 presents relative computational overhead (1 = low, 3 = high). both zkpb and ecc are low complexity (1) using constant-time cryptography despite zkpb’s computationally heavy proof creation. smart contracts are medium complexity (2) using access list checks proportional to the user base. blockchain is high complexity (3) for linear block scanning of 500 blocks, consistent with sequential search requirements. these results confirm the optimality of zkpb for privacy-requiring applications. comparison of query time figure 9 depicts average query times for 100 actual-time runs. zkpb was fastest at 5.83 ms with the benefit of constant-time verification without data traversal. smart contracts had less than 0.01 ms latency, suggesting optimized access checks but just at or below the measurement sensitivity of the test environment. blockchain was 0.01 ms, with linear searches of 500 blocks hampering it. ecc was slowest at 8.72 ms due to encryption/decryption overhead. the use of simulated ipfs reduced on-chain storage to a minimum, enhancing scalability, while pos (difficulty 2) ensured ledger integrity. these results justify zkpb’s optimal trade-off between privacy and performance, with smart contracts and blockchain facilitating fine-grained control and tamper-resistance, respectively. ecc is a building-block cryptographic method best complemented by other constituents. collectively, these constituents define an efficient, scalable, and privacy-promoting paradigm for secure genomic data sharing. limitations the architecture was poc tested in real time on a local setup with a synthetic genomic dataset to be privacy-compliant. the blockchain was emulated with 500 blocks, and off-chain storage used a python dictionary to simulate ipfs. dataset size was kept small for ease of poc and smart contract query times were below the measurement threshold (<0.01 ms) since optimized access checks were performed with low precision. short-term activities include scaling to larger datasets, testing in a distributed environment using real ipfs and ethereum blockchain, and smart contract measurement optimization for higher resolution. conclusion this article outlines a blockchain-based model for private, secure genomic data sharing, which is founded by means of a poc prototype built using python. the platform integrates zkps, blockchain, smart contracts, and ecc, optimized for particular genomic use cases. real-time testing on a simulated dataset (10 dna sequences, 1,000 bases long, in synthetic_sequences.fasta) for 100 iterations with a controlled 10% failure rate demonstrated excellent performance: zkpb achieved 5.83 ms query time with fig. 9. query time performance comparison of crypto techniques. zkpb: zero-knowledge proof blockchain; ecc: elliptic curve cryptography. fig. 7. accuracy evaluation of cryptography methods. zkpb: zero-knowledge proof blockchain; ecc: elliptic curve cryptography. fig. 8. computational complexity comparison (1–3) of cryptography methods. zkpb: zero-knowledge proof blockchain; ecc: elliptic curve cryptography. https://doi.org/10.30953/bhty.v8.419 citation: blockchain in healthcare today 2025, 8: 419 https://doi.org/10.30953/bhty.v8.41912 (page number not for citation purpose) nandini k et al. 90.00% accuracy, smart contracts under 0.01 ms with 90.00% accuracy, blockchain 0.01 ms with 90.00% accuracy, and ecc 8.72 ms with 90.00% accuracy, as seen in figure 7. these experimental results confirm the efficiency of zkps for constant-time proof verification, the immutability of blockchain for auditability, rule-based access control of smart contracts, and lightweight encryption of ecc even with slower performance. the system, supplemented with scalable off-chain storage with emulated ipfs, is a feasible solution for clinical genomics, healthcare research, and personalized medicine, with future prospects including larger data sets and distributed environments. we affirm that the research was conducted with full scientific and ethical integrity, and any potential conflicts have been managed according to institutional and journal guidelines. funding  no fund was provided to publish this article. conflicts of interest the authors declare that there are no conflicts of interest related to this study. all work was conducted independently, and with no external influence affecting the results, interpretation, or reporting of this research. data availability statement (das), data sharing, reproducibility, and data repositories the data that support the findings of this study are available from the corresponding author upon reasonable request. application of ai-generated text or related technology none. acknowledgments this study was conducted with the sole interest of our research. the authors were admitted to the department of computer science and engineering, dayananda sagar university bengaluru south, india. they provided us with the resources and support to conduct this work. the authors also acknowledge the department of computer science, school of engineering dayananda sagar university bengaluru, india for research support. references 1. gudodagi r, venkata siva reddy r, riyaz ahmed m. investigations and compression of genomic data. 2020 third international conference on advances in electronics, computers and communications (icaecc) [internet]. 2020 dec 11 [cited 2025 jun 17]; pp. 1–4. available from: https://ieeexplore.ieee.org/ stamp/stamp.jsp?tp=&arnumber= 9339492 2. nandini k, girisha gs. proof of authentication for secure and digitalization of land registry using blockchain technology. 2021. 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data-sharing: blockchain-enabled federated learning in medical diagnostics. ieee trans sustain comput. 2024;10(1):1–15. https://doi.org/10.1109/ tsusc.2024.3409329 15. rao kpn, selvan c. empowering genomic data sharing in healthcare: a blockchain-driven decentralized consent model. 2024; 626–32. https://doi.org/10.1109/i-smac61858.2024.10714793 16. kanamarlapudi j, singh a, garg p. privacy preserving for electronic health records using enhanced attribute-based https://doi.org/10.30953/bhty.v8.419 https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber= 9339492 https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber= 9339492 https://doi.org/10.1007/978-981-16-0980-0_27 https://ieeexplore.ieee.org/document/10714793 https://ieeexplore.ieee.org/document/10714793 https://doi.org/10.30953/bhty.v1.34 https://doi.org/10.30953/bhty.v5.185 https://ieeexplore.ieee.org/document/10577693 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using private permissioned blockchain for telemedicine. ieee access. 2024;12:106645–57. https://doi. org/10.1109/access.2024.3436075 copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.419 https://doi.org/10.1109/access.2022.3217201 https://doi.org/10.1109/access.2022.3217201 https://doi.org/10.1109/access.2024.3436075 https://doi.org/10.1109/access.2024.3436075 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research soulbound tokens: enabler for privacy-aware and decentralized authentication mechanism in medical data storage biagio boi, phd , student; franco cirillo, phd , student; marco de santis, phd , student; and christian esposito, phd department of computer science, university of salerno, fisciano, italy corresponding author: franco cirillo, email: fracirillo@unisa.it doi: https://doi.org/10.30953/bhty.v7.334 keywords: authentication, blockchain, healthcare, medical record, sbt, self-sovereign identity, soulbound token, ssi abstract context: the digitalization of the healthcare sector faces significant challenges due to the diverse representation of data and their distribution across various hospitals. moreover, security is a key concern as healthcare-related data are subject to the legal obligations of general data protection regulation (gdpr) and similar data protection legislation. standardization efforts like health level seven (hl7) have been implemented to enhance data interoperability. however, authentication still remains a critical issue with significant challenges. aim: this research aims to improve and strengthen the authentication process by introducing a novel architecture for decentralized authentication. additionally, it proposes a new approach to decentralized data management, which is crucial for handling sensitive medical data efficiently. methodology: the proposed architecture adopts a user-centric approach, utilizing self-sovereign identity (ssi). it introduced a new non-fungible token (nft) type called soulbound token (sbt) in the medical context, which will facilitate user authentication across different hospitals, effectively creating a federation of interconnected institutions. results: the implementation of the proposed architecture demonstrated a significant reduction in authentication time across multiple hospitals. the use of sbt ensured secure and seamless user authentication, enhancing overall system interoperability and data security. the decentralized approach also mitigated the risks associated with centralized authentication servers. conclusion: this study successfully presents a novel decentralized authentication architecture for the healthcare domain, leveraging ssi and sbts. this approach accelerates the authentication process and enhances data security and interoperability among hospitals. future research should explore the scalability of this architecture and its application in other sectors requiring stringent data security measures. plain language summary this research addresses challenges in digital healthcare, particularly in data variety, distribution, and authentication. it introduces a decentralized authentication system using self-sovereign identity and a new type of non-fungible tokens called soulbound tokens. this system links hospitals, reduces authentication times, enhances data security, and improves system interoperability. by decentralizing authentication, it mitigates risks associated with centralized servers. this study results suggest that this innovative approach could benefit healthcare and potentially other industries with stringent data security needs, though further research on scalability and broader applications is recommended. submitted: july 2, 2024; accepted: august 9, 2024; published: august 31, 2024 blockchain in healthcare today issn 2573-8240 https://doi.org/0000-0003-3044-5345 https://doi.org/0009-0006-9599-5996 https://doi.org/0009-0004-6514-4168 https://doi.org/0000-0002-0085-0748 mailto:fracirillo@unisa.it https://doi.org/10.30953/bhty.v7.334 citation: blockchain in healthcare today 2024, 7: 334 https://doi.org/10.30953/bhty.v7.3342 (page number not for citation purpose) b. boi et al. improving data management, operational effectiveness, and patient care, all depend on the healthcare industry going digital. however, there are several major obstacles to this shift, especially when it comes to authentication and data compatibility. healthcare data are often dispersed throughout several systems and organizations,1 each of which uses a different set of standards and technology to manage patient data. this fragmentation makes it difficult to integrate across different systems and causes discrepancies in data representation. the absence of a standard data format makes data interchange more difficult and increases the risk of errors and inefficiencies. because many systems do not always work well together, users could have trouble easily accessing their health records. this disarray compromises the effectiveness of care coordination and could lead to mistakes or delays in patient care. conventional healthcare authentication methods usually depend on centralized servers to store and validate user credentials. these centralized systems have several difficulties, such as: • single point of failure: centralized servers used for authentication are prone to malfunctions. the entire network may be affected if the server fails or is compromised, making it impossible for staff members at different institutions to access patient data. • scalability issues: as healthcare networks develop and their user base increases, centralized systems may not be able to keep up with the demand, which could result in performance bottlenecks. • cybersecurity-related risks: centralized servers are frequently the focus of cyberattacks. if an attack on these systems is successful, critical patient data may be compromised in massive data breaches. reliance on centralized authentication systems may lead to serious security flaws that compromise the overall effectiveness and security of healthcare data management. this vulnerability is exacerbated in a decentralized environment where data are spread across various institutions. strict laws like the health insurance portability and accountability act (hipaa) and the general data protection regulation (gdpr) apply to healthcare data. to safeguard patient privacy and guarantee compliance, these regulations impose strict controls over data access and management. conventional authentication methods frequently fail to strike a compromise between user ease and security. complicated authentication standards may result in more administrative work and possible noncompliance. for traditional systems, it is difficult to guarantee that only authorized personnel will access important information while preserving a seamless user experience. the complexity of healthcare data networks is increasing along with the number of users, and existing authentication solutions might not be able to keep up.2 several conventional systems are based on inflexible infrastructures that make it difficult to modify them in response to changes in the healthcare industry or in technology. it is possible that old authentication techniques will become obsolete or require expensive modifications as healthcare systems develop and incorporate new technologies. this lack of flexibility and scalability may make it more difficult for the industry to develop and adapt to new problems. this research aims to address the authentication challenges in the healthcare sector by proposing a novel decentralized authentication architecture. the proposed solution leverages self-sovereign identity (ssi), a user-centric approach that empowers an individual’s control over their digital identities. additionally, the architecture introduces a new type of non-fungible token (nft) known as the soulbound token (sbt). the sbts are a specific type of non-transferable token introduced to represent personal credentials and achievements on the blockchain in a secure and verifiable manner. unlike traditional fungible tokens or transferable nfts, sbts are bound to a specific individual and cannot be transferred or traded to another party. the origin of sbts lies in the need for a reliable method to digitally represent and verify personal attributes and credentials such as academic degrees, professional certifications, and membership records. this need has become more pressing as human verification gains importance in various domains, from education to professional networking and beyond. the term “soulbound” metaphorically represents the idea that these tokens are inherently linked to the individual’s “soul,” meaning their personal and unique identity, and are not meant to be detached or exchanged. by implementing this decentralized approach, we aim to create a federated network of healthcare institutions, enhancing data security and interoperability while significantly reducing authentication times. to complement this decentralized authentication architecture, we propose the integration of a solid data management system (a medium for the secure, decentralized exchange of public and private data), which provides a robust framework for decentralized data storage and management. by utilizing solid, patients can store their personal health data in personal online data stores (pods), which they fully control. this ensures that patients have the authority to grant or revoke access to their health information, fostering trust and enhancing privacy.3 in this report, we detail the methodology behind the proposed architecture, including the integration of ssi and sbts, and present the results of our implementation. we demonstrate how this approach mitigates the risks associated with centralized authentication servers and improves https://doi.org/10.30953/bhty.v7.334 citation: blockchain in healthcare today 2024, 7: 334 https://doi.org/10.30953/bhty.v7.334 3 (page number not for citation purpose) soulbound tokens in medical data storage the overall efficiency and security of the healthcare data management system. finally, we discuss the potential implications of this architecture for the broader healthcare industry and suggest avenues for future research. background in the field of digital credentials management, the integration of blockchain technology and cryptographic protocols has led to significant advancements. a prominent development is the use of sbts for issuing and managing digital access credentials. this section examines various contemporary approaches and innovations in this domain, highlighting the strengths and limitations of each, with a particular focus on privacy, non-repudiation, and regulatory compliance. the digital credentials management system proposed in ref. 4 introduces an innovative approach by leveraging an enhanced version of sbts, referred to as rejectable soulbound tokens (rejsbts). this system enhances traditional credential features by embedding terms and conditions during issuance and ensuring non-repudiation of reception upon acceptance by users. the rejsbts guarantee non-repudiation of reception and origin proofs, a critical aspect for legal and security purposes. however, the current protocol lacks encryption measures, as it primarily handles non-sensitive digital access credentials. it is crucial that future integrations align with gdpr regulations to address potential privacy concerns. another notable approach is the integration of decentralized identifiers (dids) with sbts in digital authentication systems, particularly in the web3 and metaverse environments. this scheme proposed by kim and ryou (2023)5 utilizes dids for user verification via smart contracts and issues sbts for seamless integration. to enhance privacy, verification authorities’ service providers use zero-knowledge proof (zkp) systems, ensuring that critical user information remains undisclosed during the verification process. this method increases user convenience by allowing the generation of cryptographic proofs without direct user involvement. additionally, a unified wallet manages both did credentials and sbts, simplifying credential management. in the context of privacy-preserving credential systems, the use of sbts combined with selective disclosure mechanisms is gaining traction. one framework6 proposes issuing credentials as nfts stored on the interplanetary file system (ipfs) in an encrypted format. although this system empowers users with complete control over their credential information, the verification process does not employ zkps, potentially limiting its privacy assurances. an advanced method for private identity verification7 involves zero-knowledge sbts, which combine sbts with zkps. this protocol uses the identity holder’s private/ public key to encrypt data stored in an sbt. a zkp is then used for verification, ensuring that the data have not been altered, and that the identity holder meets specific requirements without revealing any personal information. this approach effectively balances privacy and security, making it a robust solution for identity verification. the metaverse presents unique challenges and opportunities for digital identity management. one implementation8 focuses on providing age-restricted access in decentraland (a 3d virtual world browser-based platform) using ethereum smart contracts and zkps. this method allows users to prove their eligibility for certain activities, such as accessing a virtual cinema, without disclosing their real identities. it leverages existing legal frameworks like eidas (electronic identification) and w3c verifiable credentials, demonstrating the practical application of blockchain technology in maintaining privacy while ensuring compliance with legal standards. a practical use case for sbts is the certification of covid-19 vaccinations. this proposed system9 employs a decentralized application, where sbts are issued as non-transferable and revocable tokens, ensuring they align with the non-transferable nature of vaccination records. while this approach addresses the administrative aspects of vaccination certification, it does not explicitly tackle privacy and confidentiality, highlighting an area for future improvement. furthermore, the concept of data decentralization extends beyond credential management, offering broader applications across various sectors. one significant challenge, aside from security, is the storage of large files on the blockchain network, as traditional blockchains lack the capacity to store extensive files like medical images. integrating decentralized storage solutions such as an ipfs and solid pods (personal data stores that provide a place to access, update, and share data) can revolutionize data management and sharing across networks. for instance, ipfs provides a peer-to-peer network for storing and sharing data in a distributed file system, enhancing data availability and mitigating the risk of central points of failure. a security model proposed for datastore on ipfs10 utilizes shamir’s secret sharing (sss) to encrypt data before storage, implemented in ethereum and operating on a proof of work consensus algorithm, necessitating high computational power. another challenge with ipfs is that it only provides a hash of the data, complicating the search for related patient records. to resolve this, an interplanetary name system-based blockchain has been proposed in ref. 11, which facilitates data searching by providing a name instead of a hash, thus reducing search time. blockchain systems are thus used for storing, sharing, using, and manipulating patient data. another solution is to use solid pods to store healthcare data. examples of https://doi.org/10.30953/bhty.v7.334 citation: blockchain in healthcare today 2024, 7: 334 https://doi.org/10.30953/bhty.v7.3344 (page number not for citation purpose) b. boi et al. their use and techniques to optimize a search have been published.12,13 based on table 1, our solution outperforms existing methods by offering a comprehensive and scalable framework that balances privacy, security, and regulatory compliance. it uses decentralized authentication with sbts, a private blockchain, and privacy-aware oracles, ensuring high privacy and strong security. unlike other approaches, it addresses key limitations such as lack of encryption, complexity, and application constraints, making it a superior and more robust option. proposed system the advancements in digital credentials management systems using sbts illustrate significant progress in enhancing privacy, security, and user convenience. however, each approach has its set of strengths and limitations, particularly concerning privacy preservation and regulatory compliance. future developments must focus on integrating robust encryption measures, comprehensive privacy protections, and adherence to regulatory standards to fully realize the potential of these innovative systems in digital credential management. the current state-of-the-art highlights a significant lack of interoperability among data generated within the healthcare domain. by leveraging standards such as hl7, it is feasible to develop interoperable solutions across different hospitals. however, the existing data storage methods, which predominantly rely on centralized servers, require careful redesign. this includes revisiting the mechanisms provided for data access. the proposed architecture aims to deliver a comprehensive framework for managing authentication in a decentralized manner while also considering the decentralized data solutions discussed in the preceding section. table 1. comparative analysis of credential management solutions source key features strengths limitations privacy non-repudiation regulatory compliance pericàs-gornals. et al. (2024)4 enhanced sbts with t&c, ensures non-repudiation of reception legal and security assurance, non-repudiation of reception and origin lacks encryption measures, primarily for non-sensitive credentials low high needs future gdpr alignment kim et al. (2023)5 dids for user verification, zkp for privacy, unified wallet enhanced privacy with zkp, seamless integration complexity of implementation high medium aligned with legal standards reddy and kushwaha (2023)6 nfts stored on ipfs, encrypted format user control over credential information lack of zkp, limited privacy assurances low medium privacy enhancements needed cabot-nadal et al. (2023)7 combines sbts with zkp, uses private/public key for encryption balances privacy and security effectively high complexity high high strong alignment with privacy regulations zichichi et al. (2023)8 ethereum smart contracts, zkps, eidas, w3c vcs practical application in metaverse, maintains privacy and compliance specific to age-restricted access high medium strong compliance with legal standards lunesu et al. (2023)9 sbts as non-transferable and revocable tokens addresses administrative aspects lacks focus on privacy and confidentiality low medium needs enhancements for privacy naz et al. (2019)10 ipfs for storage, sss for encryption, pow consensus enhanced data availability, mitigates central points of failure high computational power, search difficulties high medium strong potential but needs optimization for healthcare saharan and prasad (2020)11 facilitates data searching with names instead of hashes reduces search time, enhances data sharing and usage implementation complexity medium medium strong alignment with data management standards prop. decentralized authentication with sbts, private blockchain (hyperledger besu), poa consensus (qbft), privacy-aware oracles (chainlink) comprehensive framework, scalable, enhanced security with private smart contracts private blockchain high high strong compliance focus, private blockchain ensures privacy did: decentralized identifiers; eidas: electronic identification, authentication and trust services; gdpr: general data protection regulation; ipfs: interplanetary file system; nfts: non-fungible tokens; poa: proof of authority; pow: proof of work; qbft: quorum byzantine fault tolerant; sbts: soulbound tokens; ssi: self-sovereign identity; sss: shamir’s secret sharing; t&c: terms and conditions; vcs: verifiable credentials w3c: world wide web consortium; zkp: zero-knowledge proof. https://doi.org/10.30953/bhty.v7.334 citation: blockchain in healthcare today 2024, 7: 334 https://doi.org/10.30953/bhty.v7.334 5 (page number not for citation purpose) soulbound tokens in medical data storage the objective is to validate this approach by providing insights into the mean response time and a thorough security assessment of the protocol. introduction the use of did is not a directly applicable choice in the medical domain due to complex interaction. moreover, authenticating with ssi poses significant challenges within decentralized applications due to the requirement for signature verification on the credentials, which cannot be performed on-chain without revealing user data. these concerns become more critical when using public blockchains such as ethereum. our architecture aims to provide all the advantages of ssi while incorporating a novel authentication mechanism based on nfts, specifically an extension known as soulbound tokens (sbts). sbts are designed to bind tokens to their owner, thereby leveraging the benefits introduced by nfts. to analyze our system, we divide it into two main phases: the enrollment phase, depicted at the top of figure 1, and the authentication phase, shown at the fig. 1. system model: enrollment phase (top figure) and authentication (bottom figure). hl7: health level seven; iot: internet of things; pods: personal online data stores; sbt: soulbound token. chainlink patient doctors patient sbtauthorizer iot sensors pod medical records did wallet medicalrecordsbt 1. authenticate with sbt 2. verify sbt 3. authorize write hl7 data4. obtain data off-chain on-chain on-chain 9. provide sbt 6. verify proof off-chain 2. request sbt 8. issue sbt sbt authorizer 1001 0100 1010 hospital https://doi.org/10.30953/bhty.v7.334 citation: blockchain in healthcare today 2024, 7: 334 https://doi.org/10.30953/bhty.v7.3346 (page number not for citation purpose) b. boi et al. bottom of figure 1. before going deeper into analyzing these phases, it is necessary to clarify some technical aspects related to our architecture. credential verification is complex to on-chain; for this reason, we adopted a hybrid approach, where identity is verified off-chain, and then an sbt is released using a private blockchain, such as hyperledger besu. needs for a private blockchain come from the need to guarantee a user’s privacy when performing on-chain transactions. quorum and proof of authority the architecture has been deployed on a private blockchain using hyperledger besu, composed of four nodes as part of an experimental project. this setup is easily scalable because we only use blockchain to release sbt. in the employed proof of authority (poa) consensus mechanism, the validators, which are nodes authorized to mine blocks, are pre-authorized by the blockchain owner. each block is validated by one of these pre-authorized nodes. hyperledger besu supports various poa schemas, including qbft, ibft 2.0 (istanbul byzantine fault tolerance), and clique. for the purposes of our project, we selected qbft due to its ability to ensure the privacy of transactions, which is essential for implementing a privacy-aware oracle based on chainlink infrastructure. these transactions are secured and accessible only to the parties involved. the scalability of poa is advantageous, as it supports network growth without significant performance issues. security and trust are enhanced because validators are trusted entities, reducing the risk of malicious activity and ensuring credible issuance of sbts. we selected the qbft schema within poa for its transaction privacy features, crucial for our privacy-aware oracle using chainlink infrastructure. overall, poa’s efficiency, scalability, security, and privacy make it an ideal choice for our sbt deployment. enrollment the need for an enrollment phase arises from the complexity of creating a timely procedure for authenticating users within the system. authentication based on dids requires verification of credentials and the generation of verifiable proofs by the holder, which can introduce overhead. additionally, these processes cannot be applied to a public blockchain as previously described. in our proposal, the first phase involves nine steps to deliver an sbt to the user. during this phase, the user shares the ssi credentials and receives an sbt. the procedure begins with the holder requesting an sbt release from the hospital. during this request, the holder communicates their didh, which, in our case, is a did:eth (decentralized identifier:etherium) method identifier, to ensure compatibility with the ethereum blockchain. such didh is associated with a diddocumenth through the use of the smart contract previously registered. the hospital forwards the request to the medicalrecordsbt smart contract containing the same didh, previously deployed on the besu blockchain. the function requestsbt then forwards this request to the chainlink infrastructure, which handles off-chain communication with the external server. as illustrated in figure 1, during steps 4, 5, and 6, the requests follow the classical ssi trust triangle approach, where the verifier, which, in this case, is the chainlink node, sends a request to the off-chain server, which generates a vpr. the users generate a verifiable presentation connected to that vpr and transmits it to the off-chain server, which performs credential verification. once the validity of the transmitted credentials is confirmed, a callback is executed on the medicalrecordsbt, releasing the final sbt to the user. this sbt can now be used by the user to authenticate themselves on hospital-trusted platforms. authentication at the beginning of the authentication, the patient already possesses an sbt representing their identity within the hospital. this token is used to authenticate the user and access personal data stored on the pod. the entire procedure is managed through the sbtauthorizer smart contract, which contains references to the issued sbt and is capable of performing the authentication process. this smart contract also includes metadata related to revoked tokens and roles within the system, allowing the hospital to revoke access if a user loses ownership of their wallet. similarly to the patients, the internet of things (iot) devices and the doctors will access the data spaces by using a decentralized approach. as described in ref. 14 and in ref. 15, both doctors and iot device can implement this kind of authentication using ssi. in particular, the doctors will manage authentication through the use of the hsm as a mechanism for reducing time needed for authentication, while iot devices can leverage physical characteristics, such as static random access memory (sram), or electrocardiogram in order to create a private key used in the ssi wallet generation. finally, the proposed architecture separates the authentication process from data storage. decentralized data storage the proposed approach also includes a novel decentralized data storage system, which is fully compatible with the proposed decentralized authentication mechanism. solid, a relatively new framework, aligns with the user-centric data storage paradigm by empowering users to take responsibility for the data produced by applications. in this specific use case, this pertains to the medical data generated by both iot devices and analyses conducted by doctors. solid offers a well-structured method for storing data, utilizing knowledge graph technology. https://doi.org/10.30953/bhty.v7.334 citation: blockchain in healthcare today 2024, 7: 334 https://doi.org/10.30953/bhty.v7.334 7 (page number not for citation purpose) soulbound tokens in medical data storage this representation is fully compliant with contemporary data representation mechanisms in the medical domain, such as hl7, which provides a well-documented ontology. this approach enhances the compliancy with respect to gdpr by promoting decentralized data storage, where users can manage data produced by iot devices, without any copy on external servers. results to evaluate the quality of the proposed architecture, we mainly focus on the cost for the deployment of the solution in terms of fee for the execution of a smart contract. for the evaluation of our proposal, we deployed a hyperledger besu docker image equipped with qbft consensus over an imac 3.3 ghz intel core i5 6 cores equipped with 16 gb 2667 mhz ddr4. to implement our solutions, we deployed five smart contracts: 1. linktoken.sol: responsible for the payment of chainlink requests. initially deployed with 1.000.000 link. 2. operator.sol: responsible for operating with the chainlink node, which forwards all the requests. 3. ethereumdidregistry.sol: responsible for managing the dids architecture. 4. nationalhealthservicedidregistry.sol: responsible for managing the roles within the entire framework. 5. medicalrecordsbt.sol: containing the sbt definition and operation for minting the sbt. as reported in table 2, the smart contract deployment is the most expensive operation, together with sbt minting, needed to generate and release the sbt to the user. operational requests are required to setup the entire environment and refer to the mapping of created did to the internal registry containing the roles. the gas estimation offers insights about the complexity of the operations involved by the smart contract, but the costs will depend by multiple factors such as occupancy of the network, fee required. by assuming a cost of 3 gwei per gas needed, which is in-line with normal cost of ethereum blockchain, it is possible to make an estimation over the total cost of the different operations. the deployment phase is executed only once at the adoption of the system while enrollment and sbt minting operations are executed for each new patient belonging to the system. a total cost below $30 per new user in the system is reasonable for the advantages introduced by the proposed approach. no cost is provided for the authentication procedure, which only consists of reading data from the blockchain, without requesting any additional fee. the overall time for the enrollment procedure is about 16.03 s on average; the greatest part is spent verifying credentials (12.54 s). that is the main motivation that led us to move to a fully decentralized and on-chain approach. with our proposal, it is possible to authenticate now using the sbt and by only checking the presence of the sbt on the medicalrecordsbt smart contract. with the current research, we tried to reduce the overall time needed for authentication by providing an on-chain verification method while preserving the privacy of nodes and all the advantages introduced by ssi. the overhead introduced by the proposed architecture is relatively low, considering that the enrollment phase is executed only once for each user, and the authentication phase is reduced to a single call to the smart contract. moreover, the system is strictly based on the blockchain and on the asymmetric mechanism behind the blockchain. sbts increase security by leveraging a cryptographic wallet, which stores the private key associated with the public one. table 2. costs for the deployment of the proposed architecture operation gas needed cost ($) spent by deployment • tokenlink.(constructor) 1467527 gas 11.87 authority • operator.(constructor) 4184013 gas 33.83 authority • ethereumdidregistry.(constructor) 574518 gas 1.55 authority • nationalhealthservicedidregistry.(constructor) 1168361 gas 9.45 authority • medicalrecordsbt.(constructor) 5159457 gas 41.72 authority enrollment • ethereumdidregistry.updatediddocument(string,bytes) 742188 gas 6.00 hospital • nationalhealthservicedidregistry.authorizedid(string,string) 58569 gas 0.47 hospital • medicalrecordsbt.requestsbt(string) 164520 gas 1.33 hospital sbt minting • medicalrecordsbt.fulfillrequest(string) 2594670 gas 20.98 chainlink node did: decentralized identifiers/identity; sbt: soulbound token. https://doi.org/10.30953/bhty.v7.334 citation: blockchain in healthcare today 2024, 7: 334 https://doi.org/10.30953/bhty.v7.3348 (page number not for citation purpose) b. boi et al. conclusion in the future, we plan to enhance our evaluation of the proposed methods by integrating provider access into real-world scenarios. by incorporating provider access, we will be able to simulate more complex, multi-stakeholder environments, which will offer a more comprehensive assessment of our approach’s effectiveness in practice. this will allow us to better understand the real-world impact on medical data security and privacy. for example, in a diabetes use case, provider access would enable healthcare professionals to interact directly with patient data stored in solid pods while also contributing to and benefiting from a distributed machine learning model. this added layer of provider interaction is essential for validating the scalability and practicality of our architecture across various medical use cases. funding this work was partially supported by project serics (pe00000014) under the nrrp mur program funded by the eu—ngeu and by the project “dheal—com digital health solutions in community medicine” under the innovative health ecosystem (pnc)—national recovery and resilience plan (nrrp) program funded by the italian ministry of health. conflicts of interest none reported by the authors. contributors mr. boi contributed to the conceptualization of the system, the evaluation of the system, and the writing. mr. cirillo contributed to the conceptualization of the system, the state of the art, and the overall writing of the paper. mr. de santis contributed to the conceptualization of the system and the writing of the paper. dr. esposito contributed to the conceptualization of the system and the review of each draft. all authors have approved the manuscript and agree with its submission to blockchain in healthcare today. data availability statement (das), data sharing, reproducibility, and data repositories. contact the author. application of generated text or related technology artificial intelligence and related technologies were not used in the preparation of this article. acknowledgments this work was partially supported by project serics (pe00000014) under the nrrp mur program funded by the eu—ngeu and by the project “dheal— com-digital health solutions in community medicine” under the innovative health ecosystem (pnc)—national recovery and resilience plan (nrrp) program funded by the italian ministry of health. references 1. reegu f, abas h, jabbari a, akmam r, uddin m, wu cm, chen cl, khalaf o. interoperability requirements for blockchain-enabled electronic health records in healthcare: a systematic review and open research challenges. security and communication networks 2022; 2022(1):9227343. https://doi. org/10.1155/2022/9227343 2. gupta d, mazumdar n, nag a, singh j. secure data authentication and access control protocol for industrial healthcare system. journal of ambient intelligence and humanized computing 2023; 14(5):4853–4864. https://doi.org/10.1007/ s12652-022-04370-2 3. esposito c, horne r, robaldo l, buelens b, goesaert e. assessing the solid protocol in relation to security and privacy obligations. information 2023; 14(7):411. https://doi. org/10.3390/info14070411 4. pericàs-gornals r, mut-puigserver m, payeras-capellá mm, cabot-nadal má, ramis-bibiloni j. digital credentials management system using rejectable soulbound tokens. ann telecommun [internet]. 2024 apr 23 [cited 2024 jun 19]; available from: https://link.springer.com/10.1007/s12243-024-01032-6 5. kim g, ryou j. digital authentication system in avatar using did and sbt. mathematics. 2023 oct 22;11(20):4387. https:// doi.org/10.3390/math11204387 6. reddy s, kushwaha ds. framework for privacy preserving credential issuance and verification system using soulbound token. sumathi ac, yuvaraj n, ghazali nh, editors. itm web conf. 2023;56:06002. 7. cabot-nadal mà, playford b, payeras-capellà mm, gerske s, mut-puigserver m, pericàs-gornals r. private identity-related attribute verification protocol using soulbound tokens and zero-knowledge proofs. in: 2023 7th cyber security in networking conference (csnet) [internet]. montreal, qc, canada: ieee; 2023 [cited 2024 jun 19]. p. 153–6. available from: https:// ieeexplore.ieee.org/document/10339754/ 8. zichichi m, bomprezzi c, sorrentino g, palmirani m. protecting digital identity in the metaverse: the case of access to a cinema in decentraland. in: international conference on developments in language theory. 2023. available from: https://ceur-ws.org/vol-3460/papers/dlt_2023_paper_13. pdf 9. lunesu mi, tonelli r, pinna a, sansoni s. soulbound token for covid-19 vaccination certification. in: 2023 ieee international conference on pervasive computing and communications workshops and other affiliated events (percom workshops) [internet]. atlanta, ga, usa: ieee; 2023 [cited 2024 jun 19]. p. 10. naz m, al-zahrani fa, khalid r, javaid n, qamar am, afzal mk, et al. a secure data sharing platform using blockchain and interplanetary file system. sustainability. 2019 10;11(24):7054. https://doi.org/10.3390/su11247054 11. saharan r, prasad r. blockchain technology for healthcare data. advances in intelligent systems and computing. 2020 2;671–7. https://doi.org/10.1007/978-981-15-6014-9_81 12. ghayvat h, zuhair m, shukla n, kumar n. healthcare-ct: solid pod and blockchain-enabled cyber twin approach https://doi.org/10.30953/bhty.v7.334 https://doi.org/10.1155/2022/9227343 https://doi.org/10.1155/2022/9227343 https://doi.org/10.1007/s12652-022-04370-2 https://doi.org/10.1007/s12652-022-04370-2 https://doi.org/10.3390/info14070411 https://doi.org/10.3390/info14070411 https://link.springer.com/10.1007/s12243-024-01032-6 https://doi.org/10.3390/math11204387 https://doi.org/10.3390/math11204387 https://ieeexplore.ieee.org/document/10339754/ https://ieeexplore.ieee.org/document/10339754/ https://ceur-ws.org/vol-3460/papers/dlt_2023_paper_13.pdf https://ceur-ws.org/vol-3460/papers/dlt_2023_paper_13.pdf https://doi.org/10.3390/su11247054 https://doi.org/10.1007/978-981-15-6014-9_81 citation: blockchain in healthcare today 2024, 7: 334 https://doi.org/10.30953/bhty.v7.334 9 (page number not for citation purpose) soulbound tokens in medical data storage for healthcare 5.0 ecosystems. ieee internet of things journal. 2024 feb 15;11(4):6119–30. https://doi.org/10.1109/jiot. 2023.3312448 13. ragab m, savateev y, oliver h, tiropanis t, poulovassilis a, chapman a, et al. unlocking the potential of health data with decentralised search in personal health datastores. 2024 may 13. 14. barbareschi m, boi b, cirillo f, de santis m, esposito c. csecuring the internet of medical things using puf-based ssi authentication. in proceedings of the 8th italian conference on cyber security (itasec 2024) 2024. 15. boi b, esposito c. securing the internet of medical things with ecg-based puf encryption. iet cyber-physical systems: theory & applications 2024. appendix: acronyms defined did: decentralized identifiers/identity did:eth: decentralized identifier:etherium diddocumenth: digital identity document (holder) didh: digital identity (holder) eidas: electronic identification, authentication and trust services. gdpr: general data protection regulation hipaa: health insurance portability and accountability act hl7: health level seven hsm: hardware security module ibft: istanbul byzantine fault tolerance iot: internet of things ipfs: interplanetary file system nft: non-fungible token poa: proof of authority; pod: personal online data store pow: proof of work qbft: quorum byzantine fault tolerant rejsbts: rejectable soulbound tokens sbt: soulbound token sram: static random access memory ssi: self-sovereign identity sss: shamir’s secret sharing t&c: terms and conditions vcs: verifiable credentials vpr: verifiable presentation request w3c: world wide web consortium zkp: zero-knowledge proof copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0 https://doi.org/10.30953/bhty.v7.334 https://doi.org/10.1109/jiot.2023.3312448 https://doi.org/10.1109/jiot.2023.3312448 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 original research scalability performance analysis of blockchain using hierarchical model in healthcare lipsa sadath, msc, mca1 , deepti mehrotra, phd2 , anand kumar, phd3 1computer science engineering department, school of engineering, amity university, dubai, uae; 2computer science engineering department, amity school of engineering and technology, amity university; uttar pradesh, noida, india; 3electronics engineering department, school of engineering, amity university, dubai, uae corresponding author: lipsa sadath; email: lsadath@amitydubai.ae doi: https://doi.org/10.30953/bhty.v7.295 keywords: blockchain technology, database management, hyperledger, hyperledger caliper, network performance, privacy, security, scalability abstract blockchain technology has become crucial in improving the privacy and security of enterprise applications in the cyber world. however, scalability has become a significant concern for researchers in large organizations, especially those with complex hierarchies and access privileges. as a result, the existing models and consensus algorithms suffer from various issues. medical centers and healthcare providers are particularly affected by this problem due to the vast amount of data, making it a critical weakness of traditional database management systems. to address this issue, the authors propose a hierarchical model within the hyperledger fabric enterprise application, focusing on the healthcare sector as a use case. this model includes multiple organizations at different levels of the hierarchy, such as hospitals, hospital governance, and insurance companies. the initial implementation of this model includes two levels of hierarchy, demonstrating networks of hospitals joining an insurance company. the primary objective of the experiment is to test and improve the network’s performance using this model. the model’s performance is evaluated by manipulating and scaling environmental factors such as the number of organizations, transaction numbers, channels, block intervals, and block sizes. the benchmarking tool used for this assessment is hyperledger caliper, which measures indicators such as success and failure rates, throughput, and latency. currently, the research focuses only on testing the model’s scalability using patient data. submitted: december 5, 2023; accepted: january 20, 2024; published: april 17, 2024 the healthcare sector suffers from data privacy, security, and integrity issues as each patient’s data flows through different hospitals under a specific hospital network (hn). the biggest challenge is faced when insurance claims are rejected due to mismatches in data. the prime reason could be tampered data submitted to insurance companies by the hns. such database registries can seriously affect claims submitted to insurance companies. when a private organization’s application works with blockchain, a guarantee for better transparency and security exists, but issues related to scalability and latency arise. this is seen mainly in supply chain systems, healthcare applications, etc. one of the main reasons is the number of organizations participating in transactions and different contracts running through the network. many enterprise applications are considered sensitive to network delays because of the latency caused by the vast number of transactions. current studies revolve around changing the batch time or the network’s block size in a permissioned environment. this problem in the healthcare sector is more complex and requires more attention due to substantial patient data. the primary interest of research in this paper is to understand scalability performance and create a model in the healthcare sector using hyperledger fabric enterprise-grade open-source on the linux platform. the contributions of the research are listed here: https://orcid.org/0000-0002-4395-5320 https://orcid.org/0000-0001-5752-9800 https://orcid.org/0000-0003-0178-2799 mailto:lsadath@amitydubai.ae https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.2952 (page number not for citation purpose) lipsa sadath et al. • a performance analysis uses a linear model of hyperledger fabric with varying parameters and fixed rate controllers. • performance analysis is used to analyze the effect of more channels to compare the difference between single-channel and multi-channel networks with rate controllers. • the hierarchical model analyzes the average latency and throughput of the model, and how they overcome the effect with multiple organizations, clients, and channels. • a sequence of experiments shows the results of the model with changes in block sizes and block intervals. • performance and bottlenecks are identified to evaluate the capacity of the model. the flow diagram in figure 1 shows the order of topics discussed. a glossary of key terms used throughout the article is presented in the appendix. blockchain technology (bct): security, privacy, scalability security and privacy blockchain combines technological features, including cryptography, peer-to-peer networking, distributed system, transparency, access identity permissions, open source, autonomy, and immutability. this makes the technology secure as the transactions cannot be tweaked or tampered with, and all the entities/parties that make transactions are kept anonymous.1 thus, blockchain offers integrity, protected distributed ledgers, and transparency of the entire process by upholding a set of global states. the participating nodes agree upon the existence, value, and histories of all states. each state contains multiple transactions. hence, blockchain is a way to manage distributed transactions. the concerned entities preserve replicas of the data and jointly agree on a transaction execution order. this high-performance blockchain is a means to monitor a system completely as if under the surveillance of a legal third party. this is very important, especially in the healthcare systems, as the amount of patient data that floats from laboratories to insurance claims is immense. this data transparency is required to understand genuine patient claims. the network does not provide an opportunity for data tampering by any organization. scalability issues at the same time, concerns about data replication2 exist as immutable records are stored with every peer. this affects the throughput of the blockchain network.3 many frameworks are designed to handle this at production levels and industry applications. however, research is underway to understand the effect of different consensus mechanisms and their role in maintaining the immutability, stability, privacy, and security of the system.4 block size causes performance issues when changed, resulting in a change in the maximum message counts the blocks can hold.5 whereas, the scalability use case in the ethereum network discusses sharding (the process of separating large databases into smaller, faster, more easily managed parts)6 as a solution that reduces the load in the entire network. machine learning through proof-of-information consensus is discussed by kuo et al.,7 but the model lacks a discussion on scalability. in contrast, a model by zhang et al.8 discusses data transparency between owners and users of clinical data addresses and, to an extent, scalability and security issues. another model published by ylonen and lonvick9 that discussed scalability has an architecture with the secure shell protocol (ssh)9 that has an identity preservation method with an absolute model view controller pattern with pointers for data access from the organization’s data pool. smart contracts in web and data immutability are the highlight in a genomic study by glicksberg et al.10 that attempts to understand the identification of late cancer stages using blockchain, but they lacked clear scalability research evidence. other research by lee et al.11 using blockchain to handle patient records studied data sensitivity, privacy, integrity, and authorization using blockchain technology, but it didn’t address scalability issues. this challenge of scalability issues exists, especially when dealing with large clinical data records,12 and other types of records that need privacy and consistency. all research speaks about careful data handling with reliable smart contracts. hyperledger fabric framework the hyperledger fabric framework has peer machines with the same data. technically, there is no administrator in a blockchain system, but enterprise applications already have authenticated peers. the blocks hold the data, which fig. 1. process flow diagram. https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.295 3 (page number not for citation purpose) blockchain using hierarchical model in healthcare are otherwise known as immutable ledgers. the respective chaincode, or the smart contract, is common for a particular channel and the peers who join that transaction. the world state stores the other updates of the block data.13 scalability issues are discussed in various models, like the hierarchical model through various abstract models.14–20 this basic network architecture is shown in figure 2, where the organizations are part of the data layer, which includes the fabric. the client application part is the business layer, which interacts with the fabric blockchain and hosts the application for the user. the membership service provider (msp) are credentials used by the peers (p) and committers to participate in the hyperledger fabric network as they are the organization’s identity. the genesis block contains all the msps and the policies. clients authenticate transactions, and peers authenticate the results or endorse them using these credentials. the orderers have a shared communication channel with peers and clients. the broadcast service for messages and transactions takes place here. for scalability, they are implemented as docker containers. the peers maintain the read/write operations. the ordering services are collective nodes that order transactions into a block. the service is common for the overall network and supports pluggable implementations. each member has a cryptographic identity material that is contained within orderers. whenever the client has a transaction request (figure 3), an endorsement by the respective endorsers takes place according to the policy. then, the orderer orders the block and copies the committing peers so all peers get a copy of the latest transaction. healthcare use case this section describes the use case in a healthcare sector that involves an insurance company (ic) handling patient data from multiple hns. statement of the problem when ics need data on patients during a claim, there is a lot of missing information or gaps in the data provided by hospitals. this is due to the traditional relational database management system used to store information in hospitals and related networks. data tampering and human-caused data errors are common problems in such situations. most literature thus far has conceptual ideas that could be implemented in the blockchain. another related issue is scalability as more organizations or hospitals join the network. certain experiments in healthcare21 have only a very basic structure of blockchain implementation with a maximum of two medical organizations joining a single channel. therefore, there is no proof of absolute latency test performed in networks implemented in this sector. hence, the main aim of this paper is to design a model to test the integration of medical records between insurance companies and a large network of hospital groups where patient data mount quickly. use case scenario the key terminologies of the fabric framework are described through the use case. the experiment consists of five organizations with four hns (hn1, hn2, hn3, hn4) and one ic (figure 4). the benchmarking used for the tests is in hyperledger caliper. ideally, many data transfers take place between the insurance companies and the hospitals independently. fig. 2. hyperledger fabric network architecture. msp: membership service provider; o: orderers; org: organization 1,2…n; p: peers. fig. 3. hyperledger transaction flow. msp: membership service provider; o: orderers; p: peers (1, 2,….n). https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.2954 (page number not for citation purpose) lipsa sadath et al. 1. node: ic and each hn is considered as a node where each node joins for transactions through a peer in the network. they are also endorsers of the policy for the transactions. each peer maintains their respective ledger after every transaction. we consider the one hn as a group that has data from their hospital branches (e.g.: chicago, dubai etc.) 2. chaincode: the chaincode or the smart contract contains the business logic between the ic and the hn. there are four chaincodes or smart contracts that are deployed separately for each channel that joins the ic. we consider the basic details of the patients that are needed from hospitals by insurance companies in the following way (figure 5). the basic data structure consists of patient id, department, age, patient name, address, phone number, and bill for services. for ease of use and populating the data, the chaincodes are renamed for deploying into the channels. the patient data are duplicated (figure 6) to handle up to 15,000 transactions at times to check the scalability and performance of the network. 3. endorsement policy: only a few peers are eligible to endorse the transaction proposal. the endorsement policy specifies the required peers for endorsement. in our business logic, we have implemented “or” endorsement where not all peers are required to endorse the request. figure 7 shows that policy as  either org1 or org3 can be endorsed through channel 2. 4. channel: four channels connect the peers of the five organizations (the ic and the four hns). each  channel has a chaincode deployed on it. hence, each channel is governed by the respective  pre-agreed business logic or policy as per the  smart contract. then, the ledger functions are initiated on these smart contracts. when a specific organization wants to conduct business transactions privately, its peers join the channels separately. 5. ordering service: the model uses three orderers for ordering services to give more capacity to the network. this is to prevent any unforeseen situation where the ordering service of one orderer stops. in such situations other orderers take over to avoid any delay in the network. fig. 4. healthcare sector use case diagram. fig. 5. presentation of patient data structure in smart contract. https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.295 5 (page number not for citation purpose) blockchain using hierarchical model in healthcare 6. certificate authority (ca): each peer of an organization (ic and hns) will have their certificate authority. it is the certificate authority service that is first up in the network to establish all the organizations.25 7. ledger: the data is stored in couchdb for the respective organizations. according to the gdpr (general data protection rule)25 personal data should not be revealed in transactions, so the advantage of blockchain is that it is the hash of the data that is stored in the blocks. other data that the transactions can pass on are the treatment and diagnostic details of the patient that need to be checked by the ic. 8. workers: the current experiment considers workers as the clients. the clients can be from the ic or any of the hns. they could be doctors or agents of insurance companies, agents who try to create a transaction, or read a transaction as the business logic or the endorsement policy. 9. fabric gateway: they are a set of libraries that help to invoke the business transaction through smart contracts and the peers for endorsement. after endorsement, the transaction is saved and distributed to other peers through the fabric gateway and the channels. scalability in healthcare use case there are two ways to handle scalability: vertical scaling and horizontal scaling. in horizontal scaling, the load is distributed at peak hours to different temporary servers from the application process interfaces, whereas in vertical scaling, we assume that the capacity of the network must be increased as more organizations join and more transactions are incorporated (tps or transactions per second). however, here in this experiment, we use vertical scaling to understand the performance of the network. experiment and performance analysis experimental setup the fabric blockchain test bed is set up with five organizations carrying one peer each. the raft consensus algorithm is used with three orderers to avoid any delay even when one orderer is down. the endorsement policy requires at least one peer to be an endorser from each organization. the network is set up on a virtual machine with linux ubuntu 20.04 with 6 cpu cores at different instances to check the performance with 16 gb ram. to check scalability and performance, our fabric experiment focuses on the number of organizations, channels, number of tps, number of clients or workers, block intervals, and block sizes checked against the latency and throughput (number of successful transactions). the benchmarking tool used is hyperledger caliper, which calculates the time to create and read the transactions. most experiments22,23 consider the latency analysis using the transaction arrival rates using hyperledger caliper as the benchmarking tool. fig. 6. presentation of data types and data in smart contract. fig. 7. presentation of endorsement policy in smart contract. https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.2956 (page number not for citation purpose) lipsa sadath et al. channel distribution in the network table 1 lists the channel distribution in the network with the ic and hn (hn1, hn2, hn3, hn4). bench marking performance the performance of the network is measured by varying one parameter as a rate controller and keeping other parameters constant. this is done to understand the maximum capacity of the network. the experiment is benchmarked against two popular experiments by xu et al.22 and al-sumaidaee et al. al-sumaidaee et al.21 have performed a hyperledger fabric experiment using the concept of just two medical institutions and performed latency analysis of the network. the experiment did not consider features such as the inclusion of more than two organizations in the network, varying the number of channels, or varying other parameters like block size and block intervals. the major benchmarking we followed from al-sumaidaee et al.21 is mainly varying the number of clients or workers, and steadily increasing the number of transactions and tps to analyze the network capacity. additionally, our experiment follows other criteria set by xu et al.22 to check latency in terms of varying number of channels, block size, and block interval. the benchmarking is performed using hyperledger caliper, which gives four performance indicators in terms of tps. the indicators include the success and failure rates of transactions, latency (average time taken to complete the response), and throughput (average number of transactions/second). this section has the complete experimental setup and analysis with varying parameters. the readings are shown in table format, and the results are compared with al-sumaidaee et al.21 for the first three experiments. from experiment 4 onwards, we do not compare the results with any benchmarking as we are observing our own implementation with five organizations. each parameter impact is shown below through analysis graphs as well. each experiment is performed at least three times, and an average is taken to get a final reading results and discussion the performance of the network shall be measured in terms of three indicators such as latency, throughput, and send rates. we record the latency, throughput and send rates for creating and reading records. in the below experiments, first to test the network, we keep one, that is, channel 1— between ic and hn1 connected to test the impact of other parameters in the network. each experiment that we perform is mentioned with fixed and variable rate controllers, and recorded for their latency, throughput, and send rates to measure the success and failure of the network. rate controllers in the first set of three experiments, we fixed the block  interval = 1s and block size = 50, number of channels = 1, tps = 75, workers = 5. from experiment 1 to experiment 3, we have only one channel with variable rate controllers such as transaction numbers, number of workers, and the tps. for the remaining experiments, we increase the number of channels to understand the channel impact along with tps, block size, and block interval. experiment 1: impact of transaction numbers only transaction numbers (table 2 for create operations and table 3 for read operations) are changed as variable rate controller with five clients or end users as workers. table 1. presentation of channel distribution details in the network according to figure 4 channel number channel distribution 1 ichn1 2 ichn2 3 ic-hn3 4 ic-hn4 ic: insurance company; hn: hospital network. table 2. experiment 1: changing transaction numbers: create operation tx (n) channels (n) transactions (per second) workers (n) throughput latency (ms) 1000 1 75 5 72.5 0.56 5000 1 75 5 74.7 0.56 15,000 1 75 5 74.8 0.58 tx: transactions. table 3. experiment 1: changing transaction numbers: read operations tx (n) channels (n) transactions (per second) workers (n) throughput latency (ms) 1000 1 75 5 74.5 0.01 5000 1 75 5 74.9 0.01 15,000 1 75 5 75 0.01 tx: transactions. https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.295 7 (page number not for citation purpose) blockchain using hierarchical model in healthcare observations the latency is high for create operation. it is observed that both the throughput and latency are higher as the transaction number increases (figure 8). benchmarking benchmarking is against experiments conducted using a similar platform by al-sumaidaee et al.21 with similar indicators but with two organizations only. it is observed that when the number of organizations increases, throughput and latency (figure 8) are affected more when compared to results shown in table 4. experiment 2: impact of number of workers the variable rate controller is the number of workers or clients that join to process the transactions. table 5 shows create operations, and table 6 shows read operations. observations there is a steady decrease in throughput and latency as the number of clients or workers increase for both create and read operations (figure 9). this is a typical situation when end users are more at the network, and access write and read operations are requested by all 5 to 50 clients at the same time. table 5. changing transaction numbers of workers: create operations tx (n) channels (n) transactions (per second) workers throughput latency (ms) 1000 1 75 5 72.2 0.56 1000 1 75 25 64.0 0.99 1000 1 75 50 43.6 2.25 tx: transactions. table 4. evaluation of the impact of transaction numbers.21 org (organization) numbers = 2 create record workers (n) txnumber transactions (per second) latency (ms) throughput send rate 5 1000 75 0.1 75.1 75.4 5 5000 75 0.09 75 75.1 5 15,000 75 0.09 75.1 75 txnumber: the total number of transactions that must be sent. fig. 8. evaluation of the impact of transaction numbers, tps (transactions per second) = 75 from tables 2 and 3, org (organization) numbers = 5: latency and throughput in create and write operations. table 6. changing transaction numbers of workers: read operations tx (n) channels (n) transactions (per second) workers throughput latency (ms) 1000 1 75 5 74.8 0.01 1000 1 75 25 67.4 0.01 1000 1 75 50 56 0.01 tx: transactions. https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.2958 (page number not for citation purpose) lipsa sadath et al. table 8. changing the transactions per second: create operations tx (n) channels (n) transactions (per second) send rate (n) workers (n) throughput latency (ms) 1000 1 75 73.3 5 72.4 0.60 1000 1 150 140.3 5 122.5 1.83 1000 1 250 190.3 5 119.0 3.41 tx: transactions. benchmarking we compare our results with experiments performed by al-sumaidaee et al.,21 where the number of organizations (table 7) is just two, whereas our network supports five organizations. hence, the resources and services are shared between all the five organizations compared to two organizations. it is observed that the latency and throughput of our experiment are severely affected due to the greater number of organizations and workers. experiment 3: impact of increase in transations per second from experiment 1 and experiment 2, we found that throughput and latency are affected as more number of organizations join the network. experiment 3 is conducted by increasing the tps and keeping other parameters steady. send rate is referred to as the number of transactions that are actually sent, though we set the tps to a few values as 75, 150, and 250, as in tables 8 and 9 during the experiment. observations as the tps increases , the latency increases (figure 10) mainly for write operations. read operations give a good throughput and less latency which is almost negligable compared to the write. benchmarking the experiment is benchmarked against al-sumaidaee et al.21 and it is observed that the throughput and send rate are less when the number of organizations increase compared to the fig. 9. evaluation of impact of transaction numbers, tps = 75 from tables 5 and 6, org numbers = 5, latency and throughput in create and read operations. table 7. evaluation of the impact of workers,21 org numbers = 2 create record workers (n) txnumber transactions (per second) latency (ms) throughput send rate (n) 5 1000 75 0.1 75.1 75.4 25 1000 75 0.11 75.6 75.8 50 1000 75 0.76 76.4 76.7 txnumber: the total number of transactions that must be sent. https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.295 9 (page number not for citation purpose) blockchain using hierarchical model in healthcare two organizations in the benchmarked experiment. txnumber is the total number of transactions that must be sent. the throughput in transaction creations fell drastically compared to the benchmarking al-sumaidaee et al. (table 10).21 experiment 4: impact of channel numbers (tps = 75) here, the rate controller is set as the number of channels. we add more channels to make sure step by step, the ic communicates in parallel with hn1, hn2, hn3, and hn4, respectively. for this, we do not keep any benchmarking with al-sumaidaee et al.21 as they do not have multiple channels. from experiment 4 onwards, we escalate the experiment (tables 11 and 12) to check more performance according to our experiment model only. observations though all readings were taken each time a channel joined the ic, we observed the difference in the performance of channel table 9. changing transactions: read operations tx (n) channels (n) transactions (per second) send rate (n) workers (n) throughput latency (ms) 1000 1 75 73.3 5 74.6 0.01 1000 1 150 140.3 5 146.7 0.01 1000 1 250. 190.3 5 234.9 0.01 tx: transactions. table 11. changing the number of channels: create operations tx (n) channels (n) transactions (per second) send rate (n) workers (n) throughput latency (ms) 5000 1 75 74.6 5 74.4 0.67 5000 2 75 72.9 5 49.0 28.24 5000 3 75 74.8 5 74.6 0.65 5000 4 75 74.8 5 74.5 0.70 txnumber: the total number of transactions that must be sent. table 10. evaluation of transactions per second increase,21 org numbers = 2 transactions (per second) txnumber workers (n) latency (ms) throughput send rate (n) 75 1000 5 0.1 75.1 75.4 150 1000 5 0.08 149.6 150.8 250 1000 5 0.1 164.6 251.1 txnumber: the total number of transactions that must be sent. fig. 10. evaluation of impact of transaction numbers, tx = 1000 from tables 8 and 9, org numbers = 5, latency and throughput in create and read operations. https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.29510 (page number not for citation purpose) lipsa sadath et al. 1 each time for create (table 11) and read (table 12). in this scenario, we try to check if the addition of more channels affects the performance of channel 1, though any channel can go slow in the process. it is observed that as channel 2 [hn2] joined to ic, there is a sudden latency (28.24 ms) experienced in the network (table 11) and a fall in throughput to 49.0. this could be due to several clients’ requests being processed at the same time and the network sharing the resources parallelly all of a sudden. as always, the latency is higher in create than in read operations. experiment 5: impact of channel numbers (with tps = 100) the next effort is to understand how the addition of channels affect the network as tps changes to 100 from 75 (experiment 4). we conduct the experiment for both create (table 13) and read (table 14) operations. observations similar to the pattern that is observed for tps = 75 (table  11), it is noted that during the write operation, while the second channel joins there is a sudden latency experienced (16.43 ms) in the network. overall latency increases as the number of channels increases from one through four. it is also observed that a high rate in tps also affects the network (tps = 75 to tps = 100) (figure 11). experiment 6: impact of block interval (tps = 75) block interval is the time taken to create a new block. with all the changes being observed, it is essential to now learn if there is any impact on the block intervals. therefore, we set the block interval to 2s from 1s, with the block size continuing to be 50 itself for create (table 15) and read (table 16) operations. observations compared to experiment 4 with bt = 1s, it is observed that there is less latency in the network even with more number of channels joining when the batch time or block interval increases to 2s (figure 12). but there is no evidence of a heavy change in throughput. figure 12 shows a comparison in latencies for write operation for bt = 1s and bt = 2s for channel numbers increasing from 1 through 4. experiment 7: impact of block size (tps = 75, block time = 2s) it is essential to understand the impact of block size in the network. hence, we keep block size as the rate controller in experiment 7 for create (table 17) and read (table 18) operations. observations it is observed that with an increase in bs (100), the throughput is steady. and the latency reduces compared table 12. changing the number of channels: read operations tx (n) channels (n) transactions (per second) send rate (n) workers (n) throughput latency (ms) 5000 1 75 74.7 5 74.7 0.01 5000 2 75 74.7 5 74.6 0.02 5000 3 75 75.0 5 74.9 0.01 5000 4 75 74.9 5 74.9 0.01 table 14. changing the number of channels. read operations tx (n) channel transactions (per second) send rate (n) workers throughput latency (ms) 5000 1 100 99.7 5 98.8 0.95 5000 2 100 99.0 5 60.5 16.43 5000 3 100 99.7 5 99.4 0.74 5000 4 100 99.7 5 97.4 1.39 table 13. changing the number of channels: create operations tx (n) channel transactions (per second) send rate (n) workers throughput latency (ms) 5000 1 100 99.3 5 98.8 0.95 5000 2 100 97.6 5 60.5 16.43 5000 3 100 99.7 5 99.4 0.74 5000 4 100 99.6 5 97.4 1.39 https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.295 11 (page number not for citation purpose) blockchain using hierarchical model in healthcare to bs = 50. even when more number of channels joined, the throughput is not much affected when the block size is increased, but there is an increase in latency when the block size increases (figure 13). this section completes a detailed analysis of the proposed two-layer hierarchical model. we experiment with the entire network with a total of five organizations. the prime one is the ic, and all the four hns them. each channel is deployed with its own smart contract. the performance and latency analysis of the network are benchmarked and evaluated against a two-organization network that has only two channels. the study varies the parameters and uses different rate controllers during the experiment. it is observed that as the number of organizations, channels, workers, and transactions increases, the latency of the network and the performance gradually falls. effectively we conclude that vertical scaling is recommended for any organization that needs to improve the scalability of their network. table 15. changing block interval = 2s, transactions per second = 75: create operations tx (n) channel transactions (per second) send rate (n) workers throughput latency (ms) 5000 1 75 74.6 5 74.5 0.63 5000 2 75 74.8 5 74.6 0.62 5000 3 75 74.9 5 74.6 0.57 5000 4 75 74.6 5 74.4 0.61 tx: transactions. fig. 11. evaluation of latency for tps (transactions per second) = 75 and tps = 100 (tables 11 and 13) in write operation with the number of channels increased from 1 through 4. table 16. changing block interval = 2s: read operations tx (n) channel transactions (per second) send rate (n) workers throughput latency (ms) 5000 1 75 74.8 5 74.8 0.01 5000 2 75 74.9 5 74.9 0.01 5000 3 75 74.9 5 74.7 0.01 5000 4 75 74.8 5 74.8 0.01 tx: transactions. https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.29512 (page number not for citation purpose) lipsa sadath et al. discussion impact on healthcare while we evaluate the performance of the entire model in terms of scalability, we would like to comment on how this entire exercise is useful for the healthcare community. as more users, clients, transactions, and patient data increase in the network, it becomes important to understand the capacity of the organization’s network (ic in the experiment). this study does not cover trust issues as we already understand bct guarantees the security of patient data that is abundantly floating between insurance companies and different hns. hence, we deal with scalability issues only in such systems that require vertical scaling, that is, increasing the capacity of the network. so, if more clients (workers in the experiment) post more patient transactions, the network could be scaled to handle such situations. otherwise, as shown in the experiment, there could be severe latency as more hospital groups join, and the network might crash. related works in blockchain the technology is being widely tested across many use cases like supply chains for agriculture sustainiability21 fig. 12. evaluation of block interval (1s and 2s) for tps = 75 (tables 11 and 15) in write operation with the number of channels increase from 1 through 4. tps: transactions per second. table 17. changing block size = 100, block interval or block time = 2s: create operations tx (n) channel transactions (per second) send rate (n) workers (n) throughput latency (ms) 5000 1 75 74.8 5 74.5 0.67 5000 2 75 74.9 5 74.6 0.65 5000 3 75 74.7 5 74.5 0.67 5000 4 75 74.9 5 74.4 0.66 tx: transactions. table 18. changing block size = 100. block interval or block time = 2s: read operations tx (n) channel transactions (per second) send rate (n) workers (n) throughput latency (ms) 5000 1 75 74.9 5 74.9 0.01 5000 2 75 74.8 5 74.8 0.01 5000 3 75 74.9 5 74.9 0.01 5000 4 75 74.8 5 74.8 0.01 tx: transactions; tps: transactions per second. https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.295 13 (page number not for citation purpose) blockchain using hierarchical model in healthcare and semiconductor industry26 and other areas27 for performance analysis. bag et al. conducted a study on how bct can be effective in small and medium enterprises (smes).28 it is not always the performance analysis, but as research is delving more into this area, there is confidence now shown by organizations to implement blockchain technology. another sector that recently made an impact was research in the automotive industry.29 while most studies are still progressing in this technology, keeping track of sustainability and supply chain, interesting use cases from different parts of the world like jordan are impressive.30 enormous studies have also been conducted in healthcare supply chain systems using blockchain technology.31 though all sectors are trying to learn the implementation of blockchain, it is the supply chain that is mainly trying to research from all ends to check the resilience of the technology.32 conclusion in this work, a two-level hierarchical model is implemented using hyperledger fabric as a solution to the scalability issues faced in bct in the healthcare sector. detailed performance analysis of the model is done by varying the number of transactions, workers or clients, channels, block size, and block intervals. when more channels are added, the major performance analysis on latency is made on the records creation operations rather than on read operations. this is because the read operations mostly show a negligible amount of latency. the benchmarking tool used for the analysis is hyperledger fabric. each channel has a patient contract deployed on it. the entire experiment tries to analyze how more patient data can be managed at hospitals while interacting with insurance companies for insurance claims. when block interval increases, there is less latency experienced, but it is studied that the block size increase is not recommended as there is higher latency experienced. though more readings and experiments were conducted in this study, limited tables are included to show the performance of the network through worker size, channels, block size, and block intervals. the current model has only one smart contract implemented per channel. the main limitation of the experiment is memory capacity. during the experiment at different stages, we scaled our network vertically, and it was observed that as we scale more, more organizations can be added to the network. hence, the study emphasizes doing further experiments to check the maximum ability of a healthcare system network. future work one major challenge that might occur in a running real-time network could be that the network might crash if the scalability issues during heavy transactions are not foreseen. the initial overhead and usage of more memory during major transactions could be high. however, this is required to protect patient data and make sure smooth and effective implementations are deployed to handle healthcare data well in advance. future work will extend to further test the scalability of the network to the maximum, and the implementation of more organizations in the proposed hierarchical model to maintain more levels of hns. fig. 13. evaluation of block size (50 and 100) for tps = 75 (tables 13 and 15) in write operation with the number of channels increase from 1 through 4. tps: transactions per second. https://doi.org/10.30953/bhty.v7.295 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.29514 (page number not for citation purpose) lipsa sadath et al. funding the was no funding for the 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provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/ licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.295 https://arxiv.org/abs/1808.07338v1 https://doi.org/10.1080/13675567.2022.2088707 https://doi.org/10.1080/13675567.2022.2088707 https://doi.org/10.1108/imds-12-2021-0784 https://doi.org/10.1108/imds-12-2021-0784 https://doi.org/10.1108/ijlm-01-2022-0017 https://doi.org/10.1108/ijlm-01-2022-0017 https://doi.org/10.1007/s10479-021-04129-6 https://doi.org/10.1007/s10479-021-04129-6 https://doi.org/10.1108/mrr-04-2022-0298 https://doi.org/10.1108/mrr-04-2022-0298 https://doi.org/10.1108/meq-02-2022-0025 https://doi.org/10.1016/j.tre.2022.102773 https://doi.org/10.1016/j.tre.2022.102773 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 citation: blockchain in healthcare today 2024, 7: 295 https://doi.org/10.30953/bhty.v7.29516 (page number not for citation purpose) lipsa sadath et al. appendix. key terminologies of the fabric framework are described through the use case.* terminology defined application node • the ic and each hn are considered as a node where each node joins for transactions through a peer in the network. • they also endorse the policy for the transactions. • each peer maintains their respective ledger after every transaction. • we consider the one hn as a group that has data from their hospital branches (e.g., chicago, dubai, etc.). chaincode • the chaincode (aka smart contract) contains the business logic between the ic and the hn. • four chaincodes are deployed separately for each channel that joins the ic. • the basic data structure consists of patient id, department, age, patient name, address, phone number, and bill amount (table 5). • for ease of use and populating the data, the chaincodes are renamed for deployment into the channels. • patient data are duplicated (figure 6) to handle up to 15,000 transactions at times in order to check the scalability and performance of the network. endorsement policy • the endorsement policy specifies the required peers needed for endorsement. • in our business logic, we implemented “or” endorsement, where not all peers are required to endorse the request. figure 7 illustrates a policy that either org1 or org3 can endorse through channel 2. • only a few peers are eligible to endorse the transaction proposal. channel • four channels connect the peers of the five organizations (i.e., the ic and the four hns). • each channel is assigned a chaincode. • hence, each channel is governed by the respective pre-agreed business logic or policy as per the smart contract. • then, the ledger functions are initiated on these smart contracts. • when a specific organization wants to transact business privately, its peers join the channels separately. ordering service • in the model, three orderers request services to provide greater capacity to the network. • this prevents unforeseen situations where the ordering service of one orderer stops. • in such situations. other orderers take over to avoid network delays. certificate authority • each peer of an organization (ic and hns) has their ca. • ca is first up in the network to establish all the organizations. ledger • the data are stored in couch db for the respective organizations. • according to the gdpr, 25 personal data should not be revealed in transactions. • accordingly, the advantage of blockchain is that it is the hash of the data stored in the blocks. • other data the transactions can pass on are the treatment and diagnostic details of the patient that must be checked by the ic. workers • the current experiment considers workers as the clients which come from the ic or any of the hns. • clients can be doctors or ic agents who attempt to create a transaction or read a transaction as the business logic or the endorsement policy. fabric gateway • a set of libraries that help invoke the business transaction through sc and the peers for endorsement. • following endorsement, the transaction is saved and distributed to other peers through the fabric gateway and the channels. *the authors propose a hierarchical model within the hyperledger fabric enterprise application, focusing on the healthcare sector as a use case. **patient data structure in a smart contract is illustrated in figure 5. ca: certificate of authority; couch db: a clustered database that allows running a single logical database server on any number of servers; gdpr: data protection rule; hn: hospital network; ic: insurance company; id: identification; or: endorsement where not all peers are required to endorse the request. org: organization number (e.g., org1). figure 7 shows that policy as either org1 or org3 can be endorsed through channel 2.; sc: smart contract. https://doi.org/10.30953/bhty.v7.295 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 narrative/systematic reviews/meta-analysis ethics of blockchain by design: guiding a responsible future for healthcare innovation muthu ramachandran, phd1,2 1forti5 tech ltd., london, england; 2centre for augmented intelligence and data science (caids), school of computing, college of science, engineering and technology, university of south africa, pretoria, south africa corresponding author: dr. muthu ramachandran, email: muthuram@ieee.org doi: https://doi.org/10.30953/bhty.v7.362 abstract the rapid evolution of blockchain technology in healthcare presents unparalleled opportunities for advancements, including enhanced patient data security, decentralized systems for trustless operations, and transparent supply chain management. however, as blockchain reshapes the healthcare landscape, it demands a robust ethical framework that guides its design and implementation. “ethics of blockchain by design” emphasizes embedding ethical principles at the heart of blockchain innovation, fostering public trust, equity, and longterm societal benefits. in this article, the author proposes a set of best practices guidelines on the ethics of blockchain by design. plain language summary this paper explores the ethical challenges and opportunities of using blockchain technology in healthcare, emphasizing the need for responsible design. blockchain can not only improve data security, transparency, and patient trust but also raises concerns about inequality, access, and unintended consequences. the author proposes an ethical framework to guide the development and use of blockchain in healthcare, ensuring it aligns with principles like fairness, inclusivity, and accountability. by involving diverse stakeholders and prioritizing human-centric design, this study aims to foster innovation that benefits while minimizing harm. the findings highlight the importance of considering equity and societal impact in healthcare technology. this study is conceptual and does not include empirical data or case-specific applications. the ethical framework proposed is based on a synthesis of existing literature and theoretical analysis, which may not capture the full diversity of perspectives or real-world complexities in implementing blockchain systems across varied healthcare contexts. future work should consider field-specific studies, practical deployments, and stakeholder-driven research to validate and refine the framework, ensuring its applicability across diverse healthcare environments. received: november 12, 2024; accepted: november 26, 2024; published: december 16, 2024 the ethical imperative in blockchain development the applications of blockchain in healthcare, from immutable patient records to efficient clinical trial management, illustrate its transformative potential.1,2 however, this potential raises significant ethical challenges, including data privacy, patient autonomy, governance, and accessibility. as highlighted by zwitter and boisse despiaux,3 ethical frameworks are essential to ensure that emerging technologies do not inadvertently harm those they aim to serve. to develop ethically sound systems, blockchain must prioritize data protection, equitable access, and transparent governance structures. shah and de filippi4 argue that data permanence, a hallmark of blockchain’s immutability, creates ethical concerns surrounding patients’ right to amend or remove their data. mechanisms that respect individual autonomy while maintaining system https://orcid.org/0000-0002-5303-3100 mailto:muthuram@ieee.org https://doi.org/10.30953/bhty.v7.362 citation: blockchain in healthcare today 2024, 7: 362 https://doi.org/10.30953/bhty.v7.3622 (page number not for citation purpose) muthu ramachandran transparency and security are critical. figure 1 illustrates key ethical dimensions such as privacy, security, governance, data sovereignty, and inclusivity, showing their interconnected nature within a healthcare blockchain system. the ethical dimensions of blockchain design—privacy, security, governance, data sovereignty, and inclusivity— are deeply interconnected. as illustrated in figure 1, the effectiveness of ethical frameworks relies on addressing these dimensions holistically rather than in isolation. each component influences and shapes the others, emphasizing the need for a comprehensive, integrated approach to ethical blockchain design. figure 1 illustrates the concept of an ethical blockchain in healthcare, organized into a circular model emphasizing interconnected principles and outcomes. at its center is the main idea: leveraging blockchain technology to address ethical challenges in healthcare. surrounding this core are the core ethical dimensions that underpin its implementation, including (patient-controlled access to their data), security (encryption and decentralized identities for protection), governance (smart contracts enabling stakeholder consensus), inclusivity (ensuring multi-language accessibility), and data sovereignty (compliance with local jurisdiction laws for data storage). these ethical dimensions lead to tangible outcomes, such as enhanced patient trust in the system, adherence to regulatory compliance, and greater accessibility for users. this model provides a holistic framework for integrating blockchain into healthcare ethically and effectively. by embedding these values into the technological core, stakeholders can ensure that blockchain solutions in healthcare uphold transparency, fairness, and human rights, fostering public trust and enhancing patient outcomes. privacy, security, and decentralization privacy and security remain paramount in blockchain healthcare applications. dagher et al.5 argue that protecting patient data from breaches and misuse requires robust cryptographic methods and decentralized access control mechanisms. however, decentralization presents challenges regarding shared responsibility and governance among network participants. kumar et al.6 propose that ethical frameworks must incorporate controls like encrypted keys, pseudonymization, and consent-based smart contracts. decentralized architectures empower patients through transparency and data control. however, as noted by werbach,7 decentralized systems often pose ethical questions regarding governance and accountability. decentralized autonomous organizations (daos) can provide democratic governance models that emphasize fairness, accountability, and diverse stakeholder input. incorporating explainability principles for blockchain-based systems, as explored in ramachandran,8,9 is essential to ensure decisions made by autonomous processes can be understood and evaluated by human stakeholders. alignment with international regulations ensuring that blockchain systems align with international regulations is vital for their ethical and legal implementation in healthcare. frameworks like the general data protection regulation (gdpr) in europe and the health insurance portability and accountability act of 1996 (hipaa)10 in the united states provide stringent guidelines for data protection and privacy. by addressing these regulatory requirements, blockchain systems can uphold ethical principles while fostering trust among stakeholders. gdpr compliance: off-chain storage and patient consent the gdpr mandates that individuals have control over their personal data, including the “right to be forgotten,” which conflicts with blockchain’s immutable nature.11–13 to reconcile this, ethical blockchain frameworks can adopt fig. 1. ethical dimensions of blockchain by design in healthcare. https://doi.org/10.30953/bhty.v7.362 citation: blockchain in healthcare today 2024, 7: 362 https://doi.org/10.30953/bhty.v7.362 3 (page number not for citation purpose) blockchain: guiding the future for innovation off-chain storage for sensitive data. in this model, only references or hashes of the data are stored on the blockchain, while the actual data reside in secure, modifiable storage off-chain. if data must be updated or deleted, the hash becomes obsolete without altering the blockchain’s integrity. patient consent mechanisms are another gdpr-compliant feature enabled by blockchain. patients can grant or revoke access to their records through smart contracts, ensuring explicit, informed consent for every data transaction. for example, a patient could permit access to their health data for a specific duration or purpose, aligning blockchain functionality with gdpr’s transparency and accountability principles. hipaa compliance: encryption and permissioned access the hipaa focuses on protecting protected health information (phi) by mandating safeguards like encryption and role-based access controls.14 blockchain systems inherently support encryption, ensuring that phi is only accessible to authorized parties. advanced techniques, such as homomorphic encryption, allow healthcare providers to perform computations on encrypted data without exposing sensitive information, maintaining compliance with hipaa’s security standards. additionally, permissioned blockchain networks enable role-based access. unlike public blockchains, permissioned systems ensure that only verified stakeholders, such as healthcare providers, patients, and insurers, can interact with the data. smart contracts further enhance compliance by automating access permissions, ensuring adherence to hipaa’s minimum necessary standard. case study: estonia’s national blockchain system estonia has become a global leader in blockchain-driven healthcare systems, providing a practical example of regulatory compliance in action.15 the country’s ehealth system uses blockchain to secure over 95% of citizens’ health data. by integrating off-chain storage for sensitive data and blockchain-based logging for access transparency, estonia aligns its system with gdpr while ensuring patient trust. citizens can track who accessed their data and for what purpose, exemplifying a human-centric approach to blockchain implementation. data sovereignty, inclusivity, and accountability data sovereignty is critical for ethical blockchain applications. haque et al.16 and lindman et al.17 state that patients should control their data and decide its usage, fostering trust and autonomy. inclusivity should also be a guiding principle, ensuring blockchain benefits all populations and does not exacerbate existing healthcare disparities (e.g., frameworks such as the quality framework for explainable artificial intelligence (ai))17,18 offer tools to ensure accessibility and equitable engagement. accountability remains a critical challenge in decentralized systems, where responsibility for decisions is dispersed. raval19 states that ethical-by-design frameworks must include clear accountability structures to ensure network participants adhere to established standards. best practice guidelines for ethics of blockchain by design to support ethical blockchain development in healthcare, ramachandran8,9,18 proposes the following best practices, building on established research and frameworks such as the secure and sustainable software engineering framework for healthcare blockchain applications (s3efhbca)5 and ai-blockchain frameworks,18 which include the following concepts. principle of data ownership and consent patients should maintain ownership of their data and retain control over its use and sharing. real-time consent management systems embedded within blockchain-based healthcare applications offer one way to ensure patient autonomy. privacy-preserving mechanisms incorporating privacy-preserving cryptographic protocols and security measures ensures that patient data remain confidential and secure.5 the s3ef-hbca framework focuses on sustainable and secure healthcare blockchain systems.8 equitable access and inclusivity lindman et al.17 state that blockchain systems must be accessible to all populations, and mitigating healthcare disparities and ensuring inclusivity is a core value. this aligns with the ethical principles outlined in explainable ai frameworks to ensure interpretability and equitable decision-making.9 transparent and accountable governance governance mechanisms should be transparent, allowing for democratic participation from all stakeholders.3,7 ethical frameworks should prioritize decentralized, inclusive governance models such as daos. interoperability and sustainable design systems must integrate seamlessly with existing healthcare infrastructure without compromising security or sustainability. approaches like ai-blockchain integrated frameworks can enhance system interoperability while promoting secure data exchange.18 figure 2 illustrates “best https://doi.org/10.30953/bhty.v7.362 citation: blockchain in healthcare today 2024, 7: 362 https://doi.org/10.30953/bhty.v7.3624 (page number not for citation purpose) muthu ramachandran practice guidelines for blockchain ethics” in healthcare, depicted as an interconnected framework highlighting key ethical dimensions and practices. the proposed best practices for ethical blockchain development in healthcare provide a comprehensive framework to guide the design, implementation, and governance of blockchain-based systems. by upholding principles of data ownership, privacy, equitable access, transparent governance, and sustainable interoperability, these guidelines ensure that blockchain technology is leveraged in a manner that empowers patients, protects sensitive information, and promotes inclusive and accountable healthcare services. as the adoption of blockchain in the medical sector continues to grow, adherence to these ethical considerations will be crucial in realizing the full transformative potential of this technology while safeguarding the rights and wellbeing of patients. ongoing research and collaboration among healthcare stakeholders, technologists, and ethicists will be essential to further refine and operationalize these best practices, ultimately shaping the ethical development of blockchain in the healthcare domain. conclusion: moving toward an ethical blockchain future in healthcare “ethics of blockchain by design” calls on developers, healthcare professionals, policymakers, and stakeholders to collaborate on ethical innovation. tsanidis20 proposes that by embedding ethics into every phase of blockchain system design and regulation, we can protect patient autonomy, foster trust, and maximize blockchain’s potential for social good. ethics are not a barrier to innovation but a catalyst for responsible technology development. it ensures that blockchain systems align with human dignity, uphold the mission to “do no harm,” and enhance health outcomes globally. through thoughtful design, ethical governance, and continuous evaluation, we can build blockchain solutions that truly serve patients’ needs. future research directions while blockchain offers transformative potential for healthcare, scaling ethical solutions globally presents significant challenges. future research should focus on several key areas. fig. 2. best practice guidelines for blockchain ethics illustrating the interconnected framework, highlighting key ethical dimensions and practices. https://doi.org/10.30953/bhty.v7.362 citation: blockchain in healthcare today 2024, 7: 362 https://doi.org/10.30953/bhty.v7.362 5 (page number not for citation purpose) blockchain: guiding the future for innovation scaling ethical solutions globally implementing blockchain across diverse healthcare systems requires accommodating varying levels of infrastructure, technological maturity, and regulatory frameworks. research should explore modular and adaptive blockchain frameworks that can be tailored to both high-resource and low-resource settings. this includes simplifying deployment processes and reducing costs to ensure accessibility. blockchain integration with ai and the internet of things the integration of blockchain with emerging technologies like ai and the internet of things (iot) promises enhanced interoperability and predictive analytics in healthcare. however, ethical considerations, such as bias in ai models or privacy risks in iot device data, must be addressed. future studies should focus on designing governance frameworks that balance innovation with ethical safeguards. for example, ai-driven diagnostic tools can use blockchain for secure data sharing and model transparency.18 addressing equity and the digital divide blockchain solutions risk exacerbating existing inequities if underserved populations lack access to the necessary technology or infrastructure. research must prioritize inclusive blockchain designs that address the digital divide by: 1. supporting low-bandwidth networks. 2. designing user-friendly interfaces for populations with limited digital literacy. 3. partnering with governments and non-governmental organizations (ngos) to subsidize access to blockchain-based healthcare tools. by addressing these areas, the healthcare community can advance blockchain’s potential while ensuring it serves as an equitable and ethical tool for global health innovation. funding the author did not receive support from any organization for the submitted work. financial and non-financial relationship and activities this article is an individual contribution of the author. there are no relevant relationships to report. contributor the author is responsible for all aspects of the article. application of ai-generated text or related technology chatgpt4o was used to check for grammatical errors, rewrite, and proofread some sections in this article. data availability statement (das), data sharing, reproducibility, and data repositories the data that support the findings of this study are openly available in the published literature. references 1. kuo t-t, kim h-e, ohno-machado l. blockchain distributed ledger technologies for biomedical and health care applications. j am med inform assoc. 2017;24(6):1211–20. https://doi. 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harnessing bitcoin’s blockchain technology. o’reilly media; 2016. 20. tsanidis c. ethical frameworks for blockchain governance. technol soc. 2019;21(3):49–63. copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.362 https://doi.org/10.24251/hicss.2017.185 https://doi.org/10.24251/hicss.2017.185 https://doi.org/10.2298/fuee240​1169r http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 abstract over the past 50 years, although categorized as the “information age” or “digital age,” the vast amounts of digitized data have been sorely underutilized. only recently, in response to the covid-19 pandemic, efforts have accelerated to harness these data using blockchain technology as it pertains to healthcare. today, through the blockchain infrastructure and its tokenization applications, we are able to leverage healthcare data effectively into more efficient business processes. in addition, we can secure better patient engagement and outcomes, while generating new revenue streams for an array of healthcare stakeholders. it is in the application of blockchain technology to compile these stockpiled data into new, compliant business models that we can reap the full potential of the blockchain. here are predictions by members of the bhty editorial board members on how we might further advance the role of blockchain in healthcare in 2023. submitted: 12 december 2022; accepted: 19 december 2022; published 06 january 2023 monetization, consumerization of health data, and the metaverse talisha shine, mba, cbe and jane thomason, phd, msc the covid-19 pandemic was an accelerator for blockchain in healthcare. we saw rapid advances in blockchain in information exchange, pandemic prediction, tracking, supply chain integrity, provenance, and payments. the covid-19 pandemic removed barriers and accelerated the drive to care for patients remotely. we have become more reliant on data, and this trend will continue. in the next few years, we will witness the monetization of health data through health data marketplaces, the consumerization of healthcare, and the growth of metahealth. when combined with rapidly growing smart phone penetration and increasing adoption of smart wearable devices, web 3.0, “the internet of value,” will underpin significant changes in healthcare. access to medical data is needed for training artificial intelligence (ai), drug development, scientific discovery, medical research, and precision medicine. yet, much medical data are not being used or monetized. blockchain and non-fungible tokens (nfts) allow people to exchange value on a decentralized network. humans are also being digitalized with new devices, apps, and monitoring technologies that generate extraordinary volumes of data. this will enable data owners to monetize their data, leading to the development of health data marketplaces, which connect and monetize data for data owners, making it available for scientific discovery. the progress in self-sovereign identity will make it possible for individuals to monetize their health data in the future.1 consumerization of healthcare will create opportunities for new players with low-cost, convenient services. people will expect on-demand healthcare, and consumers will want to access their health records literally in the palm of their hands. health data will become the new healthcare currency, leading to operational savings, improved treatment effectiveness and safety, faster diagnosis, and the possibility of personalized medicine. the growing focus on data analytics will turn medical devices into commodities, with patient-level data and the surrounding analytics becoming an essential source of revenue for digital health companies. editorial, discussion blockchain in healthcare: 2023 predictions from around the globe talisha shine, mba, cbe1*, jane thomason, phd, msc2 , imtiaz khan, phd3 , mohamed maher, mba3 , kohei kurihara4, and osama el-hassan, phd5 1hashed health, nashville, tennessee, usa; 2university college london, centre for blockchain technology, uae; 3cardiff metropolitan university, cardiff, uk; 4privacy by design lab, chiyoda-ku, tokyo, japan; 5dubai health authority, al jaddaf, uae *corresponding author: talisha shine, email: tshine3@gmail.com keywords: consumerization, digital twins, healthcare exchange, health data, metaverse, monetization, privacy, wearable devices https://orcid.org/0000-0003-2651-2293 https://orcid.org/0000-0001-7624-1319 https://orcid.org/0000-0003-2130-7717 mailto:tshine3@gmail.com citation: blockchain in healthcare today 2022, 5: 245 http://dx.doi.org/10.30953/bhty.v5.2452 (page number not for citation purpose) talisha shine et al. another significant change to watch will be the metaverse, the next iteration of the internet, as an immersive, always-on experience. the metaverse is an amalgamation of blockchain, virtual reality (vr, using a real-world setting), augmented reality (ar, an immersive virtual environment), as well as mobile and computer technologies. in the future, we will increasingly see the metaverse used to change, enhance, and transform healthcare. the leading use cases are collaborative working, education, clinical care, wellness, and monetization.2 in non-communicable diseases, there is potential to deploy the metaverse using gamification and incentives, as well as for education and care.3 future healthcare metaverse ecosystems are being built, and surgeons have already performed ar procedures on live patients.4 web 3.0 open metaverse will democratize access, allow peer-to-peer transactions, and give everyone free access to this immersive space. wearable devices and digital twins imtiaz khan, phd and mohamed maher, mba in recent years, we witnessed a sharp rise of wearable devices, like the smart watch, capable of measuring key health parameters with high precision in real-time.5 despite the growing use of smart watches and similar healthcare wearable devices,6 in the context of smart and connected healthcare aspirations of healthcare 4.0, the opportunities and transformative power the smart watch derived data (swdd) are yet to be realized. in coming years, we will begin to realize the value proposition of swdd, as well as how blockchain-like distributed and decentralized technologies can make that realization a reality. under traditional healthcare systems, ownership and stewardship of health data lie with healthcare service providers and are considered as the data producer. with swdd, the paradigm is reversed by replacing these providers with individuals as the data producer. from a health data consumer perspective (i.e. medical professionals, researchers, pharmaceuticals, and policy makers), the availability of high-volume real-time health data along with environmental parameters (e.g. location, time of day, weather) will forge a new type of data that can provide a holistic spatiotemporal view at personal level. these real-time contextual personal data will bring a paradigm shift in terms of value creation, since different machine learning and analytical techniques can be utilized for prognosis, discovery, diagnosis, treatment, and follow-up purposes. traditional cross-sectional and retrospective health data limit the use of machine learning algorithms and are confined to statistical analysis that has limited predictive and decision-making capability. with the change of ownership and new role as data producer, individuals will start to perceive swdd as nft-like digital assets. this perception will create the demand to establish a digital health data marketplace,7 where, similar to the accommodation sharing economic model of airbnb, individuals can earn revenue by sharing their swdd. realtime contextualized swdd will also enable us to create industry 4.0 digital twins for healthcare 4.0 as well as avatars in the metaverse. these digital twins and avatars will provide a new ai-assisted approach for monitoring and measuring our physical and mental health. asymptomatic diseases, such as stroke or mental health diseases like depression (a leading cause of disability, worldwide),8 will benefit from this approach, as new insights about the progression of these diseases will be revealed. blockchain technologies will be the enabler for the creation of such a marketplace. smart contracts will ensure fair distribution of revenues within the stakeholders, cryptography—privacy and security and cryptocoin— global micro transactions of revenues. the competition ethos of a free market economy will promote data qualit, while participation of global community promotes quantity health data. availability, accessibility, and ability to choose the right dataset at right time will boost medical services and discovery. privacy of blockchain in healthcare kohei kurihara in 2023, privacy will become one of the most significant topics, with blockchain in healthcare deploying new services at practical levels. on the application layer, some healthcare services were concerned with selling patient data for commercial purposes. under the research of duke clinical research institute, duke university, durham,9 the medical privacy regulation uncertainty covers the fundamental protection to share data with the first party against the restriction to disclose the patient’s agreement. health insurance portability and accountability act (hippa) of 1996 (a u.s. federal law requiring creation of national standards to protect sensitive patient health information from being disclosed without the patient’s consent or knowledge) and general data protection regulation (gdpr, a european union regulation on data protection and privacy) undermine the healthcare surveillance economy, but decentralized application is also applied to the legitimate requirement. earlier this year, a blog from the u.s. federal trade commission announced “the commission is committed to using the full scope of its legal authorities to protect consumers’ privacy.10 we will vigorously enforce the law if we uncover illegal conduct that exploits americans’ location, health, or other sensitive data and investigate illegal action regardless of decentralized infrastructure, but prioritize the consumer safety and concerns. http://dx.doi.org/10.30953/bhty.v5.245 citation: blockchain in healthcare today 2022, 5: 245 http://dx.doi.org/10.30953/bhty.v5.245 3 (page number not for citation purpose) blockchain in healthcare: 2023 predictions from around the glob healthcare data are no longer the information from hospitals and medical care centers that is censored in our home or physical devices such as smart watches. location data become more sensitive to use for commercial purposes. blockchain has the capacity to integrate, under a secure decentralized network, an application causing the claim from regulatory authorities. to predict the risk against healthcare data abuse, blockchain experts should consider multiple actions in 2023 as follows: 1. proper risk assessment containing application layer 2. third party and network evaluation 3. cross-border healthcare data transfer risk assessment is not sufficient with security checkboxes because risk categories are diversified and enlarge the definition of sensitive data. a few years ago, location data were not a pain point for commercial utilities, but regulatory authorities have changed their stance for vigorous enforcement in accordance with the transition from social feedback. compliance is the methodology for the patients to access healthcare service safeguards. in addition, application providers should reconsider the definition of compliance with an eye on diversifying products. patients will find that third party connections will be beneficial for monitoring their status and for personalized medical care with comprehensive data integration, although they prohibit sharing the patient’s data with commercial third parties such as advertisers or e-commerce platforms. blockchain has an essential benefit to develop a wider network on a holistic system and process the data automatically. of course, this system is superior to a centralized database but embraces the impact with wrong connections. cross-border healthcare data become realistic by lower server cost on the cloud; however, cloud systems rely on local regulation and government action to access corporate databases in case of national emergency. the eu and usa have promised to commit to a mutual relationship to create a new framework in consecutive data flows based on the same level of data protection. blockchain may have to follow when it comes to the selection on chain or off chain to store patient data. it explicitly falls on the healthcare industry to use patient data under different jurisdictions. changes in 2023 will exhibit an important shift to the application level in order to apply new rules of regulatory regimes. blockchain enabled patient-mediated healthcare exchange osama el-hassan, phd today, we still have very few convincing blockchain enabled solutions for healthcare. this is due to the complexity of the domain and the nature of healthcare data, which is still relatively large, unstructured, and amenable to changes. in 2023, i predict there will be greater emphasis on utilizing blockchain to enable patient-mediated healthcare exchange, especially with pharma and clinical trials platforms. patients with interesting data (e.g. rare diseases) will be able to monetize their information as nft. additionally, blockchain technology will find its way to empower the workforce through talent management and career development frameworks that provide incentives for training, mentoring, and knowledge-transfer through digital attraction (i.e. cryptocurrencies-based rewards). funding statement no funding was provided for the writing or publication of this article. financial and non-financial relationship and activities the authors declare no financial or non-financial relationships in the development of this article. contributors each author contributed their section of the article. acknowledgments none. references 1. thomason j. big tech, big data and the new world of digital health. global health j. 2021;5(4):165–8. https://doi. org/10.1016/j.glohj.2021.11.003 2. thomason j. metahealth – how will the metaverse change health care? journal of metaverse. 2021;1(1):13–6. available from: https://dergipark.org.tr/en/download/article-file/2167692 [cited 10 december 2022]. 3. thomason j. metaverse, token economies, and noncommunicable diseases. global health j. 2022;6(3):164–7. https://doi. org/10.1016/j.glohj.2022.07.001 4. witham t. johns hopkins performs its first augmented reality surgeries in patients. johns hopkins medicine. available from: https://www.hopkinsmedicine.org/news/articles/johnshopkinsperforms-its-first-augmented-reality-surgeries-in-patients [cited 16 february 2021]. 5. basha nk, cheng-xi aw e, chuah sh. are we so over smartwatches? or can technology, fashion, and psychographic attributes sustain smartwatch usage? tech in soc. 2022 may;69:101952 6. iqbal sma, mahdeldin i, du e, leavitt, ma, asghar w. advances in healthcare wearable devices. flex electron. 2021;5:9. 7. maher m, khan i. from sharing to selling: challenges and opportunities of establishing a digital health data marketplace using blockchain technologies. blockchain in healthcare today. 5:184. https://doi.org/10.30953/bhty.v5.184 8. world health organization. depression. 2021. available from: https://www.who.int/news-room/fact-sheets/detail/ 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[cited 30 october 2022]. 9. downing a, peraklss eric p. health advertising on facebook: privacy and policy considerations, patterns. 2022 aug 15;(1):2–3. https://doi.org/10.1016/j.patter.2022.100561 http://dx.doi.org/10.30953/bhty.v5.245 https://doi.org/10.1016/j.glohj.2021.11.003 https://doi.org/10.1016/j.glohj.2021.11.003 https://dergipark.org.tr/en/download/article-file/2167692 https://doi.org/10.1016/j.glohj.2022.07.001 https://doi.org/10.1016/j.glohj.2022.07.001 https://www.hopkinsmedicine.org/news/articles/johns-­hopkins-performs-its-first-augmented-reality-surgeries-in-patients https://www.hopkinsmedicine.org/news/articles/johns-­hopkins-performs-its-first-augmented-reality-surgeries-in-patients https://doi.org/10.30953/bhty.v5.184 https://www.who.int/news-room/fact-sheets/detail/depression https://doi.org/10.1016/j.patter.2022.100561 citation: blockchain in healthcare today 2022, 5: 245 http://dx.doi.org/10.30953/bhty.v5.2454 (page number not for citation purpose) talisha shine et al. 10. cohen k. location, health, and other sensitive information: ftc committed to fully enforcing the law against illegal use and sharing of highly sensitive data. federal trade commission. available from: https://www.ftc.gov/business-guidance/blog/2022/07/ location-health-and-other-sensitive-information-ftc-committed-fully-enforcing-law-against-illegal [cited 11 july 2022]. copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/licenses/by-nc/4.0 http://dx.doi.org/10.30953/bhty.v5.245 https://www.ftc.gov/business-guidance/blog/2022/07/location-health-and-other-sensitive-information-ftc-committed-fully-enforcing-law-against-illegal https://www.ftc.gov/business-guidance/blog/2022/07/location-health-and-other-sensitive-information-ftc-committed-fully-enforcing-law-against-illegal https://www.ftc.gov/business-guidance/blog/2022/07/location-health-and-other-sensitive-information-ftc-committed-fully-enforcing-law-against-illegal http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 narrative/systematic reviews/meta-analysis use of blockchain technology to accelerate digital health transformation programs regien sumo1  and simcha jong2,3,4  1post doctoral researcher, leiden university, faculty of science, leiden netherlands; 2professor, university college ucl global business school for health, london, united kingdom; 3university of eastern finland, faculty of social sciences and business studies, joensuu finland; 4école polytechnique department of management, innovation, and entrepreneurship, palaiseau, france corresponding author: regien sumor, email: a.f.sumo@sbb.leidenuniv.nl doi: https://doi.org/10.30953/bhty.v8.399 keywords: blockchain, data management, decentralization, distributed ledger technology, medical information systems, healthcare, health transformation, medical data abstract disruptive digital health technologies are reshaping how patients interact with health professionals, how data are shared among providers, and how treatment plans and health outcomes are determined. while the covid-19 pandemic has accelerated the adoption of digital technologies, challenges remain in realizing the potential of digital transformation programs in healthcare. specifically, health data need to remain secure, usable, and shareable across multiple stakeholder groups in a world where silos between organizations and information systems persist. the implementation of innovative and disruptive digital technologies such as blockchain can offer a solution to these challenges. this article explores how blockchain technology can be used to accelerate digital health transformation programs. it provides an overview of the technology applications (i.e. data management, internet of medical things [iomt], supply chain management, and health insurance) and key players based on a literature review and secondary data. it also identifies challenges and success factors in implementing blockchain in healthcare. at the organizational level, we discuss the careful planning and specialized expertise required to overcome the technical, regulatory, and adoption-related hurdles associated with implementing blockchain technology. at the system level, the authors discuss the regulatory constraints, standardization and interoperability issues, and stakeholder engagement challenges linked to implementing blockchain technology. plain language summary disruptive digital health technologies are reshaping how patients interact with health professionals, how data are shared among providers, and how treatment plans and health outcomes are determined. while the covid19 pandemic has accelerated adoption of digital technologies, challenges remain. specifically, health data need to remain secure, usable, and shareable across multiple stakeholder groups. blockchain offers a promising solution by improving data security, interoperability, and transparency. this article reviews blockchain’s roles in managing medical records, iot, supply chain management, and health insurance, while also highlighting the organizational and regulatory hurdles and what it takes to implement this technology successfully. submitted: april 24, 2025; accepted: august 5, 2025; published: august 31, 2025 advances in disruptive digital health technologies have brought to the forefront several challenges in realizing the potential of successfully implementing digital health transformation programs.1–3,7–11 specifically, it is imperative that the enormous volumes of health data generated remain secure while still being easily usable and shareable across various stakeholder groups. while conventional security solutions continue becoming more sophisticated, these solutions remain prone to security breaches and exposure of personal and/or confidential information. moreover, to make greater use of and realize the significant potential of all the data that the https://orcid.org/0000-0002-0176-5713 https://orcid.org/0000-0002-3281-5381 mailto:a.f.sumo@sbb.leidenuniv.nl https://doi.org/10.30953/bhty.v8.399 2 (page number not for citation purpose) r. sumo and s. jong citation: blockchain in healthcare today 2025, 8: 399 https://doi.org/10.30953/bhty.v8.399 healthcare sector is collecting across various stakeholders, the silos between those stakeholders and different health information systems need to be broken down.2,3,7,9–11 finally, patients, who are the primary source of data, should be empowered and have greater control over the data they share.12 as the healthcare industry plans for a future where health data can be securely and transparently exchanged and used—while still retaining consumer control, privacy, and confidentiality—blockchain technology has often been suggested as a valuable solution to the challenges faced in digital health transformation programs.6,13–17 existing studies on the use of blockchain technology in healthcare consist of systematic literature and scoping reviews.2,3,5,8,12,15,18–22 studies on the use of blockchain in a specific area of the healthcare sector,12,13,16,23–28 and discussions of the technology itself from a computer science angle.22,24,25,29–32 nevertheless, extant studies remain inadequate in explaining the current state of real-life use of the blockchain technology across the healthcare sector, who the main players are, the challenges faced by healthcare stakeholders in implementing the technology, and an overall assessment of what areas of the healthcare sector can be served by the blockchain technology. the aim of this article is to delve into how blockchain technology can address challenges in digital technology use in the healthcare sector by providing an overview of the technology’s applications (i.e. data management, iot, supply chain management, and health insurance) and by identifying the key companies in this space. a total of 108 blockchain technology companies active in the healthcare sector are identified using industry databases such as s&p capital and crunchbase. in the concluding section, key takeaways for healthcare stakeholders and avenues for future research are outlined. previous work what is blockchain? blockchain (public or private) is a distributed ledger technology that enables a set of peers to work together to create a unified, decentralized network. to make it decentralized, each network of users (nodes or participants) carries a copy of the ledger, which is a constantly growing list of electronic records (blocks) that are linked using cryptography.13–17,33,34 the list of records is stored and maintained by the nodes on the blockchain. the role of the nodes on the blockchain is to validate every new block after receiving a notification when a transaction takes place.35,36 peers can communicate and share information or data with the help of the consensus algorithm (which differs depending on which blockchain is used; e.g. the bitcoin blockchain uses a “proof of work” consensus method, whereas the vechain blockchain uses a “proof of stake” consensus method).37–39 hence, three key components underlie the blockchain technology. first, these include blocks stored linearly, where the latest block is attached to the previous, and each block contains a “hash” used to determine any block’s authenticity. second, transactions that take place within the network when one peer sends information (sender, receiver, value) to another peer. third, a consensus method through which a transaction is validated. these features result in key benefits.6,13–17,37,40–44 • less vulnerability to attacks or fraudulent transactions due to encryption technology • better transparency, allowing for real-time monitoring of asset movements • enhanced traceability of transactions, as encrypted data cannot be altered or deleted • transactional verification without the use of a thirdparty vendor • faster recovery of data, as the full dataset is chronologically stored on every node on the chain • utilization of smart contracts, allowing for automatic instruction of processes on ledgers. smart contracts are a set of agreed orders that are executed only once certain conditions are met, that is, if a is selling a house to b, then once a makes the full payment, the smart contract will execute the transfer of title from a to b therefore, given the challenges of digital programs in healthcare delivery laid out in the introduction section and the features and benefits of blockchain, blockchain has the potential to disrupt existing technologies and change the way healthcare is managed and delivered. it puts patients at the center of healthcare delivery while at the same time lowering costs and improving accessibility, security, and privacy. blockchain could save the healthcare industry up to $100 billion annually by 2025 in data breach, it, operation, and personnel costs, and health insurance-related frauds, and health insurance is expected to realize up to $10 billion of cost savings annually.45 nevertheless, compared to other industries, the healthcare sector is slower when it comes to implementing digital technologies in general and blockchain in particular.27,31,32,46–49 literature review we conducted a literature review and searched for english-language articles that contained the word “blockchain in healthcare” in the title, keywords, and/ or abstract. the review of the articles focused on identifying blockchain use cases in the healthcare sector and the main implementation challenges and success factors of using blockchain in healthcare sector (appendix 1). articles were selected from the web of science, applied science & technology source, and various internet websites (e.g. google scholar and researchgate). in total, https://doi.org/10.30953/bhty.v8.399 3 (page number not for citation purpose) blockchain to accelerate digital health transformation citation: blockchain in healthcare today 2025, 8: 399 https://doi.org/10.30953/bhty.v8.399 29 articles were identified with a specific focus on applications of blockchain technology in the healthcare sector between 2016 and 2023. the outcome of the review represents descriptive insights, supported by secondary data.* the results point to the scarcity of research on the implementation of blockchain in healthcare and limited use in practice of this technology. to address this challenge, we also looked into other articles that discussed the implementation challenges and success factors of blockchain in other industries. we have used the findings of these papers that are relevant for the healthcare sector. as highlighted in figure 1, extant research remains inadequate in providing an aggregated healthcare sector overview. moreover, appendix 1 reveals that existing studies focus mainly on systematic literature and scoping reviews on the applications of blockchain in the healthcare sector with a limited focus on empirical studies. use cases of blockchain technology in healthcare to identify which blockchain technology companies use and which area of the healthcare sector they serve, we make use of existing large-scale databases (i.e., s&p capital and crunchbase) and desk research by searching for and analyzing company websites. we identified 108 global blockchain technology companies that are active in the healthcare sector (figure 1). blockchain technology companies that focus on the healthcare industry are concentrated in the americas (usa).† africa and the middle east lag in terms of companies that are active in blockchain healthcare services (figure 2). *, company websites, s&p capital, crunchbase. n = 108. †, company websites, s&p capital, crunchbase. n = 108. blockchain technology has been deployed across four key areas in the healthcare sector: the majority of blockchain healthcare companies are active in services related to data management, followed by iot, supply chain management, and health insurance (figure 3).* data management healthcare data management is the compilation of patient data from multiple sources, which allows healthcare providers to enter patient information into a singular database where it can be securely stored, analyzed, and shared.50–52 it creates a holistic view of patients that will allow providers to personalize treatments, improve communication, and enhance health outcomes.52,53 challenges prevail across three fig. 2. overview of blockchain healthcare companies across regions. 60%20% 19% americaseurope asia� pacific 1% africa� middle east fig. 1. blockchain in the healthcare sector. iot: internet of things; r&d: research and delivery. data management internet of things supply chain mgt (scm) health insurance • electronic health records • medical staff credential verification • scientific r&d data sharing clinical research • healthcare and medical devices iot • infrastructure and data security • pharma medical device scm transparency • scm smart contract settlements • smart contract for insurance settlements • crypto payments https://doi.org/10.30953/bhty.v8.399 4 (page number not for citation purpose) r. sumo and s. jong citation: blockchain in healthcare today 2025, 8: 399 https://doi.org/10.30953/bhty.v8.399 main areas that can be alleviated through blockchain technology. electronic health records prevalence of data silos can be decreased by compiling data from multiple sources into one central hub for a comprehensive view of patient history and providing patient documentation in a digital format securely, anytime, and anywhere by using blockchain technology.6,12,16,32,54 it can offer the “whole story” and a “single source of truth” to health providers,26,31,47 hence providing better diagnostics that are more secure and transparent. the blockchain technology in this regard is considered more secure given that the actual patient data do not go on the blockchain, but each new record is appended to the blockchain, whether a physician’s note, a prescription, or a lab result, and is translated into a unique hash function (small string of letters and numbers). every hash function is unique and can be decoded only if the person who owns the data—in this case, the patient—gives their consent. patients can also choose to share their medical records, or part of their medical records, with researchers. this blockchain-based medical record technology is provided by, for example, a successful uk company, which offers a decentralized platform where providers can build digital health solutions, provide virtual consultation services, and sell their anonymized medical data to medical data exchanges through blockchain. medical staff credentials healthcare credentialing plays a vital role in ensuring the competence and integrity of healthcare professionals. however, the current credentials verification process suffers from time-consuming procedures due to the large number of intermediaries, limited information access, data fragmentation, and the persistent risk of fraudulent credentials, leading to delayed hiring, increased administrative burden, and loss of trust and reputation in the healthcare system.55 globally, medical document forgery stands at 2.32%.56 for example, in the middle east alone, an estimated 3% have used fake or misrepresented academic credentials, professional licenses, or work histories in their visa or licensing applications.57 nurses were most likely to misrepresent their backgrounds (4.4%), followed by allied healthcare professionals such as pharmacists and medical technologists (3.8%). the most commonly misrepresented information is employment history, particularly the tenure, position, and type of institution. it is estimated that fraudulent degree mills generate $7 billion a year in sales worldwide (from $1 billion in 2004), with much of that market in the united states and the middle east, particularly the gulf region.58  the prevalence of fake medical credentials can be decreased by using blockchain technology to track the experience of medical professionals throughout their careers in medical institutions and healthcare organizations.59–61 this will result in faster credentialing for healthcare organizations during the hiring process, more opportunities for medical institutions, insurers, and healthcare providers to monetize their existing credentials data on past and existing staff, and increased transparency and reassurance for partners, for example, in emerging virtual health delivery models to inform patients on medical staff experience. a u.s. company has developed a fig. 3. overview of blockchain technology services offered in the healthcare sector. https://doi.org/10.30953/bhty.v8.399 5 (page number not for citation purpose) blockchain to accelerate digital health transformation citation: blockchain in healthcare today 2025, 8: 399 https://doi.org/10.30953/bhty.v8.399 medical credential verification system using the r3 corda blockchain protocol. this company’s validation engine, distributed ledger technology, and member network contain the following features: • establish the provenance of every member-contributed data point. • render the data immutable and permanently traceable. • analyze the data and produce actionable insights. • curate the data to meet an organization’s unique requirements. • share the data with trusted and authorized partners. scientific r&d data sharing and clinical research lack of trusted and high-quality data and a tampering of trial events during the pharmaceutical development process can be reduced by offering more reliable and widespread population data on blockchain. personalized medicine is a promising field, but its development is hindered by a lack of sufficient high-quality data and tools for analyzing data.60,62–64 blockchain could be used to ease access to more reliable and widespread population level data throughout the whole pharma development process.65–70 blockchain could preserve the privacy and ensure security by using a distributed ledger, thus ensuring that every trial event is recorded on the blockchain nodes, which are tamper-proof. this technology will enable much more powerful segmentation and analysis of targeted medicine research outcomes. internet of medical things the internet of medical things (iomt) refers to the collection of medical devices and applications that connect to and transfer data through online computer networks without any man–machine interactions, which is crucial in monitoring patients and collecting data.71–73 adoption of remote monitoring solutions, where all kinds of sensors measuring patients’ vital signs are used to provide healthcare practitioners more visibility into patients’ health, enabling more proactive and preventative care. security is a major issue in iomt, both in terms of ensuring patient data are private and secure and is not tampered with to create false information. through its blockchain cryptography, only permitted parties can gain access to personal data, which is stored on the blockchain as a unique hash function, making it difficult to receive ddos attacks.6,71 a successful u.s.-based company (hipaaand gdprcompliant platform) leverages blockchain, security, big data, and machine intelligence to enable a health data network. the result is a global, secure data network that allows health systems, payers, digital health companies, pharma & life science companies, and governments to collaborate, share, analyze, and unlock a more in-depth understanding of the diverse factors that influence health. nevertheless, blockchains suffer from scalability challenges in iomt applications that need to be addressed (please refer to the challenges section related to this). supply chain management (scm) scm transparency many countries are facing challenges related to counterfeit medical drugs and shortages.74,75 these challenges can be alleviated by using blockchain technology that can track items from the manufacturing point and at each stage through the supply chain to have full visibility and transparency of the items.6,61,76–78 customer confidence customer confidence increases as customers can track each item’s end-to-end authenticity, with the ability to integrate the players in the supply chain, from manufacturers and wholesalers to transportation players and pharmacies. in addition, aggregating supply chain data into one system helps streamline compliance, and because data across the supply chain are stored in one place, companies apply ai to better predict demand and optimize supply accordingly. a large uk company provides a global blockchain-based pharmaceutical provenance system that eliminates counterfeit drugs, automates various pharmaceutical processes such as automated law enforcement notifications when an issue is discovered, and provides valuable data insights to its customers. recently, glaxosmithkline engaged in a blockchain program using a decentralized approach and an immutable audit trail built for its drug supply chain. the program is designed to eliminate manual, time-consuming processes and help reduce the risk of fraud and errors, ultimately creating frictionless connectivity across supply chains. scm smart contract settlement disputes over payment chargeback claims for prescription medicines and other medical goods may be solved by blockchain-based smart contracts that enable trading partners and insurance providers to operate based on fully digital and, in some cases, automated contract terms.40,42–44,69 digitally shared contracts are logged on a blockchain ledger, rather than each player having their own version of contracts. smart contracts will authenticate the identities of the related organizations, log contract details, and track transaction of goods, and payment settlement details. health insurance fraud claims, contract-related disputes, and long durations for processing and settling claims are key challenges https://doi.org/10.30953/bhty.v8.399 6 (page number not for citation purpose) r. sumo and s. jong citation: blockchain in healthcare today 2025, 8: 399 https://doi.org/10.30953/bhty.v8.399 in the health insurance industry.79,80 for example, abuse of health services in, for example, the uae costs 10%– 15% of total collected premiums, equaling arab emirates dirham ~3bn (~$0.8 u.s.).81 these challenges can be alleviated by using smart contracts for insurance settlements, which can automatically process claims on blockchain when predefined conditions are met.18,79,82,83 insurers can thus easily authenticate their identities as organizations, log contract details, and track claims and payment settlement  details. this technology will significantly reduce disputes and lower operational expenses due to decreased duplication of procedures, diminished counterparty risks, expanded automation of procedures, and secure and decentralized exchanges being used.24,25,84–86 moreover, once data are digitized and easily accessible, insurance providers can use more advanced analytics to optimize health outcomes and costs. note that disadvantages of smart contracts are that, given the immutability of the technology, it is cumbersome to modify the contracts (especially long-term contracts), visibility of the transactions on the blockchain, and the legal enforceability of smart contracts.87 another area of blockchain usage in the health sector is the use of crypto payments for health services and the settlement of health insurance claims through crypto payments. however, given the high crypto fluctuation and limited regulations in the crypto payment space, there is limited uptake of this technology in the health sector, currently. the main implementation challenges involved in deploying blockchain technology implementing blockchain technologies can present several challenges, including the following. regulatory compliance currently, there is no regulatory framework in place for blockchain solutions, and the introduction of new regulation may also lead to extra costs for early adopters of blockchain technology in healthcare, who may need to modify their systems and processes to comply with new regulatory rules.46,60,88–90 several regulatory challenges have been identified.91 first, by definition shared distributed ledgers have no specific location, and there may be no single party ultimately responsible for the functioning of the blockchain. hence, regulatory challenges related to territoriality and liability remain to be solved. second, there are no regulatory frameworks in place to recognize blockchain as immutable and tamper-proof, ensuring the accuracy of the information. third, the tamper-proof characteristic of blockchain is in contrast with existing regulation related to protecting personal data, including health data (e.g. “right to be forgotten,” which grants patients the right to delete information). finally, there are no regulatory frameworks in place regarding the validity of data, documents, assets stored on blockchain as evidence of possession or existence. as blockchain technology continues to evolve and gains acceptance, regulators will need to find ways to balance innovation and security to create a regulatory environment that enables the benefits of blockchain in the sector while minimizing the risks. in the meantime, organizations that are considering blockchain solutions will need to work closely with regulators to ensure that their systems and processes are compliant with existing regulations and that they are prepared to adapt to changes. technical complexity and scalability challenges, negatively impacting the blockchain’s performance blockchain technology is very complex to implement and requires specialized expertise in areas such as cryptography, distributed systems, and consensus algorithms.90,92,93 it faces scalability challenges related to transaction throughput and latency (i.e. transaction processing time), storage capacity of blockchains, block sizes on the blockchain (smaller blocks limit the number of transactions whereas larger blocks impact transaction throughput and latency), number of nodes connecting to the blockchain, which may impact the performance, which are all related to the consensus mechanisms of blockchains.85,86 moreover, ensuring interoperability between different blockchain systems and integrating them with existing healthcare systems is challenging, as there are currently no widely accepted standards for blockchain interoperability.94 the issue of data reliability blockchain technology is designed to be immutable, meaning that once data are recorded on the blockchain, it cannot be easily modified or deleted.85,86 accordingly, instances where incorrect initial data are collected or inputted either on purpose or accidentally (which cannot be verified by blockchain) may lead to incorrect data shown in the blockchain system. as a result, incorrect data will persist and be visible in the system, potentially leading to inaccurate analyses or conclusions. this underscores the importance of ensuring that accurate data are collected and inputted into the blockchain system from the outset. hence, robust data quality control measures are critical for blockchain adopters in the healthcare sector. energy usage of blockchain technology, resulting in higher costs energy consumption of blockchain technology is a significant concern, as processing data on a blockchain requires a substantial amount of power.95,96 this is https://doi.org/10.30953/bhty.v8.399 7 (page number not for citation purpose) blockchain to accelerate digital health transformation citation: blockchain in healthcare today 2025, 8: 399 https://doi.org/10.30953/bhty.v8.399 because blockchain networks rely on distributed computing power to verify and validate transactions, which requires a significant amount of computational resources. in addition, a sizable amount of energy is required to cool down the computers used to process blockchain transactions, further adding to the overall energy consumption of the system. the energy consumption of blockchain technology has been criticized by some as being unsustainable and environmentally harmful, especially in light of the increasing global demand for energy.96,97 to address this issue, there has been a growing focus on developing more energy-efficient blockchain solutions, such as proof-of-stake consensus algorithms, that can reduce the amount of energy required to process transactions on the blockchain. adoption of blockchain technology in the healthcare sector one of the major challenges in implementing blockchain technology in the healthcare sector is getting various stakeholders to adopt and use the technology.46 moreover, stakeholders such as healthcare provider organizations, payers, patients, regulators, and biomedical deviceand pharmaceutical companies often bring competing agendas and conflicting interests to the implementation of new blockchain technologies. however, the implementation of blockchain technology requires buy-in from all parties involved and a willingness to invest resources, time, and money into the new technology. thus, efforts to reconcile oftentimes conflicting agendas and interests represent a major challenge in the adoption of new blockchain technologies. overall, implementing blockchain technology can be a challenging process that requires careful planning, specialized expertise, and a willingness to overcome technical, regulatory, and adoption-related hurdles. what is next for a successful implementation of blockchain technology in the healthcare sector? the use of blockchain in the healthcare sector seems to be rapidly becoming a reality as researchers and companies are deploying blockchain technology across various aspects of health systems. in the near term, healthcare blockchain technology can be used for business impact (i.e. process excellence, efficiency gains, tracking and traceability, and identity) and competitive differentiation (i.e. reimagined it infrastructure, redefined transaction management, and trust in multi-party collaboration). these near-term opportunities mostly offer incremental improvements in the healthcare industry by optimizing existing structures, processes, and value propositions and do not represent major disruptions to the roles of different actors in the healthcare sector. however, the more transformative changes that new blockchain technologies promise to deliver in terms of the creation of new business models that remove various intermediaries and that can disrupt traditional business models are contingent on several factors. addressing these factors will be critical to supporting the mass adoption of blockchain technology in the healthcare sector. regulatory change regulatory bodies (incl. health regulators) are facing obstacles in defining policies that require collaboration.46,60,88–90 in fact, the cryptocurrency crash led to a backlash from regulators against blockchain technology. key building blocks of blockchain remain unregulated (e.g. smart contracts). regulations need to be enacted as that will ease adoption of blockchain in the healthcare sector and, more specifically, work on defining policies that will preserve the privacy of users’ medical records. improved performance blockchain systems can be slow and cumbersome due to their complexity as well as encrypted and distributed nature. these chains have the potential to become slow as they grow and the number of computers accessing the network, noting that the majority of nodes on the system need to verify the transactions.90,92,93 new technologies and advances in engineering and processing speeds can enhance consensus on the blockchain, leading to improved performance. increased standardization many types of blockchains are currently deployed and cannot interact with each other.94 for blockchain’s successful deployment in healthcare, standards must be developed by standardization bodies. decreased costs costs of operating a blockchain are high, mainly due to energy costs.95,96 as the complex blockchain algorithm runs, a large amount of computing power is required. companies like amazon, microsoft, hewlett packard, oracle, and ibm are now offering blockchain-as-a-service to healthcare entities looking to use blockchain, benefitting from scale. participation of key stakeholders given the siloed nature of health data and service offerings,21,31,98,99 it is imperative that key stakeholders in the blockchain healthcare ecosystem participate in the enablement and use of blockchain technology. for a safe and secure use of blockchain in the healthcare sector, regulators need to put in place regulations on data security, standardization bodies need to develop blockchain standards in the healthcare sector, patients need to be willing to share their data on the blockchain, and health professionals and providers need to be willing to use the technology. https://doi.org/10.30953/bhty.v8.399 8 (page number not for citation purpose) r. sumo and s. jong citation: blockchain in healthcare today 2025, 8: 399 https://doi.org/10.30953/bhty.v8.399 conclusion this contribution outlined some key opportunities and challenges of implementing blockchain technology in healthcare. we provided an overview of the technology’s applications across the domains of data management, iot, supply chain management, and health insurance. we also identified some key players in the field, as well as the main challenges and success factors in implementing blockchain technology in healthcare. our study highlights the fact that the healthcare sector has been slower than many other sectors in implementing blockchain technology. nevertheless, this study has provided insights into a growing group of companies offering blockchain technology solutions for the healthcare sector, particularly in the americas. other continents still lag in terms of blockchain technology companies that focus on healthcare, particularly in the middle east and africa. moreover, we found that the majority of blockchain healthcare companies are active in services related to data management, followed by iot, supply chain management, and health insurance. using blockchain technology in healthcare can be a challenging process requiring careful planning, specialized expertise, and a willingness to overcome technical, regulatory, and adoption-related hurdles. the use of blockchain technology in healthcare seems to be rapidly becoming a reality as researchers and companies are developing various aspects of blockchain-enabled healthcare systems. we identified several factors that will support the wider adoption of blockchain technology in the healthcare sector. these include (1) regulatory changes to ease the adoption of blockchain and preserve the privacy of users’ medical records, (2) improved performance and standardization of blockchain technology to ensure interoperability between different blockchains, (3) reduced costs of blockchain technology, and (4) participation of key stakeholders such as regulators, patients, health professionals, and providers in the enablement and use of blockchain technology for a safe and secure implementation in the healthcare sector. our conclusions are in line with existing studies that examine the components and advantages of blockchain technology in the healthcare sector, explore its potential applications in the healthcare sector, and identify the challenges and factors that contribute to successful implementation. yet, our contribution extends this existing work by providing a more comprehensive overview of initiatives that have been launched around the world to deploy blockchain technology in healthcare settings. finally, this contribution provides practitioners with a deeper understanding of the challenges and success factors associated with the implementation of blockchain technology in the healthcare sector. the information in this article paves the way for future research as blockchain technology is gaining momentum in the healthcare sector. specifically, future research could expand on our findings by utilizing primary data collection methods such as interviews, surveys, and in-depth qualitative case studies to gain richer insights into this nascent field.100 funding there was no funding involved for this research authors’ contributions regien sumo collected and analysed the data. she also wrote the first draft of the paper. simcha jong further expanded on the manuscript and both authors reviewed the manuscript. conflicts of interest none declared disclaimer the overview of blockchain 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[a q 6] https://doi.org/10.30953/bhty.v8.399 1 (page number not for citation purpose) editorial or discussion the evolution of healthcare economics: blockchain integration amidst medicare reform ryan m. wright, mba, cdaa ceo & founder, nvlope, inc., kansas city, mo, usa corresponding author: ryan m. wright, email: doi: https://doi.org/10.30953/bhty.v8.383 keywords: blockchain, healthcare institutions, medicare, medicaid, funding, transparency received: february 13, 2025; accepted: april 9, 2025; published: april 30, 2025 healthcare institutions face unprecedented financial pressures as potential federal budget cuts threaten medicare and medicaid funding. according to the latest cms national health expenditure data, these programs represent significant revenue streams, with medicare accounting for 21% and medicaid for 18% of total national health expenditures.1 more critically, medicare and medicaid together account for more than 60% of all care provided by hospitals, with medicare alone accounting for 44% of hospital care revenue.2 recent polls indicate that transparency in healthcare data handling is a primary concern, with 61% of americans expressing distrust in how their health information is managed and protected.3 despite strong public support for healthcare modernization—with 72% of americans believing current health information systems need significant improvement3—healthcare providers must prepare for the reality of reductions in traditional revenue sources while addressing these trust concerns.  blockchain in healthcare began as an initiative to reduce infrastructure costs in america’s increasingly expensive healthcare system.4 now, it must evolve into a crucial mission addressing revenue shortfalls while solving fundamental patient concerns. blockchain technologies are emerging with solutions that address core public concerns: healthcare cost transparency, insurance claim efficiency, and prescription drug pricing. placing data “on-chain” creates immutable records that remain anchored, malleable, and accessible to patients and practitioners while maintaining cryptographic security guarantees.  medical digital assets fundamentally transform the healthcare landscape through their unique capability to create immutable, patient-controlled health information. this revolution enables real-time health metrics that respond dynamically to patient conditions while ensuring that the individual—not the institution—remains the sovereign authority over their personal health narrative. by encoding patient rights directly into the infrastructure of healthcare data management, blockchain creates a paradigm where information flows freely when authorized but remains protected by cryptographic guarantees that conventional systems cannot match.  from data silos to value streams: blockchain’s transformative potential  healthcare organizations operating traditional enterprise resource planning (erp) systems face limitations in data interoperability and value extraction.5 the transition from centralized systems like epic or sap and hyperledger to decentralized platforms represents more than technological evolution—it’s a fundamental shift in how healthcare value is created and distributed. traditional systems create artificial viscosity—resistance to movement and adaptation—with patient data trapped in silos, preventing information from flowing to where it generates the most value.  blockchain technology reduces this viscosity dramatically. when patient data becomes fluid, its potential value multiplies across the entire healthcare ecosystem. at the patient-provider level, physicians access complete information instantly, reducing redundant tests and enabling precise diagnostics. within healthcare systems, departments coordinate seamlessly without administrative friction. in the research domain, patterns emerge across diverse populations while maintaining individual privacy protections through cryptographic guarantees.  blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-9719-415x https://doi.org/10.30953/bhty.v8.383 citation: blockchain in healthcare today 2025, 8: 383 https://doi.org/10.30953/bhty.v8.3832 (page number not for citation purpose) ryan m. wright the current paradigm—where patients pay for their health data’s storage yet have little ownership or control over it—stands at a critical juncture. proposed legislation like h.r.8818, the american privacy rights act, could fundamentally reshape this landscape by empowering patients with increased ownership of their health information.6 this shift would force healthcare institutions to transition from data custodians to data service providers, creating new models of asset distribution.  semi-fungible tokens represent a powerful tool in this transformation, enabling healthcare providers to track and monetize health outcomes beyond traditional clinical encounters.7 unlike standard cryptocurrencies, these specialized tokens can encode complex health metrics while maintaining partial fungibility, creating a new asset class specifically designed for healthcare value exchange. value-based healthcare (vbhc) extends patient monitoring beyond single encounters through these tokens, enabling treatment efficacy tracking while creating opportunities for encrypted, anonymized data containers that advance both research and population health outcomes. clinical trial management systems using blockchain technology have already demonstrated significant improvements in data integrity and process efficiency.7  studies indicate that transparent data sharing practices could increase patient engagement and improve treatment adherence.5 individuals are awakening to the value of their personal health portfolios, with blockchain technology offering new pathways for enhanced data stewardship and new values for all stakeholders.  blockchain-ai synergy: creating fluid healthcare systems  while blockchain creates secure, transparent infrastructure, artificial intelligence amplifies its impact through analytical power. the erc-6551 architecture represents a transformative advancement by tokenizing patient identity and binding it to functional accounts, turning static health records into dynamic, programmable assets. this approach decreases data viscosity dramatically, allowing information to flow to where it creates the most value while maintaining strict permission boundaries through smart contracts rather than administrative bottlenecks.  blockchain can define the parameters of ai for medical service lines, creating an auditable trail for algorithmic decision-making. by recording model versions, training parameters, and decision pathways on an immutable ledger, blockchain brings transparency to ai systems that would otherwise operate as inscrutable black boxes. smart contracts require reliable inputs from trusted sources, and ai systems help validate and process real-world health data to ensure the integrity of automated healthcare processes.  consider a diabetes management program where wearable devices monitor patient glucose levels continuously. ai algorithms detect patterns and potential complications, while smart contracts automatically adjust treatment protocols when specific thresholds are reached. the entire process maintains a blockchain-secured audit trail accessible to authorized providers. this marriage of technologies addresses the notorious “black box” problem in healthcare ai—the inability to trace how systems reach specific conclusions. post quantum cryptography (pqc) confronts temporal causalities in reasoning and reduces the risk of “harvest now, decrypt later” attacks.  blockchain’s programmable value transforms healthcare delivery by embedding medical veracity within smart contract triggers. when sensors indicate a medication need, blockchain systems simultaneously verify necessity, authorize prescriptions, and execute payment through tokenomics—creating verifiable economic consequences tied directly to health outcomes. this programmable verification establishes proof of humanity while addressing ai’s critical weakness of bias through anchored data provenance. this could be seen as the beginning of formal framework for standardizing proof of need (pon).  patient data monetization becomes more fluid when value is established rather than unknown. research shows that 78.6% of patients want to know how their data is being used, and 75.9% want transparency about who is using their data.8 while patients express concerns about data privacy and security, they show increased willingness to share data when given transparency and control over its use.9 the erc-6551 architecture creates a framework where this value becomes explicit and programmable, allowing patients to selectively license anonymized data through automated smart contracts. this approach transforms previously static health information into productive capital with clearly defined economic parameters, addressing patient preferences for opt-in systems and individual control over data sharing.10 decentralized physical infrastructure networks (depins) represent a crucial advancement in healthcare’s technological evolution. these networks connect physical sensors and devices to blockchain infrastructure, creating verifiable data streams from real-world health monitoring. by implementing w3c standards for decentralized identifiers (dids), healthcare systems can ensure both the authenticity of patient identity and the integrity of medical data. this approach maintains privacy through self-sovereign identity principles while creating new models for patient participation in their own care.  the path forward: implementing blockchain in healthcare  why now? the emergence of web3 and decentralized governance structures is reshaping traditional concepts of https://doi.org/10.30953/bhty.v8.383 citation: blockchain in healthcare today 2025, 8: 383 https://doi.org/10.30953/bhty.v8.383 3 (page number not for citation purpose) blockchain integration in medicare reform statehood and service delivery.10 these new forms of digital sovereignty create opportunities for reimagining healthcare delivery beyond traditional geographical boundaries to digital boundaries. while conventional healthcare systems have been bound by nation-state areas, the evolution of network-based governance opens new possibilities for cross-border healthcare services and global token models for payments, research, and telehealth. this evolution is reflected in broader societal shifts, as evidenced during the recent presidential inauguration, where tech titans stood alongside traditional political figures for the first time, symbolizing the growing confluence of technological and political governance.  this convergence creates a profound shift in the locus of control over one’s health information. as network states emerge alongside traditional nation states, citizens increasingly navigate dual identities—their geographical citizenship and their digital sovereignty. healthcare becomes a frontline in this transformation, where individual autonomy through blockchain-secured health data challenges traditional models of centralized medical authority. the promise of technology materializes in verifiable, patient-controlled health records that transcend borders and bureaucracies, creating a new citizenry empowered through technological self-determination.  the “caveat emptor” ethos of web3, where individuals must shoulder the burden of their own due diligence, fundamentally fails in healthcare contexts where human lives hang in the balance. blockchain technology cannot merely disrupt and iterate in this sensitive domain—it must demonstrate wisdom through measurable results and unimpeachable business ethics. the promise lies in blockchain’s potential to illuminate the shadowy corners of medical billing and protected health information, creating a marketplace where stakeholders—not gatekeepers—determine value. information liberty exists within this framework, but requires active participation to flourish, with healthcare blockchain solutions earning trust through competence, not merely demanding it through decentralization.  the tokenization of healthcare data enables each health record to function simultaneously as clinical documentation, research data, reimbursement evidence, and personal health narrative—with each layer accessible to different stakeholders according to permissions encoded in the token itself. this multi-dimensional characteristic allows physicians to view diagnostic details while researchers access anonymized patterns and patients maintain control over their complete information, all without duplicating or fragmenting the underlying record. studies show that transparent data governance structures significantly impact patients’ willingness to share their personal health information for both care improvement and research purposes.11 as healthcare institutions navigate potential medicare and medicaid funding reductions, blockchain-based financial mechanisms in healthcare show promise in creating new revenue streams while addressing public concerns. token systems enable novel approaches to creating credit and securing equipment financing. patient-centric token economies can reduce costs through community pooling and staking of established stablecoins. fully homomorphic encryption (fhe) creates opportunities for substantial value generation for research hospitals also facing nih overhead funding cuts. essentially turning cost centers into revenue generators.    the future of healthcare economics lies in the balanced integration of traditional healthcare delivery with emerging blockchain solutions. success will be measured not just in revenue generation but in tangible improvements to healthcare accessibility, transparency, and efficiency. global institutions are already demonstrating that blockchain integration can simultaneously address revenue shortfalls and fundamental patient concerns, paving the way for a more sustainable and transparent healthcare system that places patients at the center of their own health data ecosystem while navigating the complex intersection of digital and geographic sovereignty.  funding this editorial received no external funding. conflicts of interest as the ceo of nvlope, inc., the author declares no conflicts of interest relevant to this editorial. contributors ryan wright, ceo of nvlope, inc., is the sole author of this editorial. data availability statement (das), data sharing, reproducibility, and data repositories not applicable. this editorial does not contain any primary data analysis or new datasets. application of ai-generated text or related technology ai-assisted tools were used for proofreading and formatting this editorial to conform with vancouver style. all content was conceived, written, and approved by the human author. acknowledgments the author acknowledges all pioneers using blockchain in their healthcare solutions. proper stewardship of human artifacts and their composability with respect to public usage are of paramount importance to human freedom and the advancement of science. https://doi.org/10.30953/bhty.v8.383 citation: blockchain in healthcare today 2025, 8: 383 https://doi.org/10.30953/bhty.v8.3834 (page number not for citation purpose) ryan m. wright references 1. centers for medicare & medicaid services. national health expenditure data [internet]. baltimore: cms. gov; 2024 [cited 2024 feb 13]. available from: https:// www.cms.gov/data-research/statistics-trends-and-reports/ national-health-expenditure-data  2. american hospital association. fast facts on u.s. hospitals, 2024 [internet]. chicago: aha; 2024 jan [cited 2024 feb 13]. available from: https://www.aha.org/statistics/fast-facts-us-hospitals  3. kaiser family foundation. kff health tracking poll on health information and trust: january 2025 [internet]. san francisco: kff; 2025 jan [cited 2024 feb 13]. available from: https://www. kff.org/health-information-and-trust/poll-finding/kff-trackingpoll-on-health-information-and-trust-january-2025/  4. dhillon v, metcalf d, hooper m. the future of healthcare. in: blockchain enabled applications [internet]. berkeley, ca: apress; 2021. p. 126-148. available from: https://link.springer.com/ book/10.1007/978-1-4842-6534-5 doi: 10.1007/978-1-4842-7647-2_5  5. mckinsey & company. the transformation imperative: igniting value creation in medtech [internet]. new york: mckinsey & company; 2024 jan [cited 2024 feb 13]. available from: https://www.mckinsey.com/industries/life-sciences/our-insights/ the-transformation-imperative-igniting-value-creation-in medtech (subscription required)  6. managed healthcare executive. majority of americans support medicare and medicaid amid federal spending cut talks [internet]. mhe; 2024 jan [cited 2024 feb 13]. available from: https://www.managedhealthcareexecutive.com/view/majority-of-americans-support-medicare-and-medicaid-amid-federal-spending-cut-talks  7. zhuang y, zhang l, gao x, shae z, tsai j, li p, shyu c. re-engineering a clinical trial management system using blockchain technology: system design, development, and case studies. j med internet res. 2022;24(6). available from: https://www.jmir.org/2022/6/e36774/ doi: 10.2196/36774  8. khan g, thompson r, dean j, tran m, zhang s, shuvo s, et al. patient perceptions of data sharing and trust in health-related artificial intelligence: a global online survey. plos one. 2023;18(12). available from: https://journals.plos.org/plosone/ article?id=10.1371/journal.pone.0309161 doi: 10.1371/journal. pone.0309161  9. kuo t, takla a, lee d. leveraging blockchain and hybrid cloud models to preserve patient health data privacy. blockchain healthcare today. 2023;6(11):357. available from: https://blockchainhealthcaretoday.com/index.php/journal/article/view/357  10. calzada i. decentralized web3 reshaping internet governance: towards the emergence of new forms of nation-statehood? future internet. 2023;16(10):361. available from: https://www. mdpi.com/1999-5903/16/10/361 doi: 10.3390/fi16100361  11. esmaeilzadeh p, mirzaei t, dharaiya m. the effects of data source, privacy and security concerns, and digital identity on personal health information sharing intentions: mixed methods study. jmir hum factors. 2022;9(3) . available from: https://humanfactors.jmir.org/2022/3/e36797 doi: 10.2196/36797 copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. please note: the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.383 https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data https://www.aha.org/statistics/fast-facts-us-hospitals https://www.kff.org/health-information-and-trust/poll-finding/kff-tracking-poll-on-health-information-and-trust-january-2025/ https://www.kff.org/health-information-and-trust/poll-finding/kff-tracking-poll-on-health-information-and-trust-january-2025/ https://www.kff.org/health-information-and-trust/poll-finding/kff-tracking-poll-on-health-information-and-trust-january-2025/ https://link.springer.com/book/10.1007/978-1-4842-6534-5 https://link.springer.com/book/10.1007/978-1-4842-6534-5 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https://blockchainhealthcaretoday.com/index.php/journal/article/view/357 https://blockchainhealthcaretoday.com/index.php/journal/article/view/357 https://www.mdpi.com/1999-5903/16/10/361 https://www.mdpi.com/1999-5903/16/10/361 https://humanfactors.jmir.org/2022/3/e36797 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 original research predictors of commercial success in blockchain healthcare insurance: a mixed-methods analysis raaga likhitha musunuri, bds, ms   ; kimberly s. brooks, md, ms   ; swapna ashish patel, bpharm, ms   ; trupti jayesh majgunkar, ms   ; krisha patel, bs, ms   ; and erin o’neill, ms   masters students in health informatics, bouvé college of health sciences and khoury college of computer sciences, northeastern university, boston, massachusetts, usa corresponding author: raaga likhitha musunuri, email: dr.raagalikhitha@gmail.com doi: https://doi.org/10.30953/bhty.v8.401 keywords: blockchain, commercial success, general data protection regulation, healthcare insurance, health insurance portability and accountability act of 1996, market capitalization, regulatory factors abstract background: blockchain healthcare insurance offers substantial potential for improving data transparency, efficiency, and security. however, many blockchain healthcare projects struggle to scale up and achieve commercial success. despite technical feasibility studies, limited research exists on the commercial success of these projects. this study explores the technical, regulatory, and strategic factors that contribute to market success in blockchain healthcare insurance initiatives. methods: using exploratory mixed methods, we combined quantitative market data analysis from coingecko application programming interface (api) with manual metadata curation from publicly available data. we conducted descriptive statistics, pearson correlation, and multiple regression analysis to evaluate relationships between key variables and market success. results: the health insurance portability and accountability act of 1996 (hipaa) compliance emerged as a significant predictor of higher market cap (β = +12.6, p = 0.006), while insurance partnerships negatively impacted success due to early-stage complexity (β = –15.3, p = 0.009). the model explains 95.7% of market cap variance (adjusted r² = 0.957). findings: these findings demonstrate how crucial technical preparedness and regulatory alignment are to the successful commercialization of blockchain-based health insurance. the importance of organizational scale is highlighted by a moderate association (r = 0.83) between team size and market success. the importance of strategic alliances and regulatory compliance is further shown by the theme analysis of white papers. investors should concentrate on projects with well-defined regulatory policies, while entrepreneurs should give hipaa compliance top priority early on. it is recommended that policymakers create more precise regulatory frameworks for blockchain in the medical field. for more thorough insights, future studies should increase the sample size. plain language summary blockchain technologies have the potential to transform healthcare insurance by improving data security, transparency, and efficiency. however, many blockchain healthcare projects struggle to progress beyond the pilot stage. our study explored factors influencing the market success of blockchain healthcare insurance projects, revealing that compliance with the health insurance portability and accountability act of 1996 (hipaa) and regulatory standards are key predictors of success. additionally, having too many early-stage partnerships can hinder progress, while strategic partnerships are more beneficial. larger project teams lead to better market performance. entrepreneurs should prioritize regulatory compliance, and policymakers should establish clearer blockchain frameworks. these factors are crucial for both technical functionality and commercial viability. submitted: april 28, 2025; accepted: july 8, 2025; published: august 1, 2025 https://orcid.org/0009-0008-6823-8054 https://orcid.org/0009-0000-2675-4060 https://orcid.org/0009-0009-8905-8404 https://orcid.org/0009-0009-2195-7091 https://orcid.org/0009-0007-7815-9678 https://orcid.org/0009-0000-3404-3007 mailto:dr.raagalikhitha@gmail.com https://doi.org/10.30953/bhty.v8.401 citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.4012 (page number not for citation purpose) raaga likhitha musunuri et al. as the 21st century began, the healthcare sector faced enormous pressure to offer high-quality services while keeping costs in check. blockchain has evolved into a promising approach for implementing a distributed and secure healthcare data management and sharing service. from a conceptual standpoint, blockchain can be viewed as a distributed collaborative system upheld by various entities in an environment where trust cannot be assumed. blockchain enables secure data processing among multiple parties without the need for trusted third parties. secure data processing among multiple parties can occur, and since the launch of bitcoin in 2008, blockchain has garnered intense focus from academia and industry. financial projections based on research and from markets indicate substantial growth in this sector, with the global blockchain market expected to expand from approximately $17.21 billion (€15.45 billion) in 2023 to $29.35 billion (€26.35 billion) in 2024, reflecting a compound annual growth rate of 70.6%. this revolutionary approach to data security and verification is transforming sectors where privacy and integrity are paramount. due to its outstanding characteristics, such as data traceability, tamper-proof data storage, and service availability, blockchain-based healthcare data management has been the subject of extensive research. with the addition of access control mechanisms, blockchain-based solutions enable flexible data sharing, promote accountability, and guarantee data authentication. as an example, patientory (ptoy) leverages blockchain technology for storage and transfer of healthcare data, which guarantees secure and efficient data exchange.1 health insurance represents a critical domain where blockchain’s transformative potential is particularly significant. the current healthcare insurance framework faces substantial challenges such as claim fraud, data security, interoperability, and trust challenges. the financial impact of these fraudulent activities is staggering, with estimates suggesting that healthcare fraud diverts approximately 10% of global healthcare spending, threatening economic stability worldwide. despite extensive research, the healthcare insurance industry still lacks real-time, comprehensive solutions for analyzing complex data from diverse sources. blockchain technology combined with ensemble learning offers a promising solution through its decentralized, tamper-resistant ledger, enhanced security, and smart contract capabilities. by distributing patient data across networks, blockchain reduces centralized system risks while ensuring data immutability creates verifiable, unalterable medical records.2 however, there is a critical research gap between technical feasibility and commercial success in real-world implementations. studies reveal that commercially successful blockchain healthcare projects represent merely 0.24% of tracked blockchain initiatives, as highlighted by fang3 with most enterprise efforts remaining confined to exploratory groups and not progressing beyond pilots or limited trials into mainstream adoption, as emphasized by krishnasamy and gopalakrishnan.4 this research addresses the critical commercialization gap in blockchain healthcare insurance by investigating which factors meaningfully differentiate market-successful implementations from failed initiatives. our research moves beyond theoretical feasibility to examine concrete determinants of commercial viability. to fill the research gap in understanding what makes blockchain healthcare insurance projects successful, the important factors were grouped into three main categories: technical, regulatory, and strategic. we developed a conceptual framework (figure 1) to address this identified research gap in commercialization success factors for blockchain healthcare insurance projects, emphasizing real-world implementations rather than merely theoretical possibilities. our framework synthesizes insights from previous blockchain healthcare adoption studies3,4 while explicitly focusing on market success rather than implementation feasibility. technical factors include the underlying blockchain platform (e.g., ethereum vs. custom solutions), specific use case orientation (e.g., insurance claims processing vs. patient record management), and system architecture design. regulatory factors center on compliance readiness, particularly adherence to the general data protection regulation (gdpr) and the hipaa, which may serve as critical signals of institutional trustworthiness and market credibility. strategic factors encompass organizational characteristics such as the number and quality of partnerships, team size, and geographic focus. these connected factors together affect how the market views a project, how ready institutions are to adopt it, and how it stands against competitors, which ultimately impacts the project’s success in the market, measured here as log-transformed market capitalization. based on earlier research that highlights the importance of trust in regulations for adopting blockchain, we suggest that regulatory factors, especially hipaa compliance, might have the biggest impact on a project’s success in the market, possibly accounting for major differences in how well it commercializes. literature review contemporary healthcare insurance systems face mounting challenges from fragmented data architectures that impede effective information exchange and create vulnerabilities to widespread fraud. this fragmentation manifests as siloed patient information across providers and insurers, generating significant interoperability issues as https://doi.org/10.30953/bhty.v8.401 citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.401 3 (page number not for citation purpose) success in blockchain healthcare insurance disparate systems employ incompatible data formats and standards. the financial impact of these systematic weaknesses is staggering. medical identity theft alone costs the healthcare industry over $30 billion (€27 billion) annually, while healthcare fraud schemes worldwide result in approximate losses of $260 billion (€233 billion) to medical insurance funds. these figures highlight the urgent need for innovative approaches to manage healthcare insurance data that address interoperability and security concerns. the scope and variety of healthcare insurance fraud further highlight this pressing challenge. in 2016, a significant federal investigation resulted in charges against more than 300 healthcare professionals involved in fraudulent billing schemes totaling approximately $900 million (€807 billion).5 beyond financial fraud, practices such as inappropriate opioid prescribing, often incentivized by deceptive pharmaceutical marketing campaigns that minimize addiction risks, compromise patient safety. these fraudulent activities collectively threaten the financial sustainability of healthcare insurance systems and patient well-being, creating momentum for technological solutions that can enhance transparency, security, and trust across the healthcare insurance ecosystem while preserving appropriate privacy protections. the evolution of healthcare reimbursement models introduced new complexities in fraud detection. traditional item-based payment systems have gradually shifted toward diagnosis-based models such as diagnosisrelated groups (drgs), where insurers pay fixed amounts based on diagnostic categories rather than individual services rendered. while this approach helps control excessive medical treatment and rising costs by standardizing payments, it also creates new avenues for fraud through diagnostic code manipulation. healthcare providers may intentionally upgrade low-cost disease codes to higher-paying alternatives to maximize reimbursement. the sheer volume of patient cases, with some chinese provinces processing 15 million inpatients annually, makes manual auditing impractical, necessitating advanced technological solutions that can efficiently identify suspicious patterns while maintaining data integrity across the healthcare insurance ecosystem.6 blockchain serves as a distributed solution to the growing complexity of healthcare data, offering realtime analysis, traceability, and counterfeit resistance.20 the immutable ledger and cryptographic protections of this system are ideal for the insurance sector, where data integrity and audibility are essential.1 by identifying unusual claim trends automatically, blockchain-enabled fraud detection systems enable proactive interventions, thereby preserving data privacy. nevertheless, the adoption of blockchain in health insurance is inconsistent due to regulatory misalignment and technological innovation.2 only 0.24% of blockchain ventures in the healthcare sector achieved commercial viability, primarily due to regulatory uncertainties and limited ecosystem integration.3 healthcare data are subject to privacy regulations like hipaa, and projects that prioritize regulatory alignment and confidentiality are more likely to gain institutional trust and be successful.7 integrating decentralized identification systems with compliant blockchain frameworks elevates patient autonomy, privacy, and trustworthiness, which are key factors concerning the uptake of healthcare. however, the fragmented regulatory environment and absence of interoperability standards pose significant fig. 1. conceptual framework of determinants of success in blockchain health insurance. hipaa: health insurance portability and accountability act of 1996; edpr: elcomsoft distributed password recovery (software). https://doi.org/10.30953/bhty.v5.xxx citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.4014 (page number not for citation purpose) raaga likhitha musunuri et al. challenges to institutional adoption and scalability in healthcare blockchain endeavors.8 according to studies on blockchain healthcare insurance initiatives, team size and strategy alignment are critical factors. the lack of resources or an extremely limited focus tends to make teams unable to secure and sustain ongoing development and scaling.8 conversely, larger, multidisciplinary teams are generally better attuned to address technical, legal, and regulatory issues associated with products.2 collaborations among providers, regulators, insurers, and other stakeholders are essential for the success of programs. excessive early-stage insurance partnerships can complicate operations and postpone financial returns.9 the results of literature studies indicate that strategic quality is more significant than quantity in these programs. blockchain offers a workable answer to the problems that healthcare insurance systems face, including fraud, data fragmentation, and compliance, but only if technical implementation, regulatory alignment, and organizational maturity all line up. methods and findings study design and rationale this study uses a combination of methods (e.g., looking at numbers from the market and gathering information by hand) to understand what makes blockchain healthcare insurance projects successful (figure 2). the market data help us see how successful the market is (e.g., market cap), while manually pulling information from white papers adds important details about things like rules and partnerships, which are key to understanding how blockchain healthcare insurance projects work. prior research focused on the technical feasibility of the blockchain in healthcare, while the market success factors remain understudied and unclear. our study aims to do an exploratory analysis of the regulatory, technical, and strategic implications that enable market success and adaptability, such as the role of hipaa compliance and partnerships with insurance companies. fig. 2. mixed methods methodology. hipaa: health insurance portability and accountability act of 1996. https://doi.org/10.30953/bhty.v8.401 citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.401 5 (page number not for citation purpose) success in blockchain healthcare insurance data sources and collection the primary data sources include market data from the coingecko api (https://www.coingecko.com/en/ap) and publicly available white papers and project documentation (snapshot collected on april 20, 2025). the data retrieval used the standardized /coins/markets and /coins/id endpoints, extracting project-level information including market capitalization (us dollars), current trading volume, circulating supply, launch year (genesis date), and platform type. retrieved data were saved in comma-separated values (csv) format and processed in python (pandas v2.2.1). in addition to market data, metadata was manually extracted from the white papers and public documentation of the selected blockchain healthcare insurance projects. these documents provided valuable insights into various factors influencing the market success of these projects, including technical architecture, regulatory compliance, and strategic focus. the metadata collected focused on several key variables: hipaa compliance, gdpr compliance, partnerships, platform type, use case, and team size. sampling strategy a purposive sampling method was used to select 10 blockchain healthcare insurance projects. the selection was based on the availability of white papers, market presence, and relevance to the healthcare insurance sector. projects were included if they had publicly available white papers and market data, such as market cap and trading volume. additionally, the projects selected were operational or had a visible market presence, indicating they were beyond the proof-of-concept phase. defining a successful project the sample captured a cross-section of geographically diverse, thematically varied, and technically distinct projects in the blockchain healthcare ecosystem, despite its small size, which limits generalizability. but it was appropriate for an exploratory analysis in a niche and evolving sector. the hipaa compliance was assessed as a critical factor in determining whether the project adheres to united states health information privacy standards.10 compliance with gdpr was considered for projects operating in european markets, ensuring they meet the data protection and privacy regulations.11 the nature of partnerships with insurance companies, healthcare providers, and other relevant organizations was examined, as these partnerships can enhance the project’s credibility and integration into the healthcare system. while hipaa compliance was evaluated for projects targeting the us market and gdpr for those operating in europe, other relevant data protection laws such as the california consumer privacy act (cpra) were not systematically included in this analysis; future research should expand the regulatory scope to capture additional regional frameworks. the type of blockchain platform used by the projects was also documented, as it influences scalability, security, and interoperability. the use case of each project was also recorded to determine whether it focuses on insurance claims processing, medical record management, or another aspect of healthcare insurance. finally, team size was noted as it could correlate with the project’s ability to scale and deliver successful outcomes. data processing and statistical analysis key variables were “cleansed” (identify and resolve potential data inconsistencies or errors) and standardized. the dependent variable, market capitalization, was log-transformed (log1p) to normalize skewness, as raw market caps varied widely across projects. the study used several statistical methods for data analysis and was conducted using python (pandas, statsmodels, and seaborn libraries). descriptive statistics were calculated to summarize the key characteristics of the selected projects, including market cap, team size, and compliance rates. pearson correlation coefficients were computed to explore the relationships between different variables, such as team size and market cap or hipaa compliance and market success. multiple regression analysis was also conducted using log-transformed market cap as the dependent variable. this method allowed for the evaluation of how different independent variables contributed to market cap while controlling for the influence of other variables. the log transformation of market cap was necessary to normalize the data, as market caps can vary significantly across projects. analyses were performed using python libraries: statsmodels (v0.14.0) for regression modeling and scipy (v1.14.1) for correlation analysis. visualization of key findings was performed using matplotlib (v3.10.0) and seaborn (v0.13.2). the use of mixed methods in this study is crucial to understanding the factors influencing market success. quantitative data, such as market caps, alone would miss the critical business, technical, and regulatory context that is essential for understanding why certain projects succeed or fail. manual metadata extraction from white papers and documentation provided important contextual information regarding the organizational, strategic, and regulatory dimensions of the projects, which could not be captured by market data alone. market data collected from coingecko was validated against secondary sources (e.g., coinmarketcap) to ensure accuracy within a 5% threshold. thematic analysis methodology this study also used a thematic analysis to qualitatively assess the white papers and public documents of selected (n = 10) blockchain healthcare insurance projects, which helped us understand the organizational, strategic, and regulatory factors that may not be captured through quantitative market data alone. to achieve this, we collected https://doi.org/10.30953/bhty.v5.xxx https://www.coingecko.com/en/ap citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.4016 (page number not for citation purpose) raaga likhitha musunuri et al. the data from the white papers and public project documentation, which primarily had insights about various factors such as technical architecture, partnerships, regulatory compliance, and strategic focus that added insights on market success. after this, we systematically coded these white papers using a deductive approach, which was derived from our research questions and literature review. this included categories such as regulatory compliance (e.g., hipaa, gdpr), partnerships (with healthcare providers, insurers, etc.), platform type (ethereum, custom, etc.), tokenomics (utility, rewards systems), and data privacy and artificial intelligence (ai) focus. this also involved identifying references to these themes within the white papers and assigning corresponding codes. for example, a reference to hipaa compliance in a white paper was coded under the regulatory compliance theme, and other such things. two independent researchers conducted the coding process, ensuring the reliability and consistency of the results. any discrepancies were discussed and resolved through consensus. this process aimed to minimize coding bias and improve the accuracy of the analysis. after the initial coding, themes were further refined and categorized into broader patterns of strategic focus. for example, partnerships were further divided into strategic partnerships with healthcare providers, insurance partnerships, and other partnerships (e.g., technology partners and investors). two independent researchers reviewed manual metadata coding to ensure consistency. discrepancies were resolved through consensus discussion. reliability metrics (e.g., inter-rater agreement) are reported in appendix a. this process minimized errors and ensured the data’s accuracy. however, future research should apply structured coding frameworks to standardize the extraction and analysis of metadata, which would further improve the reproducibility of the study and reduce potential biases introduced by manual data collection. the themes were analyzed to identify the most influential factors impacting market success. these insights were then triangulated with quantitative data (e.g., regression results, correlations) to provide a more comprehensive understanding of the factors contributing to market success. methodological challenges the primary methodological challenges that arose were handling multicollinearity and bias minimization. moderate multicollinearity was detected (e.g., hipaa compliance variance inflation factors [vif] ≈ 9) which was addressed through careful interpretation of coefficients and acknowledging limitations in extrapolation beyond exploratory findings. manual metadata extraction was standardized through predefined coding categories. sampling bias was minimized by adhering to explicit project selection criteria. in addition, keeping the market data volatility in mind, all the coingecko data was collected within the same 1-week window (april 2025) to control cryptocurrency market fluctuations. ethical considerations all data utilized were publicly available through open access sources. no human subjects or patient-level data were involved. institutional review board (irb) approval was not required. results descriptive statistics and project characteristics table 1 presents the descriptive statistics for the 10 blockchain healthcare insurance projects included in this study. the table includes various factors such as market cap, team size, hipaa compliance, gdpr compliance, and insurance partnerships, which helped in understanding the interdependencies that affect the performance of these projects in the market. the projects exhibit considerable variation in market capitalization, with a mean of $3.76  million (standard deviation [sd] = 3.04) and a range from $0.3 to $8.2 million. team sizes averaged 43.5 members (sd = 24.27), ranging from small teams of 10 to larger organizations with 80 members. half of the analyzed projects demonstrated hipaa compliance, while a larger proportion (80%) were gdpr-compliant. the projects maintained an average of 1.5 insurance partnerships (sd  = 1.08), ranging from 0 to 3 partnerships. table 1. descriptive statistics of 10 projects. variable count mean sd min 25% 50% 75% max market cap (in millions usd) 10 3.76 3.04 0.3 0.83 3.9 6.13 8.2 team size 10 43.5 24.27 10 22.5 45 60 80 hipaa compliance 10 0.5 0.53 0 0 0.5 1 1 gdpr compliance 10 0.8 0.42 0 1 1 1 1 insurance partnerships 10 1.5 1.08 0 1 1.5 2 3 gdpr: general data protection regulation; hipaa: health insurance portability and accountability act of 1996; max: maximum; min: minimum; sd: standard deviation; usd: united states dollars. https://doi.org/10.30953/bhty.v8.401 citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.401 7 (page number not for citation purpose) success in blockchain healthcare insurance market capitalization distribution as seen in figure 3, the market capitalization distribution is heavily skewed, with medibloc (med), a blockchain healthcare platform for secure medical data management, dominating the chart. this supports the finding of regulatory compliance being a significant success factor, as med was one of the projects with the highest compliance and market cap, with close to $74.5 million (€66.73 million), substantially outperforming other projects in the sample. interestingly, as we can see from figure 3, there is no active market value, despite technical prowess and promise in projects like humanscape (hum), a patient-centered blockchain healthcare ecosystem, doc. com (mtc), a blockchain-based telemedicine platform, lympo (lym), a blockchain-based health and wellness platform with incentivized fitness activities, ai doctor (aidoc), a blockchain-based healthcare platform using ai for medical diagnostics, and remote patient monitoring (rpm) applications. this points to the significant challenges faced by many blockchain healthcare initiatives in achieving substantial market traction. this skewed distribution emphasizes the finding that successful projects possess distinctive characteristics that separate them from less successful counterparts in this emerging sector. the correlation matrix visually represented in figure 4 reveals important relationships between key variables in our study. as observed in the figure, there is a strong positive correlation between hipaa compliance and market capitalization (r = 0.81), which indicates that regulatory compliance is one of the primary factors that drive a blockchain health insurance project’s success. team size also showed a remarkable correlation with market capitalization (r = 0.83), suggesting that organizational scale contributes to project success, as they might provide a better competitive edge. insurance partnerships displayed a moderately strong correlation with market capitalization (r = 0.71), highlighting the importance of strategic alliances in achieving market success. interestingly, gdpr compliance showed a weak correlation with market capitalization (r = 0.05), suggesting that while european data protection regulations may be important for global operations, they appear to have less direct impact on market success compared to us healthcare regulations in this sample. however, gdpr compliance showed moderate correlations with team size (r = 0.51) and insurance partnerships (r = 0.49), which may indicate indirect effects on market performance through organizational maturity and partnership development. hipaa compliance and market success as observed in figure 5, hipaa compliance played a major influence on market success with these projects. hipaa-compliant projects showed a higher market capitalization compared to non-compliant counterparts. this also strongly supports our regression findings, which identified hipaa compliance as a significant positive predictor of market success (β = 12.6, p = 0.006). the distinct statistical significance that this relation has shown is that regulatory compliance is critical for blockchain fig. 3. market capitalization. aidoc: ai doctor; lym: lympo; med: medibloc; mtc: doc.com; mtn: medicalchain; hum: humanscape; ptoy: patientory; rbm: robomed network; solve: solve.care; usd: us dollars. https://doi.org/10.30953/bhty.v5.xxx http://doc.com http://doc.com http://doc.com citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.4018 (page number not for citation purpose) raaga likhitha musunuri et al. healthcare projects. the compliant projects had nearly 12.6 times higher market capitalization than those that do not. this reiterates that regulatory trust and institutionality are important in the healthcare blockchain sector. insurance partnerships and market performance in addition, when we look at insurance partnerships and market capitalization, there are further interesting relationships that emerge (figure 6). the scatterplot shows an overall moderate positive correlation with fig. 4. correlation heatmap. hipaa: health insurance portability and accountability act of 1996. fig. 5. hipaa compliance versus log market cap boxplot. hipaa: health insurance portability and accountability act of 1996; usd: us dollars. https://doi.org/10.30953/bhty.v8.401 citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.401 9 (page number not for citation purpose) success in blockchain healthcare insurance r = 0.61; it also shows reduced returns, which resulted out of operational complexity from too many partnerships, and there are few other projects with higher returns despite lower partnership numbers. this could mean that while partnerships enhance and improve a project’s market outcomes, strategic alignment and quality are more important than numbers. this is also substantiated by our findings from regression analysis which showed a negative correlation between insurance partnerships and market success, when we controlled for other variables (β = –15.3, p = 0.009). in addition to this, it could be plausible that projects that had fig. 7. compliance and partnerships quadrant bubble chart. aidoc: ai doctor; dcn: dentacoin (blockchain platform focused on dental healthcare); hipaa: health insurance portability and accountability act of 1996; hum: humanscape; lym: lympo; med: medibloc; mtc: doc.com; mtn: medicalchain; ptoy: patientory; rbm: robomed network; solve: solve.care. fig. 6. partnerships versus market cap scatter plot. usd: us dollars. https://doi.org/10.30953/bhty.v5.xxx http://doc.com citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.40110 (page number not for citation purpose) raaga likhitha musunuri et al. numerous early-stage partnerships might have faced operational complexity without immediate financial returns, which often ends up delaying the project’s ability to scale and further impact its market growth. confluence of compliance and partnerships figure 7 shows the combined effect of hipaa compliance and strategic partnerships that affect market success. as observed in projects such as med and medicalchain (mtn), a blockchain platform for secure electronic health record management, which are hipaa-compliant with multiple partnerships at the top-right quadrant, they show a simple, straightforward correlation of market success. on the other hand, other projects, such as robomed network (rbm), a blockchain platform connecting healthcare providers and patients using smart contracts, aidoc, and doc. com (mtc), either lack compliance or rely solely on partnerships or are located in the lower quadrants with weaker market caps. the positioning of projects like med and solve.care (solve), a blockchain platform for healthcare administration and payments, in the right quadrant confirms their superior market performance backed by both regulatory compliance and strategic partnership development. this reiterates the idea that market success is not just determined by the number of partnerships but by a combination of regulatory readiness and strategic alliances. projects that focus on both compliance and meaningful partnerships appear to be more successful in capturing market value. temporal analysis of project launch year and market success the next part of our analysis relates to the project launch year and market success relations, where we identified that the earlier launched projects (2015– 2016) achieved a higher market capitalization than later entrants (2017 and beyond). the early market entry provided a first-mover advantage to these projects. in the project ptoy, a blockchain healthcare platform for patient-controlled health data management, which was launched in 2017, strategic execution and regulatory compliance were likely more significant factors in its market performance than simply being an early entrant, which implies that this is not a deterministic relationship. the trend line in figure 8, with a correlation of r = 0.24, suggests a mild relationship between earlier launch years and market success. however, it highlights that the quality of execution remains crucial for long-term success. this finding indicates that while being an early mover provides certain advantages in the blockchain healthcare space, project longevity alone does not guarantee commercial success without proper regulatory alignment and strategic execution. fig. 8. project timeline: launch year vs. current market status. med: medibloc; dcn: dentacoin; mtn: medicalchain; ptoy: patientory; solve: solve.care. https://doi.org/10.30953/bhty.v8.401 http://doc.com http://doc.com citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.401 11 (page number not for citation purpose) success in blockchain healthcare insurance thematic analysis of white papers our thematic analysis of blockchain healthcare insurance project white papers revealed several key themes that influence market success. table 2 summarizes these themes and the supporting evidence from our data analysis. the quantitative findings were reiterated by the thematic analysis we conducted, during which we identified more nuanced insights about regulatory compliance, partnership strategy, platform choice, use case, tokenomics, and ai/data privacy. during the analysis, we found that hipaa compliance is a defining theme among leading projects. hipaa-compliant projects are strongly associated with higher market capitalization, as shown by both correlation (r = 0.79) and regression analysis (β = 12.63, p = 0.006). this suggests that regulatory alignment is not only a technical or legal checkbox but a key market signal for trust and adoption in blockchain healthcare. a couple of projects, such as solve care and med, highlighted hipaa compliance as a key driver of their success, ensuring that their platforms gained trust and institutional adoption. the number of partnerships is positively correlated with market capitalization (r = 0.75), highlighting that ecosystem integration and external collaborations are central to project visibility and value. projects with more partnerships tend to have greater resources, reach, and legitimacy, which translates into higher market cap. med, for example, formed a key partnership with south korean healthcare providers, boosting its market credibility. however, partnerships specifically with insurance companies showed a negative association with market cap in regression (β = –15.11, p = 0.009). this unexpected finding may reflect the challenges and slow pace of insurance sector adoption or that insurance-focused partnerships alone do not guarantee broader market traction. team size is another strong theme that we identified, which was reinforced from quantitative insights, where we saw larger teams are associated with higher market capitalization (correlation r = 0.83). this suggests that organizational capacity – reflected in human resources – enables better execution, product development, and market engagement. our thematic analysis also revealed that most of the projects use ethereum or custom blockchain platforms, which employ dual or multichain architectures, but this was not a significant predictor in our regression analysis, which implies that for this sample, technical stack alone is not a differentiator for market success. another significant finding from this analysis is that projects like med migrated from qtum/ethereum to their custom “panacea” blockchain to improve scalability and interoperability. this revealed that the interoperability with existing healthcare systems, such as electronic health records (ehr) integration, was a stronger predictor of success than just the choice of platform. another key factor identified was data privacy and ai diagnostics, as in the case of ptoy and tokenomics in the case of dentacoin. this had less impact on market success, which implies that data ownership and privacy in the healthcare space are more valued than rewards. finally, from our thematic analysis, we identified that projects spanned across different use cases (insurance, personal health records, dental, wellness, diagnostics, claims) and geographic focuses (global, us, asia, europe). dummy variable analysis and regression suggest that global focus may have a borderline positive effect (β = 8.84, p = 0.057), but use case category and platform are not statistically significant predictors. the correlation heatmap reveals that hipaa compliance, team size, and partnerships are themselves interrelated, reflecting that successful projects tend to align across multiple strategic and organizational dimensions. regression analysis and multicollinearity our multiple regression analysis, using log-transformed market capitalization as the dependent variable and various independent variables such as hipaa compliance, team size, and insurance partnerships, yielded significant insights into the determinants of market success in blockchain healthcare projects. we used log transformation of the market cap variable to normalize distribution, as raw market caps have high variability across projects. hipaa compliance emerged as a strong positive predictor (β = 12.6, p = 0.006), indicating that hipaa-compliant projects exhibited substantially higher market capitalization compared to non-compliant projects. the complete regression analysis results are presented in appendix a. contrary to expectations, insurance partnerships showed a negative effect on market capitalization when controlling for other factors (β = –15.3, p = 0.009). this table 2. summary table of key themes. theme evidence from data/analysis. regulatory compliance hipaa compliance is the strongest positive predictor of market cap. strategic partnerships more partnerships = higher market cap; insurance partnerships ≠ success. team scale larger teams correlate with higher market cap. technical/platform choices no significant effect on market cap. use case/geographic focus diverse, but not significant predictors. adoption challenges many projects struggle to achieve scale; insurance focus is not enough. interconnectedness key success factors are correlated (compliance, partnerships, team). hipaa: health insurance portability and accountability act of 1996. https://doi.org/10.30953/bhty.v5.xxx citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.40112 (page number not for citation purpose) raaga likhitha musunuri et al. finding suggests that while partnerships are important, they must be strategically managed to avoid operational complexity that could hinder growth. team size demonstrated a positive relationship with market success (β  =  0.83, p = 0.031), supporting the notion that organizational scale contributes to project success. platform type (ethereum vs. custom) did not demonstrate statistical significance in our regression model (β = 0.17, p = 0.248), supporting our thematic analysis finding that the choice of blockchain platform alone may not be a determining factor for market success. the high adjusted r² value (0.957) indicates that our model explains a substantial proportion of the variance in market capitalization, suggesting that the included variables are strong predictors of market success in blockchain healthcare insurance projects. as noted in our methodology, we detected moderate multicollinearity in our regression analysis, particularly between hipaa compliance and insurance partnerships variables (vif ≈ 9). while this does not invalidate our findings, it does suggest caution in interpreting the independent effects of these variables and highlights the need for larger samples in future research. the multicollinearity detected between compliance and partnership variables aligns with our qualitative observation that successful projects tend to address regulatory and strategic dimensions simultaneously, making it challenging to completely isolate their individual effects. future studies should employ larger samples and explore techniques such as regularization to address multicollinearity more effectively. the detailed statistical output of our regression analysis can be found in supplementary appendix a. discussion the findings from this study provide a practical framework for stakeholders operating at the intersection of blockchain and healthcare (table 3). a stakeholder-oriented decision matrix is proposed to help align strategic priorities with regulatory requirements and prevailing market conditions. as commercial success in this domain is closely linked to strategic positioning, trust-building, and operational readiness, the matrix offers a structured approach to informed decision-making. project launch year this study explored whether the launch year of a blockchain healthcare project influenced its market success and long-term sustainability. early movers, particularly those initiated between 2015 and 2017, tended to capture greater market value compared to projects launched in later years. this trend suggests early movers benefit from high market visibility and early investment potential. however, long-term survival appears to depend more on a project’s ability to adapt to evolving compliance requirements and interoperability standards that exist in the healthcare space. several early projects, like rbm, despite initial traction, eventually lost momentum due to a lack of alignment with regulatory expectations.12 as fang3 emphasizes, commercial viability in blockchain healthcare is not solely determined by timing in the market but by organizational maturity and adequate integration with the healthcare ecosystem. team size organizational scale, reflected in larger team size, showed a moderately strong positive correlation with market success (r = 0.83), suggesting that startups with greater organizational maturity are more likely to possess the resources needed to navigate regulatory complexity and translate their solutions into real-world healthcare applications. prior use cases show that many projects fail to progress beyond the conceptual phase, often due to limited resources.3 additionally, under-resourced teams may lack the necessary diversity and expertise to navigate legal, regulatory, interoperability, and technical challenges associated with large-scale adoption. effective strategic leadership is crucial to advancing these projects through the final stages required for substantial market uptake. future research could focus on identifying specific thresholds in team composition and organizational structure that confer significant market success. table 3. stakeholder priorities and recommended actions based on study findings. stakeholder priority strategic action entrepreneurs compliance interoperability align with hipaa and ehr standards early investors regulatory readiness team quality prioritize regulatory maturity and organizational depth healthcare organizations risk mitigation conduct readiness assessments before partnering. hipaa: health insurance portability and accountability act of 1996; ehr: electronic health records. https://doi.org/10.30953/bhty.v8.401 citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.401 13 (page number not for citation purpose) success in blockchain healthcare insurance platform type platform type did not show a significant correlation with market capitalization in this study. ethereum, as a public blockchain, may confer reputational trust and familiarity with developers; however, its general-purpose design may not always align with the specific privacy, scalability, and interoperability needs of healthcare applications.13 while ethereum-based projects exhibited minor positive trends, this suggests that platform choice may be a secondary factor compared to governance models, regulatory strategies, and integration with existing health systems. projects that failed to achieve market success it is important to acknowledge those projects that failed to reach commercial viability or maintain sustainable capitalization. notably, rbm, a patient-centered platform combining ai and blockchain to connect patients and providers,13 gained early visibility and funding success but later struggled due to interoperability limitations and inadequate compliance with institutional standards. several studies have cited the inability of rbm to navigate and integrate with dominant standards like fhir (fast healthcare interoperability resources) as a key factor limiting its scalability in the health insurance domain.14,15 this case emphasizes that market capitalization is not only influenced by technical innovation or platform type but also by integration with healthcare data standards and adherence to evolving privacy regulations. project examples that failed to achieve sufficient market success were excluded from this analysis, revealing a key limitation in the form of survivorship bias. since only successful projects were included in the dataset, the analysis may underrepresent those factors like partnership quality, organizational structure, and so on that contributed to project failure in those cases. future research should consider a wide variety of projects, including delisted projects, to better understand the full spectrum of success and failure in blockchain-based health insurance ventures. contextual interpretation of regulatory findings the observed stronger association between hipaa compliance and market success in this study is specific to the sample’s geographic and operational focus. this does not imply that hipaa is inherently more important than gdpr or other data protection frameworks globally. rather, it highlights that alignment with the dominant regulatory standards in a project’s target market is a key determinant of commercial viability. jurisdictional relevance of regulatory compliance regulatory compliance was assessed regarding the primary jurisdictional frameworks relevant to each project’s operational geography – namely, hipaa for us-focused projects and gdpr for those operating in the european union. while our findings indicate that hipaa compliance was a strong predictor of market success in this sample, this reflects the predominance of us-centric projects and should not be interpreted as a universal hierarchy of regulatory importance. we did not systematically analyze compliance with other regional data protection laws, such as the cpra, which may also play a significant role in us markets. future studies should expand the regulatory scope to include such frameworks for a more comprehensive analysis. hipaa compliance had a stronger predictive value for market success (β = 12.6) compared to gdpr, which showed no statistical significance. this suggests that for blockchain healthcare projects targeting the u.s. market, the hipaa compliance serves as a more crucial institutional trust signal than gdpr does for eu markets. entrepreneur insights entrepreneurs in blockchain should prioritize early alignment with regulatory frameworks like hipaa, which governs healthcare data privacy in the us, and the gdpr, which applies to personal data in the european union. early compliance builds trust and signals institutional credibility to potential partners and investors. the regression and outlier analyses show that early onboarding to these frameworks is a strong, positive predictor of successful market signaling and partner acquisition. in addition, interoperability with existing systems plays an important role in ensuring market uptake and widespread adoption. this approach not only reduces costs and implementation time, but also positions startups as collaborative contributors to the current ecosystem, rather than as isolated disruptors.4 investor insights investors should prioritize the strategic quality of partnerships over their sheer quantity. this study identified a negative correlation between the number of insurance partnerships and market capitalization (regression β  =  –15.3, p = 0.009), indicating that an excessive or unfocused partnership strategy may lead to operational inefficiencies. such complexities can hinder a project’s scalability, particularly in the healthcare sector, where technological adoption tends to be slow and risk averse. however, this relationship might be confounded by project maturity or strategic focus. less mature projects may overextend by forming numerous partnerships in the absence of a well-defined integration roadmap or regulatory foundation. as a result, the negative effect of excessive partnerships may reflect the challenges faced by unfocused projects in executing complex integrations. it is possible that fewer, more strategically aligned partnerships focusing on integration and compliance are https://doi.org/10.30953/bhty.v5.xxx citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.40114 (page number not for citation purpose) raaga likhitha musunuri et al. better predictors of long-term success. therefore, investors should focus on strategic partnership alignment and robust organizational infrastructure when evaluating blockchain ventures.3 policy recommendations policy reform is an important factor in facilitating the broader adoption of blockchain technologies within the healthcare sector. clear, comprehensive regulatory frameworks can help streamline implementation and enhance trust among key stakeholders, including patients, providers, and institutional partners, by reducing ambiguity and demonstrating governance.16 policymakers should prioritize the creation of standardized compliance frameworks specifically tailored to healthcare’s complex privacy and interoperability demands. yeung (2021) 8 and krishnasamy and gopalakrishna (2023)4 emphasize that inconsistent or fragmented regulatory environments continue to pose significant obstacles to both the adoption and investment viability of blockchain-based healthcare solutions. in contrast, regulatory clarity has been shown to improve investor confidence and facilitate sustainable scaling. compliance with local and international data protection standards (hipaa, gdpr) will also be essential to ensure interoperability across borders, trustworthiness, and legal certainty.17 regulatory frameworks encompassing these standards should also be established. governance mechanisms such as decentralized identity systems should be developed in tandem with legal reforms to enhance patient agency and transparency.7 historical use cases also indicate that projects aligned with standardized policy frameworks tend to demonstrate stronger market performance and more durable institutional integration than those that sought to circumvent regulatory norms.3 furthermore, it is important that these policies be made in conjunction with all stakeholders in mind, that is industry leaders, healthcare providers, and patients, to enable successful adoption, considering appropriate ethical considerations in addition to technical relevance to ensure market sustainability.18,19 limitations the study has several limitations that should be considered when interpreting the results. firstly, the sample size is small. only 10 projects were included; hence, the findings might not apply to the wider range of blockchain healthcare projects. additionally, the manual extraction of project metadata could also introduce some biases, as it is not always easy to capture all the relevant details consistently. it is important to note that this study exclusively analyzed projects with ongoing market presence and available capitalization data, thereby excluding projects that failed to reach or sustain commercial viability. this introduces a survivorship bias, as the analysis may underrepresent factors contributing to project failure. we recognize that including failed or delisted projects in future research will be essential to fully understand the determinants of both success and failure in blockchain healthcare insurance. furthermore, the study’s reliance on a market snapshot further limits its accuracy, as market capitalization fluctuates over time, making the data potentially unrepresentative of long-term trends. lastly, some multicollinearity was observed in the regression analysis, with vif indicating some overlap among predictor variables. this is somewhat common in exploratory studies, but it’s still something to keep in mind when interpreting the findings. conclusion and future recommendations our mixed-methods analysis demonstrated that regulatory compliance, particularly hipaa compliance, is the strongest predictor of market success in this emerging sector. strategic partnerships contribute to success when properly aligned with project objectives, though excessive early partnerships may introduce operational complexities. team size and early market entry provide additional advantages but appear less influential than regulatory compliance and strategic partnerships in determining market outcomes. the finding that hipaa-compliant projects demonstrated significantly higher market capitalization (β = 12.6, p = 0.006) underscores the critical importance of regulatory alignment in the healthcare blockchain sector. projects that prioritize compliance with healthcare privacy regulations are better positioned to gain market trust and achieve commercial success in this highly regulated industry. the results reinforce the importance of balancing regulatory readiness with strategic execution, ensuring scalability through organizational maturity, and effectively navigating the technical landscape of blockchain. future research should systematically examine the influence of additional data protection laws, such as the california consumer privacy act and other state or national regulations, to capture the full spectrum of compliance requirements impacting blockchain healthcare insurance projects in diverse markets. call to action our call to action is threefold, focusing on entrepreneurs, policymakers, investors, and venture capital to ensure the presence of blockchain in the healthcare insurance space more tactfully. for entrepreneurs and innovators, the focus primarily is on prioritizing regulatory compliance at the project initiation stages to ensure compliance of these projects with the u.s. market and is particularly hipaa-compliant. https://doi.org/10.30953/bhty.v8.401 citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.401 15 (page number not for citation purpose) success in blockchain healthcare insurance in addition, entrepreneurs should focus on developing strategic partnerships with established healthcare institutions rather than pursuing numerous partnerships without clear integration plans, which enable them to achieve a product-market fit and scaling feasibility. for this to happen, the entrepreneurs should ensure that their token utility models are in alignment with healthcare workflows and provide tangible benefits to the stakeholders, which enables a functional and strategic partnership. the startups and entrepreneurs should ensure that their focus is on technical integration and interoperability capabilities too, which help in integrating with the existing health infrastructure to improve adoption rates. the investors and venture capital should ensure that the evaluation of blockchain healthcare projects for potential investment is based on their regulatory readiness and compliance frameworks. these organizations should thoroughly investigate strategic alignment of partnerships rather than merely counting the number of partnerships and ensure that their focus is on the regulatory and strategic execution rather than just the selection of the platform (ethereum and custom solutions) by the startup. the policymakers and regulators, on the other hand, should focus on developing standard and clear regulatory frameworks, specifically addressing blockchain applications in healthcare insurance. this includes a strategic alignment between regulations such as hipaa and privacy protections to accommodate blockchain innovations. there is a sincere need for the creation of regulatory sandboxes to facilitate compliant innovation. finally, there is a need for establishing guidelines for tokenization models in healthcare to enhance market confidence. policy frameworks should address regulatory compliance and provide incentives for blockchain adoption through clear guidelines on tokenomics and patient data ownership. future research this study represents an initial exploration of the factors influencing market success in blockchain healthcare insurance projects. while our findings provide valuable insights for entrepreneurs, investors, and policymakers, continued research is necessary to fully understand this rapidly evolving field and to develop comprehensive frameworks for evaluating and predicting project success. we realize the need for expansion of the dataset to include a larger sample of blockchain health insurance projects. there is a need to investigate the failed projects, which will help us identify common pitfalls and risk factors. the need to develop a sophisticated metric to evaluate partnership quality beyond simple counting is of prime importance in the short term. furthermore, there is a need to conduct longitudinal studies that track blockchain healthcare projects over time to assess the market trajectory based on regulatory compliance and partnership influence and explore the impact of different tokenomics models on long-term sustainability. this will further be influenced by the technical architecture decisions and market outcomes. finally, there is a need to understand and focus on regulatory convergence patterns that are present across global markets and their impact on blockchain healthcare adoption rates, especially in insurance. it is also imperative that as blockchain is growing, other emerging markets such as ai and iot coexist, and it is very important to understand their market outcomes when they are integrated with blockchain. funding this editorial received no external funding. disclosure of financial and non-financial relationships and activities the authors declare no financial or non-financial relationships or activities that could appear to have influenced the submitted work. contributors raaga likhitha musunuri, kimberly s. brooks, swapna ashish patel, trupti jayesh majgunkar, krisha patel, and erin o’neill all contributed to conceptualization, methodology, investigation, data curation, formal analysis, visualization, software, writing original draft, and writing review & editing. all authors contributed equally to this work. all authors have read and agreed to the published version of the manuscript. data availability statement (das), data sharing, reproducibility, and data repositories the data supporting the findings of this study are included within the article and its supplementary materials. the market data was collected from coingecko api (https:// www.coingecko.com/en/api) during april 2025, with specific snapshot dates noted in the methodology section. metadata from white papers was manually extracted following the coding framework described in the methods and summarized in appendix a. the complete dataset of market capitalization values, compliance metrics, and partnership data used for statistical analyses is available from the corresponding author upon reasonable request. python code used for statistical analysis and visualization (using pandas v2.2.1, statsmodels v0.14.0, scipy v1.14.1, matplotlib v3.10.0, and seaborn v0.13.2) is available upon request. ai assistance was utilized for code optimization and debugging during the analysis phase. acknowledgments the authors would like to acknowledge northeastern university for providing resources and support for this https://doi.org/10.30953/bhty.v5.xxx https://www.coingecko.com/en/api https://www.coingecko.com/en/api citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.40116 (page number not for citation purpose) raaga likhitha musunuri et al. research. we also thank the blockchain healthcare projects whose public data made this study possible. application of ai-generated text or related technology ai tools were used to improve efficiency and fix syntax errors in the python code (pandas v2.2.1, statsmodels v0.14.0, scipy v1.14.1, matplotlib v3.10.0, and seaborn v0.13.2) used for statistical analysis. this assistance was limited to technical implementation and did not influence analytical design, interpretation of results, or research conclusions. no ai tools were used to generate research concepts, design the study methodology, interpret findings, or draft significant portions of text. all writing reflects the authors’ original thoughts and expertise. this disclosure follows bhty’s ethical publication practices regarding ai use in research. references 1. zhou f, huang y, li c, feng x, yin w, zhang g, et al. blockchain 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https://doi.org/10.56294/dm2023166 18. tabari p, costagliola g, rosa md, boeker m. state-of-theart fhir-based data model and structure implementations: a systematic scoping review (preprint). jmir med inform. 2024;12:e58445. https://doi.org/10.2196/58445 19. aich s, tripathy s, joo mi, kim hc. critical dimensions of blockchain technology implementation in the healthcare industry: an integrated systems management approach. sustainability. 2021;13(9):5269. https://doi.org/10.3390/su13095269 20. jafri r, singh s. blockchain applications for the healthcare sector: uses beyond bitcoin. blockchain appl healthc inform [internet]. 2022 [cited 2022 dec 1];71–92. available from: https:// www.ncbi.nlm.nih.gov/pmc/articles/pmc9212252/ copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons. org/licenses/by-nc/4.0. the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.401 https://www.sciencedirect.com/science/article/pii/s2667102624000627?via%3dihub https://www.sciencedirect.com/science/article/pii/s2667102624000627?via%3dihub https://doi.org/10.1016/j.compeleceng.2024.109898 https://doi.org/10.30953/bhty.v4.166 https://doi.org/10.30953/bhty.v6.280 https://doi.org/10.30953/bhty.v6.280 https://www.sciencedirect.com/science/article/pii/s2096720924000551 https://www.sciencedirect.com/science/article/pii/s2096720924000551 https://doi.org/10.1016/j.future.2021.12.006 https://www.jmir.org/2024/1/e46556/ https://www.jmir.org/2024/1/e46556/ https://doi.org/10.2196/24109 https://doi.org/10.2196/24109 https://blockchainhealthcaretoday.com/index.php/journal/article/view/298 https://blockchainhealthcaretoday.com/index.php/journal/article/view/298 https://doi.org/10.1080/20479700.2016.1270875 https://www.thinkmind.org/articles/etelemed_2020_3_250_40097.pdf https://www.thinkmind.org/articles/etelemed_2020_3_250_40097.pdf https://pmc.ncbi.nlm.nih.gov/articles/pmc11082361/ https://pmc.ncbi.nlm.nih.gov/articles/pmc11082361/ https://www.dre.vanderbilt.edu/~schmidt/pdf/fhirchain-jnca.pdf https://www.dre.vanderbilt.edu/~schmidt/pdf/fhirchain-jnca.pdf https://doi.org/10.2196/17475 https://doi.org/10.3390/digital2010005 https://doi.org/10.56294/dm2023166 https://doi.org/10.2196/58445 https://doi.org/10.3390/su13095269 https://www.ncbi.nlm.nih.gov/pmc/articles/pmc9212252/ https://www.ncbi.nlm.nih.gov/pmc/articles/pmc9212252/ http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.401 17 (page number not for citation purpose) success in blockchain healthcare insurance appendix a regression analysis addendum: acronyms blockchain project names and tokens • aidoc: ai doctor (blockchain-based healthcare platform using artificial intelligence for medical diagnostics) • dcn: dentacoin (blockchain platform focused on dental healthcare) • hum: humanscape (patient-centered blockchain healthcare ecosystem) • lym: lympo (blockchain-based health and wellness platform with incentivized fitness activities) • med: medibloc (blockchain healthcare platform for secure medical data management) • mtc: doc.com (blockchain-based telemedicine platform) • mtn: medicalchain (blockchain platform for secure electronic health record management) • ptoy: patientory (blockchain healthcare platform for patient-controlled health data management) • rbm: robomed network (blockchain platform connecting healthcare providers and patients using smart contracts) • solve: solve.care (blockchain platform for healthcare administration and payments) • uhc: universal health coin (blockchain-based healthcare payment solution) technical terms • csv: comma-separated values (data file format) • das: data availability statement • drgs: diagnosis-related groups (patient classification system for healthcare payments) • edpr: elcomsoft distributed password recovery (software) • ehr: electronic health record • fhir: fast healthcare interoperability resources (standard for healthcare data exchange) • hipaa: health insurance portability and accountability act of 1996 • mtm: medication therapy management (healthcare service to optimize medication use) • rpm: remote patient monitoring https://doi.org/10.30953/bhty.v5.xxx http://doc.com citation: blockchain in healthcare today 2025, 8: 401 https://doi.org/10.30953/bhty.v8.40118 (page number not for citation purpose) raaga likhitha musunuri et al. • vif: variance inflation factor (statistical measure used in regression analysis) regulatory terms • gdpr: general data protection regulation (european union data privacy regulation) • irb: institutional review board analysis tools • ai: artificial intelligence https://doi.org/10.30953/bhty.v8.401 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 editorial blockchain technology predictions 2024: transformations in healthcare, patient identity, and public health gianluca de novi, phd1, natalia sofia, pharmd, msc2, ingrid vasiliu-feltes, md, emba3 , christine yan zang, phd, ceo4 , frank ricotta5 1imaging department, harvard medical school, massachusetts general hospital, boston, massachusetts, usa; 2drug sciences & toxicology, digital patient access, zurich, switzerland; 3universityof miami, miami, florida, usa; 4the metaverse institute, london, uk; 5ceo & founder, burstiq, englewood, colorado, usa *corresponding author: gianluca, email: denovi.gianluca@mgh.harvard.edu keywords: ai-assisted learning; artificial intelligence; chatgpt; blockchain; digital twin; healthcare; multi-omics; multiversity; public health; web3. abstract in an era characterized by the convergence of cutting-edge technologies, the world of healthcare and public health is on the brink of a profound transformation that will shape the future of medicine and wellness. this transformation is not merely an incremental step forward but a paradigm shift driven by the synergistic integration of digital twins, blockchain technology, artificial intelligence, and multi-omics platforms collectively propelling us into uncharted territory. integrating these innovations holds the potential to rewrite the rules of engagement in clinical trials, revamp the strategies for preventing public health crises, and redefine how we manage, share, and secure healthcare data. as we embark on this journey of exploration and innovation, we find ourselves at a pivotal juncture, akin to the invention of the microscope in biology or the discovery of antibiotics in medicine. we are at the crossroads of a new era with immense promise and transformative power. received: october 23, 2023; accepted: october 24, 2023; published: november 24, 2023 the concept of creating a digital twin (dt) is rapidly evolving from a visionary idea into a reality that promises to redefine our approach to healthcare and personal well-being. in this emerging landscape, we will have the privilege of possessing a virtual alter ego—an entity we can use for experimentation and exploration before making critical decisions about our health and lifestyle. however, as this transformation gains momentum, a central challenge emerges—ensuring the quality and authenticity of the data that fuel these dt models. the integrity of the data that fuel the artificial intelligence (ai) algorithms responsible for constructing dts is paramount. to achieve this, the integration of blockchain technology becomes indispensable at every stage where patient data are collected, processed, and utilized, particularly in the context of clinical trials. with its capacity to establish proof of data provenance and facilitate robust data auditing, blockchain is poised to become an essential component of this evolving ecosystem. in the near future, it is anticipated that proof of data origin and meticulous data auditing will not merely be optional but standard practices in creating dependable and internally consistent dts. in this way, blockchain technology will play a pivotal role in assuring the reliability and coherence of these virtual counterparts, ensuring that they serve as trusted allies in our journey toward healthier and more informed decision-making. gianluca de novi, phd the infusion of connected devices into patients’ lives and clinical trials has ushered in an era of unprecedented data generation. this data deluge, coupled with advancements in big data analytics, cloud computing, machine learning (ml), and ai, has paved the way for the emergence of dt in healthcare. dts, virtual representations of real-world entities or processes, offer a novel approach to clinical trials. by leveraging historical and real-time data, dts can simulate patient trajectories, aiding in more efficient and effective trial design and personalized medicine. however, creating dts requires vast amounts of patient data, https://orcid.org/0000-0001-7276-354x https://orcid.org/0009-0008-3462-3245 mailto:denovi.gianluca@mgh.harvard.edu citation: blockchain in healthcare today 2023, 6: 287 https://doi.org/10.30953/bhty.v6.2872 (page number not for citation purpose) gianluca de novi et al. raising concerns about data security, privacy, and integrity. this is where blockchain technology steps in. blockchain, with its decentralized and secure ledger system, ensures data integrity and confidentiality, making it an ideal companion for dts in healthcare. blockchain provides a reliable audit trail and access management, addressing critical concerns related to sensitive medical data. if you want to ensure that your dt is reliable, the data ingested to the ml must be genuine. looking beyond clinical trials, the convergence of blockchain, dts, and multi-omics platforms promises to redefine public health prevention. blockchain enhances data security and interoperability, while dts offer the ability to simulate health trajectories, enabling proactive interventions. when integrated with multi-omics data, a comprehensive understanding of individual health is achieved, paving the way for tailored preventive strategies. nevertheless, these innovations also raise ethical questions and challenges. the responsible deployment of these technologies is imperative to protect data privacy and individual rights. the battle for self-sovereignty and digital rights is becoming increasingly relevant as we navigate the digital age, where ai and predictive models can influence and mold human behavior. natalia sofia, pharmd, msc digital twins and blockchain: data-driven innovation in clinical trials during the last decades, the infusion of connected devices across patients’ daily lives and within clinical trials has grown considerably. such devices produce large volumes of data, and along with the advances in big data analytics, cloud computing, and new technologies such as ml and ai, they are altering how we store, process, and exchange health data. looking into the potential application of dt models in healthcare is challenging due to the lack of an adequate and secure data collection process. meet your twin dts are considered virtual representations of real-world physical entities/processes from a physical space. dts bring extraordinary data-driven research, optimization, and innovation by leveraging real-time and historical data to represent the past and present and simulate predicted futures. one key requirement for building a dt environment is that the virtual and physical properties must be congruous at any moment for the corresponding purpose. within the healthcare scope, a virtual patient in a digital space requires a bulk of data, representing the patient from a physical space. in other words, dts consist of the relevant it components for status updates and connectivity. their unique technological capabilities help untangle complex environments via abstraction, streamline business models, implement ai/ml solutions, and increase analytical power and confidence in the desired outcomes. but how can we create more efficient and effective clinical trial designs and personalized medicines? dts can be created by referring to patients in the clinical trial sample. they add information for existing subjects and generate a predicted clinical trajectory according to the clinical study timelines. by using large historical datasets of longitudinal patient information to generate virtual patients, dt models estimate how these patients would evolve over the course of the trial if they were to be given a placebo. dts can be applied to any therapeutic area, and such innovations may contribute to prospectively designing more efficient and effective trials with higher statistical power or to recover power in ongoing trials impacted by low enrollment or high dropout rates. empowering digital twins with blockchain within clinical research, there is a great variety of medical data sources, such as electronic health records, open clinical datasets, data from social networks, and other external applications, which, if acquired properly, could provide a rich repository for creating dt models. nonetheless, there are security and privacy concerns as medical data are very sensitive and can be used in malicious ways, and dt models focus on creating the virtual environment and do not provide any necessary steps about data integrity and confidentiality breaches. as data are the cornerstone of creating a successful dt model, ensuring data integrity, quality, and confidentiality is critical for a reliable dt system. to that direction, blockchain technology, as a distributed ledger, ensures that all containing blocks’ data are distributed to all the peer nodes over the network, and members of disparate groups can carry out transactions and validate them in a distributed environment without needing a centralized system. this provides a highly secure, timestamped, and reliable audit trail and access management. in this abstract, we aim to explore the different capabilities of dts as an emerging technology, raise awareness, and stay two steps ahead. we believe that dts, along with other advanced technologies such as ml, ai, and blockchain, can offer a major opportunity for clinical research and enable operational efficiency for clinical trials and data management. ingrid vasiliu-feltes, md, emba for 2024, the convergence of blockchain technology, dt concepts, and multi-omics platforms is set to usher in a new era of healthcare, profoundly impacting precision public health strategies. this amalgamation of technological advancements holds transformative potential across various domains of public health, with prevention being a prominent focal point. blockchain technology, recognized for its incorruptible decentralized ledger system, is poised to catalyze data https://doi.org/10.30953/bhty.v6.287 citation: blockchain in healthcare today 2023, 6: 287 https://doi.org/10.30953/bhty.v6.287 3 (page number not for citation purpose) blockchain technology predictions 2024 security and interoperability in preventive public health measures. by establishing cryptographic hashes and distributed consensus mechanisms, blockchain ensures the veracity and privacy of health data. blockchain can amplify the impact of dt platforms to monitor public health data. in the context of prevention, this synergy will facilitate real-time tracking of physiological parameters and the early detection of anomalies, thereby allowing for real-time, decisive interventions and personalized preventive strategies. dts, as virtual replicas of individual organs, disease states, medical devices, novel therapeutics, hospital rooms, or complex health ecosystems, exhibit their prowess in enhancing prevention efforts when coupled with blockchain capabilities. these intricate models, fed with data from wearables, sensors or bio-implants, and other environmental sources, can simulate potential health trajectories in public health surveillance. these data simulations can change the public and global health paradigm when paired with blockchain’s data integrity and provenance capabilities. this predictive prowess enables proactive interventions, effectively targeting potential threats before they escalate into a full-blown health crisis. in parallel, integrating multi-omics platforms, encompassing genomics, proteomics, and metabolomics data, further augments public health prevention strategies. the blockchain application ensures secure storage and traceability of multi-omics data, maintaining data integrity and accessibility. when combined with dts, multi-omics insights offer a comprehensive understanding of an individual’s genetic predispositions and molecular health indicators. this holistic perspective equips public healthcare stakeholders with the insights to tailor preventive interventions based on intricate biological signatures. the impending convergence of blockchain technology, dts, and multi-omics platforms in 2024 promises to reshape the landscape of public health prevention. the fusion of these advancements, accentuating blockchain’s data security, dt’s simulation capabilities, and multi-omics intricate biological insights, presents a paradigm shift in the precision of preventive measures. however, it is important to emphasize that the success of this trifecta relies on responsible deployment. proactive, robust cyber-ethics programs must be diligently integrated to safeguard data privacy, ensure responsible data usage, and maintain the digital trust for all public health stakeholders. only through such responsible deployment, can we harness the full potential of this innovative convergence as we transition to a new era of precision public health? christine yan zang, phd by 2040, blockchain can potentially improve healthcare data and services’ security, privacy, interoperability, and innovation. however, it also faces many challenges and limitations, such as data quality, scalability, performance, regulation, governance, and adoption.1 blockchain could enable smart contracts that automate the execution of agreements between parties based on predefined rules and conditions.2 for example, smart contracts could facilitate the payment and reimbursement of healthcare services, the verification and credentialing of healthcare providers, the management and distribution of medical supplies and drugs, and the enforcement of data sharing and consent policies.1 blockchain could support digital identity systems that provide a secure and verifiable way of identifying individuals and entities in the healthcare sector.2 for example, digital identity could be used to authenticate patients, providers, insurers, researchers, and other stakeholders and to link their health records, claims, prescriptions, test results, and other data across different platforms and sources.1 blockchain could empower patient-centric approaches that give patients more control and ownership over their health data and decisions.2 for example, patients could use blockchain to store their personal health records in a decentralized manner, to grant or revoke access permissions to their data, to participate in health research or clinical trials, to access personalized health recommendations or treatments, and to benefit from data monetization or incentives.1 blockchain could foster collaboration and innovation among different stakeholders in the healthcare sector by enabling data sharing and exchange securely and transparently.2 for example, blockchain could facilitate the creation of health data commons or marketplaces that allow researchers, providers, insurers, regulators, and others to access and use health data for various purposes, such as improving quality of care, advancing medical knowledge, developing new drugs or devices, or creating new business models or services.1 frank ricotta, ceo & founder, burstiq in 2024, humans and ai will converge. in 2023, ai stepped out from the shadows with a roar and created one of the most disruptive societal forces we have witnessed in a long time. chatgpt (generative pre-trained transformer) reached 100 million users in just two months. it took facebook and youtube over four years, twitter over five years, instagram over two years, and tiktok nine months to reach this milestone.1 with three social platforms boasting an active user base of over 1 billion, it is safe to say our world is truly connected. it makes sense that chatgpt had such wide adoption so quickly. with this level of connection, the curiosity and appetite for ai are increasing, which means innovation cycles will continue to accelerate. these are exciting times, and it is fun to have a frontrow seat to watch and participate in the explosion of ai. https://doi.org/10.30953/bhty.v6.287 citation: blockchain in healthcare today 2023, 6: 287 https://doi.org/10.30953/bhty.v6.2874 (page number not for citation purpose) gianluca de novi et al. here are my four predictions and two cautions heading into 2024. prediction 1: the rise of digital twins in 2024, we will witness the emergence of commercially viable dts for people. what is a dt? dr. sofia discussed this earlier. i will add that until now, dt technology has focused on creating a virtual representation of a physical object or system used, for example, for virtual simulations before real devices are designed and deployed. as the industry begins to push the boundaries of this technology coupled with the advancements in ai, particularly generative ai, we are reaching an inflection point where we can now start to represent a person in a variety of ways, including their medical history and records, professional accomplishments, lifestyle, personal iot (internet of things) devices, and relationships. these representations move beyond data about a person; instead, the data generate digital personas that highly reflect the person and, by extension, what a person desires to become. the data you generate are more than just data—it is your digital dna. like all dna, it is the foundation for life. ultimately, we will hit a point where your dt can act on your behalf, but that won’t happen until well beyond 2024. ai companions and the workforce of tomorrow in 2024, we will experience the wide-scale adoption of ai buddies based on generative ai engines, marking a major inflection point for the nature of work and learning. a recent report by mckinsey and company3 stated between now and 2030, up to 12 million or more occupational transitions may be needed, and up to 30% of all hours currently worked could be automated (accelerated by generative ai) across the u.s. economy. as a result, companies will revamp their workforce development programs and focus on attitude and aptitude augmented with the ability to teach and verify required skills. university versus multiversity college enrollment has been declining since 2010, and this trend has accelerated since the start of the pandemic, resulting in a decline of 9% between the spring of 2019 and the spring of 2023.4 ten years from now, we will remember 2024 as the time when the educational institutions that managed to thrive adopted the idea of multiversity, prioritized skill-based lifelong learning, and ai-assisted learning (i.e., ai teaching assistants, mentors, and ai-generated learning content) over degree-based education. self-sovereign identity adoption blockchain and, by extension, web3 (a new iteration of the world wide web) will finally find a breakthrough application outside of cryptocurrencies—creating, managing, and protecting a true self-sovereign identity. the world bank estimates that over 1.1 billion people do not have a legal identity, 2 billion people do not have access to a bank account or financial services, and nearly 1 billion do not have access to adequate healthcare.5 trusted and verifiable identity is foundational to creating a world that supports broader access to critical services and human flourishing, including healthcare, property ownership, free and fair elections, and financial access. more importantly, a self-sovereign identity is essential for establishing a trusted digital personal and dt. in the short term, this will allow people lacking a valid id to gain one and greatly reduce identity theft. caution: navigating the digital age and protecting your digital self while ai has tremendous potential for good, there is a reason that most sci-fi descriptions of the future are dystopian. we live in a world where our every move is tracked and recorded, whether we want it or not. big tech uses our data to support their business interests—usually to influence and monetize our behavior. our behavior in these digital worlds is already used to create predictive models. ai, in particular generative ai, will evolve these models into psychological profiles that will be used to mold how we think and act. privacy laws have no chance of keeping up with the rate of advancements. caution: social credit scoring over 130 countries, which account for 98% of the global gdp, are exploring central bank digital currencies (cbdcs). nineteen of the g20 countries are now in the advanced stage of cbdc development. eleven countries have launched cbdcs, and 21 are in pilot phases.4 as a blockchain and web3 enthusiast, i am a fan of the underlying technology. the caution comes when implemented in a centralized versus decentralized manner. when you combine a cbdc with a centralized implementation of a digital identity and this next generation of surveillance, we will have created a means of control that should not be trusted to any single corporation or government. we will see radical expansion of those pushing for social credit scores, starting with solutions that may seem beneficial for society. it will be the year that the battle for self-sovereignty will come to the forefront of society. this will no longer simply be an academic discussion. instead, 2024 will bring the battle for individual digital rights and privacy to center stage. in summary the year ahead will reveal how dedicated societies and governments are to strengthening individual privacy. legislation will undoubtedly come into play, but privacy-enhanced technology will evolve from a competitive differentiator to an operational necessity. decentralized networks, https://doi.org/10.30953/bhty.v6.287 citation: blockchain in healthcare today 2023, 6: 287 https://doi.org/10.30953/bhty.v6.287 5 (page number not for citation purpose) blockchain technology predictions 2024 individual data ownership, transparency, and cracking the ai black box without compromising privacy will build organizational trust. done effectively, the world will revere ai and its potential to innovate for the greater good. conclusion as we stand on the cusp of 2024, the fusion of blockchain, dts, ai, and multi-omics platforms holds the promise of reshaping healthcare and public health. these technologies offer unprecedented opportunities to enhance clinical trials, enable precision public health strategies, and empower individuals with control over their health data. however, these advancements are responsible for safeguarding privacy and ensuring ethical deployment. in the years ahead, we must navigate the complexities of this new era with caution and foresight. legislation and privacy-enhancing technologies will play crucial roles in balancing innovation with individual rights. decentralized networks, data ownership, transparency, and responsible ai usage are key pillars in building trust and harnessing the potential of these innovations for the greater good. the year ahead will undoubtedly be a pivotal moment in the battle for digital rights and privacy as we strive to realize the full potential of these transformative technologies while upholding our ethical responsibilities. funding statement no funding was provided for the preparation of this article. financial and non-financial relationships and activities ingrid vasiliu-feltes reports no conflict of interest to report. each contributor is affiliated with the organization listed in the article. dr. de novi and dr. vasiliu-feltes are members of the bhty editorial board. author contributors each author contributed their section of the article. references 1. oderkirk j, slawomirski l. opportunities and challenges of blockchain technologies in health care 2020 [internet]. 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https://www.worldbank.org/en/news/press-release/2017/10/12/11-billion-invisible-people-without-id-are-priority-for-new-high-level-advisory-council-on-identification-for-development#:~:text=washington%2c%20october%2012%2c%202017-,are%20children%20who%20are https://www.worldbank.org/en/news/press-release/2017/10/12/11-billion-invisible-people-without-id-are-priority-for-new-high-level-advisory-council-on-identification-for-development#:~:text=washington%2c%20october%2012%2c%202017-,are%20children%20who%20are https://www.worldbank.org/en/news/press-release/2017/10/12/11-billion-invisible-people-without-id-are-priority-for-new-high-level-advisory-council-on-identification-for-development#:~:text=washington%2c%20october%2012%2c%202017-,are%20children%20who%20are http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) narrative/systematic reviews/meta-analysis rich data versus quantity of data in code generation ai: a paradigm shift for healthcare muthu ramachandran, phd1,2 and steven fouracre3 1research consultant atforti5 tech and at self-evolving software (ses) systems group, london, uk; 2professor extraordinarous at university of south africa (unisa), pretoria, south africa; 3ceo, self-evolving software (ses) systems group, london, uk corresponding author: muthu ramachandran, email: muthuram@ieee.org doi: https://doi.org/10.30953/bhty.v8.396 keywords: code generative ai, code gen ai in healthcare, large language models, llm, rich data, self-evolving software, ses, software engineering abstract in the context of code generation ai (code gen ai), “rich” and “quality” data refer to datasets that are not only syntactically and structurally sound but also context-aware, domain-specific, and semantically aligned with the target application. unlike large-scale, general-purpose code corpora scraped from open repositories, rich datasets are curated to reflect regulatory requirements, architectural patterns, and problem-solving conventions within a given field. this distinction is critically important when deploying code gen ai in the healthcare sector, where software must meet rigorous standards for safety, auditability, and compliance. blindly scaling models with low-quality or irrelevant data may lead to brittle, error-prone systems—posing risks not only to patients and providers but also to the integrity of digital healthcare infrastructure. this issue has not been fully addressed in the code gen ai research to date. this article evaluates the critical trade-offs between “rich data” and “data quantity” strategies in code gen ai and autonomous code agents, focusing on high-integrity sectors such as healthcare. while code gen ai can enhance productivity by up to 55% in controlled environments, models trained on unfiltered, large-scale datasets often increase code duplication, churn, and error rates. the central challenge is balancing performance gains with reliability, maintainability, and ethical accountability. in healthcare, codebases must embody accuracy, traceability, and data privacy—attributes often diluted in large but uncurated training sets. using self-evolving software as a case study, this article contrasts the outcomes of both approaches and introduces a weighted data selection matrix tailored to code gen ai systems. the findings demonstrate that rich, curated, domain-specific datasets consistently produce more robust, compliant, and sustainable code, especially in sectors where quality and governance are non-negotiable. plain language summary the authors compare two different approaches to training ai systems that write computer code: one that uses massive amounts of general code data (“quantity approach”) versus one that uses smaller but higher-quality, specialized data (“rich data approach”). the research reveals that while the quantity approach might be faster to set up and works for general coding tasks, it often creates serious problems in sensitive areas like healthcare software. these problems include duplicate code, security vulnerabilities, and code that does not comply with regulations like the health insurance portability and accountability act of 1996. the researchers studied a system called self-evolving software (ses) that uses the rich data approach. in healthcare settings, ses produced better results by generating code that results in less rework time and elimination of repeated documentation errors, all fully compliant with healthcare privacy laws. the authors conclude that for important software in regulated industries like healthcare, the quality of data used to train artificial intelligence coding tools matters much more than the quantity. using carefully selected, blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-5303-3100 mailto:muthuram@ieee.org https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.3962 (page number not for citation purpose) muthu ramachandran and steven fouracre well-documented code examples rather than scraping massive amounts of code from public repositories is recommended. this approach creates more reliable, secure, and maintainable software, especially when health or privacy is at stake. received: april 16, 2025; accepted: may 27, 2025; published: june 18, 2025 code generation ai (code gen ai) is transforming software development, particularly in high-integrity sectors like healthcare and regulated enterprises. in this article, the authors evaluate critical trade-offs between “rich data” and “data quantity” in code gen ai and autonomous code agents. while code gen ai can enhance productivity up to 55% in controlled environments, it can also significantly increase code duplication, churn, and error rates when trained or operated with unfiltered, large-scale data.1–16 in high-integrity sectors where accuracy, auditability, and privacy are paramount, data richness often outperforms brute-force scaling strategies. this article contrasts outcomes from data quantity versus data richness paradigms using self-evolving software (ses) as a case study and proposes a weighted matrix for data selection in code gen ai systems.6 the rise of code gen ai is rapidly transforming the landscape of software development, especially in highly regulated and high-integrity domains such as healthcare, finance, and aerospace. these systems, powered by large language models (llms), can produce code from natural language prompts, optimize legacy codebases, and automate substantial portions of the software development lifecycle. however, their performance and trustworthiness are deeply shaped by the nature and quality of the data they are trained on.1 the authors investigated a pivotal trade-off in the design and deployment of code gen ai systems: the use of “rich, curated data” versus “large-scale, unfiltered datasets.” while the industry has favored scale for quick wins in general-purpose tasks, this study highlights the risks of such an approach in sensitive sectors where reliability, traceability, and compliance are critical. through the lens of a proprietary system—ses—we present empirical and architectural insights into how rich data contribute to superior outcomes, from reduced technical debt to enhanced compliance with privacy regulations. to guide practitioners and policymakers, the authors also propose a weighted decision matrix for selecting appropriate data sources when deploying code gen ai systems in high-stakes environments. this article is structured to explore the trade-offs systematically between rich data and data quantity in code gen ai, with a specific focus on high-integrity domains like healthcare. it begins by providing foundational context on the emergence and implications of code gen ai appendix systems. following this, the authors delve into the workings of llms, explaining their architecture, capabilities, and limitations in the context of software development. the discussion transitions to a comparison between data-rich and quantity-based approaches, using the ses platform as a real-world case study to highlight the advantages of curated, domain-specific datasets. a detailed architectural analysis of ses is presented to illustrate how it operationalizes these principles. subsequently, a healthcare-focused case study illustrates ses’s practical impact on code quality, compliance, and reusability. finally, the hidden costs and risks of unvetted code reuse are addressed—including security, legal, and ethical implications—before introducing a weighted decision matrix for evaluating data sources for code gen ai. this article concludes with a synthesis of findings and best practice recommendations for implementing ethical and effective ai-assisted development in regulated environments. large language models for code gen ai the llms are foundational to code gen ai. these are sophisticated neural networks—often based on transformer architectures like generative pre-trained transformer (gpt)—trained on massive corpora of text and code. their capacity to model linguistic structure, context, and semantic nuance enables them to generate syntactically correct and semantically relevant outputs, including source code. when fine-tuned or purpose-trained on programming data, these models become code gen ai systems, capable of producing, editing, and explaining code in a variety of programming languages. notable examples include github copilot, amazon codewhisperer, and anthropic claude.1–5 these tools can convert natural language descriptions into functional code, assist with debugging, and even generate test cases. however, the effectiveness and safety of these systems are deeply linked to the nature of their training data. studies14,15 reveal that llms trained on unfiltered code from open repositories are prone to producing duplicated, insecure, or outdated code patterns, potentially introducing regulatory risks or security flaws. thus, the quality and curation of data—not just the size of the dataset—become critical variables in system performance, particularly in safety-critical domains like healthcare. https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.396 3 (page number not for citation purpose) rich data versus data quantity in code generation ai recent advances underscore the importance of training data quality in shaping the effectiveness, safety, and usability of code generation systems. the study results point out significant risks associated with using uncurated datasets, including security vulnerabilities, legal liabilities, and the inadvertent perpetuation of outdated code practices.5 code generation ai for healthcare the healthcare industry stands at a critical intersection of technological innovation and strict regulatory oversight. as software increasingly becomes the backbone of modern healthcare delivery, the demand for efficient, reliable development approaches has never been greater. code gen ai represents a potentially transformative technology in this space—promising to accelerate development cycles, reduce human error, and democratize access to sophisticated healthcare applications. however, the unique characteristics of healthcare— where software failures can have life-threatening consequences and where privacy breaches can violate fundamental patient rights—require special consideration when deploying these emerging ai technologies. the healthcare domain presents distinct challenges for ai-assisted development beyond those faced in less regulated sectors, including compliance with frameworks like the health insurance portability and accountability act of 1996 (hipaa) and u.s. food and drug administration regulations for medical software and international standards such as iso 13485 for medical devices. this section explores the specific applications, challenges, and considerations for implementing code gen ai within healthcare contexts. it examines how this technology can be harnessed responsibly to advance healthcare capabilities while maintaining the rigorous standards of safety, efficacy, and ethical compliance that patients and regulators rightfully demand. as healthcare organizations navigate this complex landscape, understanding the potential benefits and inherent risks of ai-generated code becomes essential to realizing its promise while safeguarding patient welfare. in healthcare, code gen ai is used to automate the creation of software for applications such as electronic health records (ehrs), clinical decision support systems, healthcare data analytics, medical device software, regulatory compliance tools, and telemedicine platforms. however, concerns include accuracy and safety, bias in training data, data privacy and security, ethical and legal risks, explainability and traceability, and technical debt. while non-proprietary code gen ai models—such as those trained on open-source or publicly available datasets—offer accessibility and scalability, they also present a range of significant concerns, particularly in high-stakes or regulated environments. accuracy and safety are major issues, as these models may generate syntactically correct but functionally flawed code, which can lead to system malfunctions, especially in critical domains like healthcare or finance. ethical and legal risks arise when these models inadvertently reproduce licensed or plagiarized code, creating potential violations of intellectual property laws and open-source licenses. another concern is the lack of explainability and traceability. non-proprietary models often operate as “black boxes,” making it difficult for developers to understand how decisions are made or trace the origin of generated code. this undermines trust and complicates debugging and auditing processes. technical debt is frequently introduced through low-quality or redundant code generated from noisy datasets, which can accumulate rapidly over time and increase long-term maintenance expenses. furthermore, data privacy and security risks are amplified when models trained on scraped or unvetted data inadvertently expose sensitive information or reproduce known vulnerabilities. finally, bias in training data can lead to inequitable software behaviors, especially if underrepresented groups or domains are inadequately represented in the model’s training corpus. these systemic issues highlight the importance of adopting more curated, domain-specific, and ethically governed approaches to ai-assisted code generation. in summary, while code gen ai holds immense potential to revolutionize healthcare by improving efficiency and reducing development time, its adoption must be guided by principles of data quality, ethical compliance, and rigorous validation to ensure safe and equitable outcomes. self-evolving software: proprietary code gen ai the ses represents a paradigm shift in the application of proprietary llms for code generation. the ses is a cloud-native salesforce application designed to address the limitations of traditional code gen ai tools by leveraging rich, curated datasets and integrating advanced features for quality assurance and compliance. as software systems become increasingly central to mission-critical applications in sectors like healthcare, finance, and public services, the reliability and transparency of code have never been more important. code gen ai—powered by llms—has emerged as a powerful tool to accelerate development, reduce manual coding effort, and enhance productivity. yet, a growing body of evidence reveals the risks of relying on generic, quantity-focused ai models trained on unvetted public data. these risks include code duplication, undocumented logic, compliance failures, and systemic security vulnerabilities. https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.3964 (page number not for citation purpose) muthu ramachandran and steven fouracre to address these limitations, the industry is seeing a transition toward proprietary, domain-specific solutions—most notably, ses. the ses is a paradigm-shifting, cloud-native ai platform built within the salesforce ecosystem, designed specifically for high-integrity and regulated environments. unlike traditional ai tools that prioritize data scale, ses leverages rich, curated, and context-aware datasets to generate high-quality, maintainable, and compliant code. the system incorporates several advanced features that distinguish it from open-source counterparts. these include automated requirements capture, which streamlines the translation of business needs into structured development tasks; ai-driven code generation that ensures context alignment; and integrated code quality analysis, which evaluates complexity, redundancy, and adherence to standards in real-time. in addition, ses offers automated documentation, embedding traceability and compliance metadata directly into the codebase, and a continuous improvement loop, allowing the model to learn from user feedback and project outcomes over time. by focusing on rich data and regulatory alignment, ses minimizes code churn, reduces duplication and defects, and supports seamless compliance with frameworks such as hipaa and general data protection regulation (gdpr). its architecture prioritizes explainability, traceability, and sustainability—qualities essential for long-term use in sensitive sectors. the ses not only delivers immediate productivity gains but also supports ethical, scalable, and secure software development. in this context, the key features of ses include ai-driven code generation, automated documentation, automated requirements capture, continuous improvement, and integrated code quality analysis. the ses outperforms traditional tools by reducing code churn, duplication, and defects while maintaining compliance with regulatory standards such as gdpr/hipaa. its focus on rich data ensures generated code is functional, ethical, sustainable, and aligned with industry-specific requirements. figure 1 compares the four main features of ses with the relative capabilities of other typical ai code generation tools. the comparative analysis of figure 1 clearly demonstrates that ses offers significant advantages over traditional code gen ai tools. while conventional solutions may excel in producing generic code, they frequently fall short in critical areas such as code quality analysis, duplicate/dead code detection, and comprehensive documentation. the ses addresses these limitations through its proprietary ai technology, delivering consistently high-quality outputs across all key metrics. by integrating advanced mathematical analysis, efficient code optimization, and automated technical documentation that links directly to application lifecycle management (alm) tools, ses provides a comprehensive solution that enhances both developer productivity and code quality. this revolutionary approach to ai-assisted software development establishes a new standard for intelligent coding assistance that evolves alongside project requirements and organizational needs. now, let us examine the architectural view of ses to understand how these capabilities are structured and implemented. architectural view of ses code gen ai based on llms refers to ai systems that use deep learning—specifically, transformer-based architectures like gpt—to write, understand, and assist with programming tasks. these models are trained on massive datasets containing source code from multiple languages (e.g. python, javascript, and c++) as well as documentation, comments, and developer discussions. by learning patterns in this data, they can generate code from natural language prompts (e.g. “write a function to sort a list using merge sort”), explain code by translating it into plain language, refactor, or debug code by suggesting improvements or identifying errors, and complete code based on context, like an advanced autocomplete. popular examples include github copilot (powered by openai codex) and codewhisperer (by amazon). these tools aim to boost developer productivity and reduce repetitive coding work. figure 2 illustrates the architectural view of ses. the architectural diagram shown in figure 2 illustrates how ses integrates code generative ai within the salesforce community cloud to enable the intelligent reuse of proprietary code across trusted systems. at the core of this ecosystem lies code gen ai, which learns from various trusted code sources to generate solutions for customer-specific needs. the key components of ses are listed in table 1. benefits of proprietary code generation ai tools proprietary code gen ai tools, such as ses, are purpose-built for enterprise use, particularly in regulated industries like healthcare. unlike generic ai coding assistants, these tools address specific challenges, ensuring compliance, efficiency, and tailored solutions for complex environments. some key benefits are listed as follows. increased productivity proprietary tools significantly boost developer efficiency by automating routine tasks, leveraging domain-specific knowledge to generate contextually appropriate code. this allows teams to focus on innovation and high-value work, with organizations reporting productivity gains of 30% to 40%. customization tools like ses adapt to an organization’s unique needs, aligning with existing codebases and architectural standards. this ensures consistency and relevance, even in specialized healthcare domains where generic tools often fall short. https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.396 5 (page number not for citation purpose) rich data versus data quantity in code generation ai scalability and accessibility these tools support large-scale development with standardized patterns and interoperability across projects. they also enable non-developers, like clinicians, to contribute through intuitive interfaces, fostering collaboration and democratizing development. enhanced collaboration by bridging technical and clinical domains, ses facilitates better communication among stakeholders. this reduces misalignment and ensures the development of clinically relevant applications. cost efficiency despite upfront investment, proprietary tools reduce longterm costs by minimizing defects, maintenance burdens, and technical debt. healthcare organizations report maintenance cost reductions of up to 50% with faster feature rollouts. improved code quality the ses emphasizes security, maintainability, and compliance, generating high-quality code that meets regulatory standards. metrics such as reduced complexity and vulnerability density highlight the superiority of proprietary solutions in regulated settings. fig. 1. comparative analysis of ses proprietary code gen ai. ai: artificial intelligence; alm: application lifecycle management; code gen ai: code generative ai; ses: self-evolving software. https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.3966 (page number not for citation purpose) muthu ramachandran and steven fouracre advanced capabilities proprietary tools like ses stand out for their explainability, regulatory alignment, and technical debt prevention. they proactively update to meet evolving healthcare regulations and provide interfaces tailored for both developers and non-technical stakeholders, enabling seamless collaboration. in summary, proprietary code gen ai tools like ses offer several benefits.17 these include increased productivity, customization for specific needs, scalability and accessibility, enhanced collaboration, cost efficiency, and improved code quality. ses is designed to be transparent, explainable, and aligned with evolving regulatory expectations for ethical ai. it mitigates code duplication, reduces churn, enhances security, and eliminates technical debt while empowering both technical and non-technical users through an intuitive interface. proprietary code gen ai tools offer transformative benefits for healthcare organizations. by combining domain expertise, regulatory compliance, and quality-focused designs, these tools deliver measurable improvements in productivity, code quality, and cost efficiency—making them indispensable for sustainable digital transformation. defining “rich data” in code generation context in the realm of ai-assisted software development, “rich data” refers to high-quality, annotated, domain-specific datasets that are ethically sourced, legally compliant, and embedded with structural context such as task relevance, regulatory constraints, and traceability metadata. unlike general-purpose datasets scraped from public repositories (plagued by duplication, deprecated syntax, and undocumented dependencies), rich datasets provide actionable context for generating reliable and reusable software components. for instance, in healthcare application, fhir (fast healthcare interoperability resources) is a standard for exchanging healthcare information electronically, which is developed by hl7 (health level seven international). fhir defines how healthcare data should fig. 2. architectural view of self-evolving software (ses). ai, artificial intelligence. table 1. the key components of ses components of ses description organization private (ses customer) the internal code base within a customer’s own ses environment. the ses ai learns from this proprietary code to build solutions tailored to that specific organization. it is the most secure and self-contained source of intelligence. ses global a central repository owned by ses, accessible to all ses customers. this global system allows users to upload and share code solutions, which ses ai can reuse across customers. it ensures scalability and broad solution availability with unlimited storage for contributions. global group organization also ses-owned, this system offers code access to selected customers based on permission. while similar to ses global, it introduces selective sharing, offering a middle ground between private and global access. solutions uploaded here are reusable by a specific group of ses customers. hub & spoke (salesforce spoke) connected external systems that ses customers can link to if granted secure access. the ses ai can leverage code from these spokes to generate innovative solutions, encouraging cross-system intelligence and reuse. snippets reusable libraries of complete or partial programming solutions created by ses customers or externally sourced. snippets are programming language agnostic, greatly expanding the appeal of ses ai and snippets. ses ai learns from these snippets, using them as foundational blocks to generate more complex solutions. with no limit on the number of snippets, they become a vital source for ongoing ai learning. ai: artificial intelligence; ses: self-evolving software. https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.396 7 (page number not for citation purpose) rich data versus data quantity in code generation ai be formatted and transmitted between different systems, such as ehrs, hospitals, clinics, and other healthcare applications. clinical validation rules are linked to icd-10 codes, which describes automated checks that validate medical data against the international classification of diseases, 10th revision—the global standard for diagnosing and classifying diseases. this is one of the hard issues to tackle in healthcare to validate against the standard framework. for example, a rich dataset in healthcare could comprise fhir-compliant ehr logic, clinical validation rules linked to the international classification of diseases (icd-10) codes, and automated documentation templates—all version-controlled and privacy-tagged to align with hipaa/gdpr standards. table 2 illustrates rich data versus the quantity-first model and compares rich data and quantity-based approaches across seven key development features. the rich data approach demonstrates superior performance with high domain specificity, consistently high code quality, and strong reusability. it excels in ethical compliance, poses low technical debt risk, and provides structured, traceable explanations that lead to highly reliable outputs. in contrast, the quantity-based approach shows significant limitations, featuring low domain specificity, inconsistent (often poor) code quality, low reusability, weak or unverified ethical compliance, high technical debt risk, unstructured/random explanations, and mixed reliability of outputs. this comparison highlights the substantial advantages of the rich data approach across all measured development parameters. the rich data approach prioritizes quality, domain specificity, and ethical compliance, making it ideal for applications requiring precision and reliability. on the other hand, the quantity-based approach focuses on amassing large datasets, which can be useful for general-purpose models but often sacrifices quality, reusability, and explainability. the role of ses in rich data-driven code ai the ses exemplifies the principles of rich data-driven code generation. the platform integrates rich data throughout its lifecycle, from requirements capture to post-development analysis. key outcomes of ses include reduced code churn (reusing existing, validated code, ses reduces unnecessary duplication), improved code quality (ses quality analysis tools ensure that the generated code meets high standards of maintainability and compliance), and enhanced documentation (ses automates the creation of technical documentation, embedding metadata for traceability and explainability). the ses is a salesforce-native app that gathers requirements, auto-generates code, evaluates code quality, and produces documentation using rich data-centric principles. it has been shown to reduce code churn by 39%, cut released code defects by 67%, and limit code duplication by 26%. weighted matrix: evaluating data for code gen ai and agents to systematically assess the utility of various data sources for code gen ai and autonomous coding agents, this article introduces a weighted decision matrix. this matrix helps evaluate datasets across critical criteria such as domain relevance, error risk, privacy compliance, explainability, and potential for technical debt.10–15 table 3 illustrates the data selection matrix for code gen ai. table 3 presents a weighted evaluation criteria matrix comparing rich data versus large quantity data approaches for ai code generation. the matrix assigns importance weights (1–5) to seven critical factors and scores each approach accordingly. rich data excel in domain relevance (5/5), versioning and documentation (5/5), privacy and regulatory fit (5/5), and explainability and traceability (5/5), while maintaining minimal risk (1/5) for code duplication, error rates, and technical debt propagation. in contrast, large quantity data perform poorly in most highweight categories, scoring just 1–2 points in domain relevance, versioning, privacy compliance, and explainability while showing high-risk levels (5/5) for code duplication, production errors, and technical debt. this weighted comparison demonstrates the superior risk-adjusted table 2. a comparison between rich data versus quantity-first model feature rich data approach quantity-based approach domain specificity high low code quality high mixed (often poor) reusability high low ethical compliance strong weak/unverified technical debt risk low high explainability structured/traceable unstructured/random reliable output high mixed (often poor) table 3. data selection matrix for code gen ai critical factors weight rich data large quantity data domain relevance 5 5 2 versioning & documentation 4 5 2 code duplication risk 4 1 5 error rate in production 5 1 5 privacy & regulatory fit 5 5 1 explainability & traceability 4 5 2 technical debt propagation 5 1 5 ai: artificial intelligence; ses: self-evolving software. https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.3968 (page number not for citation purpose) muthu ramachandran and steven fouracre performance of the rich data approach across the most critical evaluation criteria. rich data consistently score higher for ethical and longterm quality-driven development. this analysis shows that rich data provide a more sustainable, reliable, and ethical foundation for code gen ai, especially in domains like healthcare where lives, regulations, and long-term system integrity are at stake. the matrix assigns importance weights to the seven critical factors and scores each data approach (rich data vs. large quantity data) on a scale where 1 = lowest performance/suitability and 5 = highest performance/suitability. case study: rich data in healthcare code agents the development and maintenance of ehr systems present some of the most complex and high-stakes challenges in modern software engineering. these systems must not only support clinical workflows and maintain interoperability across healthcare networks but also comply with strict regulatory requirements such as gdpr, hipaa, and iso 13485. the stakes are incredibly high; errors in code can lead to life-threatening consequences, privacy violations, and legal repercussions. despite this, many ai code generation tools used in the industry are trained on unfiltered, general-purpose datasets that lack the specificity, compliance tagging, and domain knowledge required for medical-grade software development. to evaluate the potential of a rich data-driven approach, a pilot project was launched in a hospital setting. this initiative aimed to automate key components of the ehr development lifecycle using ses—a proprietary code gen ai platform purpose-built for regulated domains. ses was compared against a widely used, quantity-based ai tool such as github copilot, which draws from massive but uncurated codebases. the goal of the pilot was to assess how effectively each system could generate accurate, compliant, and contextually relevant code in a clinical environment. the ses was assigned three mission-critical tasks. these include developing a class to determine the priority level of a patient based on clinical parameters, implementation of a class to schedule appointments within hospital systems while respecting existing booking rules, and generation of a class to anonymize patient information in accordance with gdpr and hospital data governance policies. unlike non-proprietary tools that generate generic code with little contextual alignment or legal awareness, ses offers advanced capabilities specifically tailored for the healthcare domain. as shown in figure 3, ses allows developers to initiate and refine code generation through natural language prompts enhanced by point-and-click adjustments. this intuitive interface minimizes the risk of human error and accelerates development. in addition, figure 4 highlights ses’s quick templates feature, which enables developers to apply pre-approved configurations with a single click—embedding compliance logic, domain-specific settings, and documentation standards directly into the generated code. figure 4 then illustrates how the ses engine synthesizes all this input to build reliable, well-documented, and regulation-aligned code. by grounding its learning process in rich, curated datasets rather than bulk-scraped repositories, ses bridges a crucial gap left by open models: the ability to produce safe, explainable, and legally compliant code in domains where mistakes are costly. this case study not only demonstrates fig. 3. example of chat message in ses conversation for code gen ai. (ai: artificial intelligence, ses: self-evolving software). https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.396 9 (page number not for citation purpose) rich data versus data quantity in code generation ai ses’s superior performance in ehr-related tasks but also underscores the broader necessity of domain-specific ai systems in safety-critical environments. a pilot project conducted in a hospital environment sought to automate parts of the ehr development lifecycle using code gen ai. the study was designed to evaluate the performance difference between a rich data-driven platform (ses) and a quantity-based ai tool (e.g. github copilot). as part of the pilot implementation in a hospital environment, ses was evaluated against critical, realworld healthcare coding tasks. these use cases were carefully chosen to reflect the functional, regulatory, and operational complexity inherent to ehr systems. unlike general-purpose ai tools, ses was designed to interpret both the clinical intent and the regulatory obligations embedded within these tasks, leveraging rich, domain-specific datasets. determine patient priority level the first task involved generating a class to “find the priority of a patient” based on medical inputs such as vitals, diagnosis codes, and triage indicators. this function is pivotal in clinical environments where triage and response time can directly impact patient outcomes. the challenge lies in designing logic that accurately interprets clinical urgency while conforming to hospital-specific workflows and national triage standards. the ses, trained on curated datasets that include hospital protocols, (icd-10) mappings, and clinical guidelines, was able to produce clean, maintainable code with embedded validation logic. the generated output incorporated commentary for explainability, structured error handling, and built-in hooks for future extensibility. automated appointment scheduling the second use case required ses to “arrange appointments for patients,” a task that involves integrating with backend systems for clinician availability, avoiding scheduling conflicts, and handling constraints such as insurance authorizations or specialist referrals. in many ehr systems, poor appointment logic leads to bottlenecks and administrative rework. ses tackled this by generating modular, api-ready code aligned with existing scheduling schemas, including time-slot validation and resource locking mechanisms. importantly, it also incorporated data privacy safeguards and audit trails—features often missing in code generated by non-proprietary tools, which may not understand the broader context of sensitive scheduling data. gdpr-compliant anonymization of patient data the third and most compliance-sensitive task was to “anonymize patient information to comply with gdpr.” this functionality is vital for ensuring data protection during analytics, research, or third-party integrations. standard anonymization requires field-level transformations, encryption, and redaction rules—all dictated by legal frameworks and institutional policies. fig. 4. the operation and quick template creation process in ses, while the computer readout in the appendix demonstrates the execution of ses ai and its resulting code output. ai: artificial intelligence, ses: self-evolving software. https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.39610 (page number not for citation purpose) muthu ramachandran and steven fouracre the ses’s rich data foundation allowed it to generate code that not only redacted identifiable fields (e.g. names and contact info) but also included metadata for auditability and compliance verification. the output adhered to gdpr’s standards for data minimization, traceability, and subject rights, providing explainable and defensible code that would stand up in a regulatory audit. these use case scenarios underscore the practical advantage of rich data and proprietary architecture in ses. where general-purpose ai tools may produce superficial or context-blind code, ses delivered precise, standards-aligned, and scalable solutions tailored for healthcare systems. the flexibility and explainability embedded in its outputs make ses a strong model for responsible, ai-assisted development in high-integrity sectors. in this context, ses was tasked with three key objectives. these include the first class to “find the priority of a patient,” the next class to “arrange appointments for patients,” and a final class to “anonymize patient information to comply with gdpr.” an example of the chat message posed to ses is shown in figure 3. unlike other code generative ai systems, ses allows the code output to be finely adjusted using clicks to speed up user operation, and quick templates allows predefined configurations to be set with 1 click, as shown in figure 5. the output from ses, which accurately anonymized the patient data, is displayed in the appendix. complementing the code outputs, ses also creates skeleton structures of unit test code to accelerate test code development that should always remain the domain of the developer but aided by ai. key outcomes from the pilot implementation include ses reduced rework time by 41%, eliminated 87% of repeated documentation errors, and achieved 100% alignment with hipaa requirements. discussion this case clearly demonstrates that code gen ai in healthcare must prioritize data quality and contextual alignment over generic scale. the structured and curated datasets powering ses allowed it to understand not only the functional requirements but also the clinical, legal, and ethical implications of the code it generated. technical debt is particularly insidious in ai-assisted development because it can accumulate silently and at scale. when developers rely on llms trained on vast, unvetted data repositories, they often incorporate code without understanding its underlying mechanisms or weaknesses. this “black box” approach creates several cascading problems. the technical debt crisis in ai-assisted development ai-generated code may solve immediate problems while introducing complex dependencies or inefficient implementations that remain hidden until they cause critical failures. code generated from mass data sources often lacks proper documentation or comes with generic comments that fail to explain context-specific design decisions. security: the hidden cost of unvetted code reuse the integration of ai into software development has accelerated rapidly, but this advancement comes with significant hidden costs when implemented without proper data governance. this section examines the security, legal, ethical, and quality implications of current ai development approaches while offering a more sustainable alternative through ses. the security implications of indiscriminate data usage in ai development present clear dangers, including known security flaws from public repositories that can be unintentionally replicated, the introduction of entire dependency chains of projects and their security risks, and the potential for “poisoned” repositories with subtle security flaws. the security implications of indiscriminate data usage in ai development present clear dangers to organizations. when ai systems train on vast repositories of unvetted code, they inevitably incorporate and potentially amplify existing vulnerabilities. known security flaws from public repositories can be unintentionally replicated through ai-generated code, creating a dangerous propagation mechanism for vulnerable code patterns. additionally, ai systems may introduce entire dependency chains of projects and their associated security risks, significantly expanding the attack surface of applications. perhaps most concerning is the potential for “poisoned” repositories with subtle security flaws designed specifically to evade detection while creating exploitable backdoors in systems that incorporate ai-generated code. legal and ethical complications the legal landscape around ai-assisted development remains complex and filled with potential pitfalls. code generated from murky origins and uncertain licensing status creates the intellectual property disputes that can fig. 5. run ses ai to build the code output. ai: artificial intelligence, ses: self-evolving software. https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.396 11 (page number not for citation purpose) rich data versus data quantity in code generation ai result in costly litigation and project delays. organizations may inadvertently violate open-source licenses when ai systems incorporate code with incompatible licensing terms. there are also significant challenges in determining responsibility for defects—whether they lie with the developers, the ai system providers, or the organizations that created the training data. these ambiguities can lead to potential compliance failures in regulated industries where code provenance and security guarantees are mandated by law. the legal landscape around ai-assisted development remains complex and potentially clouded because murky origins and licensing status create potential ip disputes, inadvertent violation of open-source licenses, challenges in determining responsibility for defects, and potential compliance failures in regulated industries. the self-evolving software alternative the ses offers a fundamentally different approach that addresses many of these concerns through rigorous data governance. unlike traditional ai code generation, ses uses carefully vetted, high-quality code solutions that meet predetermined security and quality standards. the system continuously learns from successful code practices using reinforced learning and proactive learning mechanisms, improving over time without compromising security. importantly, ses maintains clear provenance information for all generated code, creating an auditable trail that satisfies compliance requirements. by prioritizing well-tested, secure, and properly documented code, ses creates a foundation for sustainable ai-assisted development. ses offers a fundamentally different approach that includes using carefully vetted, high-quality code solutions; continuous learning from successful code practices using reinforced learning and proactive learning mechanisms; maintaining clear provenance information; and prioritizing well-tested, secure, and properly documented code. measuring the impact: quality over quantity empirical evidence increasingly supports the ses approach as not just more secure but more economical in the long term. organizations implementing ses report up  to 60% fewer defects compared to traditional ai-assisted development, dramatically reducing the resources needed for bug fixes and security patches. these quality improvements translate to 40% to 50% lower maintenance expenses over a 3-year period, creating a substantial return on investment for organizations that prioritize code quality. perhaps most compelling is the significantly fewer security incidents—a 70% reduction in some cases—demonstrating that the initial investment in proper data governance pays dividends in reduced security risks and breach-related costs. empirical evidence increasingly supports the ses approach, with up to 60% fewer defects compared to traditional ai-assisted development, 40% to 50% lower maintenance expenses over a 3-years, and significantly fewer security incidents (70% reduction in some cases). the importance of this is underlined by a research paper conducted by gitclear,1 16th january 2024, which analyzed 153 million lines of code across multiple publicly available projects over a 4-years from 2020 to 2024. the hidden costs of unvetted code reuse in ai development extend far beyond immediate development time, creating significant security vulnerabilities, legal exposures, and long-term maintenance burdens. the ses approach demonstrates that prioritizing quality over quantity in training data not only produces more secure and compliant code but also ultimately delivers superior economic outcomes. as ai becomes increasingly central to software development, organizations must critically evaluate the true costs and benefits of their ai integration strategies, looking beyond short-term productivity gains to consider the full lifecycle implications of their approach. conclusion and recommendations as code gen ai transitions from an experimental technology to a core component of enterprise software development, its success will increasingly hinge on how responsibly and intelligently it is implemented. this is especially true in high-stakes environments like healthcare, where the consequences of malfunctioning code extend beyond system performance to encompass human safety, privacy, and legal accountability. this article demonstrates that the richness and contextual relevance of data are more critical than dataset size alone. through case studies and comparative metrics, we show that platforms like ses, which adopt domain-specific, curated, and legally compliant datasets, outperform quantity-based ai systems across virtually all key dimensions— code quality, maintainability, security, and compliance. we recommend that organizations adopt rich data strategies when implementing code gen ai. these should include curated datasets, embedded regulatory logic, explainable outputs, and continuous feedback loops. while this approach may require more initial investment and configuration, it results in long-term benefits—reduced technical debt, enhanced trust, and significantly lower maintenance costs. the future of trustworthy ai in software development lies not in the indiscriminate scaling of models but in scaling with intention, ethics, and contextual intelligence. this principle will form the cornerstone of sustainable and secure ai-driven engineering in the years to come. best practice guidelines for code gen ai in regulated sectors the implementation of ai-driven code generation in regulated sectors presents unique challenges that demand https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.39612 (page number not for citation purpose) muthu ramachandran and steven fouracre specialized approaches. healthcare, finance, aerospace, and other highly regulated industries operate under strict compliance frameworks where software failures can have serious consequences, including harm to patients, financial losses, or threats to public safety. traditional ai code generation techniques that prioritize quantity over quality create unacceptable risks in these environments. this section presents a comprehensive framework of best practices specifically designed for organizations deploying code gen ai in contexts where regulatory compliance, security, and reliability are non-negotiable requirements. these guidelines represent a synthesis of emerging industry standards, lessons learned from early adopters, and principles derived from successful implementations in regulated environments. by following these recommendations, organizations can harness the productivity benefits of ai code generation while maintaining the high standards of quality, security, and compliance that their industries demand. table 4 presents guidelines that offer a practical roadmap for implementing code gen ai responsibly, with special attention to the unique requirements of healthcare and other regulated sectors where patient safety, data privacy, and regulatory adherence must remain paramount concerns throughout the development process. the future of sustainable, safe, and responsible software engineering does not lie in scaling indiscriminately, but in scaling ethically and intelligently. rich data, combined with thoughtful design and governance frameworks, will be the cornerstone of trustworthy ai-driven development—particularly in healthcare, where the stakes are too high for anything less. funding there was no funding received for this research. conflicts of interest no. contributors both authors wrote the article and reviewed by each other. data availability statement (das), data sharing, reproducibility, and data repositories the written code is available in the appendix. application of ai-generated text or related technology chatgpt was used to help with grammar checking and initial literature review. all ai-generated content was reviewed and verified by the authors. acknowledgments we would like to thank russo john, bhty, for providing an excellent support for proofreading our article. references 1. gitclear. the impact of ai on code duplication, churn and defects [internet]. 2024 [cited 2025 apr 20]. available from: https://arc.dev/talent-blog/impact-of-ai-on-code/ 2. eu ai act & gdpr regulations [internet]. [cited 2025 apr 20]. available from: https://artificialintelligenceact.eu/ 3. tantithamthavor c, cito j. hemmati h, chandra s. explainable ai for se. ieee software; 2023. 4. self-evolving software [internet]. [cited 2025 apr 20]. available from: https://www.selfevolvingsoftware.com 5. brundage m. lessons learned on language model safety and misuse. openai; 2022, available from: https://openai.com/ index/language-model-safety-and-misuse/ 6. ses overview. internal whitepaper; 2024. 7. nijkamp e, zhao j, poesia g, xiong c. codegen: an open large language model for code with multi-turn program synthesis. arxiv preprint arxiv:2303.17568. 2023. 8. perry n, srivastava m, kumar d, vonneh d. do users write more insecure code with ai assistants? arxiv preprint arxiv: 2211.03622. 2022. https://doi.org/10.1145/3576915.3623157 9. bell e. generative ai vs. large language models (llms): what’s the difference? [internet]. 2024 [cited 2025 apr 20]. table 4. guidelines that offer a practical roadmap for implementing code generation ai responsibly guideline implementation prioritize rich, domain-specific data. use curated, annotated, and context-aware datasets that reflect real-world requirements and legal constraints. apply weighted decision matrices for data selection. assess training and operational data using multidimensional criteria. implement human-in-the-loop validation. maintain human oversight through subject-matter experts. choose architectures that embed explainability and traceability. select ai tools and platforms that support end-to-end traceability. incorporate continuous feedback loops. leverage performance metrics, error rates, and user feedback to iteratively improve outputs. ensure compliance by design. integrate legal and regulatory logic directly into the ai development lifecycle. monitor for technical debt accumulation. regularly evaluate ai-generated code for duplication, complexity, and maintainability. adopt platforms that align with ethical ai principles. use tools designed around fairness, accountability, transparency, and sustainability. ai: artificial intelligence. https://doi.org/10.30953/bhty.v8.396 https://arc.dev/talent-blog/impact-of-ai-on-code/ https://artificialintelligenceact.eu/ https://www.selfevolvingsoftware.com https://openai.com/index/language-model-safety-and-misuse/ https://openai.com/index/language-model-safety-and-misuse/ https://doi.org/10.1145/3576915.3623157 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.396 13 (page number not for citation purpose) rich data versus data quantity in code generation ai available from: https://appian.com/blog/acp/process-automation/ generative-aivs-large-language-models 10. ibm. ai code-generation software: what it is and how it works? [internet]. 2023 [cited 2025 apr 20]. available from: https:// www.ibm.com/think/topics/ai-code-generation 11. american psychological association. publication manual of the american psychological association. 7th ed. washington, dc; 2019. 12. bommasanin r, hudson da, adeli e, altman r, arora s, von arx s. on the opportunities and risks of foundation models. arxiv preprint arxiv:2108.07258. 2021. 13. brown tb, mann b, ryder n, subbiah m, kaplan j, dhariwal p. language models are few-shot learners. adv neural inf process syst. 2020;33:1877–901. 14. chen m, tworek, j, jun h, yuan q, pinto p, kaplan j. evaluating large language models trained on code. arxiv preprint arxiv:2107.03374. 2021. 15. fried d, aghajanyan a, lin j, wang s, wallace e, shi f. incoder: a generative model for code infilling and synthesis. arxiv preprint arxiv:2204.05999. 2022. 16. li y, choi d, chung j, kuhman n, shrittwieser j, leblond r, eccles t. competition-level code generation with alphacode. science. 2022;378(6624):1092–7. https://doi.org/10.1126/science.abq1158 17. sherje n. enhancing software development efficiency through ai-powered code generation. res j comput syst eng. 2024;5(1):01–12. copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is  non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.396 https://appian.com/blog/acp/process-automation/generative-ai-vs-large-language-models https://appian.com/blog/acp/process-automation/generative-ai-vs-large-language-models https://www.ibm.com/think/topics/ai-code-generation https://www.ibm.com/think/topics/ai-code-generation https://doi.org/10.1126/science.abq1158 http://creativecommons.org/licenses/by-nc/4.0 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.39614 (page number not for citation purpose) muthu ramachandran and steven fouracre appendix the output from ses, which accurately anonymized the patient data. the other 2 requirements produced the following code outputs: “find the priority of a patient” https://doi.org/10.30953/bhty.v8.396 citation: blockchain in healthcare today 2025, 8: 396 https://doi.org/10.30953/bhty.v8.396 15 (page number not for citation purpose) rich data versus data quantity in code generation ai “to arrange appointments for patients” https://doi.org/10.30953/bhty.v8.396 1 (page number not for citation purpose) opinions/perspectives/point of view the self-sovereign patient as a cornerstone of healthcare 4.0 tomer jordi chaffer, msc1  , joe littlejohn, md2  , arun nadarasa, mrpharms3  and claudia lamschtein, md4  1faculty of law, mcgill university, quebec, canada; 2zucker school of medicine, new york, new york, usa; 3international social prescribing pharmacy association (isppa), london, united kingdom; 4department of psychiatry, faculty of medicine, university of manitoba, manitoba, canada corresponding author: tomer jordi chaffer, email: tomer.chaffer@mail.mcgill.ca doi: https://doi.org/10.30953/bhty.v8.414 keywords: blockchain, decentralized ai, healthcare 4.0, self-sovereign identity abstract in healthcare 4.0, we are witnessing a fundamental shift from provider-centric systems to patient-centric models, where individuals, empowered by technologies such as blockchain, the internet of medical things, and artificial intelligence (ai), assume the role of the self-sovereign patient, exercising control over their health data and care journey. these technologies enable new forms of data ownership, interoperability, and personalized care, building on the structured reliability of legacy systems. however, significant challenges remain. tensions between blockchain immutability and regulatory rights such as data erasure, the unresolved question of digital inheritance, and ethical concerns surrounding consent, monetization, and health equity must all be addressed. in addition, institutional barriers such as clinical integration, data governance, and uneven access to digital infrastructure pose risks of deepening existing disparities. ai agents, when responsibly deployed, offer promising pathways to augment care delivery and alleviate workforce burdens. realizing this vision requires coordinated action across clinical, technical, legal, and ethical domains to design trustworthy, privacy-preserving systems that enhance transparency and accountability. plain language summary this article explores how healthcare 4.0, driven by technologies such as blockchain, artificial intelligence, and internet of medical things, is shifting control of health data from institutions to patients. it explores the concept of the self-sovereign patient, who actively manages and shares their own health information. by using secure, decentralized technologies, patients can give consent, protect their privacy, and participate in new health data ecosystems. the article highlights real-world examples and ethical considerations, showing how these tools can support better care, reduce administrative burden, and increase trust. it also warns that digital access and literacy are essential to ensure these benefits are shared fairly. submitted: june 16, 2025; accepted: august 4, 2025; published: august 18, 2025 healthcare is being transformed, not only by new machines but also by a shift in who controls the flow of data, decisions, and trust. patients are no longer passive recipients of care; they are becoming architects of their own health data ecosystems. the transition to healthcare 4.0, inspired by industry 4.0, redefines the patient’s role by integrating advanced technologies such as blockchain, artificial intelligence (ai), the internet of medical things (iomt), and wearables to enhance healthcare delivery, management, and outcomes.1 traditionally, medical records, rooted in physician-centric paper charts and later electronic medical records (emrs), have evolved into electronic health records (ehrs), improving legibility and transferability but maintaining centralized, siloed systems due to inconsistent data standards.2 personal health records (phrs) aim to blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-1388-7339 https://orcid.org/0000-0002-7483-1777 https://orcid.org/0009-0004-3755-7926 https://orcid.org/0009-0003-2286-8046 mailto:tomer.chaffer@mail.mcgill.ca https://doi.org/10.30953/bhty.v8.414 citation: blockchain in healthcare today 2025, 8: 414 https://doi.org/10.30953/bhty.v8.4142 (page number not for citation purpose) tomer jordi chaffer et al. empower patients by granting data control and requiring consent for access, yet their adoption remains limited, with patient engagement a key challenge.3 healthcare 4.0 not only introduces a new technological toolkit but also signals a shift toward decentralized, patient-driven ecosystems that challenge traditional provider-centric models of care. at the heart of this shift is the self-sovereign patient, an emerging paradigm that positions patients as empowered custodians of their health information, and ultimately as active stakeholders of their health journeys. here, the self-sovereign patient is one who manages, shares, and benefits from their own health data, acting as a digital custodian within a decentralized healthcare network. to realize this vision, it is essential to view healthcare 4.0 as a layered architecture: one grounded in legacy systems that manage structured clinical data, and another powered by decentralized, intelligent tools that enable automation, personalization, and patient sovereignty. indeed, legacy platforms, including emrs and clinical data warehouses,4 continue to be the most reliable source of structured, longitudinal data. despite current limitations such as interoperability challenges, inconsistent data formats, and limited patient access, they serve as the backbone upon which newer technologies will be developed and deployed. to align healthcare 4.0’s promise with the pace of technological innovation, it is crucial to understand the transformative role of technology in shaping healthcare delivery and to anticipate emerging models of care. preparing clinicians, administrators, bioethicists, and policymakers for these technological shifts is critical to realizing this transformation. blockchain technology, when applied to healthcare, offers a distributed and tamper-resistant ledger that ensures data provenance and immutability.5 a prominent use case of blockchain in healthcare includes estonia’s partnership with guardtime, which provided a blockchainbased solution to health records of over 1 million citizens.6 another notable use case is the mit media lab’s medrec, a decentralized record management system tested within the harvard medical school teaching hospital’s backend systems.7 patients, as central administrators of their selfsovereign identities, can manage granular permissions over who accesses their data, when, and for what purpose.8 this model of transparent, patient-controlled access stands in stark contrast to recent controversies such as national health service (nhs) england’s foresight project. in this case, patient data from 57 million general practitioner records, originally collected for covid-19 research, was used without additional consent to train an ai model, sparking backlash from the british medical association and the royal college of general practitioners.9 the lack of transparency and breach of scope highlights why trustpreserving technologies such as blockchain are essential: they offer verifiable access logs and enforceable consent frameworks that could prevent such overreach and restore public confidence in digital health initiatives. restoring public confidence in digital health demands scalable, verifiable, and privacy-preserving infrastructure that patients and professionals alike can trust. for instance, guardtime’s advanced blockchain-backed authentication methods such as keyless signature infrastructure (ksi) leverages hash-function cryptography to produce signatures that are cryptographically verifiable, timestamped, and immune to quantum threats. because ksi does not require the transmission or storage of raw data but only cryptographic hashes, it guarantees data privacy while enabling scalable, independently verifiable authentication across institutional and geographic boundaries.10 building on a foundation of trust, consent mechanisms can be layered with decentralized identifiers (dids),11 verifiable credentials (vcs),12 or even more recently, soulbound tokens (sbts),13,14 which makes it possible for patients to own their data and share only what is necessary with healthcare professionals, insurers, researchers, or even ai agents acting on the behalf of clinicians in the future.15 indeed, within healthcare 4.0, ai agents can act as autonomous or semi-autonomous intermediaries, analyzing real-time health streams and legacy record data to support clinical decisions, triage tasks, or even automate elements of documentation and diagnostics.16 as healthcare 4.0 infrastructure continues to be developed and integrated with legacy systems, ai agents could benefit from decentralized ai architectures, such as via secure, distributed evaluation (i.e., assessing models without exposing patient data or proprietary algorithms). in addition, decentralized ai can help reduce bias by drawing on diverse, heterogeneous datasets, which could benefit, for instance, patients with rare diseases.17 together, these technologies form the foundation of a secure, patient-directed data ecosystem where ai agents operate not as black boxes but as accountable extensions of clinical care, amplifying human decision-making while preserving patient autonomy and trust. the iomt and wearables can continuously generate real-time health data, such as for heart rate variability to sleep cycles and medication adherence.18 when integrated into a blockchain-based personal health wallet, such as the medilinker,19 these data points can inform care plans, trigger smart contracts for automated insurance reimbursement,20 or alert providers to early warning signs of chronic disease exacerbation.21 this approach exemplifies the internet of medical technologies (iomt), where decentralized blockchain infrastructure mitigates single points of failure by securely storing sensorderived vitals on an immutable ledger, thereby enhancing reliability, traceability, and system resilience.22 as these systems evolve, the aggregation of iomtand wearablederived data within secure, patient-controlled platforms https://doi.org/10.30953/bhty.v8.414 citation: blockchain in healthcare today 2025, 8: 414 https://doi.org/10.30953/bhty.v8.414 3 (page number not for citation purpose) the self-sovereign patient also lays the groundwork for broader innovation; namely, the development of blockchain-based health data marketplaces. in such models, real-time, high-resolution data streams become valuable digital assets, owned by patients and made available for research, insurance modeling, or ai training under strict consent conditions.23 if designed with guardrails against the commodification of health data and protections against coercive monetization among vulnerable groups,24 this paradigm could contribute to a secure and decentralized data-sharing economy that respects consent and incentivizes participation17 in this way, iomt-integrated health wallets could not only enhance individual care but also serve as entry points into a broader participatory data economy, one that demands robust governance, ethical safeguards, and institutional readiness to fully realize its transformative potential. recent health insurance portability and accountability act (hipaa) directives advocate for secure, patientcentric solutions, emphasizing access to health records, robust cybersecurity, telehealth expansion, and exploring interoperability solutions to address inefficiencies that hinder timely, high-quality care.25 like the principle of least-privileged access, hipaa’s minimum necessary standard aims to ensure that access to protected health information is restricted to only what is required for a given task.24 as blockchain technology continues to expand its use cases within the healthcare space, its application must be carefully aligned with hipaa’s core principles, particularly around access control, data mutability, and encryption. furthermore, the immutable nature of blockchain also introduces unresolved legal and ethical challenges.26 data stored on-chain may be considered permanent, raising tensions with regulatory principles such as the right to erasure or the “right to be forgotten,” as established in frameworks like the general data protection regulation (gdpr).27 in addition, the question of digital inheritance emerges: what happens to a patient’s health data after death? as health data gains economic significance, mechanisms must be developed to allow patients to designate trusted heirs, executors, or governance frameworks for posthumous data control and access. solutions may include social recovery pallets or legally binding digital wills that manage post-mortem data rights.28 as blockchain becomes more integrated into health ecosystems, privacy-preserving solutions such as zero-knowledge proofs or revocable sbts will be essential to reconcile permanence with consent, revocation, and inheritance.29,30 regulatory frameworks must evolve in tandem with technical innovation, emphasizing ethics by design,31 explainable and auditable systems, as well as privacy-first solutions. yet, these solutions presuppose equal technological access and capability, which is an assumption that does not hold when considering the digital divide. indeed, realizing this vision must begin with acknowledging the digital divide as a foundational barrier32 it would not be prudent to assume universal access to smartphones, broadband internet, and iomt-enabled devices as such infrastructure and affordability remain unevenly distributed across geography, income levels, age, and ability. without targeted interventions, this disparity risks creating a two-tiered system in which only the digitally resourced can exercise data sovereignty. if left unaddressed, this could undermine autonomy and expose vulnerable populations to exploitation, misinformation, or coercion. enhancing digital literacy via patient education is essential as sovereignty requires more than technical control but also demands the knowledge, resources, and institutional support that allow patients to exercise that control meaningfully. even amid equity challenges, the potential benefits of healthcare 4.0 technologies remain significant. as these technologies mature, utility applications and intelligent software interfaces—such as mobile dashboards, scheduling assistants, and patient engagement platforms—can streamline clinical workflows and reduce administrative burdens. in a time of global healthcare staffing crises,33 such tools could act as force multipliers, allowing providers to focus on high-value care activities while ai agents and automated tools manage documentation and coordination tasks. implementation must contend with challenges such as legacy system compatibility, secure data migration, and staff retraining. organizational change management will be just as vital as technical integration. workflow redesigns, cost-benefit analyses, and trust-building will determine whether these tools succeed in amplifying care delivery. ultimately, the self-sovereign patient model is a sociotechnical construct that demands institutional change. clinicians must adapt to patient-generated health data as part of the clinical narrative. administrators must rethink data governance models. bioethicists must contend with new questions around autonomy, consent, and equity. policymakers must anticipate and shape emerging standards that foster trust, transparency, and accountability across digital health ecosystems. the self-sovereign patient model must function not only at the technical layer but also as a trust architecture, thereby enabling policy-aligned data exchange along the stakeholder value chain in ways that reinforce transparency and verifiability. cross-disciplinary education and scalable systems are vital to support this transformation. and most importantly, we must center the lived experiences of patients in the design of digital health systems. in the end, healthcare 4.0 is not simply about machines, data, or automation. it is about rehumanizing care by restoring agency to the individual. funding no funding was received. https://doi.org/10.30953/bhty.v8.414 citation: blockchain in healthcare today 2025, 8: 414 https://doi.org/10.30953/bhty.v8.4144 (page number not for citation purpose) tomer jordi chaffer et al. conflicts of interest the authors have no conflicts of interest to report. financial and non-financial relationship and activities none. authors’ contributions all authors contributed to the conceptualization of the manuscript at various stages of its development. tomer jordi chaffer drafted the initial manuscript and integrated feedback from co-authors. dr. joe littlejohn contributed clinical insights and contextualized the narrative within the broader history of technological transformation in healthcare, including the role of blockchain in healthcare 4.0. arun nadarasa provided analysis of emerging use cases in the participatory data economy and industry applications of blockchain-based solutions. dr. claudia lamschtein contributed ethical analysis, particularly regarding the evolving role of the patient in this new healthcare paradigm. all authors reviewed, revised, and approved the final version of the manuscript for publication. data availability statement (das), data sharing, reproducibility, and data repositories this editorial does not contain any primary data analysis or new datasets. application of ai-generated text or related technology we acknowledge the use of chatgpt (gpt-4o model) to assist with grammar correction, spelling, and editorial refinement throughout the preparation of this manuscript. we also used claude to support adherence to the vancouver citation style. acknowledgments tomer jordi chaffer is a member of the editorial board for blockchain in healthcare today. we would like to thank muthu ramachandran for his helpful suggestions during the preparation of this manuscript. references 1. chanchaichujit j, tan a, meng f, eaimkhong s. healthcare 4.0. singapore: springer nature; 2019. 2. evans rs. electronic health records: then, now, 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workforce for health in a globalized context – global shortages and international migration. glob health action. 2014;7(1):23611. https://doi.org/ 10.3402/gha.v7.23611 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http:// creativecommons.org/licenses/by-nc/4.0. the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.414 https://doi.org/10.1038/s41598-024-68529-x https://doi.org/10.1038/s41598-024-68529-x https://doi.org/10.2196/22269 https://doi.org/10.2196/22269 https://www.hipaajournal.com/hipaa-updates-hipaa-changes/ https://www.hipaajournal.com/hipaa-updates-hipaa-changes/ https://papers.ssrn.com/sol3/papers.cfm?abstract_id=953670 https://articlegateway.com/index.php/jsis/article/view/5637/5349 https://articlegateway.com/index.php/jsis/article/view/5637/5349 https://doi.org/10.1109/wi.2018.00133 https://arxiv.org/abs/2301.11074 https://doi.org/10.3390/s22207716 https://doi.org/10.3390/s22207716 https://doi.org/10.30953/bhty.v7.334 https://link.springer.com/book/9789819643592 https://doi.org/10.1007/s11920-021-01274-4 https://doi.org/10.1007/s11920-021-01274-4 https://doi.org/10.3402/gha.v7.23611 https://doi.org/10.3402/gha.v7.23611 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research s3ef-hbcas: secure and sustainable software engineering framework for healthcare blockchain applications muthu ramachandran, phd* ai tech, leeds, england and forti5 tech, london, united kingdom *corresponding author: muthu ramachandran, email: muthuram@ieee.org keywords: blockchain architecture, ethics, privacy, engineering for blockchain, framework for blockchain, security, smart contract, software framework, sustainability abstract blockchain applications in healthcare have grown rapidly. they include record-keeping, clinical trials, medical supply chains, patient monitoring, etc., where blockchain characteristics are needed to improve safety, privacy, and security. blockchain technology is one of the most significant disruptive technologies today. however, porru et al.1 reported that it lacks processes, tools, and techniques. therefore, this paper provides a systematic framework for a secure and sustainable software engineering framework for healthcare blockchain applications (s3ef-hbca). s3ef-hbca is a significant contribution that includes requirements engineering for healthcare, business process modeling for healthcare, domain modeling for healthcare, a reference architecture for healthcare, and validation by a case study on electronic healthcare record management system (ehr), and simulation with business process modeling notation (bpmn) tools. the simulation shows it has taken 10.45 min to process 100 instances of real-time data and service requests. the overall result shows encouragement regarding process, tools, standards, and testing. submitted: october 18, 2023; accepted: december 3, 2023; published: december 22, 2023 a summary of the main highlights of this article is listed here. 1. contribution to designing blockchain applications in healthcare with built-in security, reusability, and sustainability. 2. innovative approach to building a secure and sustainable software engineering framework for healthcare blockchain applications (s3ef-hbca) framework. 3. requirements for engineering framework using business process modeling notation (bpmn) modeling and simulation for verification and validation before design and implementation. 4. non-functional requirements classification for blockchain applications in healthcare. 5. domain analysis method and classification for identifying and developing reusable smart contracts in healthcare applications. 6. blocks-point effort estimation technique, including tcf and environment complexity factors (ecf) for cost and complexity analysis. 7. design strategies for building reusable blockchains as service components. 8. innovate reference architecture (ref-arcbc) standardizes blockchain development in healthcare applications. 9. business process and reference architecture efficiency estimation metrics. this detailed report delves into the motivation behind the rise of blockchain, strategic approaches to its implementation, and its current growth trends. blockchain has impacted medicine as well as other disciplines in many ways. for example, blockchain has been adopted rapidly in most applications today as it offers trust and security. the computing technology industry association (comptia)2 defines blockchain as blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-5303-3100 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.2862 (page number not for citation purpose) ramachandran ‘a mathematical structure for storing digital transactions or data in an immutable, distributed, decentralized digital ledger consisting of blocks that are linked via cryptographic signature that is nearly impossible to fake, hack or disrupt.’ banafa3 reported that blockchain applications will generate $3.1 trillion in new business value by 2030. in addition, banafa3 also argues that ‘the basis for a dynamic distributed ledger can be applied to save time when recording transactions between parties, remove costs associated with intermediaries, and reduce risks of fraud and tampering.’ blockchain technology is a disruptive force, reshaping industries and revolutionizing transactions and data management. its core principles of decentralization, transparency, security, and immutability have motivated organizations across various sectors to explore and adopt blockchain solutions. blockchain applications in healthcare have expanded rapidly, and they include record keeping, clinical trials, medical supply chains, patient monitoring, etc., where blockchain characteristics are needed to blockchain has been adopted rapidly in most applications today as it offers trust and security. comptia2 defines blockchain as ‘a mathematical structure for storing digital transactions or data in an immutable, distributed, decentralized digital ledger consisting of blocks that are linked via cryptographic signature that is nearly impossible to fake, hack or disrupt.’ banafa3 reported that blockchain applications will generate $3.1 trillion in new business value by 2030. in addition, banafa3 also argues that ‘the basis for a dynamic distributed ledger can be applied to save time when recording transactions between parties, remove costs associated with intermediaries, and reduce risks of fraud and tampering.’ overall, blockchain technology in healthcare continues to grow, driven by the industry’s need for data security, transparency, and efficiency. these metrics highlight the increasing adoption of blockchain solutions across various healthcare applications, promising a transformative impact on the sector in the coming years. as of today, blockchain technology continues to experience robust growth and development: • increased adoption: businesses across industries, including finance, supply chain, healthcare, and real estate, are actively adopting blockchain solutions to enhance efficiency and transparency. • evolving ecosystem: the blockchain ecosystem is continuously evolving with the emergence of new blockchain platforms, projects, and protocols, each catering to specific use cases and requirements. • cryptocurrency market: cryptocurrencies, such as bitcoin and ethereum, continue to gain mainstream acceptance as alternative investments and digital assets. • defi and nfts: the decentralized finance (defi) and non-fungible token (nft) markets have exploded, showcasing the versatility and potential of blockchain technology. • regulatory developments: governments and regulatory bodies are actively working to create a regulatory framework for blockchain and cryptocurrencies, aiming to balance innovation with consumer protection. • institutional involvement: institutional investors and financial institutions are increasingly participating in the cryptocurrency and blockchain space, indicating growing confidence in the technology. • this research is based on an experimental case study on her and doesn’t involve any datasets. in conclusion, the growth of blockchain technology is driven by its ability to provide trust, transparency, and security in various applications. organizations are strategically adopting blockchain to innovate, optimize processes, and remain competitive in an ever-changing digital landscape. as the ecosystem matures and regulatory clarity improves, blockchain’s role in shaping the future of industries is poised to expand further. destefanis et al.4 reported a need for smart contract programming based on a disciplined approach. they reported several vulnerabilities causing the freezing of more than 500k users with a loss of $150 million. in addition, studies on software engineering (se) for blockchain dapps sought a systematic approach [beller and hejderup5; destefanis et al. 4; chung l, do prado leite jcs6]. these articles focus on exploring vulnerabilities in smart contracts and emphasize the need for proper se practices in the context of blockchain technology. smart contracts are self-executing contracts with predefined rules and conditions encoded on a blockchain. they are an integral part of many blockchain platforms, such as ethereum. smart contracts are powerful tools, but they are not immune to vulnerabilities. the article discusses various security issues that can arise in smart contracts, such as coding errors, design flaws, and implementation issues. these vulnerabilities can potentially lead to financial losses, exploitation, or disruptions in blockchain applications. the author argues that traditional se practices should be applied to developing and testing smart contracts to enhance their security and reliability. they may suggest specific approaches or methodologies for ensuring the correctness and robustness of smart contracts. overall, the articles highlight the importance of addressing vulnerabilities in smart contracts and advocate for adopting best practices from se to improve the quality and security of blockchain-based applications. what do you gain from this article (learning outcome) and highlighting some of the key research questions? https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 3 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications the organization is as follows: introduction provides key questions and learning outcomes; a critical evaluation of the literature survey on blockchain in healthcare today, key challenges and characteristics of blockchain, the s3ef-hbcas—secure and sustainable se framework, requirements engineering model for blockchain known as blockchain security quality requirements engineering (bc-square) which is the key to achieving quality development of blockchain by analyzing and evaluating healthcare customer and stakeholders requirements, design method and a reference architecture known as refarchbc which is the key to achieve standardization of blockchain apps, and evaluation techniques and best practices of reference architecture with a real-world case study. key research questions • what are the key challenges in blockchain for healthcare applications? • how do we systematically apply blockchain to be secure, safe, and sustainable technology in healthcare applications? • what are the design principles for a driven reference architecture for a secure, sustainable, and software engineering approach to healthcare blockchain applications (s3ef-hbcas)? • what are the services comprising reference architecture for s3ef-hbcas in healthcare? • how to classify technologies and services for blockchain applications? • how do we classify the application domain for building sustainable and reusable blockchain in healthcare? • what are the key challenges of sustainability in s3hbcas mean? • what are the key challenges of security in hbcas? learning outcomes • understand the current trends in healthcare blockchain applications (hbcas) • identify some of the key characteristics of blockchain relevant to hbcas • to understand the security, sustainability, and software engineering approach in the hbcas framework (s3ef-hbcas) • understand requirements engineering for s3ef-hbcas • acquire knowledge of domain classification for blockchain applications • understand blocks-points effort and complexity estimation • understand reference architecture for blockchain in s3ef-hbcas • understand bpmn modeling and simulation using a case study and evaluation of ref-arcbc with bpmn: electronic healthcare record (ehr) • understand the evaluation techniques of bpmn simulation for reference architecture for s3ef-hbcas. background and critical evaluation blockchain technology is a distributed ledger technology that gained prominence with the advent of cryptocurrencies like bitcoin. it has since found applications in various industries, including healthcare. the core concept behind blockchain is the creation of a decentralized and immutable ledger that records transactions securely and transparently. in healthcare, this technology promises to enhance data security, interoperability, and transparency. figure 1 shows the benefits of blockchain applications in healthcare, including key blockchain characteristics such as decentralization, improved data security, and privacy, as blockchain provides trust, health data ownership, availability, robustness, integrity, transparency and trust, and data verifiability. agbo, mahmoud, and eklund7 discuss the potential applications of blockchain in healthcare and its benefits in terms of security and privacy and also provide a list of benefits of blockchain to healthcare applications: decentralization, improved data security and privacy, health data ownership, availability/robustness, transparency and trust, and data verifiability. mayer et al.8 explore the use of blockchain technology for managing electronic health records and discuss the challenges associated with privacy. christidis and devetsikiotis9 provide insights into the concept of smart contracts and their application in various domains, including healthcare. khezr et al.10 discuss different blockchain applications in healthcare, such as supply chain management and health records. hasselgren et al.11 explore blockchain technology’s challenges and use cases in healthcare. decentraliza�on improved data security and privacy health data ownership availability, robustness and integrity transparency and trust data verifiability fig. 1. benefits of blockchain in healthcare applications. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.2864 (page number not for citation purpose) ramachandran besides several benefits, blockchain technology also poses some key challenges, including ethical and legal issues. ethical considerations on blockchain technology are crucial to ensure patient privacy, data security, and overall fairness. for example, on data privacy and consent, we must consider the following aspects: • obtain explicit and informed patient consent for data sharing and storage on the blockchain. • ensure compliance with data protection regulations like gdpr, hipaa, or other local laws. tang et al.12 provide a systematic discussion on the ethics of blockchain applications and map the main social challenges raised by its technology and applications. de filippi and wright13 provide a detailed discussion on the legal aspects of blockchain concerning code as a rule versus code as law (blockchain), as smart contracts promise to support decentralized autonomous organizations (dao) by automating the law through code. this creates a major fear amongst lawyers, lawmakers, and governments alike. in addition, there are major strengths and research challenges on security and privacy, interoperability, building reusable smart contracts for sustainability and productivity, and scalability. sustainability is an important factor for blockchain technology. this paper defines sustainability in terms of several factors: reusability of the blockchain, such as smart contract, as it has the potential to reduce energy consumption across the blockchain network, reproducibility of the results, energy consumption, social impact on the lifestyle improvement, and economic impact on the society. to this end, giungato et al.14 have reported that transitioning monetary systems to cryptocurrency will result in unacceptable energy consumption. they also reported that 25 new bitcoins are being generated every 10 min globally. this amounts to difficulty and the complex process of mining bitcoins involves a larger number of complex networks, pcs, virtual memory, etc. the following section identifies some of the key characteristics of blockchain and its significant contribution to dapps development, process, and identifying requirements. characteristics of blockchain identifying the characteristics of blockchain is essential for understanding its potential business opportunities, applications, advantages, and limitations. blockchain technology has gained significant attention due to its unique attributes, which set it apart from traditional databases and ledgers. here are some key rationales for identifying the characteristics of blockchain: 1. innovation and disruption: blockchain is a relatively new technology that can potentially disrupt various industries, including finance, supply chain, healthcare, and more. understanding its characteristics helps businesses and individuals assess how it can be used to innovate and improve existing processes. 2. security: blockchain is often touted for its security features, including cryptographic encryption and immutability. recognizing these characteristics is crucial for evaluating its suitability in applications where data security and integrity are paramount, such as financial transactions or medical records. 3. transparency and trust: blockchain’s transparent and decentralized nature allows multiple parties to trust a single source of truth without relying on intermediaries. this transparency is important in industries like supply chain management, where stakeholders need to trace the origin and journey of products. 4. decentralization: blockchain operates on a decentralized network of nodes, eliminating the need for a central authority. this characteristic can reduce the risk of a single point of failure, censorship, or manipulation, making it relevant in scenarios where trust in centralized institutions is eroded. 5. immutability: once data is added to a blockchain, it is challenging to alter or delete. this immutability can be advantageous in scenarios that require an unchangeable record, such as land registries or legal contracts. 6. smart contracts: blockchain platforms like ethereum enable the creation and execution of smart contracts, self-executing agreements with predefined rules. identifying this characteristic is essential for exploring automation possibilities in various industries. 7. consensus mechanisms: blockchain networks rely on consensus mechanisms like proof of work (pow) or proof of stake (pos) to validate and add new transactions to the chain. understanding these mechanisms is crucial for assessing the network’s security, scalability, and energy efficiency. 8. cryptocurrency: many blockchains have their native cryptocurrencies (e.g. bitcoin, ethereum’s ether). recognizing this characteristic is important for comprehending the role of digital assets in the blockchain ecosystem, including their use for payments, incentives, or governance. 9. scalability and performance: different blockchain platforms have varying scalability and performance characteristics. identifying these factors helps organizations select the right blockchain for their specific use cases and understand its limitations. 10. privacy: while blockchains are often associated with transparency, some offer privacy features, such as confidential transactions or zero-knowledge proofs. recognizing these privacy characteristics is vital for https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 5 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications industries like healthcare or finance, which require data confidentiality. 11. energy efficiency: blockchain’s energy consumption has been a topic of debate. identifying this characteristic is important for making informed decisions about its environmental impact and sustainability. 12. regulatory and legal implications: understanding blockchain characteristics helps regulators and legal authorities address legal and compliance issues related to its use, such as data protection, taxation, and liability. in summary, identifying the characteristics of blockchain is essential for making informed decisions about its adoption, assessing its suitability for specific use cases, and navigating the evolving landscape of blockchain technology and its applications. it allows businesses, policymakers, and individuals to harness the benefits of blockchain while addressing its challenges effectively. figure 2 shows the core concepts of blockchain. the core concepts of blockchain include a distributed system of record (blocks), security, verifiability and provenance, embedded business terms (smart contracts), and consensus and agreement. in addition, viriyasitavat and hoonsopon15 state that some of the key characteristics of integrity, transparency, and resiliency are attractive to modern business process management (bpm), service workflow, internet of things (iot), industrial internet of things (iiot), healthcare, cloud computing, big data, cyber-physical systems (cps), etc. hakak et al.16 have identified four key characteristics of blockchain that are relevant for building smart cities: consensus helps to prevent fraudulent transactions, transparency helps to establish easy validation of information, robustness helps to establish no single point of failure, and incorruptible helps to establish secured information and to build trust which is paramount and promise of blockchain. de filippi and wright13 discuss some of the characteristics of blockchain that exhibit a set of core characteristics such as peer-to-peer networking, public-private key cryptography, and consensus mechanisms. figure 3 provides a set of organized characteristics of blockchain and its benefits for healthcare applications. these include data mining, immutability, blockchain as software connected to emerging technologies such as data science and ai, blockchain data structures, and its limitations on scalability. for example, mainstream public blockchains can only handle an average of 3 to 20 transactions per second, whereas mainstream payment services, like visa, can handle an average of 1,700 transactions per second. therefore, it is important to consider closely the requirements for dapp development on the security and privacy of blockchain. in conclusion, in this section, the characteristics of blockchain play a major role in fine-tuning requirements for the development of dapp healthcare applications, and it is important to adopt a systematic approach based on the se framework presented in the following sections. s3ef-bc: secure and sustainable software engineering framework for blockchain applications in the ever-evolving landscape of evolving modern and integrated technologies such as ai, machine learning, and data science, blockchain has emerged as a transformative force with the potential to revolutionize industries ranging from finance and supply chain management to healthcare and beyond. this decentralized and tamper-resistant ledger technology has garnered significant attention for its promise to enhance transparency, security, and efficiency in various domains. however, deploying blockchain applications also brings forth unique challenges, particularly regarding security and sustainability. as we venture deeper into the digital age, the need for a robust framework that ensures the security of blockchain systems and promotes their sustainability becomes increasingly paramount. in response to this imperative, the concept of a secure and sustainable software engineering framework for blockchain applications has emerged as a critical area of focus. this framework seeks to establish best practices, methodologies, and tools that not only fortify the security of blockchain-based solutions but also address the environmental and resource-intensive concerns associated with blockchain technology. in this exploration, we delve into this framework’s core principles and components, shedding light on its significance in shaping the future of blockchain applications. a software framework provides a structure to follow from requirements to deployment. two fields of specialization have emerged: software engineering for blockchain (se for bc) and blockchain for software engineering (bc for se). beller and hejderup5 distributed system of record (blocks) security, verifiability, and provenance (each transac�ons has a unique hash which can’t be changed/traced) embedded business terms (smart contracts) consensus and agreement fig. 2. core concepts of blockchain. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.2866 (page number not for citation purpose) ramachandran have proposed how software engineering can benefit from blockchain in terms of achieving continuous integration and packaging mechanism as such apt-get. in addition, they claim that blockchain can democratize and professionalize the se profession ethically and legally, which can improve the quality of se artifacts and increase the trust in software repositories and open-source software. vacca et al.17 proposed how se can help the non-standard development of smart contracts. the framework shown in figure 4 is known as s3ef-hbcas: secure and sustainable software engineering framework for healthcare blockchain applications, which provides best practices on full life-cycle support for the development of dapps: • the secure requirements engineering method is known as bc-square, a systematic approach and steps in identifying, verifying, and validating blockchain applications using user stories and business process modeling and simulation (bpmn). • the design method involves identifying and developing blockchain service components (bsc) and design assets with the build security in (bsi) approach proposed by ramachandran.18 • reference architecture for blockchain dapps (reffor-bc) is one of the key stages in the framework to map design assets identified during the design stage into four architectural layers such as blockchain ai and iot application and prediction layer at the top, followed by the application layer where healthcare and or application specific bsc and assets such as api, interfaces, security, privacy, and test specific smart contract services. bc fundamental characteris�cs: benefits and limita�ons data mining (blocks of distributed clouds similar to linked data structures) immutable (if data is contained in a commi�ed transac�on, it will eventually become in prac�ce immutable) blockchain, as a so�ware connector, has a complex internal structure and has many configura�ons and variants blockchains have technical limita�ons. privacy is impacted because informa�on on a blockchain is available to all par�cipants.limited throughput scalability, mainstream public blockchains can only handle on average 3-20 transac�ons per second, whereas mainstream payment services, like visa, can handle an average of 1,700 transac�ons per second data structure driven (bc, ghost, blockdag, segregated witness techniques) bc scope (public, private, and consor�um/co mmunity deploy ment models) connector based not interface based as opposed to cloud services fig. 3. characteristics of blockchain applications requirements. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 7 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications • blockchain platform and tools (hyperledger fabric, ethereum: building decentralized applications (dapps). for example, medrec for healthcare, corda is a permissioned blockchain platform designed for enterprise use cases, and eosio is designed for high-performance decentralized applications. • s3ef-bc applications: bc_fintech, bc4se, bc4spi, bc4qi, bc-landregistry, service level agreement as a service (slaas), cps-iot energy management, cps-iot service delivery, service security and privacy protection, cps-iot smart city, cps-iot smart grid, cps-iot smart transportation, cps-iot smart e-government, smart home, etc. healthcare applications with hyperledger fabric for building ehr systems, clinical trial management platforms, and medical supply chain solutions. • s3ef-bc adoption models. this involves creating awareness workshops, training, identifying strategic business innovation within the organization, ethical policies, and governance awareness on dao. • blockchain testing (smart contract testing and blockchain transaction testing), evaluation, and applications. adopting a systematic framework will help us achieve quality and standardization in establishing decentralized organizations with blockchain technology. the following section is devoted to presenting detailed processes and techniques of s3ef-hbca. requirements engineering method for blockchain (bc-square) requirements engineering plays a major role in any software development paradigm both in agile se as well as in traditional se. requirements engineering is a critical phase in software development that involves gathering, documenting, analyzing, and validating the needs and constraints of a system or application. it plays a pivotal role in ensuring that software meets the desired functionality, quality, and performance criteria. in the context of blockchain applications, requirements engineering is equally essential to guarantee the success of blockchain projects. blockchain technology, known for its decentralized and immutable nature, is being increasingly adopted in various industries such as finance, supply chain management, healthcare, and more. to harness the full potential of blockchain, it is essential to define and manage the specific requirements of blockchain applications effectively. in addition, there is also another major role blockchain secure requirements engineering for blockchain with bc-square method andbpmn modelling and simula�on methods and design principles: service components with soaml reference architecture for s3ef-bc (ref-for-bc) blockchain pla�orm and tools (hyperledger fabric, ethereum: building decentralized applica�ons (dapps). example medrec for healthcare, corda: permissioned blockchain pla�orm that is designed for enterprise use cases, eosio: designed for high-performance decentralized applica�ons. s3ef-bc adop�on models blockchain tes�ng (smart contract tes�ng and blockchain transac�on tes�ng), evalua�on and applica�ons. s3ef-bc applica�ons: bc_fintech, bc4se, bc4spi, bc4qi, bc-landregistry, service level agreement as a service (slaas), cps-iot energy management, cps-iot service delivery, service security and privacy protec�on, cps-iot smart city, cps-iot smart grid, cps-iot smart transporta�on, cps-iot smart e-government, smart home, etc. healthcare applica�ons with hyperledger fabric for building electronic health record (ehr) systems, clinical trial management pla�orms, and medical supply chain solu�ons. fig. 4. s3ef-hbcas: secure and sustainable software engineering framework for healthcare blockchain applications. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.2868 (page number not for citation purpose) ramachandran technology plays in securing software development process and creating trust. as mentioned in the earlier section, there are two fields of research: se for blockchain, which adopts systematic processes and techniques for developing blockchain applications, and blockchain for se, which adopts the application of blockchain to improve the quality and security of software development process and assets. in requirements engineering, blockchain plays a major role in creating a smart contract between clients and software development organizations, as discussed by dzhalila et al.19 a fundamental reference for understanding the importance of requirements engineering in developing blockchain applications is the paper titled ‘a systematic literature review on blockchain technology in software engineering’.19 this paper highlights the challenges and best practices in se for blockchain applications, emphasizing this technology’s unique characteristics and considerations. in the realm of blockchain, requirements engineering involves: • defining use cases: identifying and describing the specific use cases for the blockchain application, such as smart contracts for automated transactions or decentralized identity management. • security and consensus mechanisms: determining the security requirements and consensus mechanisms to ensure the integrity and trustworthiness of the blockchain network. • scalability and performance: addressing the scalability and performance requirements to handle a potentially large number of transactions and participants. • interoperability: ensuring interoperability with existing systems and other blockchain networks, if necessary. • privacy and compliance: meeting privacy and regulatory compliance requirements, especially in industries like finance and healthcare. • user experience: designing user-friendly interfaces and experiences for interacting with the blockchain application. • testing and validation: developing test cases and validation criteria to ensure that the blockchain application meets its intended requirements. • maintenance and evolution: consider how the blockchain application will evolve and plan for maintenance and upgrades. in summary, requirements engineering is a crucial step in developing blockchain applications. it helps ensure that blockchain technology is effectively leveraged to address specific business needs while considering the unique characteristics and challenges of blockchain networks. the development of requirements engineering for blockchain applications involves a structured approach to ensure the successful integration of security, privacy, and trust considerations. in addition, the requirements engineering for security methodology, known as security quality requirements engineering (square), has evolved at sei (software engineering institute).20 this provides a framework for collecting basic security requirements early in the lifecycle. however, it lacks details and doesn’t go beyond a nine-step process. therefore, this paper proposes a requirements engineering method known as bc-square, as shown in figure 5. bcsquare method has been fine-tuned for identifying, collecting, validating by bpmn modeling and simulation, and evaluating quality blockchain quality requirements, which has been evaluated in a real-world case study presented in section 7 of this paper. this consists of several stages as follows: • agreed definitions for domain-specific blockchain applications involve identifying common domain-specific terminology and definitions; therefore, they create an understandable communication amongst stakeholders. • identify build security, privacy, and trust in (bspti) goals. °° ‘build security, privacy, and trust in’ (bspti) goals are fundamental objectives in software development and system design that aim to incorporate security, privacy, and trust considerations into the development process from the outset. these goals are essential for creating robust and reliable systems that protect user data, maintain system integrity, and foster trust among users and stakeholders. here are some key bspti goals. 1. security goals: °° data protection: ensure the confidentiality and integrity of sensitive data by implementing encryption, access controls, and secure storage mechanisms. °° authentication and authorization: implement strong authentication methods and granular authorization controls to prevent unauthorized resource access. °° vulnerability mitigation: identify and address potential security vulnerabilities through techniques such as code reviews, penetration testing, and security scanning. °° incident response: develop a robust incident response plan to effectively detect and respond to security breaches. °° secure coding practices: enforce secure coding standards and best practices to minimize the introduction of security flaws during development. • privacy goals: °° data minimization: collect and process only the minimum amount of personal data necessary for the intended purpose. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 9 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications °° consent management: obtain informed consent from individuals before collecting and processing their personal information. °° data transparency: provide users with clear and understandable privacy policies and notices regarding data collection and processing. °° data portability: enable users to access and transfer their data between services easily. °° data retention: define data retention policies and ensure the secure deletion of data when it is no longer needed. • trust goals: °° user trustworthiness: build systems that users can trust by being transparent about data use and adhering to ethical and legal standards. °° reliability and availability: ensure the system is highly available, reliable, and resilient to minimize downtime and disruptions. °° compliance: adhere to industry-specific regulations and standards, such as gdpr, hipaa, or iso 27001, to demonstrate a commitment to compliance. °° third-party trust: assess and verify the trustworthiness of third-party components, libraries, and services integrated into the system. °° user education: educate users and stakeholders about security and privacy practices, empowering them to make informed decisions. these bspti goals should be integrated into the software development lifecycle, from requirements gathering validate, verify, and inspect functional and nfr requirements using bpmn modelling and simulations tool and smart contract. adopt static analyser for smart contract for vulnerability analysis (slither tool, fiest, j. et al. 2019) agreed de�nitions for domain-speci�c blockchain applications identify build security, privacy and trust in (bspti) goals develop bsti artefacts perform risk assessments and mitigation plan identify and select a requirement elicitation technique elicit nfr, security and privacy requirements categorise and create smart contract for nfr, security and privacy requirements identify, classify, and combine (link and associate) relevant smart contracts for nfr blockchain domani-speci�c functional requirements prioritise functional and nfr blockchain requirements create and adopt a process, standard and structures for smart contracts map and associate smart contract with blockchain functions/services (functional requirements for blockchain (for each blocks and transactions)) fig. 5. requirements engineering method for blockchain (bc-square) applications. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28610 (page number not for citation purpose) ramachandran to design, coding, testing, deployment, and ongoing maintenance. incorporating security, privacy, and trust considerations from the beginning helps reduce the risk of vulnerabilities and data breaches and fosters user confidence in the system. • develop build security, privacy, and trust in (bsti) artifacts. this involves beginning by creating artifacts that document your ‘build security, privacy, and trust in’ (bsti) goals, outlining your objectives for security, privacy, and trust within the blockchain application. • perform a risk assessment and mitigation plan involving a comprehensive risk assessment to identify potential threats and vulnerabilities related to your blockchain application. develop a mitigation plan to address these risks. • identify and select a requirement elicitation technique involves choosing appropriate requirement elicitation techniques (e.g. interviews, surveys, workshops) to gather requirements effectively from stakeholders and users. • elicit non-functions requirements (nfr) for blockchain applications based on the characteristics identified in section 3, trust, security, and privacy requirements involves eliciting nfrs, trust, security requirements, and privacy requirements specific to your blockchain application. these requirements should align with your bsti goals. • categorizing and creating smart contracts for nfr, trust, security, and privacy requirements involves translating the elicited nfrs, trust security, and privacy requirements into service-level agreements (slas) and smart contracts that will enforce these constraints on the blockchain. • identifying, classifying, and combining (linking and associating) relevant smart contracts for nfr blockchain domani-specific functional requirements involves identifying and classifying the smart contracts that address specific functional requirements within the blockchain domain. combine or link smart contracts as needed to meet functional requirements. • prioritizing functional and nfr blockchain requirements involves prioritizing functional and non-functional requirements to allocate resources effectively and ensure that the most critical requirements are addressed first. • creating and adopting a process, standard, and structures for smart contracts based on sla elicitations, sla analysis, and sla specification consists of defining the structure and logic for the smart contracts based on the identified requirements, ensuring that they are designed to meet trust, security, and privacy goals. • map and associate smart contracts with blockchain functions/services (functional requirements for blockchain (for each block and transaction)) consists of linking and associating the smart contracts with the relevant blockchain functions and services, aligning them with specific transactions and blocks as required by your application. • validate, verify, and inspect functional and nfr requirements using bpmn modelling and simulation tools. smart contract involves utilizing bpmn modeling and simulation tools to validate and verify the functional and nfr requirements. ensure that the smart contracts align with the intended business strategies and processes. • adopting a static analyzer for smart contracts for vulnerability analysis (feist et al.21 slither tool) involves deploying static analysis tools like slither, fiest, or other suitable tools to conduct vulnerability analysis on your smart contracts. identify and rectify security vulnerabilities and weaknesses. this systematic approach ensures that your blockchain application not only meets its functional requirements but also incorporates essential security, privacy, and trust elements from the early stages of development. it helps mitigate risks and enhances the reliability and trustworthiness of your blockchain application. furthermore, according to singh and lee,22 there needs to be more standards and a systematic process that follows re principles to model smart contracts for blockchain-based cloud (bbc) systems. as a result, the development quality is not assured, and issues such as scalability, trade-off between block size and security, and privacy leakage plague the development of smart contracts for bbc applications. therefore, they have proposed a sla-based re for blockchain systems to identify and develop smart contracts: sla elicitation, sla analysis and negotiation, sla specification, and sla assessment and validation. however, their approach to developing smart contracts is very interesting. however, it lacks a development process for complete blockchain application requirements where we believe bc-square provides complete support for both functional and non-functional requirements and develops a reusable smart contract that aims to provide sustainability for blockchain technology as discussed in the introduction section. figure 6 illustrates requirements engineering classification for blockchain applications, consisting of reusable functional blockchain services such as domain-specific bcs and new generic bcs. the domain-specific bcs are further classified into a set of compute services and cloud-based bcs for secure data and transaction services. the newly developed generic bcs also support bc repository services for reuse and on-thefly composition of blockchains. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 11 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications furthermore, the nfr smart contracts are classified into several nfr bcs, such as resource management bcs, dependability bcs which are further classified into build security in (bsi) bcs, build privacy in (bpi) bcs, and build trust in (bti) bcs. furthermore, the classification of nfr bcs includes extensibility bcs, autonomous recovery, reliability, reusability, low latency, energy efficiency, offloading, and traceability. as discussed, there are two types of requirements known as functional requirements for blockchain (we can also call functional blocks) and non-functional requirements for blockchain, which are presented in the following sub-section. non-functional requirements for blockchain as reusable smart contracts designing nfrs as reusable smart contracts in blockchain applications offers several significant advantages and can greatly enhance the efficiency, security, and scalability of blockchain solutions. here are the key reasons why designing nfrs as reusable smart contracts is important for standardization, consistency, efficiency, cost-effectiveness, sustainability, and productivity. designing nfrs as reusable smart contracts in blockchain applications is a strategic approach that offers numerous benefits, including efficiency, security, customization, and compliance. it streamlines the development process, promotes best practices, and contributes to the scalability and trustworthiness of blockchain solutions. identifying, rationalizing, and addressing a set of nfrs for blockchain applications is essential because blockchain possesses unique characteristics and challenges that necessitate specific considerations. here’s a brief rationale for identifying nfrs for blockchain, focusing on key characteristics such as scalability, traceability, transparency, security, privacy, performance, consensus mechanisms, interoperability, legal and regulatory, resource efficiency, resilience, availability, and usability. in summary, the characteristics of blockchain, such as decentralization, transparency, and immutability, bring about unique challenges and opportunities. identifying and defining nfrs specific to these characteristics is crucial to ensure that blockchain applications are robust, secure, and capable of meeting the needs of various industries and use cases. it helps guide the development process and aligns the technology with the business objectives. figure 7 illustrates the nfr for the medical supply chain. this kind of classifying nfr for domain-specific knowledge is useful for building reusable smart contracts. one of the nfrs is sustainability which is becoming important for the sustainability of blockchain technology compute blockchain services transferring blockchains requested data to the cloud and back blockchain service repository composing new generic blockchain services resource management (storage and caching management, resource alloca�on, energy efficient algorithms for longer ba�ery life, low bandwidth, etc.) load balancing non-func�onal re as smart contracts re for blockchain extensibility autonomic recovery reliability reusability (smart contracts) low latency offloading/transferring/uploading energy efficiency traceability func�onal services re build security in (bsi) build privacy in (bpi) build trust in (bti) dependability (understanding data consumers and data producers, ability to compose simple services, and cogni�ve edge compu�ng capabili�es) develop newly required blockchain for domain-specific applica�ons fig. 6. requirements for engineering classification for blockchain applications. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28612 (page number not for citation purpose) ramachandran as well as addressing building reusable smart contracts, reproducible blockchains and energy-efficient algorithms, and optimization techniques in particular service transactions from cloud-based blockchains (cbb). the following section looks at how to granularize blockchains and how to identify reusable smart contracts and services using domain analysis methodologies, which include ontologies. this is useful for building reusable and sustainable dapps and establishing a product line approach. domain classification for blockchain applications domain analysis in se is a systematic process of studying and understanding a specific problem domain or application area to gather knowledge and insights that can inform the development of software systems within that domain. this process helps software engineers and developers create more effective and tailored solutions by identifying domain-specific requirements, constraints, and characteristics. it is often considered a critical initial step in software development when building systems for specialized or complex domains. several studies on the application of domain analysis include consumer electronic products and blockchain in the supply chain.24–30 here are some key aspects of the domain analysis process in se for the benefit of blockchain in business: 1. understanding the domain: this involves studying the domain’s concepts, terminology, and underlying principles. it includes collaborating with domain experts to gain a deeper understanding of the problem space. 2. understanding the scope of the domain to develop dapps: this involves identifying the business scope and boundaries and will also help us build vertical and horizontal dapps product lines. 3. identifying requirements: domain analysis helps in identifying domain-specific requirements and constraints that need to be addressed in the software system. these requirements may not be apparent without a thorough domain analysis. 4. reusability: domain analysis can lead to the identification of common patterns, components, or frameworks that can be reused across multiple projects within the same domain, promoting efficiency and consistency in software development. 5. reducing risks: by understanding the domain intricacies, software developers can make more informed decisions, reducing the risk of misunderstandings and costly errors during the development process. 6. customization: domain-specific knowledge obtained through analysis allows developers to customize the software to better meet the unique needs of the domain. 7. documentation: domain analysis often results in documentation that serves as a valuable reference for developers, stakeholders, and future maintainers of the software. 8. validation: the findings of domain analysis can be used to validate and refine the software requirements, ensuring that the resulting system aligns with the domain’s expectations. blockchain applications can be classified into different domains based on their intended use and functionality. some common classes of application domain classification for blockchain applications include: 1. financial applications: these blockchain applications are primarily used in the financial sector, such as cryptocurrencies, digital wallets, and payment systems. they allow users to transfer funds securely and transparently without the need for intermediaries. 2. supply chain management: blockchain-based supply chain management applications are designed to provide greater visibility and accountability in the n fr fo rb c traceability interoperability scalability integrity confiden�ality latency availability security performance throughput trust privacy sustainability (building reusable, reproducible and energy efficient bc services) fig. 7. non-functional requirements (nfr) for medical supply chain by khatter and devanjalirelan.23 https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 13 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications movement of goods and services. they allow stakeholders to track the journey of a product from its origin to its final destination, ensuring the authenticity and quality of the product. 3. healthcare: blockchain applications in healthcare can improve the efficiency and security of medical data storage and sharing, ensuring patient privacy and data integrity. they can be used to create a secure and tamper-proof system for managing patient health records and facilitating medical research. 4. identity and access management: blockchain-based identity and access management applications can provide secure and decentralized verification of identity and access to services. they can be used for authentication and authorization of users, ensuring secure access to data and systems. 5. e-government: blockchain applications in government can improve the transparency, efficiency, and accountability of public services. they can be used for voting systems, public records management, and tax collection, among other use cases. 6. smart energy and environment: blockchain-based applications in the energy and environment sector can enable the tracking of renewable energy production and consumption, reducing carbon emissions and facilitating the trading of energy certificates. figure 8 shows a few examples of the different application domains for blockchain technology, and the list continues to grow as more use cases and adaptations of blockchain are discovered and developed. there are several sub-domain and blockchain product lines and challenges that exist in healthcare applications, some of which include: • health records management: blockchain can securely store and share ehrs among healthcare providers, ensuring data accuracy and patient consent. • drug traceability: it can be used to trace the origin and distribution of pharmaceuticals, reducing counterfeit drugs and ensuring patient safety. • clinical trials: blockchain can improve transparency and integrity in clinical trials by recording and verifying trial data in a tamper-proof manner. • supply chain management: it enhances the tracking of medical supplies and devices, ensuring their authenticity and quality. • software engineering approach to dapps development: • developing dapps in healthcare requires rigorous se practices, including: • solid codebase: ensuring the dapp’s codebase is robust, well-documented, and follows best practices to minimize vulnerabilities. • security audits: regular security audits and penetration testing to identify and mitigate vulnerabilities. • legal compliance: adhering to healthcare regulations like hipaa (in the united states) to protect patient privacy and data security. • user experience: creating a user-friendly interface for healthcare professionals and patients to interact with the dapp. • scalability: choosing a blockchain platform that suits the specific healthcare use case and can scale as needed. • maintenance and updates: regular maintenance and updates to adapt to changing healthcare requirements and blockchain technology advancements. in conclusion, blockchain technology has significant potential in healthcare, but its implementation requires careful consideration of security, privacy, scalability, and a strong se approach. the success of blockchain applications in healthcare will depend on addressing these challenges and ensuring the technology’s seamless integration with existing healthcare systems and regulatory frameworks. blocks-points effort estimation we also need to estimate the complexity of service level requirements and there have been several approaches such as the use case points (ucp) method, and user stories point estimation method in agile projects and they have also proposed a set of values for technical complexity factors (tcf) and environmental complexity factors depending bl oc kc ha in ap pl ica �o ns do m ai n financial applica�ons supply chain management healthcare electronic health record (ehr) smart health imaging services iden�ty and access management e-government (e-gov) land registry e-health councils smart energy and environment e-vo�ng fig. 8. application domain classification for blockchain. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28614 (page number not for citation purpose) ramachandran on the nature of applications such as distributed computing, reusability, etc., and service point estimation method in soa-based projects presented by.31 this paper proposes a concept of block-point which involves identifying a sum of blocks or nodes in a blockchain contract and so forth, and recommends the use of bpmn modelling and simulation to model first-level requirements and to validate cost, resource, and performance constraints and smart contracts which can be reusable in the proposed soa-based reference architecture discussed in the following section. in this context, this paper proposes a modified cloud cocomo model31 with weighting for cloud computing projects: a = 2, b = 2.1, c = 3, d = .2. therefore, the effort and cost estimation equations are: blockchain project effort applied (ea) = a × (block points) (human months) (1) blockchain development time (dt) = c × (effort applied)d (months) (2) number of service development engineers required = effort applied (ea)/development time (dt) (3) equations 1–3 provide cloud project effort and cost estimations based on process points which is the sum of all workflows (wf) divided by the total number of blockchain process activities (p). number of block points = wf pn n 0 0∑ ∑/ x (tcf) x (ecf) (4) tcf and ecf are useful factors for building sustainable blockchain services. identifying tcf and ecf is crucial when building sustainable blockchain services. these factors help in assessing the challenges and intricacies involved in blockchain development within specific technical and environmental contexts. here, we’ll discuss the importance of tcf and ecf and provide citations to support the discussion. technical complexity factors (tcf) technical complexity factors (tcf) impact several technical factors on blockchain such as scalability, security, organizational impact of dao, legal aspects of smart contracts and dao, interoperability, and consensus mechanisms. these factors are critically analyzed as follows: 1. scalability: tcfs related to scalability are essential for building sustainable blockchain services. as the blockchain network grows, it must handle an increasing number of transactions efficiently. identifying scalability challenges and solutions ensures that the blockchain can meet future demands.32 2. security: blockchain’s reputation is built on its security features. tcfs help identify potential vulnerabilities and security threats, enabling developers to implement robust security measures and protect the integrity of the blockchain. there are major concerns related to the security and legal aspects of blockchain, smart contracts, and organizational impact on dao.33,34,35 3. interoperability: the ability of a blockchain to interact with other systems and blockchains is vital for its sustainability. identifying tcfs related to interoperability ensures that the blockchain can collaborate seamlessly with other technologies. 4. consensus mechanisms: tcfs associated with consensus algorithms impact the blockchain’s efficiency and governance. understanding the complexities of different consensus mechanisms helps in selecting the most suitable one for a particular use case. environment complexity factors (ecf) environmental complexity factors (ecf) refer to the external factors, conditions, and dynamics in a given environment or context that can significantly impact or influence a system, project, or organization. these factors encompass a wide range of elements, such as regulatory conditions, market dynamics, competitive landscape, technological advancements, user behavior, and more. ecfs are essential to consider when making decisions, formulating strategies, or assessing the sustainability and success of initiatives because they provide insights into the challenges and opportunities posed by the external environment. understanding ecfs allows organizations to adapt and respond effectively to changes and uncertainties in their operating environment. the following are examples of ecfs that impact blockchain technology, and therefore it is essential to consider them earlier in the life cycle. 1. regulatory environment: ecfs related to regulatory compliance are critical for blockchain sustainability. different regions have varying regulations, and understanding them is essential to avoid legal issues. 2. market dynamics and market competitiveness: ecfs tied to market conditions and competition impact the adoption and sustainability of blockchain services. being aware of market trends and competitors’ strategies helps in positioning blockchain solutions effectively. 3. user adoption: ecfs related to user acceptance and adoption are essential. identifying factors that may https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 15 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications hinder or promote user engagement is crucial for building a sustainable user base. 4. technological advances: ecfs associated with technological advancements can impact the longevity of blockchain services. staying updated on emerging technologies ensures that blockchain solutions remain relevant and competitive. in conclusion, identifying tcf and ecf is essential for building sustainable blockchain services. these factors help in addressing technical challenges, complying with regulations, staying competitive, and ensuring long-term relevance in a rapidly evolving blockchain landscape. by considering tcfs and ecfs, blockchain developers and organizations can make informed decisions that contribute to the success and sustainability of their blockchain initiatives. as a systematic framework to standardize the development of blockchain applications, the next phase in the framework is to adopt a systematic design process as the focus of the following section. design strategies, method, dapp development process, reference architecture for blockchain applications (ref-arcbc) it is important to understand dapps for blockchain application development. decentralized applications, commonly known as dapps, have revolutionized the world of blockchain technology and application development. in this brief introduction, we will define what a dapp is, clarify what it is not, and outline a systematic process for dapp development. a dapp, short for decentralized application, is a software application that operates on a blockchain network. unlike traditional centralized applications that rely on a single central server, dapps leverage the principles of blockchain technology to run on a decentralized network of computers known as nodes. key characteristics of dapps include: • decentralization: dapps operate on a peer-to-peer network of nodes, eliminating the need for a central authority or intermediary. • transparency: transactions and data within dapps are recorded on a public ledger (blockchain), making them transparent and immutable. • security: the cryptographic nature of blockchain technology ensures the security and integrity of dapps, reducing the risk of fraud or data tampering. • open source: many dapps are open-source projects, encouraging community involvement and contributions. developing a dapp involves several key steps, and ethereum development resource provides more detailed platform and learning resources (https://ethereum.org/en/ developers/): 1. idea and conceptualization: start by defining the problem your dapp will solve or the unique value it will provide to users. consider the blockchain platform (e.g. ethereum, binance smart chain) that aligns with your goals. 2. design and architecture: plan the user interface (ui), user experience (ux), and overall architecture of your dapp. choose the appropriate blockchain and smart contract platform. 3. smart contract development: write and test the smart contracts that will power your dapp’s functionality. ensure security and efficiency in your code. 4. front-end development: develop the front-end interface of your dapp, which interacts with the blockchain through web3 libraries or apis. 5. testing: thoroughly test your dapp for functionality, security, and performance. use testnets to simulate blockchain environments without real assets. 6. deployment: deploy your smart contracts to the chosen blockchain network and make your dapp accessible to users. 7. user onboarding: provide clear instructions for users to interact with your dapp, including wallet setup and transaction processes. 8. community engagement: foster a community around your dapp to gather feedback, address issues, and encourage adoption. 9. maintenance and updates: continuously monitor and maintain your dapp, addressing bugs, optimizing performance, and implementing updates. 10. scaling: explore options for scaling your dapp as user demand grows, considering solutions like layer 2 scaling or sidechains. it is important to choose a blockchain dapp development process, design method, and data structure based on strategic business applications. blockchain (dapps) design consists of several steps such as elaborating and mapping requirements, design rationale, and carefully choosing blockchain algorithms for more energy efficiency, computational efficiency, scalability, etc. as shown in figure 9. this provides a development process for dapps which consists of several stages: • elaborating on dapp requirements with bcsquare involves gathering blockchain application requirements using the domain analysis method discussed in 5.2 and understanding stakeholder needs and business viability analysis. • creating proof of concept (poc) involves creating proof of concept by modeling and simulating business https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28616 (page number not for citation purpose) ramachandran requirements using business process modelling and simulation tools such as bonitasoft, bizaghi, etc. • selecting your dapp platform involves designing a rationale for selecting a decentralized application (dapp) development platform is a crucial step in the development process. your choice of platform will significantly impact the functionality, scalability, security, and overall success of your dapp. for example, blockchain platforms in the healthcare domain include ethereum which is one of the most popular blockchain platforms for dapp development, and it has been used for various healthcare applications such as patient records management, drug traceability, and telemedicine. other platforms include hyperledger fabric, corda, and medibloc. • choosing a blockchain data structure involves choosing the right blockchain data structures, which is a critical aspect of designing and building a blockchain system. the choice of data structures can significantly impact the blockchain’s efficiency, security, and functionality. • design and implement blockchain and mapping onto reference architecture involves designing a blockchain-as-a-service (baas) platform involves creating a set of components and services that make it easier for developers and organizations to implement and use blockchain technology. adopt a design strategy for creating a baas platform, implementing blockchain technology, and mapping it onto a reference architecture. • developing smart contracts involves developing smart contracts systematically involves a structured approach to ensure the reliability, security, and efficiency of your blockchain-based applications. adopt a systematic process for developing smart contracts such as defining the user requirements with use cases and bpmn as presented earlier, specifying smart contracts, choosing implementation platforms, and validating and verifying smart contracts. • choosing a front-end framework involves selecting the right front-end framework for blockchain applications, which is a crucial decision that impacts ux, development efficiency, and the overall success of your project. by following a specific design rationale, you should make an informed decision when selecting a front-end framework for your blockchain application, ensuring that it aligns with your project’s requirements, development capabilities, and user expectations. • starting the testing cycle involves testing blockchain decentralized applications (dapps) requires a systematic approach and specialized test techniques due to the unique characteristics of blockchain technology. adopt a systematic process and test techniques to start the testing cycles for blockchain dapps such as defining test objectives, creating test plans, and test environments, and creating test cases, test techniques including unit testing, functional testing, integration testing, and energy-efficiency testing. data structures play a critical role in various computer science and distributed systems, and when comparing and evaluating them, it’s essential to consider their design, purpose, and performance characteristics. in this response, i’ll compare and critically evaluate several data structures, including blockchain, ghost (greedy heaviest observed subtree), blockdag (directed acyclic graph), and segregated witness, in terms of their design, use cases, and notable features. table 1 on blockchain data structures provides a list of data structures such as blockchain, ghost, blockdag, and segregated witness. critical evaluation of blockchain data structures critical evaluation of the various blockchain data structures should be based on non-functional requirements identified for your business strategy such as security, scalability, use cases, complexity, and adoption as follows: • scalability: blockdags and segwit address scalability challenges more effectively than traditional blockchains like bitcoin. they enable higher throughput and lower transaction costs. • security: blockchain and ghost provide strong security through their consensus mechanisms. segwit enhances security by reducing the risk of transaction malleability attacks. elaborate on dapp requirements with bc-square create proof of concept (poc) select your dapp pla�orm choose a blockchain data structure design and implement blockchain and mapping onto reference architecture develop smart contracts choose a front-end framework start the tes�ng cycles fig. 9. design and development process for dapps. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 17 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications • complexity: ghost introduces complexity by considering orphaned blocks. blockdags are more complex to implement due to the lack of a linear chain. • use cases: the choice between these data structures depends on the specific use case. blockdags are suitable for high-throughput applications, while segwit is tailored for bitcoin’s needs. • adoption: blockchain and segwit have seen widespread adoption, while ghost and blockdags are adopted in specific blockchain networks. marches et al.36 propose several agile practices based on user stories and uml modeling. they also argue agile methods are well suited for dapps development as they offer for self-organized teams and requirements are not well understood initially. they also proposed other agile techniques such as continuous testing, test driven design, refactoring, continuous integration, collective code ownership, information radiators (cards, boards, burndown charts), coding standards, and pair programming (in some cases). in conclusion, the choice of a data structure depends on the requirements and goals of a given blockchain or distributed ledger system. each of these data structures has its advantages and disadvantages, and selecting the right one involves considering factors like scalability, security, complexity, and the intended use case. furthermore, the landscape of blockchain technology is continually evolving, with innovations and data structures emerging regularly. component-based software design for blockchain component-based software design for blockchain is important as it provides a natural extension of service by providing required and provider services to other blocks in the blockchain. component-based software design is crucial for blockchain development as it offers a systematic approach to building complex blockchain systems. this approach provides a natural extension of services, enabling required services to be provided to other blocks in the blockchain. here are several reasons why component-based software design is essential for blockchain: 1. modularity and reusability: component-based design promotes modularity by breaking down a blockchain system into smaller, self-contained components or smart contracts. these components can be reused in various parts of the blockchain or even in different blockchain applications. this reusability saves time and resources in development. 2. service composition of blockchain: blockchain systems can leverage a wide range of services, including oracles, data feeds, identity management, and more. component-based design enables the composition of table 1. blockchain data structures blockchain design methods and its data structures design rationale use cases notable features blockchain blockchain is a distributed, append-only ledger that relies on a linear chain of blocks, where each block contains a list of transactions. blocks are linked through cryptographic hashes, ensuring the integrity and immutability of the ledger. blockchain is popular in applications like cryptocurrencies (e.g. bitcoin), supply chain management, and smart contracts (e.g. ethereum). blockchain provides strong security through proof of work (pow) or proof of stake (pos) consensus mechanisms, but it can suffer from scalability and latency issues due to its linear structure. ghost (greedy heaviest observed subtree) ghost is a data structure used to resolve forks and reach consensus in ethereum’s blockchain. it considers not just the longest chain but also includes orphaned blocks, giving a more comprehensive view of the network. ghost is primarily used in blockchain networks that adopt ethereum’s protocol for consensus. ghost enhances security by considering more blocks in the consensus process but can lead to increased complexity in certain cases. blockdag (directed acyclic graph) blockdag is a data structure that allows multiple blocks to reference one another in a directed acyclic graph rather than a linear chain. this structure eliminates the need for a single, global consensus point. blockdag is used in cryptocurrencies like iota and nano to address scalability and throughput issues associated with traditional blockchains. blockdags offer improved scalability and reduced confirmation times compared to traditional blockchains. however, they require different consensus algorithms, such as tangle or dag-based pow. segregated witness (segwit) segwit is a data structure upgrade for bitcoin. it separates transaction data and witness data, allowing for more efficient use of block space and fixing transaction malleability issues. segwit is specific to the bitcoin network and aims to improve its scalability and security. segwit reduces the size of transactions, enabling more transactions to fit in a block. this helps reduce transaction fees and enhance the overall efficiency of the bitcoin network. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28618 (page number not for citation purpose) ramachandran these services, allowing for the creation of complex, feature-rich dapps (decentralized applications). an example of a blockchain service component is shown in figure 10, which is a service component model for data analytics consisting of several provider services (shown as lolli pop symbol) such as interface on data pre-processing (idatapreprocessing), etc. blockchain security blockchain has arisen as a potent security solution for various applications, primarily due to one of its fundamental attributes: immutability. regrettably, destefanis et al.4 highlighted vulnerabilities found in smart contract libraries and the insecure programming of smart contracts. consequently, there is a growing need for the advancement of blockchain se to address these issues. therefore, this paper proposes, as shown in figure 11, a concept of security service smart contract component and architecture which is a reusable smart contract and can be plugged into across applications. this improves the security, scalability, and immutability of smart contracts. the bsc model for security smart contracts as shown in figure 11 provides several required interfaces such as isignature for identity management, iencryption for encryption for cryptographic algorithms, etc. the following section is devoted to presenting the reference architecture for dapp development for standardization and sustainability. reference architecture for blockchain (ref-arcbc) the integration of blockchain, ai, and iot technologies can enable the development of powerful and innovative applications that can transform various industries. to standardize the development of ai-enabled blockchain applications and blockchain-driven ai applications requires a reference architecture. a reference architecture for blockchain applications is a standardized blueprint or framework that provides a structured and well-defined approach to designing and building blockchain-based solutions. it serves as a foundational guide for developers, architects, and organizations looking to leverage blockchain technology effectively. here’s a brief introduction to the importance and meaning of a reference architecture for blockchain applications: fig. 10. blockchain service component model. fig. 11. blockchain service component for security smart contract. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 19 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications 1. standardization and consistency: a reference architecture establishes a common set of design principles, best practices, and components, ensuring consistency across different blockchain applications. this standardization streamlines development and maintenance processes. 2. efficiency: it helps developers avoid reinventing the wheel. by following a reference architecture, they can leverage pre-established patterns and components, reducing development time and costs. 3. interoperability: reference architectures often consider interoperability with existing systems and other blockchain networks. this is vital for ensuring that blockchain applications can seamlessly work with other technologies. 4. security: security is a paramount concern in blockchain applications. a reference architecture typically incorporates security best practices and guidelines, which helps in reducing vulnerabilities and risks. 5. scalability: as blockchain applications grow, scalability becomes a critical factor. a reference architecture guides how to design systems that can easily scale to accommodate increased loads. 6. adoption and collaboration: a reference architecture facilitates collaboration and adoption by providing a common framework that different organizations and developers can use. this leads to faster adoption of blockchain technology. 7. regulatory compliance: compliance with regulatory requirements is a significant challenge in the blockchain space. a reference architecture may include guidelines on how to design systems that adhere to relevant regulations. 8. flexibility: while offering a structured approach, a reference architecture is often flexible enough to accommodate variations based on specific use cases or industry requirements. in summary, a reference architecture for blockchain applications is essential for establishing a standardized, efficient, and secure foundation for developing and implementing blockchain solutions. it ensures that blockchain technology is used consistently and effectively, promoting interoperability, security, and scalability, while also aiding in regulatory compliance and encouraging wider adoption. the reference architecture for blockchain is illustrated in figure 12 as reference architecture for blockchain (ref-arcbc). ref-arcbc consists of four layers namely: bc ai and iot application and prediction layer, application layer, blockchain layer, and infrastructure layer. the application layer for such applications typically consists of the following components as shown in figure 12. • blockchain layer: this layer includes the blockchain network and infrastructure that stores and validates data. it ensures the immutability, security, and transparency of the data by using cryptographic algorithms, consensus mechanisms, and smart contracts. • ai layer: this layer includes machine learning algorithms, neural networks, and other ai models that enable the analysis and processing of large amounts of data. it can be used for various purposes, such as predictive analytics, anomaly detection, and natural language processing. • iot layer: this layer includes the physical devices, sensors, and gateways that collect and transmit data to the blockchain and ai layers. it can enable real-time monitoring and control of various systems and processes, such as smart homes, smart cities, and industrial automation. • integration layer: this layer includes the middleware and apis that enable the integration and interoperability of the blockchain, ai, and iot layers. it can ensure the seamless exchange of data and transactions between the different layers, enabling the development of complex and decentralized applications. • application layer: this layer includes the uis, dashboards, and other applications that enable users to interact with the blockchain, ai, and iot layers. it can provide various features and functionalities, such as data visualization, decision-making support, and automation. these layers can be combined and customized based on the specific requirements and use cases of the applications. the integration of blockchain, ai, and iot technologies can enable the development of innovative solutions that can address various challenges and opportunities in different industries. these are just a few examples of the services provided by the blockchain application layer. the actual services and functionalities depend on the specific use cases and requirements of the applications. the blockchain layer in blockchain technology refers to the underlying network and infrastructure that stores and validates data using cryptographic algorithms, consensus mechanisms, and smart contracts. some of the services that happen at the blockchain layer include decentralized data storage, data validation and consensus, smart contract execution, cryptographic security, tokenization, digital asset management, interoperability, and integration. these are some of the services that happen at the blockchain layer in blockchain technology. the actual services and functionalities provided by the blockchain layer depend on the specific use cases and requirements of the applications. the infrastructure layer in a blockchain reference architecture provides the underlying technical infrastructure that supports the blockchain network and https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28620 (page number not for citation purpose) ramachandran enables the execution of smart contracts and other decentralized applications. the services provided by the infrastructure layer include: • node management: the infrastructure layer provides services for the management of the nodes that participate in the blockchain network. these services include node registration, node discovery, node synchronization, and node communication. • network consensus: the infrastructure layer provides services for the consensus mechanism that enables multiple nodes in the network to validate and agree on the data and transactions stored on the blockchain. these services include consensus algorithm implementation, block creation, block validation, and block propagation. • data storage and retrieval: the infrastructure layer provides services for the storage and retrieval of data on the blockchain network. these services include block storage, transaction storage, and data retrieval through apis. • smart contract execution: the infrastructure layer provides services for the execution of smart contracts and other decentralized applications on the blockchain network. these services include smart contract development, deployment, and execution. • security and privacy: the infrastructure layer provides services for the security and privacy of the blockchain network and its participants. these services include cryptographic mechanisms, access control mechanisms, and identity management services. • interoperability and integration: the infrastructure layer provides services for the interoperability and integration of the blockchain network with other networks and applications. these services include inter-chain communication, cross-chain transaction support, and application programming interfaces (apis). • scalability and performance: the infrastructure layer provides services for the scalability and performance of the blockchain network. these services include sharding, sidechains, and other techniques that enable the network to handle large volumes of data and transactions. these are some of the services provided by the infrastructure layer in a blockchain reference architecture. the actual services and functionalities depend on the specific use cases and requirements of the blockchain application. it’s crucial to recognize that assessing intricate reference architectures like ref-arcbc demands a multiyear effort to comprehensively gauge their performance in real-world scenarios and assess their adaptability for fig. 12. reference architecture for blockchain (ref-arcbc). https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 21 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications making global smart contract revisions. consequently, the subsequent sections delineate a method for evaluating ref-arcbc, which centers on a real-world case study concerning a chatbot, an application of conversational ai. this evaluation approach encompasses the utilization of bpmn tools, specifically leveraging bizaghi for modeling and simulation. case study and evaluation of ref-arcbc with bpmn: electronic health record an ehr is a digitalized, longitudinal record of a patient’s health and medical history. it includes information about a patient’s medical conditions, treatments, medications, allergies, laboratory results, and more. the ehr is designed to streamline healthcare processes, improve the quality of care, enhance patient safety, and facilitate the sharing of information among healthcare providers. they have become a fundamental part of modern healthcare systems, enabling healthcare professionals to access and update patient records securely and efficiently. ehr37 and ekblaw et al.38 describe the benefits of her and its features. a list of smart contracts that can be used for building ehr systems using the ethereum programming language are as follows: • patient registry smart contract: this contract can be used to store patient demographic information such as name, date of birth, gender, and contact information. it can also be used to store other relevant information, such as medical history and allergies. • electronic health record smart contract: this contract can be used to store patient health information such as diagnoses, medications, laboratory test results, and imaging studies. it can also be used to track changes to the patient’s health status over time. • consent smart contract: this contract can be used to manage patient consent for the use and sharing of their health information. it can be used to record the patient’s consent preferences and to manage access to their health information by healthcare providers and other authorized parties. • identity smart contract: this contract can be used to manage patient identity and authentication. it can be used to verify the patient’s identity and to ensure that only authorized users have access to their health information. • payment smart contract: this contract can be used to manage payments for healthcare services. it can be used to automatically process payments for services rendered and to manage disputes between patients and healthcare providers. • prescription smart contract: this contract can be used to manage the prescription of medications. it can be used to track prescriptions, monitor adherence to medication regimens, and manage refills. • medical device smart contract: this contract can be used to manage the use and maintenance of medical devices. it can be used to track device usage, monitor device performance, and manage device maintenance and repairs. these are just a few examples of smart contracts that can be used for building ehr systems using the ethereum programming language. smart contracts can be customized and combined in various ways to meet the specific needs of healthcare organizations and patients. business process modelling (bpmn), simulation, and evaluation can be applied to blockchain applications development (dapps) by creating a model scenario representation of a business process or system. bpmn allows for the visualization and documentation of the various steps, activities, and decisions involved in a process. this information can then be used to create a blockchain scenario, which is a simulation replica of the physical system or process. the blockchain application requirements can be modeled, simulated, and evaluated to analyze its performance, identify bottlenecks, and optimize resource allocation and business processes. there are numerous bpmn modeling and simulation tools exist including bonitasoft, bizagi, etc. chang et al.39 provide a critical evaluation of the bpmn tools. in addition, gao et al. have applied bpmn to the implementation of an enterprise resource planning (erp) system in manufacturing. therefore, we believe bpmn is extremely valuable to blockchain application modeling and simulation. by using bpmn modeling, simulation, and evaluation, organizations can gain a deeper understanding of their processes and systems. they can simulate different scenarios, test out various changes and improvements, and evaluate the impact on resource utilization and business performance. this helps in identifying inefficiencies, optimizing resource allocation, and improving overall productivity. for example, a manufacturing company can create dapps of their production line using bpmn. by simulating and evaluating different production scenarios, they can identify the optimal allocation of resources such as machines, manpower, and materials. they can analyze the impact of process changes on productivity, throughput, and quality. this enables them to optimize their resources, reduce costs, and streamline their business processes. figure 13 shows an example of how bpmn has been modeled representing the reference architecture for blockchain presented in the earlier section. business process model and notation (bpmn) is a widely used standard for representing and designing business processes in a visual, standardized format. here’s a brief introduction to bpmn notation, modeling, processes, and simulation steps: https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28622 (page number not for citation purpose) ramachandran 1. bpmn notation: bpmn provides a set of symbols and conventions for visually representing business processes. it includes various elements such as tasks, gateways, events, flows, and pools, each with specific meanings and functions. these symbols are used to create diagrams that illustrate the flow and structure of a business process. 2. modeling: bpmn allows businesses to create visual models of their processes, making it easier to understand, document, and analyze complex workflows. bpmn diagrams are typically divided into various elements, including start and end events, activities (tasks), gateways (decision points), and connecting flows that depict the sequence of actions. 3. processes: a bpmn process represents a series of connected tasks and activities that together form a complete business process. these processes can range from simple, linear workflows to complex, branching, and parallel processes, capturing the way an organization operates. 4. simulation steps: simulation is a valuable aspect of bpmn modeling, as it enables businesses to assess and optimize their processes before implementation. the steps for simulating a bpmn process typically include: °° model creation: begin by creating a bpmn diagram that represents the desired process. this includes defining tasks, decision points, and the flow of activities. °° data and resource allocation: specify data inputs and outputs for each activity and allocate resources (people, equipment) as necessary. °° parameterization: assign values to process parameters to evaluate various scenarios and assess their impact on the process. °° simulation execution: use bpmn simulation tools to run the model. this simulates the actual execution of the process, considering factors like task duration, resource availability, and decision outcomes. °° analysis and optimization: evaluate the simulation results to identify bottlenecks, inefficiencies, or areas for improvement. adjust the model and parameters as needed to optimize the process. °° documentation and reporting: document the simulation results and findings. this information can be used to make informed decisions about process enhancements or automation. furthermore, the bpmn simulation provides validation and verification of the blockchain application requirements, and the reference architecture and we can replicate architectural layers as the swim lanes and sub-systems as pools. for this paper, we have used the bizagi modeler and there are plenty of open-source tools fig. 13. ref-arc bpmn model for electronic healthcare record (bizagi modeller). https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 23 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications available in the marketplace. chang et. al.39 have provided a critical evaluation of bpmn tools. business process and reference architecture efficiency estimation metrics the bpmn model shown in figures 13 and 14 represents an example scenario for ehr health sensor data in real-time from which we can generalize the efficiency of the process execution mathematically for ref-arcbc processes. the efficiency of the bpmn models can be estimated using several bpmn model parameters such as the total number of blockchain processes (p) + the total number of parallel blockchain processes (pp) + total number of decisions and gateways (g) + total number of swim lanes (sl). the efficiency of the ref-arcbc is measured based on several key bpmn model metrics as follows: ref-arcbc efficiency (ref-arcbce%) = (no. of bpmn blockchain processes (p) + no. of parallel bpmn blockchain process (pp)) + number of decisions and gateways (g) + total number of swim lanes (sl)/total number of bpmn blockchain processes (n) ref arcbce p ppn n� � �� � �� �1 1 /n (5) business process efficiency (%) = (ref-arcbc*total number of bpmn blockchain process execution time (bpmnt/total no. of bpmn blockchain processes)*100 bpmne = (ref – arcbce * bpmnt/n) (6) equations (5) and (6) provide a measurable efficiency and sustainability of the ref-arcbc and business process modeling. in summary, bpmn notation provides a standardized way to visually represent and model business processes. simulation of these models allows organizations to analyze and optimize their processes for increased efficiency and effectiveness, ultimately leading to better decision-making and resource utilization. simulation results and analysis simulating a bpmn (business process model and notation) model for a blockchain reference architecture with swim lanes representing different layers can be a valuable exercise to understand and optimize the interactions and processes within the architecture. in this scenario, the swim lanes represent different layers such as blockchain ai, iot, prediction layer at the top, blockchain application layer, blockchain layer, and infrastructure layer at the bottom. additionally, there is a blockchain service layer responsible for coordinating communication among these layers and handling external events. let’s break down how you might approach simulating this architecture using bpmn: 1. identify key processes: begin by identifying the key processes that occur within each layer and how they interact with each other. for instance, the blockchain ai layer might have processes for data analysis and decision-making, while the iot layer may involve data collection and transmission. 2. layer swim lanes: create swim lanes for each layer in your bpmn diagram. each swim lane represents a separate layer, with the topmost swim lane being the prediction layer, followed by the blockchain application layer, blockchain layer, and infrastructure layer at the bottom. the blockchain service layer can be a separate swim lane or depicted as a coordinating element running through all the layers. 3. activities and tasks: within each swim lane, add activities and tasks that represent the processes and interactions occurring within that layer. for example, in the prediction layer, you might have tasks for data analysis and prediction. in the blockchain application layer, you can include tasks related to smart contract deployment or transaction processing. 4. gateways and events: use gateways and events to represent decision points and triggers in your processes. gateways can show where conditional flows or splits occur, while events represent occurrences that initiate processes or signal their completion. 5. message flows: use message flows to depict the communication and data exchange between layers. the blockchain service layer is crucial for coordinating communication between different layers and handling external events. message flows help illustrate the flow of information and requests between these layers. 6. exclusive and inclusive gateways: in a blockchain architecture, there may be situations where you need to make decisions based on certain conditions. use exclusive and inclusive gateways to model these decision points. exclusive gateways represent mutually exclusive paths, while inclusive gateways allow multiple paths based on conditions. 7. external events: model external events or triggers using bpmn events. these could include events like user interactions, sensor data, or incoming requests from external systems. show how these events are received and processed within the blockchain service layer and then distributed to the appropriate layers. 8. data objects: represent data objects, data stores, and data flow with the appropriate symbols in bpmn. this helps to visualize how data is collected, stored, and used across the layers. 9. simulation tools: consider using bpmn simulation tools or software that allows you to run simulations of the model. this will help you analyze how the architecture functions under various scenarios and conditions. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28624 (page number not for citation purpose) ramachandran 10. optimization: use the simulation results to identify bottlenecks, inefficiencies, or areas for improvement within the architecture. adjust the model and processes as needed to optimize the flow and performance of the blockchain reference architecture. by creating a bpmn model with swim lanes, you can visually represent the interactions and processes within your blockchain reference architecture, making it easier to understand, analyze, and optimize. simulating the model provides insights into how the architecture behaves and performs in real-world scenarios, enabling you to make informed decisions about improvements and adjustments. figure 13 shows the real-time scenario of the ehr use case with a simulation showing visually the time it takes, the waiting time, and the completion time at each node. the top layer consists of several processes such as receiving real-time data which is fed to the health analytics layer which filters the raw data and does the cleaning. after processing the data, it then passes onto the chatbot service to interpret the data and answers the requested questions, etc. the simulation shows it has taken 10.45 min to process 100 instances of real-time data and service requests. figures 14 and 15 show the resources versus utilization for energy efficiency and sustainability of the bpmn. for this scenario of the ehr and 100 user requests, the simulation has consumed 97.09% of the cloud resources, 76.33% for fig 14 simulation results fig. 15. resource utilization results. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 25 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications knowledge discovery, it has used 93.20% of blockchain scientists, etc. business process model and notation (bpmn) is a visual modeling language that is often used to represent and analyze business processes within an organization. when it comes to analyzing business processes using bpmn, resource analysis is a critical aspect. resource analysis allows organizations to evaluate various performance measures to optimize their processes. here’s how bpmn can be used to evaluate the performance measures as follows: 1. subor over-utilization of resources: 1. resource assignment: in bpmn, you can assign resources to specific tasks or activities using the ‘resource’ element. this allows you to identify which resources are used and where they are used. 2. resource pools: resource pools can be used to represent the availability of resources in the organization. by comparing the assignments to the available resources, you can identify instances of underutilization (resources are not fully utilized) or overutilization (resources are overloaded). 2. total resources costs: by associating costs with the resources, bpmn models can help calculate the total resource costs for executing a process. you can assign costs to individual resources and accumulate these costs throughout the process to determine the total resource costs. 3. total activity costs: in bpmn, you can also attach costs to activities or tasks. by aggregating these costs across all activities within a process, you can calculate the total activity costs. this includes not only the resource costs but also other costs associated with activities. 4. delays (time an activity waits for a resource): by modeling the sequence flows between activities in bpmn, you can visually represent the order in which activities are executed. analyzing these sequence flows allows you to identify delays caused by activities waiting for resources to become available. resource allocation and availability can be a crucial factor in assessing delays. 5. a more accurate expected cycle time: by considering resource assignments and analyzing the delays, you can calculate a more accurate expected cycle time for a process. this is especially useful for assessing the time it takes to complete a process from start to finish, factoring in resource-related delays. to perform these analyses effectively, bpmn models are often combined with process analysis and simulation tools. these tools can run simulations on the bpmn model to assess resource utilization, costs, and cycle times under different scenarios and resource constraints. by evaluating these performance measures, organizations can identify areas for improvement in their processes. for example, they can reallocate resources to reduce overutilization or redistribute tasks to prevent underutilization. they can also optimize processes to reduce delays, leading to shorter cycle times and more efficient operations. additionally, by calculating resource and activity costs accurately, organizations can make informed decisions to optimize their processes and achieve cost savings. the role of bpmn in conducting simulation experiments is to improve efficiency, reduce costs, and optimize resources in the context of blockchain technology within the healthcare industry. in the healthcare industry today, blockchain technology has emerged as a transformative force. it offers secure and transparent data management, which is critical for the healthcare sector. to ensure its sustainable growth and maximize its benefits, it is essential to continuously assess and optimize various parameters related to its implementation. figures 16–18 in this context likely refer to graphical representations or visual models created using bpmn. these figures could depict processes, workflows, or other aspects of blockchain technology in healthcare: 1. parameters for simulation: the paragraph mentions ‘various of these parameters.’ these parameters may include resource allocation, process efficiency, cost analysis, and resource utilization. in the healthcare context, these parameters are crucial for ensuring that blockchain technology is effectively integrated and utilized to improve data security, interoperability, and transparency. 2. bpmn as a simulation tool: bpmn serves as a powerful tool for modeling and analyzing processes. it allows you to create visual representations of complex workflows, interactions, and resource assignments. these models can be used for conducting simulation experiments. simulation involves running various scenarios to understand how changes to processes or resource allocation impact efficiency and costs. 3. simulation experiments: by conducting simulation experiments using bpmn models, healthcare organizations and stakeholders can test different strategies and scenarios. for instance, they can simulate the impact of reallocating resources within a blockchain-based healthcare system, changing the order of processes, or optimizing data-sharing protocols. 4. efficiency, cost, and resource optimization: the goal of these simulation experiments is to improve various aspects: https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28626 (page number not for citation purpose) ramachandran 1. efficiency: by adjusting processes and resource allocation, organizations can identify and implement changes that lead to more efficient operations. this can result in faster data sharing, reduced waiting times, and improved patient care. 2. cost: through cost analysis, organizations can identify cost drivers within their blockchain-based healthcare systems. by making data-driven decisions, they can reduce unnecessary expenses and allocate resources more effectively. 3. resource optimization: blockchain technology often involves the allocation of resources such as computing power, storage, and personnel. simulation experiments allow for the optimization of these resources, ensuring they are used efficiently. 5. sustainable growth: sustainable growth in the context of blockchain technology in healthcare means that the technology is continuously evolving and improving while maintaining its benefits and relevance. by using bpmn for simulation experiments, fig. 16. resources availability. fig. 17. resource utilization cost estimation. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 27 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications healthcare organizations can adapt to changing requirements, optimize their systems, and ensure the long-term sustainability and success of their blockchain implementations. in summary, figures 16 through 18 likely represent bpmn models that help healthcare organizations conduct simulation experiments to assess and enhance efficiency, reduce costs, and optimize resources in their blockchain technology implementations. this approach enables healthcare to embrace blockchain sustainably, offering long-term benefits in terms of data security, transparency, and interoperability. designing reusable smart contracts for ehr designing reusable smart contracts for an ehr system is crucial for achieving efficiency, security, and scalability in healthcare data management on a blockchain. designing reusable smart contracts helps to achieve modularity, scalability, and reusability. the reusable smart contact in her can then be customized and adopted across all healthcare applications. designing reusable smart contracts for ehr systems involves a thoughtful combination of blockchain technology, healthcare domain expertise, and a deep understanding of privacy and security considerations. these contracts should not only enhance data management but also promote patient-centric control over their healthcare data while ensuring compliance with healthcare regulations. the smart contract for patientregistry in ethereum solidity is presented in table 2. the provided solidity code defines a smart contract named patientregistry that serves as a basic patient information management system on the ethereum blockchain. let’s break down this code step by step: 1. pragma solidity ^0.8.0;: this line specifies the version of the solidity compiler that should be used. 2. contract patientregistry { ... }: this defines the patientregistry smart contract. 3. struct patient { ... }: this is a data structure that represents a patient’s information, including their id, name, date of birth, gender, contact information, medical history, and allergies. this structure is used to store patient records. 4. uint256 public patientcount: this state variable keeps track of the total number of patients in the registry. 5. mapping (uint256 ≥ patient) public patients;: this mapping associates patient ids (of type uint256) with their respective patient records. it allows you to retrieve patient information by their id. 6. event patientadded (uint256 id, string name, uint256 dateofbirth, string gender, string contactinformation);: this event is emitted when a new patient is added to the registry. it provides important details of the added patient. 7. function addpatient(string memory _name, uint256 _dateofbirth, string memory _gender, string memory _contactinformation, string memory _medicalhistory, string memory _allergies) public { ... }: this function is used to add a new patient to the registry. it fig. 18. total cost estimation. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28628 (page number not for citation purpose) ramachandran takes patient information as arguments and emits the patientadded event. 8. patientcount++;: this line increments the patientcount to keep track of the total number of patients. 9. patients[patientcount] = patient(...);: this line adds a new patient to the patients mapping by associating the patient’s id with their information. 10. function getpatient(uint256 _id) public view returns (string memory, uint256, string memory, string memory, string memory, string memory) { ... }: this function allows you to retrieve a patient’s information by providing their id. it checks if the id is valid and returns the patient’s details as a tuple. 11. require(_id > 0 andand _id <= patientcount, ‘invalid patient id’);: this line ensures that the provided patient id is within the valid range of ids stored in the registry. if the id is out of range, it throws an error. 12. patient memory patient = patients[_id];: this line retrieves the patient’s information from the patients mapping based on the provided id. 13. return (patient.name, patient.dateofbirth, patient. gender, patient.contactinformation, patient.medicalhistory, patient.allergies);: this returns the patient’s information as a tuple. in summary, this smart contract, patientregistry, allows the addition of patient records and retrieval of patient information based on their unique id. it’s a basic example of how blockchain can be used to manage sensitive healthcare data securely and transparently. please note that in real-world scenarios, more features, access control mechanisms, and security measures should be considered for the protection of patient data and privacy. in addition, design for reusable smart contracts can boost the productivity and sustainability goals of blockchain. by separating the interface from the actual implementation, you can create other contracts that adhere to the same patient record structure and can use the irecord interface for interoperability and reusability. for instance, if you want to create another contract that interacts with patient records, you can implement the same irecord interface and work with patient data seamlessly. this approach promotes code reusability and maintainability. table 2. smart contract for patientregistry in ethereum solidity. pragma solidity ^0.8.0; contract patientregistry { struct patient { uint256 id; string name; uint256 dateofbirth; string gender; string contactinformation; vstring medicalhistory; string allergies; } uint256 public patientcount; mapping (uint256 => patient) public patients; event patientadded (uint256 id, string name, uint256 dateofbirth, string gender, string contactinformation); function addpatient(string memory _name, uint256 _dateofbirth, string memory _gender, string memory _contactinformation, string memory _medicalhistory, string memory _allergies) public { patientcount++; patients[patientcount] = patient(patientcount, _name, _dateofbirth, _gender, _contactinformation, _medicalhistory, _allergies); emit patientadded(patientcount, _name, _dateofbirth, _gender, _contactinformation); } function getpatient(uint256 _id) public view returns (string memory, uint256, string memory, string memory, string memory, string memory) { require(_id > 0 andand _id <= patientcount, “invalid patient id”); patient memory patient = patients[_id]; return (patient.name, patient.dateofbirth, patient.gender, patient.contactinformation, patient.medicalhistory, patient.allergies); } } https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.286 29 (page number not for citation purpose) s3ef-hbcas for healthcare blockchain applications conclusion this paper presents a systematic approach to developing blockchain applications. a systematic framework on s3ef-hbca has been presented with best practices on requirements engineering for healthcare, business process modeling for healthcare, domain modeling for healthcare, a reference architecture for healthcare, and validation by a case study on ehr management system, and simulation with bpmn tools. the simulation shows it has taken 10.45 min to process 100 instances of real-time data and service requests. the overall result shows encouragement in terms of process, tools, standards, and testing. funding statement this research is not funded by any organization or government. acknowledgment the author would like to thank chatgpt 3.5 for re-writing and proofreading some of the texts in this paper. blockchain concepts glossary application programming interface (api): a set of rules and protocols that allows one software application to interact with another, facilitating integration and communication between different systems. blockchain for software engineering: adopts the application of blockchain to improve the quality and security of software development processes and assets. blockchain: a mathematical structure for storing digital transactions or data in an immutable, distributed, decentralized digital ledger consisting of blocks that are linked via cryptographic signature that is nearly impossible to fake, hack, or disrupt. consensus algorithm: a mechanism used to achieve agreement on a single data value among distributed processes or systems, crucial for validating transactions on a blockchain. consensus layer: the part of a blockchain system responsible for achieving agreement on the state of the blockchain among participating nodes. cryptocurrency: a mathematical structure for storing digital transactions or data in an immutable, distributed, decentralized digital ledger consisting of blocks that are linked via cryptographic signature that is nearly impossible to fake, hack or disrupt. dapp (decentralized application): software applications that run on a decentralized network, typically a blockchain, and use smart contracts for their logic. decentralization: the distribution of control and decision-making across a network, reducing reliance on a central authority. decentralized, distributed ledger: records transactions across a network of computers, ensuring transparency and immutability. digital or virtual currency: uses cryptography for security and operates on a decentralized network, often based on blockchain technology. fork: a split in the blockchain resulting in two separate chains, usually due to a change in the protocol or a disagreement within the community. gas: the unit representing the computational effort required to execute operations or transactions on a blockchain network, often associated with transaction fees. immutable code: code that, once deployed on a blockchain, cannot be changed or updated, emphasizing the importance of thorough testing and security in the development process. immutable ledger: a ledger that cannot be altered or tampered with once a block is added to the blockchain, ensuring data integrity. interoperability: the ability of different blockchain networks and software systems to communicate, exchange data, and operate together seamlessly. mining: the process of validating transactions and adding them to the blockchain by solving complex mathematical problems, typically associated with proof-of-work consensus algorithms. nodes: devices on a blockchain network that participate in maintaining the distributed ledger by validating and relaying transactions. oracles: external agents or services that provide real-world data to a blockchain smart contract, enabling it to make decisions based on information outside the blockchain. permissioned blockchain: a blockchain where access to participate in the network and perform certain actions is restricted to a predefined group of participants. scalability: the ability of a blockchain network to handle an increasing number of transactions or users without sacrificing performance. self-executing contracts: terms of the agreement directly written into code, automating and enforcing contractual agreements on a blockchain. smart contract: software development kit (sdk): a collection of tools, libraries, and documentation that helps developers create software applications for a specific platform or framework. software engineering for blockchain glossary: software engineering (se) for blockchain which adopts systematic processes and techniques for developing blockchain applications. tokenization: the process of converting rights to an asset into a digital token on a blockchain, facilitating ownership and transfer. https://doi.org/10.30953/bhty.v6.286 citation: blockchain in healthcare today 2023, 6: 286 https://doi.org/10.30953/bhty.v6.28630 (page number not for citation purpose) ramachandran wallet: digital tools that allow users to store and manage their cryptocurrencies, providing private and public key pairs for transactions. zero-knowledge proof: a cryptographic method that allows one party to prove the authenticity of information without revealing the actual data, enhancing privacy and 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[internet]. 2023 [cited 2023 october 11]. [cited 2023 october 10]. available from: https://www.healthit.gov/ faq/what-electronic-health-record-ehr 39. chang v, yian chen y, xu qa, xiong c. evaluation and comparison of various business process management tools. int j bus inform syst. 2023;43(3):281–308. https://doi.org/10.1504/ ijbis.2023.132065 copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see: http://creativecommons.org/licenses/ by-nc/4.0. https://doi.org/10.30953/bhty.v6.286 https://www.media.mit.edu/publications/medrec-whitepaper/ https://www.media.mit.edu/publications/medrec-whitepaper/ https://www.healthit.gov/faq/what-electronic-health-record-ehr https://www.healthit.gov/faq/what-electronic-health-record-ehr https://doi.org/10.1504/ijbis.2023.132065 https://doi.org/10.1504/ijbis.2023.132065 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) blockchain in healthcare today issn 2573-8240 use case ensuring trust in pharmaceutical supply chains by data protection by design approach to blockchains halid kayhan ku leuven centre for it & ip law (citip), leuven, belgium corresponding author: halid kayhan, email: halid.kayhan@kuleuven.be keywords: blockchain, data protection by design, eu general data protection regulation (gdpr), pharma ceutical supply chain, trust abstract pharmaceutical supply chains are complex structures that include various participants. furthermore, blockchains are viewed as a promising solution to increase effectiveness and overcome some of the main challenges in these supply chains—especially lack of trust. the european union (eu) set strict rules in the domain of pharmaceutical supply chains in order to protect patient safety and public health. in addition, blockchains bring legal requirements. among these requirements, personal data protection is of utmost importance. this is because, as has been argued for years, blockchains and the eu data protection regime are in conflict by their natures. however, it is also claimed that when rightly designed and combined with other technological solutions, blockchains potentially offer great opportunities to enhance data protection. nevertheless, potential for blockchains in the pharmaceutical supply chain is not yet been realized as most use cases are in the proof of concept or pilot stage. this article examines the debates surrounding blockchains and data protection. the goal is to draw constructive conclusions on whether blockchain solutions can be designed in data protection-enhancing ways and whether this might help realize the potential for blockchain in pharmaceutical supply chains—particularly by creating trust. for this purpose, the example of an ongoing eu-funded innovative research project called pharmaledger as a case study to concretize its theoretical examinations is examined. this project is chosen because it gathers a wide variety of stakeholders representing different interests and aims to create a digital trust ecosystem in health care by providing a widely trusted platform that supports the design and adoption of blockchain-enabled healthcare solutions while accelerating the delivery of innovation that benefits the entire ecosystem from manufacturers to patients. received: april 29, 2022; revised: july 20, 2022; accepted: august 7, 2022; published: september 5, 2022 blockchain is a class of technology used for various purposes. however, the increasing number of use cases indicates that blockchain still has much to offer. on the other hand, the considerable number of projects at the proof of concept or pilot stage suggests that launching a successful blockchain use case is not always straightforward.1 it should be made clear at the start that although they are used interchangeably by many writers, blockchain is one type of distributed ledger technology. blockchain offers a sequential, verifiable, and incremental way to record data.2 basically, this particular type of technology acts as a decentralized append-only database that is maintained by a consensus algorithm and kept across a peer-to-peer network of numerous nodes (computers).3 while all nodes are located outside of or connected with one central node in centralized software systems, the nodes in decentralized systems do not require a central element of coordination or control while forming a network of connected nodes. in addition to immutability, prominent features of a blockchain network include transparency, auditability, and robustness.2 while traditional database technologies suffer from drawbacks such as the lack of information on provenance, transparency, and traceability among many others, blockchains, contrarily, benefit from traceability https://orcid.org/0000-0002-8913-7809 mailto:halid.kayhan@kuleuven.be citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.2322 (page number not for citation purpose) halid kayhan and transparency, as well as decentralization and more advanced application of business logic—as carried out by smart contracts. all this increases efficiency and accuracy of data-related processes. these advantages offered by blockchains encourage trust in health care in various use cases.4 among these, pharmaceutical supply chains require particular attention due to their critical role in the protection of patient safety and public health. the healthcare sector, and especially pharmaceutical supply chains, is highly regulated in the eu. thus, the success, or sometimes even launch, of any blockchain use case in this area strongly depends on its compliance with the applicable legal frameworks, particularly on data protection and privacy. to concretize this analysis, this article takes the example of the ongoing pharmaledger project, which aims to provide a widely trusted platform that supports the design and adoption of blockchain-enabled healthcare solutions in different domains, including the pharmaceuticals supply chain, while accelerating delivery of innovation that benefits the entire ecosystem from manufacturers to patients.5 by doing so, the author explores whether the claims that blockchain-enabled supply chain management practices could solve the main challenges in pharmaceutical supply chains,1 particularly the lack of trust,4 while also ensuring data protection compliance by the appropriate design of the technology. this paper will first briefly explain the potential of the blockchain in the pharmaceutical supply chains and, then, give an overview of the legal framework applicable to these supply chains in the eu. considering the long-lasting debates around the conflicts between the eu data protection regime and blockchains, this issue will be examined in detail in the context of pharmaceutical supply chains in a separate section. finally, as a case study, the pharmaledger project will be assessed in order to see whether this project, funded under the eu’s horizon 2020 research and innovation programme,5 manages to provide some solutions for compliance with the strict rules under the eu data protection regime. blockchain in pharmaceutical supply chains as one of the most complex supply chains in the world, the pharmaceutical supply chain is vulnerable to opacity.6 transparency in supply chains is challenging due to, but not limited to, the global nature of the industry, the size and scope of the companies, the number of different players involved, manual data processing, and the use of databases that are not interconnected, as well as complex data flow among players.7 in pharmaceutical supply chains, drugs change hands among multiple players, such as manufacturers, distributors, repackagers, wholesalers, and subcontractors before reaching the patient. this results in vulnerabilities to theft, introduction of fake medicines into legal chains, and non-compliance. in addition, these chains rely heavily on different forms of transportation and communication channels (e.g., airlines, airports, freight forwarders, trucking agencies, and third-party logistics), and this creates “dark matter gaps” in supply chain transparency.8 as noted in a recent report by the eublockchain observatory and forum, blockchain can have a significant impact on pharmaceutical supply chains where traceability and transparency are key elements. in these complex ecosystems, which include numerous parties, information sharing is asymmetrical, and the parties involved receive updates with a time lag. moreover, due to the silos, duplication of tasks and information is common. blockchain, at this point, can increase efficiency of the procedures.4 the european commission, in its pharmaceutical strategy for europe, published in 2020, supports implementation of strategic actions.9 although it does not explicitly discuss blockchain as a solution, blockchain technology has the potential to assist several actions and concerns, such as silos and transparency. indeed, it is possible to see more self-evident use cases in the pharmaceutical sector. for instance, blockchain could be used for the purposes of finished goods traceability and anti-counterfeiting.4 the world health organization, in a study dated 2017, estimates that 10% of medical products are falsified,10 and this is seen as a growing threat by the sector. considering this and the demand that occurs as a result, for use cases in the contexts of anti-counterfeiting,4 as well as finished goods traceability to identify shortages—as the covid-19 pandemic showed—blockchains could support safe and efficient processes.11 in addition, the same report by the eu blockchain observatory and forum highlights that blockchain can address inefficiencies related to supply chain and inventory management within health care. this class of technology can be used as a ledger to record the provenances of pharmaceutical products, and thus, vaccines and other life-saving drugs can be monitored and tracked throughout their journey. this will result in reducing the misplacement or mislabeling of medicines and the risk of counterfeiting. a transparent blockchain-based supply chain can also serve to improve clarity on logistics time and location and create trust that the information has not been tampered with. in case of an outbreak such as covid-19, this can offer an important opportunity for responsible entities to rearrange resources or produce contingency plans to avoid delays.4 the potential benefits of blockchains in the management of pharmaceutical supply chains are listed in the literature as follows: • decreasing or effacing frauds and errors, • decreasing the number of delays due to paperwork, • decreasing courier costs, • more rapid determination of relevant issues, • improved inventory management, • creating greater trust for consumers and partners. http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.232 3 (page number not for citation purpose) ensuring trust in pharmaceutical supply chains by data protection by design approach to blockchains it must be added that this potential is not yet fulfilled and  requires further resources, research, and maturation of real-life use cases, which are mostly at proof of concept or pilot stages, and new technological solutions.1 pharmaledger, an innovative research project funded by the eu, is designed to create a digital trust ecosystem in health care. more specifically in the context of supply chains, it aims to improve patient safety and product traceability by laying the groundwork for the use of blockchain technology and serialization throughout the medicine supply chain. by gathering numerous actors with different interests, this project also has an objective of developing a scalable, sustainable, technology agnostic, blockchain-enabled platform that can be adopted by the whole healthcare ecosystem for various use cases. this begs the question of whether it could provide the necessary solutions to fulfill the above-mentioned potential of blockchains in pharmaceutical supply chains, but also more generally in health care.5 although there may be many different blockchain applications in the context of pharmaceutical supply chains, the pharmaledger project, with its several use cases in the pharmaceutical supply chains domain, could serve as a useful example for analysis based on concrete cases. overview of the legal framework in the eu pharmaceutical supply chains are subject to strict regulations in the eu. as a result, there is heavy oversight in order to protect the public from harmful drug effects. a  comprehensive strategy covering all levels of the pharmaceutical value chain, from research and development through authorization, distribution, and patients’ access to medicines is crucial to ensure a high level of public health protection, starting prior to bringing new pharmaceuticals to market.12 the key applicable regulatory instruments, inter alia, are: • directive 2001/83 on the community code for medicinal products for human use,13 • directive 2003/94 on the principles and guidelines of good manufacturing practice,14 • directive 2011/62 on falsified medicines (falsified medicines directive),15 • council of europe convention on the counterfeiting of medical products (the medicrime convention),16 • regulation 2016/161 on safety features on the packaging of medicinal products,17 • regulation 2020/1056 on electronic freight transport information,18 and • regulation 726/2004 on authorization procedures and establishing european medicines agency.19 available at url: https://health.ec.europa.eu/system/files/201611/reg_2004_726_en_0.pdf these regulations require taking various aspects into account, including, but not limited to, market authorization requirements, safety measures, and reporting falsifying medicines in pharmaceutical supply chains regardless of deploying blockchain-enabled solutions. furthermore, sustainable management of supply chains, environmental considerations, human rights issues related to supply chains, and due diligence in sustainable supply chains should all be addressed in pharmaceutical supply chains. one reason for this is the fact that participants’ visibility in most traditional supply chain networks is limited to their direct relationships one level up and down, but transparency and integrity of those supply chains must be assured by adopting a common approach to sustainable supply chain management, which provides further insight into the whole journey of a pharmaceutical product. ensuring regulatory compliance will not be sufficient for such a strategy to be successful. long-term sustainability and respect for human rights will also be needed.20 in addition to these strict regulations applicable to pharmaceutical supply chains, privacy and data protection require particular attention because, according to many, blockchain and the principles and obligations of the eu general data protection regulation (gdpr)21 are viewed as contradictory with each other.22 for this reason, this issue will be explained separately in the following section of this article. privacy and data protection despite being used interchangeably from time to time in different jurisdictions, privacy and data protection are two separate but interrelated concepts. the charter of fundamental rights of the european union (cfreu) has, indeed, regulated these two rights in two separate articles—articles 7 and 8. while the former article defines the right to privacy as “the right for respect for his or her private and family life, home and communications.” the latter refers to “the right to the protection of personal data concerning him or her.” article 8, in addition to defining a separate right, lays down the core principles for the effective implementation of this right in paragraphs 2 and 3. it states that the processing of personal data must be fair, for specific purposes, and based on either the individual’s consent or a legal basis. individuals must have the right to access and correct their personal data; and compliance with these principles must be monitored by an independent authority.23 the two rights are closely related because they both aim to uphold similar values, such as individual autonomy and human dignity. for this reason, they provide individuals with a personal sphere to freely develop their personalities and opinions. nevertheless, their formulations and scopes differ under the cfreu. while the right to privacy entails a general prohibition http://dx.doi.org/10.30953/bhty.v5.232 https://health.ec.europa.eu/system/files/2016-11/reg_2004_726_en_0.pdf https://health.ec.europa.eu/system/files/2016-11/reg_2004_726_en_0.pdf citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.2324 (page number not for citation purpose) halid kayhan on interference with one’s private life unless some public interest criteria are met to justify such interference in certain circumstances, the right to personal data protection is considered an active right bringing a system of checks and balances in order to protect individuals whenever they are subject to data processing. this makes the right to personal data protection broader than the right to privacy because a demonstration of an infringement of privacy is not necessary for the personal data protection principles to apply.24 with recent technological and organizational developments processing more and more personal data, data protection has become a prominent concern worldwide, and this has resulted in numerous legislation worldwide. in this paradigm shift, the eu is seen to have a particular role since, with its strict rules, it is considered the standard-setter on how new technologies should be developed and used to process personal data.12 the gdpr, adopted in may 2016 and entered into force on 25 may 2018 by replacing the data protection directive (directive 95/46/ec),25 is the main instrument in the eu’s data protection law. different from the directive, the gdpr does not need to be transposed into the national laws of each eu member state, but its rules are directly applicable in every eu member state. furthermore, considering the gdpr being widely seen as a high-watermark of data protection laws worldwide (and becoming a template for more and more countries’ own legislation), developing a gdpr-compliant blockchain solution will support achieving data protection and privacy compliance not only at the european level but also globally as well.26 legal issues to tackle while deploying blockchain not only are related to healthcare applications but also have greater significance for these applications due to the health sector’s critical nature, and data protection is one of the most pressing legal concerns. a considerable amount of the data circulating within health care is sensitive, which requires greater protection. thus, the principles of medical confidentiality, in addition to privacy and data protection, are of high importance. certain risks associated with patient visibility and monitoring may be brought by blockchain-based applications, as well as the creation of aggregated profiles of patients if multiple sources data are combined. stigmatization and discrimination may be seriously suffered by patients in case of data breaches. for these reasons, privacy and data protection should be seen as the main legal and ethical issues to be addressed while designing, deploying, and maintaining a blockchain-based healthcare solution.4 it is alleged by many that blockchains (particularly public, permissionless blockchains) are incompatible with the gdpr by their very nature since the gdpr was designed for centralized methods of data collection, processing, and storage while blockchains decentralize these methods.3 years after the gdpr came into force, its compatibility with the public permissionless blockchains is still highly disputable, but addressing the same issue in the cases of private, permissioned blockchains is more straightforward. since the core of this article is how to use blockchain technology in a gdpr-compliant way for the purposes of pharmaceutical supply chains, the focus is on private, permissioned blockchains. this is because supply chains require known parties in order for the participants to ascertain the source and quality of their inventory.27 as the disputes around data protection and blockchains are not only related to supply chain use cases, explanations will be given with a broader approach in this section, and, when needed, elaboration will be provided in the context of supply chains. in other words, these explanations can mostly be applied to different types of blockchain use cases as well. a closer look at the blockchain-gdpr relationship taking the above-mentioned tensions into account, any blockchain use case that might process personal data should only take place after in-depth considerations and assessments regarding data protection. this is, indeed, true for pharmaceutical supply chains as privacy and data protection aspects with regard to health care are seen as one of the greatest challenges to realizing the potential of blockchains in this domain.1 a study conducted for the members and staff of the european parliament explains that there are numerous tensions between the gdpr and blockchains due to two main reasons as listed below: 1. the gdpr is founded on the assumption that in each data processing activity, there is always at least one natural or legal person (“data controller”) who is responsible for compliance with the gdpr and who can be requested to fulfill the rights of the data subjects in case such a request comes from their side. however, in blockchains and particularly in public, permissionless blockchains, there is not a central actor in control, as this is sought to be achieved by those blockchains. as a consequence, it is difficult to allocate the responsibility and accountability. 2. while the gdpr was being written, it was also assumed that in case of necessity to comply with articles 16 (“right to rectification”) and 17 (“right to erasure”) of the gdpr, data could be modified or deleted. however, blockchains purposefully make such modifications extremely difficult, with the objective of ensuring data integrity and providing greater trust in the network. uncertainties in the eu data protection regime, such as how the “erasure” concept needs to be understood, make it even more difficult to comply with the law.22 before diving into the domains where these factors cause further difficulties, it is worth stating that despite http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.232 5 (page number not for citation purpose) ensuring trust in pharmaceutical supply chains by data protection by design approach to blockchains the tensions, blockchains also propose opportunities to achieve certain objectives of the gdpr. first of all, besides decentralized handling of personal data, blockchains promise data sovereignty, which is a concept focusing on giving data subjects control over their data and the opportunity for them to share their personal data only with the parties they trust. as indicated in its recital 7, the gdpr has set data sovereignty as one of its objectives by giving natural persons “control over their data.” article 20 gdpr on the right to data portability enshrines this objective of data sovereignty by allowing data subjects the ability to receive their personal data from the data controller and to give it to another data controller. this right is seen as a concept stipulated in the gdpr with the purpose of giving more control to data subjects over their personal data.3 as noted by the article 29 working party, which is replaced by the european data protection board (edpb), the “primary aim of the data portability is enhancing individuals’ control over their personal data and making sure that they play an active part in the data ecosystem.”28 another important point noted in the literature is the lack of exact definitions of data portability and data sovereignty in the gdpr or elsewhere. this is highly important because there are currently no solutions giving individuals full control over their personal data. some solutions provide more control compared to others.3 according to many, blockchains can be designed in a way that only the user is able to access the public and private keys and freely decide when to share their personal data with external parties.29 with blockchains, selective data sharing is possible through applications, which ensure privacy and decrease the risk of identity theft.3 hence, new forms of identity management could be facilitated by blockchain by enabling individuals to control not just their identifiers but also the data associated with them.30 although there are still questions about certain points, such as whether parties with access to once revealed data will be able to copy and extract that data and store it permanently,3 many new technological proposals for blockchains are under development in order to empower individuals to own and control their personal data.31 such developments in technology may offer means to achieve certain objectives of the gdpr.3 it is crucial to stress that blockchains do not, in themselves, provide guarantees to protect personal data, and they must be developed and deployed in combination with additional mechanisms in order to achieve data sovereignty objectives. despite its strong promises for data sovereignty, blockchains may also reveal all the data stored on them unless the necessary safeguards are put in place. since blockchains are still an emergent class of technology, they should be designed and developed in a fashion to fulfill technical and legal requirements and also according to policy considerations and what is desirable for the public good. in other words, despite the conceptual tensions with the gdpr, blockchains might realize some of the objectives of the gdpr through rightly developed technological means, which might be different from the mechanisms envisaged by the gdpr.3 for instance, the append-only feature, which is also referred to as immutability, of blockchains offers the trust that data stored on the ledger have not been tampered with or manipulated. blockchains can also enable better accountability by allowing visibility and traceability over who accesses data, thanks to time-stamped logs on the ledger. furthermore, since the datasets are replicated across several computers, there is no single point of failure, and this guarantees data integrity and security.2 after setting the high-level scene, it is worth exploring the domains where blockchains are seen in conflict with the gdpr, and how these can be addressed. however, for the sake of brevity, these explanations will be succinct. types of personal data processed on blockchains while the gdpr, as per article 2, applies to the processing activities of personal data, recital 26 states that anonymous data are not subject to the gdpr. this requires assessing what is personal data, and whether personal data processed on blockchains are classified as anonymous. article 4 gdpr defines “personal data” as any information relating to an identified or, either directly or indirectly, identifiable natural person. on the other hand, article 29 working party notes that anonymization occurs only in the cases of “processing personal data in order to irreversibly prevent identification.”32 there are two types of data stored on blockchains that can be classified as personal data in the sense of the gdpr: transactional data and public keys. when transactional data are stored on a blockchain in plain text, it is obvious that it will be subject to the gdpr. however, even if data are encrypted, it will still be possible to access that data with the right keys, and, thus, those data will not be irreversibly anonymized.3 under the eu data protection law, encryption is regarded as a pseudonymization technique because an individual can still be indirectly identified.32 in case transactional data are subject to hashing algorithms, those data will still be qualified as personal data for gdpr purposes3 as article 29 working party considers hashing as a pseudonymization method, given that the data subject and the dataset are still linkable.32 nevertheless, it is also possible to store transactional data off-chain and only link those data to the blockchain with a hash pointer. thus, personal data will be stored in a modifiable and encrypted database instead of the blockchain itself. by doing so, the concerns caused by the special features of blockchains from a data protection perspective will be avoided with regard to data stored off-chain.3 http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.2326 (page number not for citation purpose) halid kayhan on the other hand, each participant of a blockchain network also has a unique personal identifier shared on the blockchain, which consists series of random-looking alphanumeric characters called the public key. this is a key to the participant’s account, and when combined with the private key, which is known only by the participant, data encrypted with these keys will be decrypted. participants randomly create this pair of public–private keys in their digital wallets, which is an application on a smartphone, computer, or another similar device. when a public key is associated with a natural person, public keys constitute personal data. although these public keys are long strings of random-looking alphanumeric characters, they are not anonymous data under the eu data protection regime.12 this is because the article 29 working party classifies encryption with a secret key as a pseudonymization technique because “the holder of the [private] key [which is only known to the data subject in order to relate off-chain data with the public key] can trivially re-identify each data subject through decryption of the dataset.”32 this absolute approach of the article 29 working party means that encrypted public keys on the chains should be treated as pseudonymous data because it is possible to reveal the identity behind those public keys by using additional information, which is the corresponding private key. when a public key is linked to a natural person, it will be possible to identify all previous transactions carried out by that person.12 different from transactional data, it is not possible to move public keys off-chain since they constitute part of the metadata transactions and are required for their validation, and, thus, they are essential for the functioning of the blockchain technology. as a result, it is more difficult to find gdpr-compliant solutions for public keys, compared to transactional data, which can be stored off-chain.3 responsible parties to ensure compliance since blockchain technology is based on the idea of decentralization, it is difficult, particularly in public, permissionless blockchains, to allocate the responsibility and accountability that are crucial for data subjects to find parties to address in order to enforce their rights under the gdpr. however, in supply chains, parties need to know other involved parties in order to ascertain the source and quality of their inventory. this will be only possible by using private, permissioned blockchains. as recognized by the eu blockchain observatory and forum, public permissionless blockchains represent the greatest challenge in terms of gdpr compliance, while it is easier for private permissioned blockchains to comply with the gdpr.33 the strongly highlighted challenge of determining the controller significantly diverges from the initial public blockchains. with the technology and related business models being developed, this challenge appears to be less relevant for the many new multi-layered ecosystems that deploy new types of permissioned and consortium blockchains. arguably, permissioned blockchains have been developed exactly in response to the shortcomings of public, permissionless blockchains.34 when blockchain applications are built for proof and value transfer on top of a blockchain platform, there will almost certainly be a set of governance rules that represent the terms agreed upon by the ecosystem’s participants to regulate their relationship.35 these blockchains are permissioned in the sense that, depending on the governance model, one or several entities determine which parties are going to have permission to write.2 in a private, permissioned blockchain, the participants should determine their rights and obligations and document those in a governance model. a robust governance model should address the issue of responsibility allocation with regard to personal data protection as well as other regulatory issues, the competencies of the participants, and the procedures to ensure that data subjects can enforce their rights. indeed, the french data protection authority (cnil) highlights the importance of taking a common decision about the data controllers’ responsibilities in cases where a group of entities decides to carry out processing operations on a blockchain for a common purpose. the cnil recommends either creating a legal person to be the data controller or designating the participant who makes decisions for the group as the data controller, and further notes that if neither of these is the case, all participants are likely to be considered as joint controllers.36 thus, it is of utmost importance not only to establish a robust governance model to allocate the responsibility of data controllership but also to limit network access by allowing only trusted members to have the ability to add data onto the chain. by doing so, in addition to identifying the parties to be accountable by the authorities and data subjects, the number of threat actors and their affordance or capacity to enact privacy violations will be reduced.4 the adopted governance model for any permissioned blockchain network should require all participants to agree to abide by the gdpr-compliant terms as a condition of being granted permission. the governance model should carefully consider the international data transfers and identify the most suitable mechanism and safeguards for the potential transfers of personal data to controllers and processors based outside the eu and not covered by an adequacy decision of the european commission.12 as per the schrems ii decision of the court of justice of the eu,37 the european data protection board’s recommendations on measures that supplement transfer tools,38 and clause 14 of the new standard contractual http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.232 7 (page number not for citation purpose) ensuring trust in pharmaceutical supply chains by data protection by design approach to blockchains clauses (sccs) approved by the ec in june 2021— which will have completely replaced the three sets of sccs adopted under the previous data protection directive 95/46 by 27 december 202239—an appropriate approach would be to oblige the members of the governance model to carry out a transfer impact assessment, through which data exporter and importer assess the impact on data protection of an international data transfer,40 prior to each data transfer to a third country not covered by an adequacy decision. although this analysis is based on data protection, it is important to note that for blockchain use cases in the context of pharmaceutical supply chains, the governance model is critical beyond data protection and privacy.2 one of the most important functions of product traceability is to provide the opportunity to identify the parties who are responsible for adverse impacts on consumers’ safety, human rights, and sustainability. identifying responsible actors is key to conducting a thorough examination of business relationships and assigning responsibility to them for adverse impacts. the governance model implemented by the authorized stakeholders is the basis of the transparency of the whole ecosystem. thus, explicit guidelines for network administration should be provided by the governance model. it would be necessary to have legally enforceable contracts in order to document the relationship between the participants and network operators. key issues that could be addressed by the governance models are as follows: • participants’ rights as well as obligations, • procedures to make decisions and implement them, • details and limits of the centralized control to be maintained, • procedures to grant access permissions to the blockchain, • parties that will validate transactions, • responsibilities regarding the maintenance of the blockchain, commitments to participate in the platform’s operation at the service level, and remedies for possible network downtime, • measures to safeguard the maintenance of the network security, • due diligence and participant monitoring mechanisms, • rules and procedures to maintain data confidentiality among the participants.41 regarding data controllership, as blockchains can grant data subjects more control over their data, another important question arises on whether they can be seen as data controllers where they hash their own data to a blockchain.3 however, whether this argumentation will be accepted by the authorities is unclear for the time being.2 data protection principles european data protection regime is based on a number of key data protection principles, which are either explicitly stipulated in the relevant legislation, such as the gdpr and the council of europe convention 108, or established through the case-law of the european court of human rights and the court of justice of the european union.12 the data protection principles stipulated by article 5 gdpr are as follows: • lawfulness, fairness, and transparency; • purpose limitation; • data minimization; • accuracy; • storage limitation; • integrity and confidentiality; • accountability. it is important to note that among these principles, two seem particularly problematic: purpose limitation, which requires personal data to be processed only for an initially specified purpose, and data minimization, which requires processing only a minimum amount of data and for as long as it is necessary to achieve the purposes of the processing.2 with regard to the purpose limitation principle, the discussions are around whether it is compatible to further process personal data added to blocks following the execution of a transaction for which personal data were added to the chain in the first place.22 as per the objective of enabling trust and reliability in a blockchain network, consensus algorithms establish a procedure in which each new block is added to the chain by involving some data from the previous block in order to maintain the ledger integrity. this makes those algorithms a crucial component of blockchain networks. thus, it is argued that processing the hashed data continuously with the purpose of validation can be seen in compliance with the principle of purpose limitation. nevertheless, it is also noted that such an argumentation will not be valid if the data are stored in plain text in public blockchains since disclosing personal data publicly will allow its further use by unknown third parties for whichever purposes.2 turning to data minimization, article 5(1)(b) gdpr stipulates that personal data must be “collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes.” this principle is in conflict with the nature of blockchains, which are append-only databases and expand continuously. this is because once data are added to the chain, it will permanently be a part of the chain and when each block is added to the chain, more data will have been accumulated in the chain. furthermore, contrary to the data minimization principle, each full node http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.2328 (page number not for citation purpose) halid kayhan stores a copy of the entire blockchain, and when new data are added to the chain, in principle, it is not possible to amend or delete it. however, by storing transactional data off-chain, it will be possible to minimize and amend those data without touching the chain itself in compliance with the data minimization principle. nevertheless, it is more problematic to comply with this principle with regard to the pseudonymous public keys since it is not possible to remove those retroactively from the chain.3 noteworthy is that the french data protection authority cnil considers that these public keys are essential to the blockchain’s proper functioning, and it is not possible to further minimize them. thus, their retention period, which is the lifetime of the blockchain, is in line with the gdpr.36 data subject rights the effective exercise of data subject rights is essential to ensure the protection of personal data, and this is why data controllers are required to facilitate the exercise of these rights. the following is the list of rights granted to data subjects by the gdpr: • right to information (articles 13 and 14 gdpr) • right to access (article 15 gdpr) • right to rectification (article 16 gdpr) • right to erasure (article 17 gdpr) • right to restriction of processing (article 18 gdpr) • right to data portability (article 20 gdpr) • right to object (article 21 gdpr) • right not to be subject to automated individual decision-making (article 22 gdpr) two of these rights require attention since they raise particular challenges in the context of blockchains: the right to rectification and the right to erasure. as stipulated by article 16 gdpr, data subjects have the right to rectification, and this includes the right to obtain rectification from the data controller of inaccurate personal data without undue delay and to have incomplete personal data completed. this right is a reflection of the principle of accuracy under article 5(1)(d) gdpr. according to this principle, controllers are obliged to take every reasonable step to ensure that personal data are accurate and, where necessary, kept up to date. the (near-)immutability feature is built into the blockchain protocols with the purpose of creating trust in the network, and this feature, in principle, prevents any altering of data. however, this brings hurdles to fulfilling rectification requests that may come from data subjects.2 while it can be possible to effectively exercise this right in permissioned blockchains by way of re-hashing subsequent blocks, it is not straightforward to find a solution in the case of permissionless blockchains.42 as an alternative solution to direct modification of data on an append-only network, adding new information showing that the previously added data are incorrect is also suggested. in such a case, the most recent version will rectify the previous data and show the current status of that particular data. however, it is unclear, from a legal perspective, whether this is sufficient to comply with article 16 of gdpr since the previously added, out-of-date, or inaccurate data will still be on the chain. it is also worth noting that the lack of the exact definition of “accuracy” in the gdpr does not help to solve this issue.2 a more straightforward solution is to keep transactional data off-chain, and, by doing so, it will be possible to fulfill the request of rectification from data subjects in compliance with the gdpr since those data stored off-chain can be amended without touching the chain itself. however, this does not facilitate gdpr compliance in relation to public keys.3 on the other hand, article 17 gdpr grants data subjects the right to erasure (also referred to as the “right to be forgotten”), which allows obtaining the data controller “the erasure of personal data concerning him or her without undue delay.” it is important to note that this is not an absolute right as certain exceptions have been included in article 17(2) gdpr. the tensions between this right and blockchains have been highlighted by many since blockchains’ persistent and distributed architecture may render a straightforward deletion of data upon a request by data subjects impossible.3,43 while erasing data in a single computer is always technically possible, erasure from one node in a blockchain network does not result in erasure in all nodes. furthermore, such erasure, in most cases, would invalidate the node and pose a risk to the integrity of the blockchain network, which is crucial to creating trust.2 it is once again essential to make a distinction between transactional data and public keys. while storing transactional data off-chain will significantly facilitate compliance with the requirements under article 17, it is not straightforward to comply with erasure requests in the case of public keys, and this requires further explanations.3 as noted previously, the right to erasure is not an absolute right. article 17(2) gdpr, indeed, says that in cases where data subjects request the erasure of their data, the data controller must take “account of available technology and the cost of implementation” and take “reasonable steps, including technical measures, to inform controllers which are processing the personal data that the data subject has requested the erasure by such controllers of any links to, or copy or replication of those personal data.” considering this provision and blockchains’ technical limitations to erasure as well as the lack of the exact definition of “erasure” in the gdpr, the question of whether a solution other than the outright erasure of data can be used in compliance with the gdpr becomes highly important. some data protection authorities in europe have indicated that “erasure” does not have to be the http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.232 9 (page number not for citation purpose) ensuring trust in pharmaceutical supply chains by data protection by design approach to blockchains outright destruction of personal data.2 the uk information commissioner’s office tolerates, to a certain extent, putting data “beyond use” if it is not possible to delete the data for technical reasons.44 the german act for adapting the data protection rules in germany, adopted in accordance with the gdpr and german law, also puts a derogation to article 17 gdpr and mandates that data controllers are not obliged to erase personal data where erasure is impossible or requires a disproportionate effort because of the specific mode of storage.45 instead, restriction of processing might be sufficient if data subjects have minimal interest in erasure.2 the french authority cnil, in the context of blockchains, suggests that deleting the keyed-hash function’s secret key together with the data stored off-chain could be sufficient to fulfill erasure requests as this process renders data on the chain valueless, and it would be highly difficult, if not impossible, to retrieve information.36 to ensure legal certainty with regard to the implementation of the gdpr, there is a need for guidance at the european level with regard to the meaning of “erasure”, and how it can be fulfilled under the gdpr.2 data protection by design and default the gdpr has set strict principles of personal data protection and obligations on responsible parties and rights for data subjects. however, there should also be a technical infrastructure to support all these and ensure compliance with the gdpr. with such an infrastructure, blockchain may offer great advantages. taking into account the long-lasting debates around the compatibility of blockchain with the gdpr, it is worth stressing that blockchain is a class of technology, and there is not only one way to design it. with its specific features, blockchain may help achieve some objectives of the gdpr. gdpr compliance of a blockchain use case, to a significant extent, can be ensured by implementing privacy-preserving features in the design of the blockchain protocol, and this is what article 25 of the gdpr, by stipulating the principles of data protection by design (which is also called “privacy by design”) and by default, requires. as noted by the french data protection authority cnil, data controllers, according to this principle, are required to select the format and methods without impact on data subjects’ rights and freedoms to the greatest extent possible.36 although data protection by design and data protection by default principles are sometimes referred to as one, a distinction between the two is made by article 25 gdpr as follows: • data protection by design requires the implementation of appropriate technical and organizational measures, such as pseudonymization, that are designed to implement data protection principles, such as data minimization principle, and to integrate the necessary safeguards into the processing to fulfill the gdpr requirements and protect the right of the data subjects. • on the other hand, data protection by default requires the implementation of appropriate technical and organizational measures in order to ensure that, by default, only necessary personal data for each specific processing purpose are processed, and that, by default, personal data are not made accessible to others without the individual’s intervention to an indefinite number of natural persons. especially when combined with off-chain solutions, blockchains can be designed and deployed in a more data protection-enhancing way. in order to comply with the gdpr, blockchain can be used on a layer above databases, and this can allow monitoring transactions on the data exchange and access information while all personal data are stored off the blockchain. thus, the data stored off-chain will be anchored to the blockchain with a cryptographic reference, and the blockchain will merely be used to keep a record of the processing operations that take place off-chain.4 the cnil, in its study to examine how blockchains can be used in the most privacy preserving way, recommends solutions, in which personal data are processed outside of the blockchain, and only one of the following cryptographic identifiers is stored on the blockchain: • a commitment of the data; • a hash generated by a keyed hash function on the data; • a ciphertext of the data.36 as explained previously, even when the personal data are stored off-chain and only anchored to it with one of these cryptographic identifiers, these identifiers will still be classified as personal data. however, it is argued that keeping these public keys on-chain is the key to blockchains’ proper functioning and they cannot be further minimized, and, thus, it is seen in compliance with the gdpr to store them as long as the blockchain exists.36 the cnil further notes that the choice of a proper cryptological method to store the data off-chain not only supports risk minimization but also allows data subjects to move closer to an effective exercise of their data protection rights. erasing the data stored off-chain and the elements enabling their verification will cut off the link with the proof recorded on-chain, and it will be extremely difficult, if not impossible, to retrieve the personal data. in addition, blockchain developers should also take data subjects’ rights into account while programming smart contracts and allow data subjects to restrict processing and request human intervention.36 http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.23210 (page number not for citation purpose) halid kayhan it is crucial to note that data protection by design and default approach should be adopted both during the design process of a technological solution and during the processing itself. these include not only technical measures but also organizational and procedural ones. in other words, in addition to the design and operation of technologies, organizational policies and business strategies should be addressed with the purpose of complying with the data protection principles and ensuring the effective implementation of data subject rights. thus, these measures may take the form of advanced technological solutions – as explained above, training for staff members, or any other appropriate measures.12 as there is not any one-size-fits-all methodology, controllers are required to assess the most suitable measures under the particular circumstances in order to ensure effective implementation of the data protection principles and data subject’s rights. putting robust and scalable measures in place is crucial since, in case of an increased risk of non-compliance, it should be possible to scale up the measure in order to achieve effective implementation of the principles and rights.46 noteworthy is that data protection impact assessments (dpias), regulated under article 35 gdpr, can be very helpful to assess the risks under certain circumstances and determine the appropriate measures. pharmaledger project as an example mentioned previously, pharmaledger establishes a blockchain-enabled platform for various healthcare use cases, with the purpose of creating a digital trust ecosystem in health care.5 from a perspective of pharmaceutical supply chains, it has a more specific objective of improving patient safety and product traceability by laying the groundwork for the use of blockchain technology and serialization throughout the medicine supply chain. the downstream traceability of completed goods would aid in the collection of important product data, enhancing the option of direct product verification by end-users and patients—something that is currently lacking throughout the whole process. the ability to quickly identify suspected or expired items gives a new degree of transparency that is critical for patient awareness and safety. these additional features will be validated by pharmaledger through four use cases: eleaflet, clinical supply chain, finished goods traceability, and anti-counterfeiting.2 since there may be many different blockchain applications in the context of pharmaceutical supply chains, the pharmaledger project will be taken as an example to concretize the explanations made so far. this approach seems appropriate considering that blockchain is a class of technology still under development, and concrete examples and design can provide more detailed and real-world-based explanations. furthermore, as highlighted in research managed by the eu parliament’s scientific foresight unit, although regulatory guidance as well as codes of conduct and certification mechanisms could increase the legal certainty regarding how the gdpr could apply to blockchains, this will not always be sufficient to ensure compliance of specific blockchain use cases with the gdpr where there are technical restraints to compliance. it could be possible to find solutions by conducting interdisciplinary research, devising technical and governance remedies, and experimenting with blockchain protocols that could be compliant by design.22 as an eu-funded project, pharmaledger aims to provide solutions that could be scalable for other use cases, which may be developed by the healthcare sector in the future, in a way compliant with numerous legal instruments including the gdpr. thus, examining these solutions offered by the pharmaledger project could also help to assess to what extent the eu-supported research activities and innovative outcomes are able to fulfill the strategy intended by the eu regulations, which is, in this case, the gdpr. overview of the pharmaledger architecture pharmaledger is designed to be built on a multi-layered hierarchical blockchain solution that is technology agnostic and, so, allows using independent blockchains for different key functions of the platform, such as decentralized identity management, as well as the use cases. the primary objectives of using blockchains are to anchor off-chain data and code and, thus, to be able to replace the blockchain infrastructure without a need to modify application code, and to have greater code and data protection, security, and confidentiality. in this structure, anchoring data only takes place in a hierarchical manner.2 a highlevel, simplified version of the pharmaledger architecture is illustrated in figure 1.2 besides the hierarchical blockchain structure, the other core component of pharmaledger is the concept of opendsu (open data sharing unit). dsus are stored off-chain as encrypted data blocks and anchored with a hash in a distributed ledger, which is anchored on a parent blockchain. the data on that blockchain are also anchored on the root blockchain with the purpose of inheriting its properties. it is possible to store dsus anywhere by avoiding the storage provider having control over their content. private keys, which are used for signing dsu contents and for client-side encryption, allow access to the dsus. a unique type of cryptographic identifier called keyssi (key self sovereign identity) is employed by dsus, and they function as both keys to decrypt dsus and self-sovereign identifiers (ssis) for the elements contained in the dsus. user-specific data are stored at a dsu component called a “digital wallet,” which runs as an application on a smartphone, computer, http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.232 11 (page number not for citation purpose) ensuring trust in pharmaceutical supply chains by data protection by design approach to blockchains or another similar device. it stores keys and certain items such as health information and credentials and controls access to these items, besides communicating with other digital wallets. therefore, the development of a digital wallet controlled by the user is crucial to achieving the objectives of pharmaledger.2 the opendsu also eliminates the need for building a specific off-chain storage solution for each different use case/application. as an open standard, this solution can be adopted by different blockchain-based environments.47 the highest layer of the pharmaledger infrastructure, and the closest one to the end-users, is the application layer to which business applications and end-user applications (both mobile and web) belong. while the business applications have an objective of enabling the creation and publication of data on the particular pharmaledger use case ledger, the end-user applications are meant to allow the end-user to access the dsu storage.2 use cases in the pharmaceutical supply chain domain the pharmaledger project has identified eight use cases in three domains, namely, clinical trials, health data, and pharmaceutical supply chains. however, in line with the focus of this paper, only four of them, which are in the context of pharmaceutical supply chains, will  be examined. these are eleaflet (also called, electronic product information, “epi”), clinical supply chain, finished goods traceability, and anti-counterfeiting use cases. these can be briefly explained as follows: • in the epi use case, the manufacturer creates the package leaflet (also known as product information, which contains information that accompanies the medicinal product) in a digital form. health authorities review and approve the epi. the manufacturer, then, makes updates to the epi in this digital form and disseminates it to the end-user, which can be a patient, healthcare practitioner, or healthcare provider. • the anti-counterfeiting use case adds additional functionalities and user experience to the epi use case, in order to create a multi-factor product authentication capability. the initial focus and prioritization of the two use cases are to provide patients with a publicly available smartphone application to easily and anonymously access the electronic leaflet of a chosen medicinal product and check its validity. patients will not be required to register to use the application. yet, the application will offer an option to enable certain functionalities, such as geolocation. this functionality will help prioritize anti-falsified medicine efforts for the public good. • the finished goods traceability use case aims to ensure product visibility and status by documenting specific product movements. a degree of certainty about the product’s provenance will be offered by the traceability of finished products. this use case will capture all key movements of a product throughout the supply chain, by creating the information flow of shipping finished goods from a manufacturing site via wholesalers and distributors to pharmacies and hospitals. access to the application will be granted at the fig. 1. the layered high-level architecture of the pharmaledger http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.23212 (page number not for citation purpose) halid kayhan organization level, where individual users (employees) register, and certain information including name, organization, organization type, and access rights (such as administrator, editor, and viewer) will be shown. • the clinical supply chain use case outlines the process of tracking products from initiation of a shipment request to end-use, which can be patient consumption, product return, or product destruction. although it is out of scope for the minimum viable product (mvp), creating a solution that allows for direct-to-patient track and trace functionality may be considered in the future. this use case begins with the initiation of a shipment request and ends with the reconciliation of an investigational product by the clinical site. therefore, the standalone mobile application will be targeted at sponsors, distributors, couriers, and sites. access to the application will be granted upon logging in with the respective credentials of the users.2 the possible ways to use blockchain for supply chains are of course not limited to the examples given above. however, the explanations in this section can be a good basis for analogy and, so, useful for other use cases developed by different actors in pharmaceutical supply chains, with regard to compliance with data protection and privacy principles. types of personal data to be processed in the pharmaledger’s finished goods traceability and clinical supply chain use cases, there is likely a very limited amount of personal data required to be processed in order to trace the products. while the data about public or private entities are not classified as personal data, the employees’ data will be classified as personal data. those data may include the names and contact details of the employees involved in the shipments and other related processes. none of these represents particularly sensitive data. on the other hand, pharmaledger’s epi use case, where end users can access the product information easily in digital form, and the collection of personal data are not required to achieve the purposes and, thus, should be avoided to comply with the data minimization principle. this is crucial since, for example, creating medical profiles of patients based on the pharmaceutical products of which they accessed the information would involve highly sensitive data and represent serious risks to the rights and freedoms of the end users. the same applies to the anti-counterfeiting use case that is being developed by pharmaledger for the purpose of fighting against falsified medicines. another important issue in the anti-counterfeiting use case is related to the use of geolocation. geolocation may result in the identification of an individual, or it may also be combined with other data such as ip address and may serve to identify an individual. for this reason, it is important to take all necessary measures to avoid any permanent personal identifiers (e.g., names, email addresses, social security numbers, any other personal identifiers, or device identifiers) to be combined with geolocation information and data of scanned medicinal products. otherwise, there may again be serious risks of profiling and inferring health information by unauthorized stakeholders. it is also recommended to give end-users an option to opt-in for sharing geolocation data. even in the cases where end-users opt-in for it, stakeholders, which are authorized to collect geolocation information, should avoid using fine-grained location information if the purpose can be achieved by sharing broader location information.2 governance of the pharmaledger project the pharmaledger project is governed by a consortium of 12 pharmaceutical companies and 18 public and private entities, including technical, legal, regulatory, academia, research organizations, and patient representative organizations.5 the research is divided into various work packages working in close coordination, and each work package and each use case are governed by one public and one private partner. furthermore, major decisions are taken by majority vote in the general assembly where all partners are represented and have an equal say. thus, the whole consortium is driving the project together. however, it is important to note that the current setting of the pharmaledger project is not designed for operating a production platform.2 the research project will come to an end in december 2022 and in order to ensure the sustainability of the platform that has been developed, and there is a clear need to put a governance model in place for the period after the end of the project. thus, it would be possible to fulfill the mission of the project  – which is to benefit all the stakeholders of the healthcare ecosystem, operate and manage the platform, ensure financial and legal oversight, and make a party accountable for decisions, strategies, and else. for these reasons, the pharmaledger association has been established as a not-for-profit swiss association in early 2022, and it will take over the platform. it is important to highlight that the use cases that reach the production level maturity will be governed by the pharmaledger association since the current research project setting does not have the mechanisms in place to ensure quality management and regulatory compliance. the pharmaledger association has been established as minimum viable governance in order to enable the development of quality management and data protection management systems for the highly regulated industry, enable a productive launch of the first pharmaledger use case, epi, in the last quarter of 2022, and establish http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.232 13 (page number not for citation purpose) ensuring trust in pharmaceutical supply chains by data protection by design approach to blockchains a baseline that enables a more in-depth definition of the future governance model. thus, the consolidated governance plan, with the details on financials, exploitation, governance, and other fundamentals, is under development. the basis could be making the pharmaledger association built on membership fees and having a governance board for decision-making.4 nevertheless, there should be further elaborations on how to ensure this association will act in a manner as decentralized as possible. while creating a single entity with decision-making power over the network seems to be the most straightforward solution to comply with the gdpr, it could be said, from a broader perspective, that this goes against the idea behind blockchains, shifting the power from centralized points of control to decentralized peer networks. when there is one party in control, the trust in the processes could be endangered. thus, it is crucial to put the right balance between the efforts to achieve compliance and functionalities to increase effectiveness, by also not endangering trust in the network, in blockchain networks creating a single entity responsible for network operation.4 although the idea is to limit the role of pharmaledger association to act as the coordinator and the policymaker but not as the network operator, this consideration of creating the right balance needs to be addressed in the further elaborations of the post-project governance model. it should also be noted that pharmaledger, as a multi-layered blockchain platform including an application layer, brings the capability of determining purposes at the application layer, and blockchains will be used as the underlying infrastructure to which these applications will be anchored. thus, the entities that determine the purposes at the application layer will qualify as data controllers for their own processing operations. this is also in line with the suggestions of the above-mentioned research managed by the eu parliament’s scientific foresight unit.22 in other words, the data controller for specific applications to be developed and deployed on the platform might be different from the data controllers for the platform. each data controller will have a liability limited with its responsibilities and limits of its processing activities.2 it would be useful to make this clear in the governance model of the multi-layered blockchain platforms like pharmaledger. measures to comply with the gdpr as explained previously, there are important factors that are at the roots of the long-lasting debates around the compatibility of the blockchain technologies with the gdpr, but it is also widely argued that with the right design and combination with different technological solutions, blockchains have a great potential to realize the underlying objectives of the gdpr, most prominently giving back individuals control over their personal data. this is only possible by implementing the data protection by design and default principles, which requires putting organizational and technical measures in place from the very beginning of the design process of any new technology which may process personal data. taking this into account, the pharmaledger project put these principles at its core and adopted a seven-step process, based on the guidelines developed by the norwegian data protection authority.48 as data protection is not a one-off practice but a continuous process, each activity in this approach represents a step leading to the next one in the circle, as illustrated in figure 2.12 the involved organizations need to determine which steps should be emphasized and where and when increased effort would be necessary. for the sake of brevity, these steps will not be explained further here, but instead, what technical and organizational measures have been, or can be, taken will be explained. the first and foremost requirement of data protection by design and default is to meet data protection principles to the greatest extent possible. thus, any processing of personal data on the pharmaledger platform is required to be lawful, fair, transparent, and carried out for specified, explicit, and legitimate purposes by minimizing the data processed.12 at this point, the opendsu concept deserves particular attention. the opendsu, as an innovative solution, offers data subjects, with smartphone applications “digital wallets,” the possibility to manage the personal data stored off-chain and the abilities of other parties to access and process it. with the control over access rights to be granted to the other parties, data subjects can be in better control over their personal data as fig. 2. data protection by design and by default process in the pharmaledger project http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.23214 (page number not for citation purpose) halid kayhan they decide whether certain information from their digital wallet is shared with requesting parties. by constituting an abstraction layer in the pharmaledger infrastructure, the opendsu offers reusability, interoperability between use cases, and data portability. the blockchain is accessible through digital wallets, which are applications handling the keys. personal data are not stored on the blockchain but outside the blockchain. this off-chain data storage solution brings an opportunity for more straightforward compliance with the data minimization principle as well as the data subject rights to erasure and rectification.2 the pharmaledger project also adopts self-sovereign identities (ssi), which aim at giving data subjects full control over their digital identities and all identity-related attributes. by having established an identity management task force (imtf), the pharmaledger project takes this technology at its focus and creates digital wallets to store credentials and confidential data and to manage access to the off-chain opendsus. users will have full freedom over what verifiable claims, which are stored in digital wallets, to share with whichever party users interact with. blockchain is the technology that allows this identity model to be established. this concept can offer significant data protection and privacy benefits, including: • using zero-knowledge proof protocols that do not provide any additional information • limiting attributes to the absolute minimum necessity • possibility to exchange verifiable claims off-chain via encrypted channels2 these concepts of opendsu and ssi uphold the gdpr principles of data security and data minimization. the latter is particularly at the heart of the most heated debates on the compatibility between blockchains and the gdpr. these solutions show that blockchain, when appropriately designed and combined with the right technological solutions, could be handy in implementing the gdpr principles and achieving some of its underlying objectives, namely, giving data subjects more control over their data. besides the technical design, organizational measures should also be well addressed, particularly in determining the details of the post-project governance structure. unless the gdpr principles are embedded in the governance model, such as by contractual requirements that need to be agreed on by the future members of the pharmaledger as a condition of being granted permissions in the permissioned blockchains to be deployed, the technical design will only have a limited impact on ensuring the effective implementation of data protection principles and data subject rights. many other organizational measures may surely be put in place for this purpose, and this is why an in-depth study taking place in the pharmaledger project at the time of writing this article is the appropriate approach. in the pharmaledger platform, where use cases are not only about pharmaceutical supply chains but also clinical trials and health data, it is necessary to go beyond the above-explained requirements focusing on supply chains and take appropriate measures to protect different sorts of personal data, including highly sensitive health data of patients. if any pharmaceutical supply chain use case is combined with the other use cases, aggregated profiles of patients may be created without these data subjects’ knowledge, and this may result in significant breaches of the gdpr. to avoid these kinds of situations, data protection by design, as well as data protection by default, procedures have a role of utmost importance. it is also key, prior to putting these use cases into the market, to conduct dpias in order to identify any high risk to the rights and freedoms of data subjects and to mitigate those risks. the success of the pharmaledger platform will mostly depend on its wide adoption by numerous actors in the healthcare sector, maturity-achieved launches of the use cases under development as well as the future use cases to be developed, effective quality and data protection management systems, and the final governance structure that will act as the coordinator of and the decision-maker over the platform. while coming to the end of the research project, the efforts put in place by all 29 partners seem to have produced favorable results to design a data protection-enhancing platform that has promises to bring the blockchain’s potential into the real world, particularly in pharmaceuticals supply chains. conclusions while pharmaceutical supply chains are complex structures including many different stakeholders, it is also a highly regulated area in the eu. besides ensuring compliance with the legal and regulatory instruments, creating trust is also key to ensuring effectiveness in these supply chains and protecting public health. this is where blockchains bring a number of promises. however, their potential still has not completely been realized, and especially in the eu, this is closely linked to discussions around how to design and deploy blockchains in compliance with the strict data protection rules under the gdpr as there are long-lasting debates around their, allegedly, conflicting natures. however, if a blockchain network is developed by adopting strong data protection by design and by default approach under the gdpr, specific features of blockchain may help achieve some objectives of the gdpr, such as data sovereignty, trust in the accuracy of data, better accountability, and data integrity and security, among others. when combined with other technological solutions as in the case of self-sovereign identities, blockchains can provide greater autonomy and control to individuals on their personal data and opportunities to enforce their data subject rights with ease. http://dx.doi.org/10.30953/bhty.v5.232 citation: blockchain in healthcare today 2022, 5: 232 http://dx.doi.org/10.30953/bhty.v5.232 15 (page number not for citation purpose) ensuring trust in pharmaceutical supply chains by data protection by design approach to blockchains there is a need for regulatory guidance on how to implement the gdpr in blockchain use cases, but, as in line with the data protection by design approach, new technological solutions can be designed to achieve certain objectives of the gdpr, though not with the mechanisms envisaged by the regulation itself. for this reason, this paper has examined whether pharmaledger, an eu-funded innovative research project, has the right mechanisms to be seen as a blueprint for pharmaceutical supply chains using blockchains. it is true that this project has an important potential, but much will depend on the details to be determined, such as the governance structure of the platform. nevertheless, it is a good example to show that there is a clear technological shift toward more gdpr-compliant blockchain designs. funding statement pharmaledger project is funded through the innovative medicines initiative (imi) 2 joint undertaking and listed under grant agreement no.853992. this joint undertaking receives support from the european union’s horizon 2020 research and innovation programme and the european federation of pharmaceutical industries and associations (efpia). a part of the budget of this project is dedicated to the dissemination of the results of the research being conducted. this research has received no external funding. the funders had no role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript. financial and non-financial relationship and activities as the representative of ku leuven in the consortium of the pharmaledger project, the author acts as the coleader for the work package on the regulatory, legal, and data privacy framework. in this capacity, his role is to provide academic research in order to guide the consortium regarding the applicable ethical, legal, and regulatory frame works. contributors the author is responsible for the content of this article. references 1. 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http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research post-quantum cryptography resilience in telehealth using quantum key distribution don roosan, pharmd, phd1 , rubayat khan, phd2 , saif nirzhor, phd3 and fahmida hai, bsc4 1associate professor, department of computer science, merrimack college, north andover, massachusetts, usa; 2research scientist, university of nebraska medical center, omaha, nebraska, usa; 3postdoctoral researcher, university of texas, southwestern medical center, dallas, texas, usa; 4research scientist, tekurai inc, san antonio, usa corresponding author: don roosan, email: roosand@merrimack.edu doi: https://doi.org/10.30953/bhty.v8.379 keywords: attribute-based encryption (abe), blockchain, post-quantum cryptography, quantum key distribution, telehealth, zero-knowledge proofs the addendum after the references defines the acronyms. abstract objective: the authors propose and evaluate a novel cybersecurity architecture for telehealth that is resilient against future quantum computing cyber threats. by integrating post-quantum cryptography (pqc) with quantum key distribution (qkd) and privacy-preserving mechanisms, data confidentiality and immutability for patient records in a post-quantum era are ensured. methods: a multi-layered design approach was adopted. the pqc algorithms (e.g. crystals-dilithium) were integrated at the blockchain consensus layer to resist quantum attacks. a directed acyclic graph (dag)based ledger managed high transaction throughput and latency constraints typical of telehealth. a qkd-enhanced key management protocol leveraged quantum channels for secure exchanges. zero-knowledge proofs (zkps) and secure multiparty computation (mpc) verified transactions without exposing sensitive patient data. a granular access control model used attribute-based encryption and smart contracts to govern which participants could view or modify encrypted medical records. results: the prototype was developed within a simulated telehealth network comprising hospitals, clinics, and patient devices. the pqc signatures at the consensus layer provided effective resistance to both classical and anticipated quantum attacks. the qkd facilitated secure key distribution, while zkps and mpc enabled validation of healthcare transactions without compromising patient privacy. despite increased computational overhead, the dag approach efficiently handled parallel transactions, indicating improved scalability compared to traditional linear blockchains. conclusion: a qkd-enhanced, pqc-driven framework successfully addresses critical security and privacy requirements, safeguarding medical data from emerging quantum threats. although overhead and infrastructural costs are significant, sustained cryptographic resilience and robust patient confidentiality underscore its suitability for next-generation healthcare systems. future studies should explore additional optimizations, homomorphic encryption, and larger-scale pilots under regulatory standards. plain language summary quantum computers threaten current encryption methods used in telehealth. this research secures remote healthcare by combining post-quantum cryptography (pqc) and quantum key distribution (qkd) with privacy tools such as zero-knowledge proofs (zkps) and attribute-based encryption (abe). a specialized directed acyclic graph (dag) ledger handles many transactions at once, storing only cryptographic references on-chain while keeping large patient data off-chain. by using crystals-dilithium, a pqc algorithm, the system remains resistant to both classical and quantum attacks. qkd locks down the exchange of keys, alerting participants if spying is detected. zkps and multiparty computation (mpc) let healthcare providers verify data and prove their authorization without revealing sensitive details. this layered approach ensures patient privacy and high security without blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0003-2482-6053 https://orcid.org/0000-0003-3264-564x https://orcid.org/0000-0003-4626-7862 https://orcid.org/0009-0009-6188-9839 mailto:roosand@merrimack.edu https://doi.org/10.30953/bhty.v8.379 citation: blockchain in healthcare today 2025, 8: 379 https://doi.org/10.30953/bhty.v8.3792 (page number not for citation purpose) don roosan et al. creating slowdowns. although setting up qkd adds some complexity and cost, the solution aims to be futureproof and adaptable as quantum computers improve. testing showed that storing only metadata on the ledger preserves privacy while maintaining system performance. next steps include exploring fully homomorphic encryption for even stronger privacy and piloting the system in real-world settings to meet regulatory requirements. this work demonstrates how telehealth can remain both accessible and secure against emerging quantum threats. submitted: january 12, 2025; accepted: march 16, 2025; published: april 25, 2025 the rapid digital transformation in healthcare offers unprecedented access to medical services across vast distances.1 by mitigating geographic barriers and enabling remote diagnostics, telehealth can significantly improve patient outcomes while reducing costs.2 however, these benefits bring heightened security risks, as sensitive data must be protected from unauthorized access and tampering.3,4 classical cryptographic protocols, including rivest-shamir-adleman (rsa) and elliptic curve cryptography (ecc), rely on computational problems that emerging quantum computers may solve.5 the advent of quantum computing thus poses a critical threat to telehealth security, potentially exposing patient records to malicious decryption.6,7 post-quantum cryptography (pqc) aims to address this vulnerability by leveraging cryptographic schemes resistant to quantum attacks.5 alongside pqc, blockchain platforms offer transparency and immutability, but they typically rely on classical signatures.8,9 as quantum capabilities mature, transitioning to quantum-resistant methods such as lattice-based or hash-based cryptography becomes vital.10,11 additionally, quantum key distribution (qkd) can strengthen key exchange processes, offering real-time interception alerts.12 innovative solutions like zero-knowledge proofs (zkps) and secure multiparty computation (mpc) further preserve privacy by limiting data exposure.13,14 against this backdrop, this article presents a novel framework integrating pqc, qkd, and blockchain to maintain confidentiality, integrity, and scalability in telehealth. methods designing a blockchain architecture that can withstand the demands of telehealth while concurrently addressing quantum-era threats requires a multi-faceted approach. telehealth data can be highly sensitive, making confidentiality and integrity paramount.4 simultaneously, the advent of quantum computing demands an upgrade from classical cryptography to post-quantum solutions.7 to reconcile these requirements, the following methodology incorporates five core elements: a post-quantum-based consensus mechanism, a directed acyclic graph (dag) ledger for scalability, qkd for secure key management, privacy-preserving tools such as zkps and secure mpc, and a robust access control model that integrates attribute-based encryption (abe) with post-quantum cryptographic schemes. collectively, these elements aim to ensure efficiency, quantum resistance, and privacy protection in telehealth systems.13 the first step involves selecting post-quantum digital signatures to replace or augment classical algorithms in the blockchain consensus. digital signatures are essential for verifying user identities and ensuring transaction integrity in telehealth applications, where data transmitted can include prescription records, patient updates, or insurance authorizations.7 classical algorithms such as rsa or ecc might be compromised by quantum attacks like those facilitated by shor’s algorithm.14 the pqc provides alternatives that remain secure against classical and quantum adversaries.4 notable candidates include crystals-dilithium, falcon, and sphincs+, each evaluated based on security level, signature size, verification speed, and ease of integration.5,6 crystals-dilithium, for example, demonstrates relatively compact signatures and robust protection, making it suitable for telehealth contexts that demand rapid transaction throughput and low latency. by testing algorithms in controlled environments that simulate telehealth traffic volumes, the proposed methodology identifies which scheme best sustains performance without compromising the system’s security. after choosing a suitable post-quantum signature algorithm, the second step addresses scalability through a dag-based ledger. linear blockchain architectures can become bottlenecks under heavy transaction loads typical in telehealth, such as when multiple providers update patient data or when large volumes of sensor information stream from wearable devices.9 a dag structure allows for parallel insertion of transactions, as each transaction references one or more predecessors instead of a single chain. this design choice enhances throughput and lowers latency.7 the telehealth network, composed of nodes representing hospitals, clinics, diagnostic centers, and patient devices, is configured so that transactions can be confirmed in parallel, alleviating computational congestion.13 in addition, the node structure supports concurrency by verifying multiple transactions simultaneously, thereby more efficiently handling the data fluctuations https://doi.org/10.30953/bhty.v8.379 citation: blockchain in healthcare today 2025, 8: 379 https://doi.org/10.30953/bhty.v8.379 3 (page number not for citation purpose) cryptography resilience in telehealth that occur throughout a typical day of telehealth operations. once integrated, the dag ledger undergoes rigorous performance assessments, measuring metrics such as transaction confirmation times and energy consumption.9 these evaluations confirm whether the dag-based approach is viable in practice for large-scale deployments. the third methodological aspect tackles key management via qkd. although adopting post-quantum algorithms protects transaction signatures, weaknesses in key exchange protocols could allow attackers to intercept or store encrypted traffic for later decryption once quantum resources become available.1 the qkd leverages the fundamental principles of quantum mechanics, particularly photon-based transmissions, to securely distribute cryptographic keys between nodes.10 any eavesdropping attempt alters the quantum state, which legitimate participants can detect as anomalies in the transmissions.4 integrating qkd into the telehealth blockchain network requires installing specialized qkd devices at critical network points, such as major hospitals or data centers, and establishing secure optical links. key lifecycle management protocols determine how often keys are rotated or revoked to maintain a minimal risk window if a key is compromised.14 the methodology also considers a hybrid approach in which qkd-derived keys are used to encrypt or protect the post-quantum signature keys themselves, thereby reinforcing layered security.7 after setup, the network is tested through penetration simulations to ensure that neither classical hacking methods nor partial interception can subvert the qkd-secured channels. once the cryptographic underpinnings are established, the fourth methodological step introduces privacy-preserving mechanisms: zkps and secure mpc. healthcare data are often subject to strict regulations that mandate minimizing patient data exposure.4 zkps enable one party to prove certain statements, such as their authorization to modify a medical record, without disclosing details about that record.6 this means that a blockchain node can confirm another node’s legitimacy to perform specific actions while maintaining confidentiality. typical implementations may utilize zk-snarks or bulletproofs, though each carries computational overhead that must be measured against system throughput. mpc permits multiple entities—for instance, different hospitals—to perform joint analytics on collectively pooled data without revealing sensitive inputs to each other.11 in a telehealth environment, mpc may facilitate collaborative research among clinics to evaluate treatment outcomes, all while preserving patient anonymity and compliance with privacy laws.13 both zkps and mpc demand significant computational resources, which may be off-loaded to specialized nodes or cloud-based accelerators.9 in parallel, the system orchestrates seamless integration of these techniques with post-quantum signatures and qkd-protected channels, ensuring end-to-end security despite elevated computational requirements. the fifth step extends privacy protections with a robust access control framework that combines abe and post-quantum cryptographic primitives. simple encryption alone is insufficient if all authorized users can freely read all patient records, as telehealth systems typically require fine-grained controls based on professional roles, jurisdictions, or certifications.6 abe enforces granular policies by encoding roles or attributes into the ciphertext, thus ensuring that only entities matching the defined attributes can decrypt patient data.7 in practical terms, this means that only a patient’s primary physician or an authorized specialist might be granted decryption rights, whereas administrative staff might have more limited access. by combining abe with post-quantum encryption, the system mitigates quantum threats to data confidentiality. the methodology also leverages smart contracts in the dag ledger, which document each policy change or key revocation as an auditable transaction.13 this creates a permanent and transparent record of when access rights are updated or revoked, thereby satisfying accountability requirements in healthcare environments. the policy logic encoded in these contracts allows administrators to adapt to changes in staff roles or regulations without re-encrypting large swaths of data, further contributing to overall system scalability. the methodology concludes with an extensive validation procedure encompassing functional, integration, performance, security, and compliance testing. functional testing isolates each component—such as digital signatures, qkd devices, or abe modules—and evaluates whether it meets specified requirements.13 integration testing then examines the interplay among components, focusing on edge cases like large network traffic or partial node failures.5 performance benchmarking simulates telehealth workloads with real-time data streams, high concurrency, and diverse user actions, measuring whether the system can uphold service-level requirements for data availability.9 security assessments include both theoretical analyses—evaluating the system’s post-quantum security guarantees—and practical penetration tests, where ethical hackers attempt to exploit potential weak points.10 finally, user experience considerations address how healthcare providers interact with features like zkps or abe-based decryption. the methodology ensures the system remains user-friendly and efficient, to encourage real-world adoption in time-critical clinical workflows.1 this multi-step methodology provides a holistic strategy for fortifying telehealth platforms against quantum-era cybersecurity threats. by introducing post-quantum signatures into the blockchain consensus, adopting a dagbased ledger for scalability, incorporating qkd to secure key exchanges, employing privacy-preserving techniques https://doi.org/10.30953/bhty.v8.379 citation: blockchain in healthcare today 2025, 8: 379 https://doi.org/10.30953/bhty.v8.3794 (page number not for citation purpose) don roosan et al. such as zkps and mpc, and enforcing a granular abedriven access control model, the architecture addresses a wide spectrum of security, privacy, and scalability challenges.7 although each step adds to system complexity and potential operational overhead, the result is a robust and future-oriented infrastructure capable of handling the escalating demands of telehealth while preserving patient data confidentiality.4 this blueprint thereby outlines a feasible path to quantum-safe telehealth implementations, offering a strategic balance between innovation and regulatory compliance. by bridging cryptographic theory with the practical realities of large-scale healthcare environments, the methodology aims to inspire further research, pilot programs, and iterative improvements as quantum computing continues to evolve. results the proposed architecture was developed and tested within a simulated telehealth network comprising a diverse set of nodes representing hospitals, clinics, patient devices, and ancillary services. nodes communicated through both classical and quantum-simulated channels, enabling the integration of a dag-based ledger for high throughput and crystals-dilithium for post-quantum digital signatures.5,7 additionally, a specialized qkd simulation layer was introduced among selected nodes, facilitating secure key exchanges and strengthening the resilience of the entire network.10 to accommodate telehealth’s unique scalability needs, off-chain storage was utilized for the majority of large patient data files, while only essential metadata and cryptographic references resided on the ledger.13 before operational testing, each node was equipped with a software module to handle crystals-dilithium signatures. this post-quantum signature scheme was integrated into the consensus algorithm to authenticate transactions and ensure that only verified participants could append new blocks or references to the ledger.6 in practice, the dag-based structure permitted parallel validation of multiple transactions from different hospitals or clinics. for instance, a cardiology clinic could update patient electrocardiogram (ecg) data, at the same time, an oncology department added a new pathology report. preliminary performance measurements showed a significant reduction in latency per transaction compared to a linear blockchain, particularly when tens of transactions arrived in close succession.9 although the computational overhead associated with pqc-based verification was noticeable, results indicated that throughput remained robust enough for telehealth applications that require near real-time responses.4 a crucial element of the design involved employing a qkd simulation layer to manage key exchanges for critical operations. within the architecture, each node participating in high-security transactions received quantum-generated keys used to encrypt or sign data, thereby minimizing the possibility of interception by an adversary with quantum decryption capabilities.1 in the simulation, these keys were periodically rotated, ensuring that even if a key were compromised, the duration for which it could be exploited remained limited.7 as part of the test environment, a hypothetical attacker was modeled to eavesdrop on the quantum channel. detected anomalies in photon transmission immediately triggered an alert that revoked the compromised key, underscoring the effectiveness of qkd in maintaining quantum-safe communications.10 figure 1 and table 1 depict the overall system architecture, illustrating the relationship between the dag ledger elements, the qkd management modules, and the cryptographic libraries that implement pqc. in addition, figure 1 highlights where zkps and abe modules interact with the ledger. the arrows within figure 1 demonstrate how nodes submit or reference transactions in the dag, how qkd channels distribute keys, and how signatures are validated with crystals-dilithium. this figure serves as a conceptual map for understanding the data flows and security layers that converge in the system. during system operation, patient data, which typically include medical images, laboratory reports, and continuous monitoring feeds, were stored off-chain to preserve ledger efficiency and user privacy.14 rather than uploading these sizable files to the dag, the architecture maintained encrypted references or hashes on-chain. such references were essential for immutability and verifiability, ensuring that any modification to patient records would be detected instantly by comparing on-chain hashes to off-chain data.4,11 in addition, this design streamlined the blockchain itself, as the ledger maintained only transaction records, cryptographic verifications, and minimal metadata. to further strengthen privacy, the system incorporated zkps for on-chain validation. the zkps enabled nodes to demonstrate the legitimacy of certain clinical transactions, such as a physician’s authorization to modify a patient’s records, without disclosing any additional patient data.6 each node possessed a local proof generator that, upon receiving a request to upload or modify a record reference, constructed a proof verifying the action’s authenticity. other nodes in the network then validated this proof before finalizing the transaction on the dag.13 in practice, this approach significantly reduced the threat of data leakage because no raw patient details were placed on the ledger. moreover, early testing revealed that incorporating zkps introduced a moderate computational overhead, but this cost was deemed acceptable in light of the amplified confidentiality.9 in tandem with zkps, abe regulated off-chain data access. whenever an authorized provider attempted to https://doi.org/10.30953/bhty.v8.379 citation: blockchain in healthcare today 2025, 8: 379 https://doi.org/10.30953/bhty.v8.379 5 (page number not for citation purpose) cryptography resilience in telehealth table 1. security layers in the proposed telehealth architecture security layers and layer name (as in figure 1) key objective principal components brief description level 1: application security ensure secure front-end interactions for telehealth users • telehealth user interfaces • secure session management • user authentication • telehealth user interfaces sit at the top of the stack. • they handle all provider/patient interactions. • this layer focuses on protecting user credentials, authenticating sessions, and safeguarding front-end data. level 2: cryptographic security protect data in transit and at rest through quantum-safe cryptography • pqc-based encryption • crystals-dilithium (or other pqc algorithms) • classical+quantum hybrid schemes • pqc-based encryption ensures resilience against both classical and quantum cryptanalysis. • crystals-dilithium provides post-quantum signatures for secure, authenticated transactions. level 3: (data integrity / access & control) verify integrity of transactions and control who reads/modifies sensitive data • zkp • abe • zkps enable validation of operations (e.g. verifying a provider’s authorization) without revealing sensitive details. • abe ensures only authorized parties can decrypt/modify data based on their attributes. level 4: quantum key management securely generate and distribute cryptographic keys, mitigating quantum-based eavesdropping • qkd key management • photon-based quantum channels • ephemeral key rotation • qkd leverages quantum properties to detect key interception in real time. • this layer refreshes keys periodically, ensuring minimal exposure if a key is compromised. level 5: ledger security provide high-throughput, tamper resistant record-keeping and transaction validation • dag-based blockchain ledger • parallel transaction handling • consensus mechanism • a dag ledger supports parallel validations, improving scalability for telehealth data. immutable blocks/“vertices” ensure accountability and traceability for patient records and clinical updates. abe: attribute-based encryption; dag: directed acyclic graph-based ledger; qc: quantum cryptography; qkd: quantum key distribution; zkp: zero-knowledge proofs. fig. 1. overview of the proposed telehealth architecture, showing the directed acyclic graph. dag: directed acyclic graph-based ledger, pqc: post-quantum cryptography; qkd: quantum key distribution; zkp: zero-knowledge proof. https://doi.org/10.30953/bhty.v8.379 citation: blockchain in healthcare today 2025, 8: 379 https://doi.org/10.30953/bhty.v8.3796 (page number not for citation purpose) don roosan et al. retrieve off-chain patient information, the system required them to demonstrate relevant attributes such as medical specialty, institutional affiliation, or authorization level that conformed to the encryption policy.6 if the system confirmed a match, the decryption keys were released, enabling the provider to view or update the records.7 simulated scenarios included multiple clinicians collaborating on a patient’s care plan, wherein each clinician had partial privileges to different segments of the patient’s health data. the granular level of control over record access proved effective in preventing unauthorized disclosures while facilitating seamless coordination among legitimate stakeholders.1 figure 2 illustrates the architecture of a telehealth system emphasizing security and privacy through various interconnected layers and components. each node represents a critical module in the telehealth system, categorized into five primary layers: application security, cryptographic security, data integrity & access control, quantum key management, and ledger security. the color coding of nodes indicates their respective layer, facilitating quick identification of their roles in the system. edges between nodes represent data flows and interactions within the system. these edges are differentiated by line styles and colors to signify different types of data flows, such as user data (red), session data (blue), auth data (green), encrypted data (purple), and others. for example, the interaction between “user authentication” and “zero-knowledge proof (zkp)” ensures the validation of authentication processes (lime). additional interactions, such as the “feedback loop” from the consensus mechanism back to the telehealth user interfaces, highlight the dynamic and iterative nature of telehealth system operations. these interactions aim to provide continuous updates and improvements based on the consensus achieved across the ledger system. the legend located below the graph explains the color coding of layers, while edge labels clarify the nature of data flows between nodes. this comprehensive visualization underscores the complexity and interdependence of telehealth components, which work together to ensure scalability, confidentiality, and post-quantum resilience in a secure and user-centric healthcare environment. by intertwining these components, the architecture ensures that each step in a transaction—from request submission to final ledger recording—remains verifiable, confidential, and resistant to quantum-level attacks.5 preliminary performance metrics, gathered over a simulated 48-h period, reflected stable throughput even during peak loads when multiple nodes accessed and updated records simultaneously.9 the overheads introduced by crystals-dilithium signature verifications, qkd key exchanges, and zkps were generally offset by the dag’s parallel transaction handling and the strategic storage of large data sets off-chain.4 nodes engaged in frequent key rotations and policy checks without fig. 2. telehealth architecture with security and privacy layers, extended interactions, and data flows. ai: artificial intelligence; dag: directed acyclic graph-based ledger; pqc: post-quantum cryptography; qkd: quantum key distribution; zkd: zero-knowledge proofs. https://doi.org/10.30953/bhty.v8.379 citation: blockchain in healthcare today 2025, 8: 379 https://doi.org/10.30953/bhty.v8.379 7 (page number not for citation purpose) cryptography resilience in telehealth experiencing unacceptable latency spikes, thus suggesting viability for real-world telehealth environments. notably, the concurrent confirmation of transactions allowed separate departments—such as radiology and pharmacy—to process updates in real time without queueing delays commonly associated with linear blockchain systems.13 these results validate that merging post-quantum cryptographic algorithms, qkd-based key management, and privacy-centric tools into a dag-based ledger can address telehealth’s stringent requirements for data security, scalability, and confidentiality.7 crystals-dilithium signatures secured on-chain transactions, the qkd layer provided quantum-resilient key distribution, and zkps maintained the privacy of sensitive medical information.6 meanwhile, abe enhanced access control over large off-chain files. despite certain trade-offs in computational complexity, the overall architecture demonstrated strong potential for ensuring secure, high-throughput telehealth record management in a post-quantum future.1,10 discussion the results presented in this study illustrate how pqc with qkd can offer a viable solution to the security and privacy challenges facing telehealth platforms. although the incorporation of pqc-based algorithms and qkd channels introduces additional computational overhead, the outcomes clearly underscore the efficacy of this design in protecting sensitive healthcare data from unauthorized access or tampering.7 as shown in figure 1 and figure 2 in the preceding results section, the interdependence of the dag-based ledger, qkd key management modules, and privacy-preserving cryptographic libraries not only fortifies transaction confidentiality but also ensures that on-chain and off-chain components are harmoniously managed.13 given the inherently sensitive nature of telehealth data, which frequently includes diagnostic images, laboratory results, and personal patient identifiers, the heightened security offered by post-quantum techniques is a compelling factor driving the feasibility of this architecture. one of the key takeaways from the experiments is the significant reduction in risk associated with quantum-based cryptographic attacks. traditionally, many healthcare systems rely on classical encryption schemes such as rsa or ecc, which are predicted to be vulnerable once quantum computers reach sufficient computational power to run algorithms like shor’s.14 by integrating pqc schemes, specifically crystals-dilithium for digital signatures, the proposed framework addresses these quantum threats proactively.5 simultaneously, qkd establishes robust key exchange mechanisms that can alert legitimate network participants to any eavesdropping attempts.10 despite the overhead incurred by generating and distributing keys via qkd, the resilience conferred on the system significantly outweighs the added complexity, particularly when the stakes involve life-critical healthcare data.4 the overarching advantage of this combined approach—pqc plus qkd—lies in its emphasis on dual security. the pqc secures stored and in-transit data, while qkd ensures that cryptographic keys are not easily intercepted or compromised.1 the synergy between these elements not only addresses present-day vulnerabilities but also anticipates future adversarial capabilities. nonetheless, the overheads documented during testing suggest that real-world telehealth networks adopting this system might benefit from additional hardware optimizations or even cloud-based post-quantum accelerators.9 the concept of offloading particularly heavy cryptographic functions to specialized nodes or services has already been explored in certain blockchain-based solutions, and it may prove crucial in maintaining the speed and efficiency necessary for time-sensitive medical operations.7 another key facet illuminated by this research is the layered approach to privacy. while blockchains inherently provide immutability, there is concern that storing unencrypted data on-chain poses a risk to patient confidentiality.4 by storing large files or highly sensitive data off-chain and placing only hashes or references on the dag, the architecture reduces chain data volume and improves privacy.11 this off-chain model can align with broader digital health equity goals, when combined with augmented reality or mixed reality solutions, which are aimed at underserved communities, ensuring equitable access without compromising security.15–18 furthermore, secure mpc extends privacy protections by enabling multiple healthcare entities to collaborate on data analyses—such as comparing anonymized patient outcomes or evaluating treatment efficacy—without ever sharing underlying personal data.13 although these protocols come with notable computational costs, they represent a crucial trade-off for compliance with privacy regulations and the ethical handling of patient information.6 at the same time, the dag-based design alleviates transaction bottlenecks by allowing for simultaneous validations.9 conventional linear blockchains can struggle under heavy telehealth workloads, especially when many providers concurrently update records. the research demonstrates that enabling parallel confirmations prevents lengthy queues, which could otherwise delay critical information updates in patient management. in large-scale deployments, however, more sophisticated node synchronization protocols might be required to handle conflicting transactions.7 for instance, two hospitals might accidentally submit differing updates to the same patient record around the same time, necessitating a robust conflict resolution mechanism that does not compromise the immutability or integrity of the ledger.13 https://doi.org/10.30953/bhty.v8.379 citation: blockchain in healthcare today 2025, 8: 379 https://doi.org/10.30953/bhty.v8.3798 (page number not for citation purpose) don roosan et al. it is important to acknowledge that dag-based ledgers, while providing distinct advantages in terms of parallel transaction processing, are not the only means of achieving high throughput for updating patient records. traditional linear blockchains can incorporate advanced smart contract designs or parallel execution frameworks to process multiple attributes of a single patient record, concurrently.19 techniques such as block partitioning, where a single block is logically divided into sub-blocks, allow different sets of transactions to be verified in parallel, thereby mitigating bottlenecks that arise from sequential consensus mechanisms.20 despite these possibilities, a dag-based approach naturally facilitates concurrency and minimizes transaction contention. however, it can introduce complexities in transaction ordering and finality, leading to potential security concerns if not carefully managed. for instance, reconciling conflicting transactions in dag architectures might require specialized conflict resolution protocols, and ensuring coherence of clinical data across multiple sites may necessitate elaborate synchronization strategies. a balanced assessment would thus compare the overhead of handling intricate dag consensus rules to the optimizations available in a traditional linear blockchain. by weighing throughput advantages against potential security or ordering complexities, telehealth system designers can tailor their choice of ledger architecture to the specific scalability demands and regulatory constraints of healthcare environments. beyond technical intricacies, there is also a substantial social and ethical dimension to adopting blockchain-based solutions in healthcare. the immutability of blockchain ledgers reassures stakeholders that patient data and clinical records have not been surreptitiously altered.13 this immutability fosters greater trust among clinicians, patients, insurers, and regulatory bodies. conversely, it also raises concerns about data permanence. even if sensitive content is encrypted, the mere existence of certain metadata on the chain might be deemed problematic under stricter interpretations of privacy laws, such as health insurance portability and accountability act in the united states or the gdpr in the european union.4 the proposed solution attempts to strike a balance by combining encryption, off-chain storage, and advanced cryptographic proofs like zkps and mpc, thereby ensuring that immutability does not equate to unwarranted transparency.6 one potential hurdle is the physical and infrastructural demand of setting up qkd channels, particularly for hospital networks that span disparate regions. although significant investments from governmental and industry partners have begun to reduce these barriers, establishing the fiber optic lines or satellite-based qkd solutions at scale is still costly.10 furthermore, key management protocols become more complex with frequent rotations and distribution to potentially thousands of clinical endpoints.1 nonetheless, the value offered by quantum-safe communications may justify these expenses over the long term, especially as the overall threat landscape expands and regulatory demands for data protection intensify.7 while this study demonstrates a promising quantum-safe approach for telehealth, several limitations must be acknowledged. the reliance on specialized hardware, such as qkd devices, can significantly increase infrastructural costs, especially for geographically dispersed healthcare facilities. additionally, the dag-based ledger introduces potential complexities in ensuring transaction ordering and finality, necessitating robust conflict resolution mechanisms. the simulation environment might not capture all real-world variables, including inconsistent network conditions, human factors, and regulatory constraints. off-chain data storage still raises trust issues regarding external data repositories, which may be vulnerable to physical attacks or insider threats. implementing privacy-preserving methods like zkps and mpc requires substantial computational overhead, potentially affecting response times in critical clinical scenarios. finally, ensuring system interoperability with existing telehealth platforms and electronic health record systems remains challenging. looking ahead, there are numerous avenues for enhancement. fully homomorphic encryption (fhe) may be integrated to allow computations on encrypted data without ever decrypting it, taking privacy-preserving analytics a step further.11 simultaneously, artificial intelligence–enhanced dashboards and caregiver support applications could streamline communication between providers and families, particularly in dementia care or bolster decision-making through data visualization.21–24 coupled with advanced pqc algorithms, fhe would enable more sophisticated machine learning or data mining processes without exposing any raw records, which is crucial in telehealth studies involving genomic data or predictive analytics. additionally, the inclusion of secure enclaves or trusted execution environments could isolate sensitive computations from untrusted parts of the network.6 these enclaves could serve as a middle layer between the blockchain ledger and off-chain data repositories, offering another protective barrier against potential breaches. in terms of validating this architecture in real-world scenarios, the next logical step is to conduct a fully operational proof-of-concept that aligns with healthcare regulations.13 this could involve partnering with actual hospitals or healthcare systems willing to pilot the architecture, implementing partial data sets and controlled qkd channels to measure performance, compliance, and scalability in situ.9 such a pilot could also uncover potential user-interface issues that might not surface in a controlled laboratory simulation, allowing for iterative refinements. ultimately, regulatory oversight would be pivotal to https://doi.org/10.30953/bhty.v8.379 citation: blockchain in healthcare today 2025, 8: 379 https://doi.org/10.30953/bhty.v8.379 9 (page number not for citation purpose) cryptography resilience in telehealth ensure that patient rights and ethical considerations are respected throughout the data lifecycle, including collection, storage, analytics, and eventual archival or deletion.4 overall, the proposed architecture paves the way for a new era of telehealth data security, one that anticipates the impending reality of quantum computing while adhering to stringent privacy demands. the synergy of pqc, qkd, zkps, mpc, and a dag-based design offers a comprehensive response to quantum threats, cryptographic vulnerabilities, and privacy concerns.7 by adopting off-chain storage strategies, abe, and parallel transaction processing, the system supports high-throughput telehealth operations without sacrificing confidentiality.6 as quantum computing evolves, maintaining proactive strategies that integrate advanced cryptographic methods will become vital. therefore, this research not only demonstrates the feasibility of a pqcand qkd-enhanced blockchain architecture but also proposes a framework adaptable to upcoming technological and regulatory landscapes. in doing so, it addresses immediate challenges and lays a foundation for continued innovation aimed at safeguarding patient records and sensitive clinical interactions in the post-quantum era.1,10 conclusion in an era where ai and quantum computing poses imminent risks to traditional cryptographic protocols, the need for robust, future-proof security in telehealth systems has become paramount.5,7 the architecture presented in this study addresses these concerns by integrating pqc at the consensus layer, harnessing qkd for secure key exchanges, and employing privacy-preserving techniques such as zkps and secure mpc. as illustrated in figure 1 and figure 2, the synergy of these components yields a system that maintains immutability and confidentiality, thereby ensuring that patient records are kept private while updates to the ledger remain verifiable.6,13 although performance assessments indicate an increase in computational overhead, particularly in the generation and validation of post-quantum signatures as well as the deployment of qkd channels, the ability to withstand potential quantum attacks ultimately justifies these resource investments.10 the dag-based approach described here further enhances scalability by enabling parallel transaction validation, which is essential in large telehealth networks where timely patient data access can be a matter of critical clinical importance.9 in tandem, a granular smart contract–based access control model ensures that only authorized entities can view or modify sensitive medical information, thereby embedding compliance with privacy regulations.4 future applications might integrate ai-driven medication management or therapy adherence tools, bridging security innovations with patient-centric outcomes.25–27 taken as a whole, this post-quantum blockchain architecture demonstrates strong potential for deployment in realworld telehealth settings. while additional optimizations and real-life pilot studies remain necessary, the research underscores that achieving quantum resistance, privacy preservation, and operational scalability within a single platform is not only feasible but also timely. this integrated approach thus offers a forward-looking blueprint for safeguarding patient care in the face of rapid technological change.1 funding none. conflicts of interest no relevant disclosures. contributors fahmida hai and don roosan contributed to conceptualization, methodology, software, data analysis, and writing the original draft. fahmida hai, rubayat khan, saif nirzhor, and don roosan contributed to investigation, review and editing, supervision, and project administration. all authors read and agreed to the published version of the manuscript. data availability statement (das), data sharing, reproducibility, and data repositories data are not available on request due to [privacy/ethical] restrictions. acknowledgments we are grateful to merrimack college for support. application of ai-generated text or related technology none reported by the authors. references 1. jeyaraman n, jeyaraman m, yadav s, ramasubramanian s, balaji s. revolutionizing healthcare: the emerging role of quantum computing in enhancing medical technology and treatment. cureus. 16(8):e67486. available from: https://www.ncbi.nlm.nih. gov/pmc/articles/pmc11416048/ 2. zastrozhin ms, sorokin as, agibalova tv, grishina ea, antonenko ap, rozochkin in, et al. using a personalized clinical decision support system for bromdihydrochlorphenylbenzodiazepine dosing in patients with anxiety disorders based on the pharmacogenomic markers. hum psychopharmacol clin exp. 2018;33(6):e2677. https://doi.org/10.1002/hup.2677 3. roosan d, chok j, baskys a, roosan mr. pgxknow: a pharmacogenomics educational hololens application of augmented reality and artificial intelligence. pharmacogenomics. 2022 mar 1;23(4):235–45. https://doi.org/10.2217/pgs-2021-0120 https://doi.org/10.30953/bhty.v8.379 https://www.ncbi.nlm.nih.gov/pmc/articles/pmc11416048/ https://www.ncbi.nlm.nih.gov/pmc/articles/pmc11416048/ https://doi.org/10.1002/hup.2677 https://doi.org/10.2217/pgs-2021-0120 citation: blockchain in healthcare today 2025, 8: 379 https://doi.org/10.30953/bhty.v8.37910 (page number not for citation purpose) don roosan et al. 4. odeh a, abdelfattah e, salameh w. privacy-preserving data sharing in telehealth services. appl sci. 2024 jan;14(23):10808. https://doi.org/10.3390/app142310808 5. opiłka f, niemiec m, gagliardi m, kourtis ma. performance analysis of post-quantum cryptography algorithms for digital signature. appl sci. 2024 jan;14(12):4994. https://doi. org/10.3390/app14124994 6. rao ys, srivastava v, mohanty t, debnath sk. designing quantum-secure attribute-based encryption. clust comput. 2024 dec 1;27(9):13075–91. https://doi.org/10.1007/s10586-024-04546-9 7. pandey s, bhushan b, hameed aa. securing healthcare 5.0: zero-knowledge proof (zkp) and post quantum cryptography (pqc) solutions for medical data security. in: ckk reddy, t sithole, m ouaissa, ö özer, mm hanafiah, editors. soft computing in industry 50 for sustainability. cham: springer nature, 2024; p. 339–55. 8. roosan d, clutter j, kendall b, weir c. power of heuristics to improve health information technology system design. aci open. 2022 dec 9;06:e114–22. https://doi.org/10.1055/s-0042-1758462 9. kumar n, reiffers-masson a, amigo i, rincón sr. the effect of network delays on distributed ledgers based on directed acyclic graphs: a mathematical model. perform eval. 2024 jan 1;163:102392. https://doi.org/10.1016/j.peva.2023.102392 10. yang j, 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https://www.researchsquare.com/ article/rs-1650806/v1 14. roosan d, chok j, li y, khou t. utilizing quantum computing-based large language transformer models to identify social determinants of health from electronic health records. in: 2024 international conference on electrical, computer and energy technologies (icecet) [internet].* 2024 [cited 2025 jan 6]; p. 1–6. available from: https://ieeexplore.ieee.org/ document/10698600 15. roosan d. integrating artificial intelligence with mixed reality to optimize health care in the metaverse. in: v geroimenko, editor. augmented and virtual reality in the metaverse. cham: springer nature switzerland, 2024; p. 247–64. 16. roosan d. the promise of digital health in healthcare equity and medication adherence in the disadvantaged dementia population. pharmacogenomics. 2022 jun;23(9):505–8. https://doi. org/10.2217/pgs-2022-0062 17. wu y, li y, baskys a, chok j, hoffman j, roosan d. health disparity in digital health technology design. health technol. 2024 [cited 2025 jan 5];1–11. available from: https://rdcu.be/ dvawv 18. roosan d, wu y, chok j, sanine c, khou t, li y, et al. artificial intelligence-powered large language transformer models for opioid abuse and social determinants of health detection for the underserved population. in: proceedings of the 13th international conference on data science, technology and applications—data. scitepress, 2024; p. 15–26. (isbn 978-989758-707-8; issn 2184-285x). 19. u.s. patent no. 11829494b2. 2023 [cited 2025 jan 5]. available from: https://patents.google.com/patent/us11829494b2 20. u.s. patent no. 11556658b2. 2023 [cited 2025 jan 5]. available from: https://patents.google.com/patent/us11556658b2 21. li y, phan h, law av, baskys a, roosan d. gamification to improve medication adherence: a mixed-method usability study for medscrab. j med syst. 2023 oct 20;47(1):108. https://doi. org/10.1007/s10916-023-02006-2 22. roosan d, kim e, chok j, nersesian t, li y, law av, et al. development of a dashboard analytics platform for dementia caregivers to understand diagnostic test results. in: e pino, r magjarevi, p de carvalho, editors. international conference on biomedical and health informatics 2022. cham: springer nature switzerland, 2022; p. 143–53. 23. roosan d, law av, karim m, roosan m. improving teambased decision-making using data analytics and informatics: protocol for a collaborative decision support 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org/10.1016/j.japh.2023.11.023 addendum acronyms defined in the article abe: attribute-based encryption dag: directed acyclic graph-based ledger ecc: elliptic curve cryptography fhe: fully homomorphic encryption mpc: multiparty computation pqc: post-quantum cryptography qkd: quantum key distribution rsa: rivest-shamir-adleman zkps: zero-knowledge proofs copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their deriva tive works on different terms, provided the original work is properly cited, and the use is non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. the authors own the copyright to this article. https://doi.org/10.30953/bhty.v8.379 https://doi.org/10.3390/app142310808 https://doi.org/10.3390/app14124994 https://doi.org/10.3390/app14124994 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http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) editorial/discussion the european digital identity wallet: a healthcare perspective danny van roijen, ms business engineering eu policy expert (independent), brussels, brussels metropolitan area, belgium corresponding author: danny van roijen, email: danny@childeroland.be doi: https://doi.org/10.30953/bhty.v7.344 keywords: cross-border healthcare, data governance, digital identity, health data, interoperability submitted: august 12, 2024; accepted: august 20, 2024; published: august 31, 2024 with the june 2024 elections, the first term of the european commission under president ursula von der leyen came to an end. within these last 5 years, major strides were made in europe to write a future-proof rulebook on data and digital. in the shadow of headline-grabbing regulations such as the digital markets act and the ai act, the eidas regulation was revised to establish a european digital identity wallet. eidas 2.0 and the european digital identity wallet published into law 10 years ago, regulation (eu) no 910/2014 on electronic identification and trust services for electronic transactions in the internal market,1 commonly referred to as the eidas (electronic identification and trust services) regulation, established a general legal framework for the (cross-border) recognition and use of electronic identification and trust services. rules were specified for electronic signatures, electronic seals, electronic time stamps, electronic documents, electronic registered delivery services and certificate services for website authentication. on april 30, 2024, the revised eidas regulation was published in the eu’s official journal2 (hereafter eidas 2.0), extending the covered trust services also to include electronic attestation of attributes, electronic archiving services and electronic ledgers. more importantly, eidas 2.0 establishes a harmonized framework supporting a european digital identity wallet (ediw). the eidas, article 3 definition is presented in table 1. the eidas should enable users to identify themselves electronically and authenticate online and in offline mode across borders to access a wide range of public and private services. the use of the ediw shall, however, be voluntary; access to public and private services by other existing identification and authentication means should remain possible. the eidas should provide a high degree of transparency, privacy, and control by the users over their personal data. this includes the ability to generate pseudonyms, registration of access activity, and integration of privacy-preserving techniques. a list of reference standards and more detailed specifications in development are listed in table 2. however, they still need to be developed and announced through secondary legislation. the first public consultations on eidas 2.0 implementing regulations launched in august 2024.3 the current role of eid solutions in access to health data in the past, patient access to health data has been limited and fragmented. the digital transformation of health and care provides opportunities to grant patients direct access and control over their health data. identification and authentication play a crucial role in enabling secure access and exchange of health data. table 1. eidas, article 3, as defined in the eu’s official journal2 (electronic ids) article 3 defined (42) “european digital identity wallet:” an electronic identification means that allows the user to securely store, manage, and validate personal identification data and electronic attestations of attributes for the purpose of providing them to relying parties and other users of european digital identity wallets, and to sign by means of qualified electronic signatures or to seal by means of qualified electronic seals. eidas: electronic identification and trust services. blockchain in healthcare today issn 2573-8240 mailto:danny@childeroland.be https://doi.org/10.30953/bhty.v7.344 citation: blockchain in healthcare today 2024, 7: 344 https://doi.org/10.30953/bhty.v7.3442 (page number not for citation purpose) d. van roijen as part of europe’s digital decade policy programme 2030, progress is being monitored towards the target of ensuring that 100% of eu citizens have access to their electronic health records (ehrs) by 20304 (the “ehealth target”). this ehealth target is being monitored through 12 sub-indicators spread across four thematic layers. according to the ‘2024 digital decade ehealth indicator study’5 reflecting the state of play as of 31 december 2023, the average composite ehealth score for the eu-27 countries has increased to 79%. currently, 17 member states enable citizens to use a secure eid to authenticate themselves when using the online access service (sub indicator 5). health data access in a cross-border context access to health data is not only important within a national context; with increasing mobility other relevant use cases are applicable within a cross-border context. this was already identified in the 2011 patient rights directive6 (also known as the cross-border healthcare directive), where member states through the ehealth network were encouraged to develop common identification and authentication measures to facilitate the transferability of data in cross-border healthcare. the exchange of ehrs is considered one of the essential building blocks in europe’s digital transformation of health and care. partially implemented across eu member states today, the european health data space7 legislative proposal will push for the eu-wide availability and cross-border accessibility of electronic patient summaries, eprescriptions, images and image reports, lab results, and discharge reports. at the moment, the european commission is funding four large scale pilot projects to test drive the specifications of the eu digital identity wallet. one of these, potential for european digital identity (potential)7 focuses on use cases in six digital identity sectors. within healthcare, the pilot is exploring, in particular, the use case of electronic prescriptions or e-prescriptions. table 3. the ehealth target is monitored through 12 sub-indicators spread across four thematic layers as part of europe’s digital decade policy programme 20304 thematic layer sub-indicator implementation of electronic access services for citizens 1. nationwide availability of electronic access service(s) categories of accessible health data 2. electronic health records summary data 3. eprescription/edispensation data 4. electronic results and reports access technology and coverage 5. access to electronic health records with an eid 6. access via an online portal or mobile application 7. percentage of the national population able to access 8. healthcare providers connected and supplying relevant data access opportunities for certain categories of people 9. access for legal guardians 10. access for authorised persons 11. assistance for disadvantaged groups 12. wcag v2.1 and web accessibility directive compliance eid: electronic identification; wcag: web content accessibility guidelines. table 2. a list of reference standards and more detailed specifications in the development of eidas (electronic identification and trust services)3 european digital wallets url certification https://ec.europa.eu/info/law/better-regulation/have-your-say/ initiatives/14337-european-digital-identity-wallets-certification_en integrity and core functionalities https://ec.europa.eu/info/law/better-regulation/have-your-say/ initiatives/14341-european-digital-identity-wallets-integrity-and-core-functionalities_en person identification data and electronic attestations of attributes https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14340-european-digitalidentity-wallets-person-identification-data-and-electronic-attestations-of-attributes_en protocols and interfaces to be supported https://ec.europa.eu/info/law/better-regulation/have-your-say/ initiatives/14339-european-digital-identity-wallets-protocols-and-interfaces-to-be-supported_en trust framework https://ec.europa.eu/info/law/better-regulation/have-your-say/ initiatives/14338-european-digital-identity-wallets-trust-framework_en source: public consultation via the european commission’s “have your say” website. https://ec.europa.eu/info/law/better-regulation/have-your-say_en https://doi.org/10.30953/bhty.v7.344 https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14337-european-digital-identity-wallets-certification_en https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14337-european-digital-identity-wallets-certification_en https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14341-european-digital-identity-wallets-integrity-and-core-functionalities_en https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14341-european-digital-identity-wallets-integrity-and-core-functionalities_en https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14340-european-digital-identity-wallets-person-identification-data-and-electronic-attestations-of-attributes_en https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14340-european-digital-identity-wallets-person-identification-data-and-electronic-attestations-of-attributes_en https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14339-european-digital-identity-wallets-protocols-and-interfaces-to-be-supported_en https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14339-european-digital-identity-wallets-protocols-and-interfaces-to-be-supported_en https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14338-european-digital-identity-wallets-trust-framework_en https://ec.europa.eu/info/law/better-regulation/have-your-say/initiatives/14338-european-digital-identity-wallets-trust-framework_en https://ec.europa.eu/info/law/better-regulation/have-your-say_en citation: blockchain in healthcare today 2024, 7: 344 https://doi.org/10.30953/bhty.v7.344 3 (page number not for citation purpose) european digital identity wallet the european digital identity wallet: use cases as mentioned above, several use cases can be facilitated through the eu digital identity wallet. these include access to health, sharing health data, cross-border use cases, and integration of digital health technology into existing care pathways. access to health data first, the eu digital identity wallet can be a trusted and secure platform to collect and/or access a person’s health data. on the one hand, this can be clinical data that have been collected upon instruction of or interaction with health and care services—public or private. the wallet can also be a repository of person-generated health data, for instance, self-reported or measured through the use of sensors and wearables. sharing health data the eu digital identity wallet will hand back control to the citizen in managing their health data. this will allow people to more actively share data with those involved in their direct, indirect, and informal care. next to that, people will be able to share data more consciously with third parties while retaining control over the level of detail they wish to share or reveal. cross-border use cases as mentioned, the use of the eu digital identity wallet could be a facilitator for cross-border healthcare services. we can make a distinction between the following scenarios (table 4). integration of digital health technology into existing care pathways the uptake of digital health technologies has been, to a certain extent, limited due to the inability to integrate easily into existing care pathways. several technical and organizational issues are at the root of this, such as a lack of interoperability, a proliferation of data silos, repetitious input of available information, or questions and doubts about data protection and cybersecurity. the eu digital identity wallet will support the once-only principle and will enable straightforward and secure data access. in this way, it can help foster trust and stimulate the use of digital health technologies in daily practice and routine. conclusion with the eidas 2.0 regulation, europe is building the foundations for the eu digital identity wallet. defining a clear legal and technical framework will help create trust and credibility for the development and application of new use cases. not only will the eu digital identity wallet empower patients to access and manage their health data, but it will also facilitate their access to cross-border health services and enable better integration of digital health technology into existing care pathways. in the end, the patient will only stand to benefit from this. funding no funding was provided. conflicts of interest danny van roijen is a member of the bhty editorial board. contributors the author developed concepts, wrote the manuscript, reviewed comments, and made all revisions. data availability statement (das), data sharing, reproducibility, and data repositories not applicable. application of ai-generated text or related technology not applicable references 1. regulation (eu) no 910/2014 of the european parliament and of the council of 23  july 2014 on electronic identification and trust services for electronic transactions in the internal market and repealing directive 1999/93/ec, eur-lex [internet]. europa.eu. 2014. available from: https://eur-lex.europa.eu/legal-content/en/ txt/?uri=celex:32014r0910 2. regulation eu) 2024/1183 of the european parliament and of the council of 11 april 2024 amending regulation (eu) no 910/2014 as regards establishing the european digital identity framework, eur-lex [internet]. europa.eu. 2024. available from: https://eurlex.europa.eu/legal-content/en/txt/?uri=celex:32024r1183 3. public consultation via the european commission’s “have your say” website. available from: https://ec.europa.eu/info/law/ better-regulation/have-your-say_en 4. european commission. europe’s digital decade: digital targets for 2030 [internet]. commission.europa.eu. 2022. available from: https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-age/ europes-digital-decade-digital-targets-2030_en table 4. eu digital identity wallet as a facilitator for cross-border healthcare services application examples physical use of cross-border healthcare services • dispensation of medicines using an eprescription while being abroad. • making imaging or lab results available when receiving care (whether planned or acute) in another country. digital or virtual use of crossborder healthcare services • access to online pharmacies or cross-border telehealth services. https://doi.org/10.30953/bhty.v7.344 http://europa.eu https://eur-lex.europa.eu/legal-content/en/txt/?uri=celex:32014r0910 https://eur-lex.europa.eu/legal-content/en/txt/?uri=celex:32014r0910 http://europa.eu https://eur-lex.europa.eu/legal-content/en/txt/?uri=celex:32024r1183 https://eur-lex.europa.eu/legal-content/en/txt/?uri=celex:32024r1183 https://ec.europa.eu/info/law/better-regulation/have-your-say_en https://ec.europa.eu/info/law/better-regulation/have-your-say_en http://commission.europa.eu https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-age/europes-digital-decade-digital-targets-2030_en https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-age/europes-digital-decade-digital-targets-2030_en https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-age/europes-digital-decade-digital-targets-2030_en citation: blockchain in healthcare today 2024, 7: 344 https://doi.org/10.30953/bhty.v7.3444 (page number not for citation purpose) d. van roijen 5. european commission, directorate-general for communications networks, content and technology, page m, winkel r, behrooz a, bussink r. 2024 digital decade ehealth indicator study: final report. publications office of the european union; 2024. 6. directive 2011/24/eu of the european parliament and of the council of 9 march 2011 on the application of patients’ rights in cross-border healthcare [internet]. 2011. available from: https://eur-lex.europa.eu/legal-content/en/txt/?uri=celex: 32011l0024 7. european commission. european health data space [internet]. health.ec.europa.eu. available from: https://health.ec.europa.eu/ ehealth-digital-health-and-care/european-health-data-space_en copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. https://doi.org/10.30953/bhty.v7.344 https://eur-lex.europa.eu/legal-content/en/txt/?uri=celex:32011l0024 https://eur-lex.europa.eu/legal-content/en/txt/?uri=celex:32011l0024 http://health.ec.europa.eu https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space_en https://health.ec.europa.eu/ehealth-digital-health-and-care/european-health-data-space_en http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) narrative/systematic review/meta-analysis tracing the blockchain challenges in healthcare: a topic modeling and bibliometric analysis mohammad mehraeen, phd , and laya mahmoudi, phd candidate department of management, ferdowsi university of mashhad, mashhad, iran corresponding author: laya mahmoudi, email: laya.mahmoudi@mail.um.ac.ir doi: https://doi.org/10.30953/bhty.v7.335 keywords: bibliometric analysis, blockchain application, healthcare, latent dirichlet allocation, topic modeling abstract the application of blockchain technology to healthcare offers promise in providing solutions to some key challenges related to data sharing, privacy, security, and access control. however, several barriers prevent the widespread adoption of blockchain and prompted research efforts. this study aims to conduct a bibliometric analysis of 196 documents indexed in the scopus database to examine their structure, impact, contributors, and journals. the bibliometric analysis provides information on the publication and citation structure, as well as the most productive authors, universities, countries, journals, and most cited studies. in addition, it identifies the most prevalent keywords and their co-occurrence patterns on blockchain challenges in healthcare. a topic modeling approach, using latent dirichlet allocation (lda), is also employed to reveal the latent topical structure of this literature. as a result of these findings, the research landscape in this area has been quantitatively analyzed, identifying six critical challenges regarding the use of blockchain in healthcare: data privacy/security, integration with smart devices, interoperability, scalability, governance, and cost. plain language summary despite the potential of blockchain technology to improve healthcare, its widespread adoption remains limited. this study examines the research conducted on blockchain challenges in healthcare. a bibliometric analysis of 196 studies was performed to clarify the current state of this research area. in addition, a topic modeling technique was used to identify the major challenges: data privacy/security, integration with smart devices, interoperability, scalability, governance, and cost. this analysis demonstrates that data privacy/security and integration with smart devices are the predominant challenges regarding their topic size. these findings provide a comprehensive overview of the obstacles blockchain faces in healthcare and highlight areas for future research. submitted: july 8, 2024; accepted: august 25, 2024; published: december 20, 2024 in the last century, technological advancements in the healthcare sector have revolutionized the industry significantly. following the advancements and increasing uses of the internet, online communication systems are becoming a priority for different users,1 resulting in generating a growing amount of personal health data. accordingly, the healthcare industry faces a significant challenge that requires the proper management and secure retrieval of massive amounts of data to address this concern. however, health data are mostly inaccessible, non-standardized across systems, and challenging to understand, use, and share.2 more specifically, as patient data are scattered throughout the value chain of the healthcare industry, and the sharing of information is subject to multiple levels of permission control, vital data are not always accessible when needed. to address this challenge, blockchain technology has received considerable attention for its potential application to healthcare. blockchain technology provides an exceptionally transparent and secure system for exchanging information with minimal risk of leakage or modifications to the security system. in the blockchain, multiple copies of information are blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-4154-8975 https://orcid.org/0009-0000-1863-2872 mailto:laya.mahmoudi@mail.um.ac.ir https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.3352 (page number not for citation purpose) m. mehraeen and l. mahmoudi shared among multiple nodes on a blockchain network.3 as a result, such distributed systems that rely on many nodes to perform different roles on the network can be assured of their integrity, security, consistency, and reliability. despite the benefits witnessed or studied by the research community in the blockchain revolution for healthcare challenges, this technology still faces significant concerns regarding its adoption by the entire global healthcare industry.4 given that blockchain technology presents challenges to the healthcare industry, this study traces and analyzes these challenges by employing bibliometric analysis to provide an overview of the field and a text analytics method based on topic modeling to identify specific challenges. the objectives of this study are twofold: 1. quantify and analyze research outputs, impact, and collaboration patterns in the field of blockchain challenges in healthcare. 2. identify key contributors, influential publications, and leading journals in this domain. to uncover the dominant themes and challenges discussed in the literature using topic modeling techniques, this article is organized as follows: section 2 presents the related works conducted on the targeted topic. section 3 discusses the methodologies used in this study. section 4 presents the findings of both bibliometric analysis and topic detection. section 5 provides the discussion and conclusions. as a novel technological development, blockchain technology has prompted considerable interest among researchers, resulting in the publication of several studies examining its innovative potential and diverse applications in the healthcare sector. despite blockchain’s transformative potential in the health domain, it is also confronted with challenges that are prompting extensive research to address these challenges from different viewpoints.5 as a result, several systematic reviews have been conducted over the past few years to synthesize and summarize the findings from various studies that addressed the applications and challenges of blockchain in healthcare. reviews providing a comprehensive analysis indicate that blockchain technology can not only help enhance the security, privacy, data sharing, and access control of health records but also faces challenges due to scalability, interoperability, storage, and costs (table 1). among these, more specific reviews,5 the focus is on identifying and proposing potential solutions for specific challenge categories such as scalability. methodology the methodological framework employed in this study is illustrated in figure 1. detailed explanations of data acquisition, preparation, and analysis using bibliometric and topic modeling tools are provided in the following subsections. data acquisition with a focus on blockchain challenges in healthcare, this study employed a rigorous data collection process from elsevier’s scopus repository, which covers academic literature extensively. one of the main advantages of scopus is its ability to organize bibliographic information into categories, codify the retrieved material, and automatically analyze the information. to find the publications, different combinations of keywords of blockchain, challenges, and healthcare were used to ensure that relevant studies are covered as widely as possible. the search, conducted in february 2024, was based solely on the article titles and keywords, utilizing logical “or” operators for synonymous phrases related to challenges, healthcare, and different forms of blockchain, all connected by three “and” conditions: (“blockchain” or “block-chain”) and (“healthcare” or “medical” or “health”) and (“challenge” or “obstacle” or “issue” or “barrier”) a total of 196 articles were retrieved from the search. these publications, written in english, included articles, reviews, and conference papers, all published between 2017 and 2023. for analysis, details related to the collected documents were exported to a csv (comma separated values) excel file. bibliometric analysis in this study, a bibliometric approach was applied to the analysis of the most important and common indicators. based on the methodology outlined by goodell and colleagues,13 we conducted a comprehensive bibliometric analysis to examine the publication and citation structure, identify the most productive authors, universities, and countries, highlight studies with the highest citation counts, determine the most productive journals, and perform keyword occurrence and co-occurrence analyses. additionally, the co-occurrence analysis was visualized using vosviewer, which features an easy-to-use interface and provides an overview of author and index keywords and their co-occurrence. in addition, this software was employed to create a network diagram of the co-authorship relationships among countries to illustrate the extent of their collaborations in producing scholarly publications. topic modeling: data preparation and analysis for topic analysis, the collected data must be preprocessed in the first step through a set of procedures to provide a https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.335 3 (page number not for citation purpose) blockchain modeling and bibliometric analysis table 1. a summary of literature reviews on blockchain in healthcare. study objective methods source years covered/ articles (n) key findings abuhalimeh & ali6 conduct a comprehensive review to identify challenges associated with data quality when using blockchain technology in healthcare. slr scopus, acm, emerald, science direct, web of science, ieee 2016–22 (49) • blockchain in healthcare poses significant challenges around data quality, classified technological, adoption, and operational factors. singh et al.7 examine blockchain technology and its application in healthcare, including the challenges, comparisons, and possible solutions. review scopus, ieee xplore, sciencedirect, acm digital library, and springerlink n/a (84) • security, privacy, interoperability, and data sharing are challenges facing current healthcare systems; blockchain can address these issues. • scalability, privacy, governance, standards, ownership, and costs are challenges in adoption of blockchain for healthcare. taherdoost8 review research on blockchain privacy and security in healthcare, focusing on practical applications and challenges. slr scopus 2017–22 (65) • blockchain in healthcare is growing and can be used to control access to medical records, share data, and enhance privacy. • blockchain adoption faces several challenges, including scalability and interoperability. kumar et al.9 examine healthcare blockchain applications powered by ai and their challenges. slr and meta-analyses (prisma) ieee xplore, pubmed, sciencedirect, google scholar, web of science, doaj, researchgate 2012–22 (100) • medical records, including health data, can be securely stored and shared using blockchain technology. • blockchain and ai in healthcare have some open challenges (e.g., privacy, bandwidth, regulations, and trust). sharma et al.10 a comprehensive overview of blockchain-based applications across various domains to identify challenges and directions for future research. slr google scholar 2015–19 (161) • blockchain is most commonly used in iot, cloud storage, and healthcare. • application challenges associated with blockchains include storage, scalability, privacy, and security. agrawal et al.1 review 10 blockchain applications and tools, addressing scalability, immutability, robustness, network latency, audibility, and traceability issues. slr ieee access, ieee transactions, acm computing surveys, computers & security, future generation computer systems 2017–22 (>150) • blockchain-based applications are identified: academics, aviation, banking, car sharing, e-voting, healthcare, iot, ipr, and supply chain. • scalability, latency, storage overhead, security vulnerabilities, lack of privacy, high energy consumption, interoperability concerns, usability concerns, and regulatory uncertainty are presented as challenges and open issues with blockchain technology. continued https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.3354 (page number not for citation purpose) m. mehraeen and l. mahmoudi cleaned corpus. the study utilized titles, abstracts, and keywords from the stored 196 articles in the csv file. for this purpose, all textual data in the dataset were converted to lowercase, a process known as lowercase conversion, which is essential to avoid duplicate representations of the same word due to capitalization variations. this results in the consistency and uniformity of the corpus. afterward, special characters and punctuation marks, making noise with the topic modeling process, were removed from the text. to further refine the dataset and emphasize terms that are more relevant and informative, the common stopwords “the,” “and,” and “in” were excluded from the dataset. by taking this step, the topic modeling process can be rationalized in terms of computational overhead. additionally, stemming and lemmatization techniques were applied to normalize the text data. the stemming process transforms words into their root forms, while lemmatization transforms words into their base forms. by converging variations of the same word, both techniques increased the quality of the dataset and improved the representation of the corpus. topic detection: latent dirichlet allocation for topic detection, latent dirichlet allocation (lda), one of the most widely used algorithms for topic modeling,14 was used to identify dominant topics. the lda relies on the assumption that each document in the corpus contains several different topics15 in various proportions, each of which represents a probability distribution over a fixed set of words. based on this assumption, the lda algorithm makes a latent topical structure of the corpus from the co-occurrence patterns of words across documents.16 to implement the lda-based topic modeling in this study, the preprocessed textual corpus, obtained through the preprocessing steps, was transformed into a document-term matrix (dtm), serving as the input for the lda algorithm. through this transformation, a numerical format of the corpus could be created, enabling lda to identify latent topics and their word distributions efficiently. the most important challenge topic modeling faces is selecting the optimum number of topics for a corpus.17 accordingly, the coherence score was used in this study as a metric to identify the optimal number table 1. (continued) a summary of literature reviews on blockchain in healthcare. study objective methods source years covered/ articles (n) key findings ratta et al.11 analyze applications of blockchain-iot integration in healthcare, identifying challenges and proposed solutions. slr ieee, elsevier, springer 2016–21 (30) • blockchain can mitigate vulnerabilities of iot in healthcare by providing decentralization, transparency, and security. • challenges faced by healthcare blockchain-iot solutions include interoperability, scalability, storage, standardization, and convincing clinicians and patients to share information. khatri et al.12 conduct a comprehensive analysis of challenges and trends in implementing blockchain solutions in the healthcare industry. slr ieee xplore, science direct, springer link, acm digital library, pubmed 2015–20 (50) • focus on areas such as data sharing, ehrs, access control, and clinical trials is a growing trend in blockchain healthcare research. • the main challenges to using blockchain in healthcare include security, privacy, scalability, interoperability, speed, lack of expertise, and high costs of healthcare infrastructure. mazlan et al.5 to undertake a systematic review of scalability challenges faced by blockchain-based healthcare applications and potential solutions slr ieee, acm, pubmed (41) • major challenges to scalability were identified: block size, high volume of data, number of transactions, and protocol limitations. • 16 solutions are proposed and categorized under three categories: storage optimization (3 solutions) and blockchain redesign (13 solutions). acm: association for computing machinery; ai: artificial intelligence; ieee: institute of electrical and electronics engineers; doaj: directory of open access journals; ipr: intellectual property rights; iot: internet of things; n/a: not available; slr: systematic literature review. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.335 5 (page number not for citation purpose) blockchain modeling and bibliometric analysis of topics. in coherence computation, words constituting a topic are checked for consistency. higher coherence scores indicate higher quality of topics,18 providing better interpretability of the topics. the optimal number of topics was six, as determined by the coherence score (figure 2 and table 2), indicating the elbow of the curve in coherence and adding topics beyond six did not significantly improve coherence. fig. 1. research framework for bibliometric and topic modeling analysis. see figures 2, 3, 5, 6, and 7 for detailed scatter plots and illustration shown in this figure. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.3356 (page number not for citation purpose) m. mehraeen and l. mahmoudi figure 3 illustrates the structure of lda topic modeling using dirichlet distributions for topic and word distributions. this model includes three levels: the corpus level, the document level, and the word level.19 at the corpus level, α and β are the global parameters, representing the distribution of topics across documents and the word distributions for each topic, respectively. the θ parameter, a document-level variable, indicates the topic proportions in a particular document. finally, the variables z and w correspond to the word level, where z specifies the topic assigned to a particular word, and w represents a word associated with a particular topic. results publication and citation structure table 3 presents information on the number of papers published in the realm of healthcare, with a focus on blockchain challenges and the general citation structures that appeared in these articles. this information is also depicted in figure 1, showing the trends in publication counts and the number of citations for this topic since 2017. starting with a single publication in 2017, there was steady growth during the early years, with more than doubling the previous year’s output. the publications of 2021 achieved 48 and revealed a significant jump. the upward trend in production continued through 2022 and 2023. as shown, the number of publications conducted to study the challenges faced by blockchain in healthcare is growing noticeably. there is also evidence that the vast majority of highly cited papers were published during the period from 2019 to 2021. specifically, table 3 and figure 4 reveal a steady increase for the first 2 years, followed by a remarkable increase to 1,695 citations in 2019. after this peak, a slight decrease was marked by a rapid decline in subsequent years. specifically, about 4.5% of the articles acquired more than 150 citations, 3.5% more than 100, 7.14% more than 20, 14.79% more than 10, 9.69% more than 5, and almost 24% received more than one citation. fig. 2. coherence score for latent dirichlet allocation. see figure 1 for greater context. table 2. coherence score by the number of topics. topics (n) coherence score 2 0.2943 4 0.2754 6 0.2955 8 0.2947 fig. 3. graphical representation of the latent dirichlet allocation (lda) model. see figure 1 for greater context. α: dirichlet hyperparameter for topic proportions; θ: topic proportions per document; z: topic assignment per word; w: observed word; β: topic-word distributions; m: document numbers; n: the word numbers in a particular document. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.335 7 (page number not for citation purpose) blockchain modeling and bibliometric analysis most productive authors a list of the 10 most productive authors who studied blockchain challenges in healthcare is presented in table 4. this table shows how many studies have been published on this topic by the highly productive authors, how many citations their studies received, and their scholarly influence and productivity in the field. in addition to these metrics, an evaluation of each author’s contribution within the targeted subject was conducted, taking into account both the number of articles produced to the total number of publications in the field and the number of citations among the total number of publications reviewed. jayaraman and salah at khalifa university in the united arab emirates demonstrate notable research outputs with a high number of citations, showing impactful contributions, with an average of 14.63% production over total studies. the highest number of citations, as shown in table 4, is attributed to seven studies authored by researchers kumar from the thapar institute of engineering and technology in india and choo from the university of texas at san antonio in the united states. it reflects the significant influence of these studies on other research and their substantial contribution to the field. moreover, the greatest h-index belongs to bhushan from the ohio state university in the united states, showcasing his high-quality studies. most productive universities table 5 presents a list of top 10 universities with the highest number of papers published on blockchain challenges in healthcare. alongside the total number of productions and received citations, these universities are compared in terms of country, qs world university rankings, and the number of publications reaching citation thresholds of 50, 100, and 150. as depicted, the university of petroleum and energy studies in india and khalifa university of science and technology in the united arab emirates stand out with the highest number of publications. with the same number of publications, khalifa university of science and technology has received a total of 266 citations, which is notable in comparison to the publications of the university of petroleum and energy studies. the more citations publications receive, the more significant their contributions to advancing knowledge, indicating they are valued highly in this field. accordingly, the university of texas at san antonio and the thapar institute of engineering & technology occupy the top two positions as the most influential universities, with 843 and 702 citations, respectively, followed by the nirma university institute of technology with 440 citations. in addition to the related information about the publications in the addressed area by each university, table 3. general citation structure of studies addressing blockchain challenges in healthcare. year >150 >100 >50 >20 >10 >5 >1 total studies total citations 2017 1 0 0 0 0 0 0 1 247 2018 0 1 3 1 0 0 1 6 364 2019 4 1 1 1 3 1 2 13 1,695 2020 2 1 4 3 5 1 3 21 1,236 2021 2 2 6 7 8 6 12 48 1,352 2022 0 2 0 6 5 7 18 46 571 2023 0 0 0 4 8 4 11 57 336 the data were retrieved in february 2024 based on scopus; the numerical values >150, >100, >50, >20, >10, >5, and >1 denote the number of citations each study has received. fig. 4. trends in publications and in citations addressing research into blockchain challenges in healthcare. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.3358 (page number not for citation purpose) m. mehraeen and l. mahmoudi the current world ranking of these universities, according to quacquarelli symonds (qs), is obtained from the qs world universities ranking website and presented. as shown, the top universities and institutes focusing on advancing blockchain technology in healthcare follow a wide range of qs world university rankings, demonstrating the varied levels of impact and expertise. in this regard, universiti putra malaysia and khalifa university of science and technology show a stronger global presence than the other institutions in table 5, with qs world university rankings of 158 and 230, respectively. most productive countries table 6 compares the academic contributions of different countries based on research output and impact metrics. table 5. top productive universities for blockchain challenges in healthcare. rank institute, country tp tc tc/total studies (%) tc/total citations (%) qs >5 >20 >50 >100 1 university of petroleum and energy studies, india 7 44 14.89 1.43 901–950 3 0 0 0 2 khalifa university of science and technology, uae 7 266 14.89 8.67 230 3 0 2 1 3 thapar institute of engineering & technology, india 5 702 10.64 22.88 951–1,000 1 1 0 2 4 nirma university, institute of technology, india 5 440 10.64 14.34 n/a 4 0 0 1 5 cleveland clinic abu dhabi, uae 5 121 10.64 3.94 n/a 1 0 2 0 6 the university of texas at san antonio, united states 4 843 8.51 27.48 1,001–1,200 1 0 1 2 7 vellore institute of technology, india 4 50 8.51 1.63 851–900 5 0 0 0 8 universiti putra malaysia, malaysia 4 278 8.51 9.06 158 0 1 0 2 9 federation university australia, australia 3 247 6.38 8.05 791–800 0 1 1 1 10 charles darwin university, australia 3 77 6.38 2.51 601–610 2 0 1 0 the data were retrieved in february 2024 based on scopus: qs: quacquarelli symonds world university rankings; tc: total citations; tc/total citations (%): the percentage of total citations each author has received relative to the total citations; tp: total publications; tp/total studies (%): the percentage of total publications by each author relative to the total publications. the numerical values >5, >20, >50, >100 denote the number of citations each study has received. table 4. the most productive authors of the study of blockchain challenges in healthcare. rank author institution tp tc tp/total studies (%) tc/total citations (%) h >5 >20 >50 >100 1 jayaraman, r. khalifa university, uae 6 254 14.63 8.7 43 2 0 2 1 2 salah, k. khalifa university, uae 6 254 14.63 8.7 66 2 0 2 1 3 ellahham, s. cleveland clinic, abu dhabi, uae 5 121 12.20 4.2 32 2 0 2 0 4 kumar, n. thapar institute of engineering and technology, india 4 702 9.76 24.2 119 1 1 0 2 5 tanwar, s. nirma university, india 4 436 9.76 15.0 72 2 0 0 1 6 yaqoob, i. charles sturt university, australia 4 193 9.76 6.7 49 1 0 1 1 7 bhushan, b. the ohio state university, united states 3 40 7.32 1.4 144 3 0 0 0 8 choo, kkr. university of texas at san antonio, united states 3 806 7.32 27.8 94 0 0 1 2 9 kumar, a. university of petroleum and energy studies, india 3 39 7.32 1.3 45 3 0 0 0 10 mantas, g. university of greenwich, uk 3 55 7.32 1.9 23 2 1 0 0 data were retrieved in february 2024 based on scopus: h: h-index; tc: total citations; tc/total citations (%): the percentage of total citations each author has received relative to the total citations; tp: total publications; tp/total studies (%): the percentage of total publications by each author relative to the total publications. the numerical values >150, >100, >50, >20, >10, >5, and >1 denote the number of citations each study has received. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.335 9 (page number not for citation purpose) blockchain modeling and bibliometric analysis there are several metrics for comparing the listed countries, including the total number of papers, the total number of citations, their publication contribution ratio relative to total studies, and their citation contribution ratio relative to total citations. furthermore, it shows how highly cited research is distributed within each country according to the number of publications with citation counts exceeding the specified thresholds (>5, >20, >50, and >100). according to the data, india is the leading contributor in both the number of publications and citations, indicating significant research impact and output. despite fewer total publication numbers, the united states received the most citations on its publications compared to india, suggesting a higher degree of impact. following the united states and india, china and pakistan hold the third and fourth positions, respectively, in terms of the number of citations for publications in the specified field. as presented in table 6, the most influential studies received more than 50 and 100 citations and were conducted by researchers from the united states and india. in particular, the united states stands out by contributing 13 articles that received more than 50 or 100 citations, with 8 of the 13 articles receiving more than 50 citations and five articles receiving more than 100 citations. comparatively, five studies published by indian researchers received more than 50 and 100 citations, including two with more than 50 citations and three with more than 100. clearly, the united states leads the way when it comes to influential studies, followed by india. on the other hand, figure 5 shows the collaborations made between researchers from different countries for the studies in the addressed field. regarding the information given in table 6, the majority of publications in the focused area are from india and the united states, followed by pakistan and the united kingdom. accordingly, the more studies published by each country, the larger the nodes representing them. moreover, the edges indicate collaborative research efforts between countries, and the thicker the edges, the more intense the collaboration. furthermore, the countries within the same clusters are differentiated by varying colors, indicating a higher frequency of regional or thematic collaborations among them. table 6. countries with the most publications on blockchain challenges in healthcare. rank name tp tc tp/total studies (%) tc/total citations (%) >5 >20 >50 >100 1 india 87 1,387 39.19 19.63 24 3 2 3 2 united states 28 1,839 12.61 26.03 7 3 8 5 3 pakistan 18 651 8.11 9.22 7 3 1 2 4 united kingdom 16 454 7.21 6.43 2 4 2 1 5 malaysia 15 421 6.76 5.96 3 2 1 2 6 saudi arabia 11 520 4.95 7.36 5 1 0 2 7 south korea 10 178 4.50 2.52 4 2 1 0 8 australia 9 382 4.05 5.41 4 1 2 1 9 china 9 931 4.05 13.18 2 4 1 2 10 united arab emirates 9 301 4.05 4.26 1 2 2 1 the data were retrieved in february 2024 based on scopus; tc: total citations; tc/total citations (%): the percentage of total citations each author has received relative to the total citations; tp: total publications; tp/total studies (%): the percentage of total publications by each author relative to the total publications; the numerical values >5, >20, >50, >100 denote the number of citations each study has received. fig. 5. collaboration among researchers from different countries. see figure 1 for greater context. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.33510 (page number not for citation purpose) m. mehraeen and l. mahmoudi studies with the most citations table 7 lists a selection of scholarly publications with the highest number of citations investigating the challenges of blockchain technology in healthcare. observing this table, the most important, influential, and popular contributions to healthcare challenges with blockchain can be identified. the total number of citations received by each paper was the criterion for ranking the publications, which were then listed in descending order. as illustrated, the article by mcghin and colleagues19 in 2019 by academic press is the most cited and contributes almost 18% to the total. in addition to discussing the applications and benefits of blockchain in healthcare, this article reviews the key challenges that remain in its deployment. as a result, five main challenges were identified and reported: scalability, mining incentives, blockchain-specific attacks, and key management/key leakage. the other most cited study was conducted by monrat and colleagues20 and published by the institute of electrical and electronics engineers inc. the authors conducted a comparative study to investigate the blockchain challenges that arise when implementing it in healthcare. they report five challenges, including scalability, privacy, interoperability, energy consumption, and regulatory issues. notably, both of the most cited articles are review types, as presented in table 7. furthermore, the table reveals that half of the top 10 most cited studies (5 out of 10) are also review articles. most productive journals table 8 illustrates an overview of the performance metrics for 10 top journals that are posing the greatest productivity rate in the studied field. the indicators of the total number of published papers, total citations, h-index, impact factor, and 5-year impact factor are provided to indicate the influence and visibility a journal has in the academic world. regarding the number of publications considered in the criteria for ranking the journals in table 8, the journal of network and computer applications and sensors, both with noteworthy impact factors and h-indices, has the highest number of notable publications. similarly, the journals sensors and lecture notes in networks and systems table 7. studies with the most received citations. rank title authors publisher year document type* tc (n) tc/total citations (%) 1 blockchain in healthcare applications: research challenges and opportunities mcghin, t, et al. academic press 2019 review 517 17.99 2 a survey of blockchain from the perspectives of applications, challenges, and opportunities monrat, aa, et al. institute of electrical and electronics engineers inc. 2019 review 516 17.98 3 blockchain for 5g-enabled iot for industrial automation: a systematic review, solutions, and challenges mistry i, et al. academic press 2020 article 416 14.50 4 applications of blockchain technology in medicine and healthcare: challenges and future perspectives siyal aa, et al. mdpi ag 2019 article 279 9.73 5 blockchain solutions for big data challenges: a literature review karafiloski e. & mishev a. institute of electrical and electronics engineers inc. 2017 conference paper 247 8.61 6 applications of blockchain in ensuring the security and privacy of electronic health record systems: a survey shi s, et al. elsevier ltd. 2020 review 233 8.12 7 a survey on the adoption of blockchain in iot: challenges and solutions uddin, ma. et al. zhejiang university 2021 review 171 5.95 8 ‘fit-for-purpose?’—challenges and opportunities for applications of blockchain technology in the future of healthcare mackey tk, et al. biomed central ltd. 2019 article 170 5.93 9 geospatial blockchain: promises, challenges, and scenarios in health and healthcare kamel boulos mn, et al. biomed central ltd. 2018 editorial 167 5.82 10 application of blockchain and internet of things in healthcare and medical sector: applications, challenges, and future perspectives ratta p. et al. hindawi limited 2021 review 159 5.54 the data were retrieved in february 2024 based on scopus; tc: total citation; tc/total citations (%): the percentage of total citations each author has received relative to the total citations. *document type as categorized by scopus. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.335 11 (page number not for citation purpose) blockchain modeling and bibliometric analysis have published the same number of articles as the journal of network and computer applications. however, they differ significantly in terms of citations and impact factors, indicating that publications in the journal of network and computer applications have a greater influence compared to the other two journals in the specified field. following the journal of network and computer applications in this field, the eee internet of things (iots) received the most citations for its total publications in this field, as shown in table 8. to assess the journals that publish the highest quality research, we provide the h-indices for each journal. among the journals recognized for their high research quality are sensors, ieee access, and the ieee iots journal, with h-indices of 219, 204, and 149, each demonstrating exceptional scholarly impact. moreover, the impact factor and 5-year impact factor are annual metrics measured by thomson reuters journal citation reports. specifically, the impact factor is calculated regarding the total number of citations received in a specified year for the last 2-year published articles, divided by the total number of those articles. the calculation method for the 5-year impact factor is similar. still, it accounts for citations received over the past 5 years and is divided by the number of articles published during that time. these indicators provide researchers with insights into the influence and citation impact of each journal within its field. with an impact factor and 5-year journal impact factor of 11.61 and 12.64, respectively, the journal of ieee internet of things stands out among the other journals. keyword occurrence and co-occurrence analyses using the vosviewer version 1.6.19 software, this section illustrates the results of the keyword occurrence and co-occurrence analysis. to achieve this, it was decided to consider index and author keywords separately for this analysis. these two types of keywords differ in their sources of origin: author keywords are provided by the author(s), whereas index keywords are generated by indexing services such as scopus.21 the co-occurrence analysis revealed a smaller number of author keywords, with 473 compared to 990 index keywords determined by scopus. table 9 presents two lists of the top 20 keywords, provided separately for each keyword type, showcasing the most frequently selected terms in the collected publications. as shown at the top of the author keyword list, “security” and “privacy” are the top priorities, with 43 and 27 mentions, respectively, after blockchain and healthcare, which are the primary terms in this area. in addition, these keywords with substantial link strengths highlight their importance and priorities. however, the order of the most frequently repeated keywords in the index list differs due to the varying terms employed by scopus for broader concepts. specifically, the concept of “security” is reflected through different terms such as “network security” and “security challenges,” which collectively appear 52 times. consequently, “security” could also be ranked at the top, followed by the main terms. in addition, the combined total of occurrences for “data privacy” and “privacy” that convey the same concept reaches 29, table 8. the most productive journals on blockchain intelligence in healthcare. rank name tp tc tp/total studies (%) tc/total citations (%) h if 5y-if >5 >20 >50 >100 1 journal of network and computer applications 6 754 14.63 62.4 129 8.7 7.3 1 1 3 1 2 sensors 6 83 14.63 6.9 219 3.9 4.1 1 2 0 0 3 lecture notes in networks and systems 6 7 14.63 0.6 27 0.54 n/a 0 0 0 0 4 ieee access 4 65 9.76 5.4 204 4.82 4.676 1 1 1 0 5 ieee internet of things journal 4 139 9.76 11.5 149 11.61 12.64 2 1 1 0 6 advances in intelligent systems and computing 4 27 9.76 2.2 58 0.21 0.63 2 0 0 0 7 lecture notes in business information processing 3 74 7.32 6.1 56 1.05 0.87 1 0 1 0 8 intelligent systems reference library 3 25 7.32 2.1 35 0.85 0.66 3 0 0 0 9 eai springer innovations in communication and computing 3 3 7.32 0.2 19 0.78 0.89 0 0 0 0 10 ieee journal of biomedical and health informatics 2 35 4.88 2.9 146 8.33 7.38 1 1 0 0 the data were retrieved in february 2024 based on scopus: h: h-index; if: impact factor; tc:total citations; tc/total citations (%): the percentage of total citations each author has received relative to the total citations; tp: total publications; tp/total studies (%): the percentage of total publications by each author relative to the total publications. the numerical values >5, >20, >50, >100 denote the number of citations each study has received. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.33512 (page number not for citation purpose) m. mehraeen and l. mahmoudi ranking after “security.” to visualize keyword occurrences in networks, a threshold of four occurrences of the keywords was determined for both types of keywords. see figures 6 and 7. as shown in the presented networks, a node represents each keyword, and the size of the node is correlated with the number of times the keyword is repeated across the studied publications. moreover, clusters of keywords that frequently occur together are displayed in different colors. this resulted in the development of five clusters for index keywords and seven clusters for author keywords, with each cluster comprising a set of related keywords that represent specific research themes. dominant topics using lda in this section, the results of topic modeling of blockchain challenges in healthcare are reported. the dominant topics in this study are organized into six clusters, showing the best coherence scores and the optimal number of topics. the topics that were extracted from the representative keywords using the lda algorithm were then labeled and shown in table 10. cluster 1: data privacy and security this cluster, showing the most significant contribution among all six clusters, primarily discusses the concerns of privacy and security as major challenges that arise when implementing blockchain technology in healthcare systems. while blockchain technology provides data transparency and improves data management due to its decentralized nature, it faces limitations related to certain attacks or security issues. this topic suggests addressing these crucial issues for the successful adoption of blockchain in healthcare.22 cluster 2: integration with iot and smart devices the predominant theme in this cluster revolves around integrating blockchain technology with the iot and smart devices within healthcare systems. by leveraging the connectivity of various smart devices, which enables secure and real-time data sharing, several key challenges arise from this integration. this cluster highlights secure data transfer between iot devices and blockchain networks as a major challenge. it also addresses the issue of resource constraints in many iot devices, which leads to the need for standardized protocols to ensure seamless data exchange (see reference23). cluster 3: interoperability these cluster studies reveal that interoperability and data standards are considered obstacles to blockchain adoption in healthcare (see reference23). organizations with varying priorities and regulations complicate the management of decentralized identity, permissions, and smart contracts. interoperability challenges in healthcare require coordinated efforts among stakeholders to develop systems based on open standards and a unified digital framework. table 9. top 20 authors and index keywords from the studied publications. rank author keyword occurrences total link strength rank index keyword occurrences total link strength 1 blockchain 138 342 1 blockchain 136 1,006 2 healthcare 87 342 2 healthcare 95 738 3 security 43 146 3 internet of things 46 382 4 privacy 27 93 4 distributed ledger 22 218 5 internet of things 20 65 5 digital storage 22 194 6 iot 16 52 6 healthcare industry 22 163 7 smart contract 11 38 7 security 21 206 8 cloud computing 9 35 8 healthcare systems 21 204 9 machine learning 8 32 9 network security 19 196 10 supply chain 8 32 10 smart contracts 12 85 11 bitcoin 8 29 11 electronic health record 16 158 12 artificial intelligence 8 28 12 data privacy 15 159 13 crypto-currency 8 28 13 healthcare sectors 15 125 14 interoperability 8 19 14 privacy 14 160 15 ethereum 7 23 15 information management 14 128 16 consensus 7 20 16 human 13 126 17 distributed ledger 7 19 17 interoperability 12 103 18 big data 6 26 18 healthcare application 12 94 19 crypto-graphy 6 22 19 security challenges 12 89 20 fog computing 6 21 20 access control 11 123 the data were retrieved in february 2024 based on scopus; rank is based on the total number of occurrences. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.335 13 (page number not for citation purpose) blockchain modeling and bibliometric analysis cluster 4: scalability this cluster is mainly dedicated to scalability challenges. as the health sector generates a growing volume of data, transactions, and participants, blockchain networks face various scalability challenges. these challenges include block size limitations and an increasing number of network nodes.5,24 although blockchain architectures have been redesigned and storage optimized, scalability issues persist, preventing widespread adoption. integration of healthcare with emerging technologies like iot and cloud computing can accelerate data generation, leading to intensifying the scalability challenges faced by blockchain. cluster 5: governance the healthcare industry can benefit from blockchain solutions by creating collaboration with patients, providers, payers, device manufacturers, and health systems. blockchain adoption requires new governance and coordination between stakeholders in the healthcare sector. therefore, governance challenges have emerged that are being addressed by the studies in this cluster (see reference25). the challenges that come along with governance include managing large networks of entities, setting policies regarding the sharing of data, resolving disputes, and aligning incentives. cluster 6: cost the cluster also delves into another challenge coming from using blockchain technology in healthcare, which is the initial investment for implementing the technology. although it is believed that blockchain technology will result in reducing costs in the long term through improving efficiency, supply chain operations, and administrative overhead, “high cost involved” is repeatedly cited across the documents as an impediment to the project’s success (see reference26). discussion and conclusion this study has conducted a bibliometric analysis and topic modeling of academic publications, addressing blockchain challenges in the healthcare industry. first, the general structure of the research’s publication and citation was provided to indicate how the research has evolved since 2017. findings demonstrate growing attention to this research area, with a notable increasing number of publications in 2021–2023. fig. 6. co-occurrence network of author keywords with a threshold of 4, displaying 44 out of 473 keywords that met the threshold. see figure 1 for greater context. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.33514 (page number not for citation purpose) m. mehraeen and l. mahmoudi fig. 7. co-occurrence network of index keywords with a threshold of 4, displaying 89 out of 990 keywords that met the threshold. see figure 1 for greater context. table 10. the dominant topics on blockchain challenges in healthcare. topic no. topic name keywords* topic size (%) 1 data privacy and security healthcare, blockchain, security, technology, data, system, health, challenge, solution, patient, manage, privacy, service, use, application 38.78 2 integration with iot and smart devices healthcare, blockchain, system, data, iot, technology, smart, challenge, network, issue, service, device, medical, information, integration 28.06 3 interoperability blockchain, technology, healthcare, application, industry, challenge, system, health, potential, record, data, domain, process, develop, digit 14.80 4 scalability blockchain, healthcare, big data, technology, challenge, application, industry, iot, platform, service, develop, evaluation, data, model, patient 7.65 5 governance blockchain, data, healthcare, security, challenge, manage, patient, governance, safety, technology, decentralized, convergence, process, adopt, design 5.61 6 cost blockchain, healthcare, technology, data, cost, challenge, security, efficiency, manage, issue, medical, system, server, feature, smart contract 5.10 *the words in each list are ordered based on their weights. higher weighting indicates greater importance or relevance to the topic. the topics were selected based on these weighted terms determining their significance within each topic. iot: internet of things. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.335 15 (page number not for citation purpose) blockchain modeling and bibliometric analysis the publications followed a sinusoidal pattern with a peak, experiencing accelerating citation rates and subsequent decline. afterward, the most productive authors, universities, countries, and journals were identified based on bibliometric indicators and presented. in addition, the studies receiving the most citations for their influential contributions were reported. table 11 provides a summary of the bibliometric analysis based on total publications and total citations. also, mcghin and colleagues19 and monrat and colleagues20 are recognized as highly cited studies, reflecting their significance in this field. moreover, both author and index keywords were utilized for keyword occurrence and co-occurrence analyses, revealing that security, data privacy, and interoperability are among the hottest themes in the research. topic modeling exposes six predominant challenge themes persistent in the literature: data privacy/security, integration with iot and smart devices, interoperability, scalability, governance, and costs. the findings align closely with previous studies, such as singh and colleagues,7 which reported scalability, privacy, governance, standards, ownership, and costs as the main challenges. while the previous review studies, such as by ratta and colleagues,11 examined the challenges that  arise when integrating blockchain networks with  iot devices, this study highlights it as a more prominent challenge area, reflecting the growing convergence of blockchain with iot in healthcare applications. the findings of this research will assist researchers in pinpointing the open issues that require additional investigation as the industry strives to capitalize on the promise of blockchain technology. this study has several limitations that need to be addressed in future work. first, this study focused on the publications indexed in scopus, missing relevant works from the other databases. second, although lda topic modeling gives valuable insights from the latent structure, more advanced topic modeling techniques can be employed to gain a deeper understanding of the literature. funding this research received no external funding. conflicts of interest no conflicts of interest. contributors dr. mehraeen contributed significantly to the study’s development through conceptualization and supervision. additionally, he played a key role in reviewing and editing the manuscript to enhance the quality and clarity of the final work. ms. mahmoudi contributed to the design and execution of the study. she played a major role in data collection and analysis, ensuring the accuracy and relevance of the findings. she also took responsibility for writing the manuscript, interpreting the results, and structuring the paper. data availability statement (das), data sharing, reproducibility, and data repositories. the data supporting the findings of this study were obtained from the scopus database. application of ai-generated text or related technology chatgpt was used to improve the manuscript grammatically whenever needed. table 11. top authors, universities, countries, and journals focusing on blockchain challenges in healthcare. total publications total citations authors • jayaraman, r. • choo, kkr. • salah, k. • kumar, n. • ellahham, s. • tanwar, s. universities • university of petroleum and energy studies • the university of texas at san antonio • khalifa university of science and technology • thapar institute of engineering & technology countries • india • united states • united states • india • pakistan • china journals • journal of network and computer applications • journal of network and computer applications • sensors • ieee internet of things journal • lecture notes in networks and systems • sensors the data were retrieved in february 2024 based on scopus. https://doi.org/10.30953/bhty.v7.335 citation: blockchain in healthcare today 2024, 7: 335 https://doi.org/10.30953/bhty.v7.33516 (page number not for citation purpose) m. mehraeen and l. mahmoudi references 1. agrawal k, aggarwal m, tanwar s, sharma g, bokoro pn, sharma r. an extensive blockchain based applications survey: tools, frameworks, opportunities, challenges and solutions. ieee access. 2022;10:116858–906. https://doi.org/10.1109/ access.2022.3219160 2. attaran m. blockchain technology in healthcare: challenges and opportunities. int j healthcare 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driving platforms and decentralized technology in healthcare and life sciences keynote address: existential times tory cenaj, ba founder and publisher, partners in digital health, blockchain in healthcare today (bhty) platform approaches journal doi: https://doi.org/10.30953/bhty.v8.458 presented: sept 25, 2025 | the foundry | cambridge, massachusetts, usa in the past i’ve given you concepts to ponder, such as humanizing technology and a north american free health agreement (nafta). i’ve asked you to explore the significance of universal basic income and a one-party payer system. in our lives, we’ve all witnessed how thematic exploration becomes a reality—and part of history. no doubt, we will witness that today. the sector brings comfort and cure to many through a business we call healthcare. today, ask yourselves, “how can you drive social impact, tech humanism, and user empowerment in your business models?” the shift in perspectives and tangible enlightenment has finally touched our sector. the dawn of a new health era has arrived, but we must keep trust at the core of policy and product development and continue to educate consumers to be better purveyors of their health data— including their financial health. today, i want to address the existential times we find ourselves in. in simple terms, “existential” means “related to human existence,” or it relates to the experience of being alive. it often refers to profound questions about the responsibilities of being human. in the era of artificial intelligence, if you have your ear to the ground, you know that fear abounds across social strata and cultural divides. the one thing we all have in common is birth and death, and along that continuum, we find health, and this industry has a responsibility to all its participants. we have an opportunity in healthcare to allay at least some existential questions and fears pondered by clients, customers, and partners. as healthcare and life science representatives, we must stand united. we do so because we first and always ask, “what is best for the patient?” how can we work smarter, faster, and more securely to save a life? this is what unites us. this is why citizens trust us, and this is why we must stand fast and deliver. today, we’ll explore the many ways we can keep that promise of life, safety, security, and financial wellness. technology has taken leaps, presenting existential questions for patients and health providers alike. understanding our role is paramount to our resilience—economic and civic. we are the purveyors of better health, better outcomes and builders of a new sustainable business model where patients not only thrive but can also be empowered to become an equal player in the business value chain. so today, think about the existential implications of our work, the changes we accelerate, the benefits they bring to world citizens, and the human dignity we instill and perpetuate. with that said, enjoy your day. give the best of yourselves, and let’s transform this industry. thank you. copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, and the use is non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. the author of this article owns the copyright. blockchain in healthcare today issn 2573-8240 https://doi.org/10.30953/bhty.v8.458 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research modeling drivers of blockchain-based ai adoption to improve financial transparency in health insurance organizations sepideh mohammadi tong andri, phd, public administration1  and sahar mohammadi tong andri, msc, information technology management2  ¹department of public administration, faculty of humanities, islamic azad university, shoushtar branch, shoushtar, iran; 2department of information technology management – intelligent business, faculty of humanities, islamic azad university, najafabad branch, najafabad, iran corresponding author: sepideh mohammadi tong andri, email: sepidehmohammaditongandri@gmail.com doi: https://doi.org/10.30953/bhty.v8.427 keywords: artificial intelligence, blockchain, financial transparency, health insurance, organizational readiness abstract the authors explored the primary organizational and environmental factors that influence the adoption of blockchain-integrated artificial intelligence systems aimed at enhancing financial transparency within health insurance institutions. building on established models of technology acceptance and organizational change, a conceptual framework was developed to examine the interaction of technological readiness, management support, regulatory compliance, and workforce capability. data collected from 272 professionals working in various health insurance entities were analyzed using structural equation modeling to assess direct and indirect pathways. the findings underscore that internal drivers—particularly employee training, executive leadership commitment, and digital infrastructure—are far more significant in shaping adoption outcomes than external forces like regulatory mandates or market competition. moreover, financial transparency emerges as a critical outcome and a mediating factor that reinforces trust in technology adoption. this article presents practical insights for policymakers and healthcare administrators to promote ethical, efficient, and transparent digital transformation in the insurance sector. plain language summary this study examines the factors influencing the adoption of blockchain-artificial intelligence integrated technologies to enhance financial transparency in health insurance organizations. using structural equation modeling and necessary condition analysis, findings reveal that technological readiness, employee training investment, and senior management support are key adoption drivers, while external factors like competitive pressure show minimal impact. the research provides practical insights for policymakers and administrators to prioritize internal organizational capabilities when implementing digital transformation initiatives in the insurance sector. submitted: april 28, 2026; accepted: august 5, 2025; published: august 31, 2025 in recent years, the health insurance industry has faced escalating challenges in financial transparency, risk management, and public trust. financial fraud, inefficiencies in claims processing, and non-compliance with data security standards are among the critical issues driving insurance organizations toward adopting innovative technologies such as artificial intelligence (ai) and blockchain.1 the integration of these technologies, particularly in modeling the factors influencing their adoption, holds the potential not only to enhance financial transparency but also enable structural transformation in insurance processes.2 however, despite their evident potential, the blockchain in healthcare today issn 2573-8240 https://orcid.org/0009-0001-5295-2623 https://orcid.org/0009-0009-6886-9160 mailto:sepidehmohammaditongandri@gmail.com https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.4272 (page number not for citation purpose) sepideh mohammadi tong andri and sahar mohammadi tong andri implementation of these systems in health insurance organizations faces complex barriers, including cultural resistance, technical challenges, and the absence of unified regulatory frameworks.3 this paradox between the urgent need for innovation and operational obstacles constitutes a core focus of the present study. financial transparency in health insurance organizations, as a critical imperative, necessitates access to precise, traceable, and tamper-proof data. artificial intelligence, with its advanced analytics and machine learning algorithms, can detect fraudulent patterns, while blockchain ensures data integrity and security through decentralized, distributed ledgers.4 for instance, blockchain-based smart contract systems automate claim payments based on predefined conditions, reducing reliance on human intermediaries.5 nevertheless, integrating these technologies into a cohesive ecosystem remains experimental, with significant knowledge gaps persisting. the first major research gap lies in the dynamic interplay between organizational and technical factors  influencing technology adoption. for example, how do organizational culture and digital readiness levels impact the success of implementation? studies indicate that projects such as the blockchain insurance industry initiative (b3i) in reinsurance failed due to stakeholder coordination complexities and mismatches between technological capabilities and operational needs.6,7 the second challenge revolves around the  tension between blockchain’s decentralization and centralized regulatory oversight  in health insurance. while blockchain is inherently decentralized, government regulations and privacy mandates (e.g., general data protection regulation [gdpr]) may necessitate hybrid, semi-centralized architectures with regulatory monitoring features.8 the third issue concerns cybersecurity risks. despite blockchain’s inherent security, integrating it with ai systems that process sensitive medical and financial data may introduce novel vulnerabilities.9 healthcare insurance fraud leads to billions of dollars in annual losses on the global economy. estimates suggest that fraudulent claims account for approximately 10% of total u.s. healthcare expenditures.10 hybrid ai-blockchain models could significantly reduce this figure by minimizing human errors and identifying anomalous patterns.11 enhanced financial transparency strengthens policyholder trust and improves access to insurance services for underserved populations. for instance, the lemonade project in africa leverages blockchain-based smart contracts to offer affordable crop insurance for smallholder farmers.12 the synergy of ai and blockchain enables the development of predictive risk assessment systems. real-time analysis of internet of things (iot)-generated data in hospitals, for example, could forecast disease outbreaks and optimize insurance resource allocation.9 however, the growing use of sensitive patient data intensifies ethical and legal concerns, particularly regarding compliance with privacy regulations such as the health insurance portability and accountability act of 1996 (hipaa) in the u.s. and gdpr in europe. blockchain systems, with advanced encryption and permissioned access, offer solutions to mitigate these challenges.13,18 the adoption of ai  and  blockchain technologies  in health insurance organizations is influenced by a dynamic interplay of technical, organizational, behavioral, and regulatory variables, with causal relationships between these factors remaining underexplored in existing literature. first, the relationship between organizational technological readiness and digital culture adoption operates as a self-reinforcing cycle. empirical studies indicate that organizations with advanced data-processing infrastructure are more likely to cultivate transparency-driven and innovation-oriented cultures, which, in turn, facilitate the adoption of complex technologies like blockchain.14,15 conversely, organizations lacking such readiness face employee resistance to automation and distrust in decentralized systems.16 second, the  tension between blockchain decentralization and regulatory compliance requirements  creates a nonlinear dynamic. while blockchain’s decentralized nature enhances transparency and reduces fraud, it conflicts with regulations such as the gdpr, which mandates centralized accountability.8 this tension has spurred the emergence of hybrid semi-centralized models, where regulatory nodes are embedded into blockchain networks to ensure legal adherence.6 the efficacy of such models depends on synchronized collaboration between technical stakeholders (developers) and legal entities (regulatory bodies). third, the relationship between  technical complexity  and  perceived usefulness—a core construct of technology acceptance model (tam)—requires recontextualization. although ai-blockchain hybrid systems are inherently complex, empirical evidence suggests that user training and intuitive interface design (e.g., visual dashboards) can enhance perceived usefulness, even when technical complexity remains high.17,18 for example, in the nexus mutual project, the integration of user-friendly interfaces to display blockchain-based insurance data improved end-user adoption rates by 40%.19 fourth,  implementation costs  and  economic returns exhibit a reciprocal relationship contingent on technological maturity. initial development costs for smart contracts and legacy system integration are substantial, but as blockchain networks achieve scalability (e.g., ethereum 2.0) and technological maturity increases roi, which grows exponentially.2,9 this relationship is particularly evident in health insurance organizations handling high transaction volumes, such as frequent claims processing. https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.427 3 (page number not for citation purpose) blockchain ai adoption to improve financial transparency fifth,  cybersecurity  and  data privacy  are interdependent variables critical to ai-blockchain convergence. while blockchain ensures data integrity via advanced cryptographic protocols (e.g., sha-256, ai systems analyzing sensitive medical records risk compromising user privacy.4 emerging solutions such as homomorphic encryption, which enables computation on encrypted data, serve as a bridge between these variables.11 ethical implications further mediate technology adoption dynamics. ai-driven health risk prediction models may perpetuate  insurance bias  against marginalized demographics unless algorithms are trained on balanced datasets and governed by ethical frameworks.13 initiatives like fairledger, which integrate algorithmic transparency mechanisms into blockchain architectures, aim to mitigate such risks.7 despite their potential benefits, modeling the factors driving ai and blockchain adoption in health insurance demands a nuanced understanding of the complex interactions among technology, organizations, and society. this study addresses existing literature gaps—such as the impact of human factors on technology adoption, the development of international standards for system integration, and ethical data governance—to provide a comprehensive framework for industry stakeholders. the adoption of hybrid ai-blockchain technologies in health insurance organizations transcends technical advantages like fraud reduction or transparency; it necessitates redefining organizational structures and revising stakeholder behavioral patterns. research underscores that  digital organizational culture  and  technological readiness  are pivotal prerequisites for successful transformation.14 for example, in public-sector institutions, executive leadership support and skilled workforce availability are critical drivers for adoption, whereas private-sector entities prioritize cost efficiency and productivity gains.15 this divergence highlights the need for  context-specific adoption models tailored to organizational nature and strategic objectives. furthermore, failed initiatives like  b3i, attributed to stakeholder misalignment and technology-reality mismatches, emphasize the importance of holistic technology ecosystem management.6 these challenges, coupled with cybersecurity risks arising from merging sensitive medical data with decentralized systems, underscore the urgency of developing  adaptive cybersecurity frameworks  and ethical standards. theoretical framework and conceptual development technology acceptance and the technology acceptance model the adoption of emerging technologies in organizations is influenced by psychological, organizational, and technical factors. the (tam) emphasizes that perceived usefulness and perceived ease of use are two key determinants of technology acceptance.17 in the healthcare domain, studies reveal that trust in ai systems plays a critical role in acceptance, facilitated by reduced perceived risk and enhanced transparency.1 for instance, in blockchain-based systems, transaction transparency and data immutability enhance user trust.4 digital culture and organizational readiness digital culture serves as a prerequisite for the adoption of complex technologies. organizations with advanced data-processing infrastructures and a well-trained workforce are more capable of integrating emerging technologies.14 conversely, organizations lacking such readiness often face employee resistance and project failure, as seen in cases like b3i.6 studies in the public sector reveal that top management support and expert personnel are critical enablers, whereas in the private sector, the focus is primarily on cost-efficiency and productivity.15 the paradox of decentralization and regulatory requirements blockchain’s inherent decentralization contradicts the need for central oversight in industries like health insurance. regulations such as the gdpr require accountability from centralized entities, which is at odds with decentralized systems.8 this paradox has led to the emergence of hybrid models, where regulatory nodes are introduced to ensure legal compliance7. cybersecurity and privacy the integration of ai and blockchain creates new security challenges. blockchain ensures data security through advanced encryption (e.g., sha-256), while ai systems may compromise user privacy.9 solutions such as homomorphic encryption enable processing of encrypted data without compromising privacy.11 in recent years, ai and blockchain technology have emerged as two fundamental pillars of digital transformation across various industries, particularly in the insurance sector and public services. with their unique capabilities in process optimization, enhancing transparency, reducing costs, and strengthening data security, these technologies have captured the attention of researchers, policymakers, and organizational leaders alike. however, the successful implementation of such technologies requires a deep understanding of facilitating factors, potential barriers, and environmental and organizational requirements. numerous global studies have examined different dimensions of this topic, each shedding light on the challenges and opportunities associated with the adoption of ai and blockchain in diverse contexts. safari and ansari (2021)14 analyzed the factors affecting ai adoption in public and private organizations in iran. based on a survey of 300 senior managers, they found that managerial support and the presence of expert personnel https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.4274 (page number not for citation purpose) sepideh mohammadi tong andri and sahar mohammadi tong andri were the most critical factors in the public sector, whereas economic cost-benefit and productivity gains were the primary drivers in the private sector. the lack of advanced data infrastructures was identified as the main barrier in public organizations.15 developed an organizational readiness framework for ai adoption, finding that organizations with an experimental culture and decentralized decision-making structures adopted new technologies 40% faster. their study emphasized the importance of investing in employee training to ensure smoother transitions during technology integration. chen and bellavitis (2020)7 analyzed the failure of the b3i project, a blockchain-based insurance platform, and found that misalignment between technical and business stakeholders was the primary cause of its collapse. developers prioritized decentralization, while insurers emphasized regulatory compliance, such as with solvency ii—a comprehensive regulatory framework in the european union. this divergence led to a system design that ultimately failed to meet operational needs. such discrepancies highlight the importance of aligning technological innovation with business objectives to ensure successful implementation. nguyen et al. (2022)9 in their study on the integration of ai and blockchain within 5g networks, warned that sophisticated cyberattacks—such as the 51% attack— could seriously undermine the integrity of insurance systems. to address these security challenges, they proposed the use of homomorphic encryption and permissioned blockchains, particularly in sectors such as health insurance where sensitive patient data is managed. these technological solutions aim to enhance both confidentiality and data integrity in high-risk environments. the european parliament (2016)8 in its gdpr framework, outlined the challenges posed by decentralized systems. one major concern was that the “right to erasure” guaranteed under gdpr directly conflicts with blockchain’s immutability. as a potential solution, the use of sidechains to store sensitive data was proposed, enabling selective deletion while preserving the core immutable characteristics of blockchain technology. in a practical application, huckle et  al. (2016)12 conducted a pilot project in africa using blockchain-based smart contracts for crop insurance. this initiative allowed smallholder farmers to receive automated compensation based on real-time iot weather data. the system yielded tangible benefits, reducing administrative costs by 35% and cutting claim processing time from three months to just 72 hours. these findings underscore the transformative potential of blockchain in improving operational efficiency and service delivery, especially in underserved regions. jiang et  al. (2021)11 analyzed over 100,000 health insurance claims in the u.s. and demonstrated that ai algorithms could detect complex fraud patterns, such as duplicate billing, with an accuracy rate of 92%. despite these advancements, the authors warned that bias in training datasets could lead to algorithmic discrimination. their findings highlight the necessity of using diverse and representative datasets to prevent unfair outcomes in ai-driven decision-making. tapscott and tapscott (2016)2 in their foundational work, explored blockchain’s transformative potential in the insurance industry. they argued that eliminating intermediaries—such as brokers—and achieving full transaction transparency could reduce operational costs by up to 50%. furthermore, they predicted that blockchain-based microinsurance could expand financial access for low-income populations, thus promoting greater inclusivity within the insurance market. zyskind et  al. (2015)4 through the development of a decentralized system of health data management, demonstrated how blockchain could restore data ownership to users. by allowing patients to control access to their medical records using private keys, their model reduced privacy breaches by up to 70%. this innovation inspired follow-up initiatives such as medrec at massachusetts institute of technology, showcasing how decentralized architecture can empower users and enhance data security in healthcare. wood et  al. (2021)5 in their evaluation of the nexus mutual project—a decentralized insurance platform— reported that a user-friendly interface significantly improved user adoption by 40%. however, they also observed that older users were more resistant to adopting blockchain technologies due to unfamiliarity. the study concluded that targeted training and education programs could mitigate this resistance and support broader adoption of emerging technologies. collectively, this body of research offers a comprehensive understanding of the multifaceted challenges and opportunities involved in adopting ai and blockchain technologies within sensitive industries such as insurance. the studies emphasize the necessity of aligning business needs with technical capacities, strengthening data infrastructures, enhancing information security, employing user-centered design, and investing in targeted training. these findings provide practical guidance for policymakers and organizational leaders navigating the path toward successful digital transformation. derived hypotheses technological readiness refers to the organization’s preparedness to implement advanced technologies such as ai and blockchain. organizations equipped with robust digital infrastructures and data systems tend to achieve higher levels of transparency, as they can collect, store, and report financial information with greater accuracy https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.427 5 (page number not for citation purpose) blockchain ai adoption to improve financial transparency and speed.15 advanced platforms like blockchain provide immutable records that reduce the risk of manipulation and ensure financial traceability.2 preliminary conclusion: based on these insights, it can be concluded that: hypothesis 1 (h1a) the level of technological readiness of the organization has a positive and significant effect on improving financial transparency. according to the resource-based view, organizational assets such as cloud infrastructure, data centers, and skilled personnel determine the capability to absorb and integrate innovative technologies.14 organizations with a high level of digital maturity adopt new technologies more quickly and successfully.15 preliminary conclusion: therefore, it can be concluded that... hypothesis 2 (h1b) the level of technological readiness of the organization has a positive and significant effect on the rate of technology adoption. employees trained in new technologies and financial systems are more capable of managing transactions transparently. training enhances procedural knowledge, reduces manual errors, and enables adherence to compliance frameworks.14 trained personnel are better at using blockchain or ai tools that promote auditability and transparency. preliminary conclusion: based on this, we infer that... hypothesis 3 (h2a) investing in employee training has a positive and significant effect on improving financial transparency. according to tam, perceived ease of use and usefulness drive adoption. training boosts both factors by reducing anxiety and enhance understanding of new systems.19 organizations that provide continuous education experience more rapid and successful technology deployment15. preliminary conclusion: therefore, it can be concluded that... hypothesis 4 (h2b) investing in employee training has a positive and significant effect on the rate of technology adoption. cybersecurity safeguards sensitive financial information and reinforces the reliability of digital records. blockchain’s cryptographic features already enhance integrity, but additional layers like homomorphic encryption further minimize tampering risks.14 secure systems increase stakeholder confidence in financial disclosures. preliminary conclusion: t is reasonable to conclude that: hypothesis 5 (h3a) improving cybersecurity has a positive and significant effect on improving financial transparency. fear of cyber threats and data breaches is a significant barrier to adopting emerging technologies.9 organizations that invest in cybersecurity infrastructure are more likely to embrace innovations like ai-blockchain, as they can mitigate associated risks. preliminary conclusion: based on this understanding, it can be concluded that... hypothesis 6 (h3b) improving cybersecurity has a positive and significant effect on the rate of technology adoption. regulations such as gdpr and hipaa demand accurate data governance and increase accountability in financial reporting.8 compliance measures often require robust systems that enhance transparency by clearly defining access and deletion rights. preliminary conclusion: thus, we can infer that... hypothesis 7 (h4a) compliance with privacy regulations has a positive and significant effect on improving financial transparency. although compliance introduces technical complexity, it builds legal and institutional legitimacy that facilitates technology implementation.7 firms with compliance-ready frameworks face fewer barriers in adopting decentralized systems. preliminary conclusion: based on this, it is logical to propose... hypothesis 8 (h4b) compliance with privacy regulations has a positive and significant effect on the rate of technology adoption. leadership influences organizational culture, priorities, and accountability mechanisms. when top management commits to transparency, it reflects in budget allocation, performance metrics, and public disclosures.15 preliminary conclusion: given these facts, it can be concluded that... hypothesis 9 (h5a) senior management support has a positive and significant effect on improving financial transparency. leadership endorsement is critical for overcoming resistance and aligning departments toward digital transformation.14 in ai-blockchain projects, active managerial involvement ensures strategic alignment and resource allocation. preliminary conclusion: accordingly, we propose... hypothesis 10 (h5b) senior management support has a positive and significant effect on the rate of technology adoption. in competitive markets, transparency is often used as a differentiator. companies disclose more financial information to build trust and attract customers, especially https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.4276 (page number not for citation purpose) sepideh mohammadi tong andri and sahar mohammadi tong andri when rivals are perceived as opaque.2 preliminary conclusion: thus, it can be inferred that... hypothesis 11 (h6a) competitive pressure in the market has a positive and significant effect on improving financial transparency. competitive intensity compels firms to innovate continuously. to remain viable, firms adopt emerging technologies that improve efficiency and reduce operational costs.19 preliminary conclusion: hence, we conclude... hypothesis 12 (h6b) competitive pressure in the market has a positive and significant effect on the rate of technology adoption. transparent financial practices build stakeholder trust and reduce uncertainty, making it easier to secure funding and institutional support for new technologies.11 it also helps in regulatory approval and partnership development. preliminary conclusion: based on this, it is concluded that... hypothesis 13 (h7) improving financial transparency has a positive and significant effect on the rate of technology adoption. the hypotheses in this study, grounded in the tam, transaction cost economics, and security frameworks, examine the complex interplay of technical, organizational, and legal factors in the adoption of ai-blockchain technologies. each hypothesis is supported by empirical evidence from realworld projects and prior studies, paving the way for future research in standardization and technology implementation. the literature reveals that the adoption of ai-blockchain in health insurance faces multilayered challenges. this study indicates that the successful adoption of ai-blockchain in health insurance depends not only on technical advancements but also on the alignment of organizational, cultural, and legal factors. future challenges include designing adaptive security frameworks (like homomorphic encryption), establishing ethical data usage standards, and training human capital. failures like the b3i and successes like nexus mutual offer key lessons on the importance of stakeholder collaboration. future research should focus on developing multilevel acceptance models and evaluating the long-term impact of these technologies on insurance equity. the operational definitions of research variables are presented in table 1. based on the conducted reviews and studies, the conceptual research model is illustrated in fig 1. this figure examines the sufficient and necessary drivers for adopting blockchain-based ai to enhance financial transparency. materials and methods given the technical and regulatory sensitivity associated with the adoption of ai-blockchain technologies and financial transparency, data were collected using a structured questionnaire. the constructs in the survey were developed based on validated scales from prior research and measured using multiple items on a likert scale. a random sampling method was employed, and the questionnaire was distributed through a multi-channel approach, including face-toface interactions, email, and social media platforms.20,21 to minimize common method bias and enhance response accuracy, several procedural remedies were implemented. first, the order of both the constructs and the individual items was randomized to reduce pattern responses and item-order effects. second, the survey included attention check items to identify and exclude careless or inattentive responses. third, respondents were assured of complete anonymity and confidentiality to reduce social desirability bias and promote honest answering (nederhof, 1985; podsakoff et al., 2003). these precautions were taken to ensure the validity and reliability of the collected data for subsequent statistical analysis. questionnaire design this study employed a standardized questionnaire consisting of three main sections. the first section provided a definition of emerging technologies, particularly the integration of artificial intelligence and blockchain in the insurance industry, to ensure a consistent understanding among respondents. the definition emphasized the role of these technologies in enhancing financial transparency, reducing fraud, and improving operational efficiency, clearly aligning with the conceptual framework of this research. the second section assessed the main research variables—including six independent variables and two dependent variables—using a five-point likert scale (ranging from “strongly disagree” to “strongly agree”). the independent variables comprised technological readiness, employee training investment, cybersecurity improvement, compliance with privacy regulations, senior management support, and competitive pressure. the dependent variables were financial transparency and technology adoption rate. the items were adapted from prior validated studies such as 14,4,15,5 and were localized to fit the insurance sector. the third section collected demographic data including respondents’ age, gender, education level, and professional experience within the insurance industry. all items were translated and back-translated to ensure linguistic consistency and conceptual accuracy. the face validity of the questionnaire was confirmed through expert review by three academic professionals in the fields of insurance and information technology. additionally, a pilot test was conducted with six insurance professionals to verify the clarity and comprehensibility of the questionnaire. based on their feedback, revisions were made to wording and structure to enhance usability. https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.427 7 (page number not for citation purpose) blockchain ai adoption to improve financial transparency data collection the main study conducted in may 2024. of the 319 responses, 47 were invalid due to incorrect comprehension of circular fashion (n = 21), failure in attention checks to verify cognitive engagement (n = 20), or completion times falling below two standard deviations (n = 6). the valid sample consisted of 272 respondents, exceeding the minimum of 253 observations needed for reliable statistical analysis with eight constructs in a structural equation model (sem). the required sample size was determined using g*power to ensure adequate power and to minimize type ii errors.22,23 additionally, this sample size meets the requirements for necessary condition analysis (nca)24, ensuring robust results. instrument validation and pilot testing to ensure face validity and clarity, a pilot test was conducted with ten professionals and managers from the healthcare insurance sector. participants were selected to represent diverse levels of technological familiarity and managerial responsibility. their feedback led to revisions in terminology, wording, and layout, enhancing the overall usability and clarity of the final instrument. data collection procedure the final questionnaire was distributed online through professional networks, organizational platforms, and digital communication channels. to mitigate social desirability bias, all participants were guaranteed full anonymity and confidentiality. a mixed sampling strategy was used, combining simple random sampling with snowball sampling to achieve a diverse and representative sample. statistical analysis the statistical analysis of this study was conducted in two phases, aligned with addressing the research questions. in the first phase, covariance-based structural equation modeling (cb-sem) was employed to test causal relationships and examine mediating effects of variables. this method, suitable for confirmatory research, requires evaluating comparative model fit indices (cfi) such as root mean square error of approximation (rmsea) and χ²/df.25,26 initially, the measurement model’s validity and reliability were assessed through confirmatory factor analysis (cfa), followed by testing the structural model to evaluate hypotheses. the cb-sem enabled analysis of both direct and indirect effects of variables such as digital culture, technological readiness, and regulatory requirements on the adoption of ai-blockchain technologies in the health insurance industry. it was also used to test the mediating roles of perceived usefulness and data privacy assurance. in the second phase, nca was applied to identify indispensable conditions without which the desired outcomes, such as fraud reduction or improved financial transparency, could not be achieved.27 this method operates on a multiplicative logic and reveals factors table 1. operational definition of research variables. variable operational definition source technological readiness the degree to which an organization possesses and utilizes advanced digital infrastructures (e.g., cloud computing, erp systems, data analytics platforms) to implement complex systems such as ai and blockchain. 14 employee training investment the extent of organizational resources allocated to educating and empowering employees on emerging technologies like ai, blockchain, and data protection. 19 cybersecurity improvement the implementation of technical security measures such as encryption, firewalls, permissioned blockchains, and intrusion detection systems to safeguard data and prevent unauthorized access. 4 compliance with privacy regulation the degree to which the organization adheres to data protection frameworks such as gdpr and hipaa, ensuring data minimization, lawful processing, and access control mechanisms. 7 senior management support the involvement and commitment of top executives in supporting digital transformation initiatives, including resource provision and active participation in decision-making processes. 15 competitive pressure the extent to which external market dynamics and actions of competitors drive an organization to adopt innovative technologies for maintaining a competitive advantage. 5 financial transparency the clarity, accessibility, and accuracy of financial records and transactions, enabling traceability and accountability in organizational processes. 2 technology adoption rate the speed and scope at which emerging technologies like ai and blockchain are integrated into organizational processes, including the number of implementations and extent of usage. 14 ai: artificial intelligence; erp: enterprise resource planning; edpr: general data protection regulation; hipaa: health insurance portability and accountability act. https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.4278 (page number not for citation purpose) sepideh mohammadi tong andri and sahar mohammadi tong andri that, although insufficient alone, are necessary for successful technology adoption. for example, technical infrastructure and managerial support were identified as necessary (but not sufficient) conditions for adoption of new technologies. nca was performed through ceiling line plots and effect size calculations to specify the critical role of specific variables in the adoption process. the combined use of cb-sem and nca allowed this research to reveal both the structural relationships among variables and the essential prerequisites for adopting ai-blockchain technologies in the health insurance sector. results sample description the demographic profile of the 272 respondents (table 2) reveals that 65% of participants were male and 35% female. this distribution reflects the workforce composition in health insurance organizations, where males typically dominate technical and managerial roles.15 most respondents (78%) were aged between 30 and 50 years, representing the primary decision-makers regarding technology adoption within these organizations.14 regarding education, 80% held at least a bachelor’s degree, indicating there is likely to be a high level of knowledge and familiarity with emerging technologies such as ai and blockchain.11 additionally, over 60% of participants were employed in information technology (it) and insurance systems development roles, which are directly related to technology acceptance in organizations. this sample provides a suitable representation for examining the factors influencing the adoption of ai-based blockchain technologies in health insurance, as it includes key individuals involved in decision-making, development, and implementation processes. consequently, it offers valuable insights into the barriers and motivators related to financial transparency. sample description the demographic profile of the 272 respondents (table 3) indicates that 65% of participants were male and 35% female. this distribution reflects the workforce composition in health insurance organizations, where males typically dominate technical and managerial roles (safari & ansari, 2021). most respondents (78%) were aged between 30 and 50 years, representing the primary decision-makers fig. 1. research model: examining sufficient and necessary drivers of blockchain-based ai adoption to improve financial transparency. https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.427 9 (page number not for citation purpose) blockchain ai adoption to improve financial transparency regarding technology adoption within these organizations (alsheibani et al., 2018). regarding education, 80% held at least a bachelor’s degree, indicating a high level of knowledge and familiarity with emerging technologies such as artificial intelligence and blockchain (jiang et al., 2021). additionally, over 60% of participants were employed in it and insurance systems development roles, which are directly related to technology acceptance in organizations. this sample provides a suitable representation for examining the factors influencing the adoption of ai-based blockchain technologies in health insurance, as it includes key individuals involved in decision-making, development, and implementation processes. consequently, it offers valuable insights into the barriers and motivators related to financial transparency. results of the measurement model the cb-sem used the r-based lavaan package.28 first, cfa confirmed convergent and discriminant validity (table 3). both the average variance extracted (ave) and composite reliability (cr) exceeded thresholds (ave > 0.5, cr > 0.7).29 the fornell-larcker criterion was met because the square root of each construct’s ave surpassed its correlations with other constructs, confirming discriminant validity.30 moreover, cross-loadings were smaller than their corresponding primary loadings, reinforcing discriminant validity (appendix table a3). to further ensure reliability, cronbach’s alpha confirmed internal consistency with values greater than 0.834. next, we assessed the measurement model (table 3), showing a good fit. the cfi and tucker-lewis index (tli) both exceeded 0.9, while the rmsea and standardized root mean square residual (srmr) were below 0.08.29,26 furthermore, the variance inflation factors (vif) ranged from 1.393 to 2.275, well below the threshold of 3, indicating that multicollinearity is not an issue. since method variance is considered a primary source of measurement errors, we used harman’s single-factor test to evaluate its potential impact. the results showed that the first component had an eigenvalue of 12.738, accounting for 41 % of the variance—below the threshold of 50%—indicating that method variance is unlikely to pose a concern (harman and harman, 1976; podsakoff et al., 2012; see appendix table a4).31,32 structural equation modeling results – sufficiency lens given that the measurement model demonstrated a good fit, the proposed structural model was tested to examine the research hypotheses. the results indicated a satisfactory model fit with the data (as shown in table 429). the path analysis of direct effects (table 5) revealed significant positive relationships between technology adoption and three key factors: technological readiness (β = 0.122; p = 0.037), employee training investment (β = 0.280; p ≤ 0.001), and senior management support (β = 0.352; p ≤ 0.001), thereby supporting hypotheses h1a, h2b, and h5e. among these, senior management support showed the strongest effect on technology adoption, highlighting the critical role of leadership commitment in facilitating blockchain-based ai adoption to enhance financial transparency. financial transparency itself had a significant positive effect on technology adoption (β = 0.329; p ≤ 0.001), confirming hypothesis h2. conversely, cybersecurity improvement (β = -0.101; p = 0.181), compliance with privacy regulation (β = -0.004; p = 0.955), and competitive pressure (β = 0.027; p = 0.653) did not show significant effects on technology adoption. as such, hypotheses h3c, h4d, and h6f were rejected. these findings suggest that internal capabilities and table 2. demographic profile of respondents. category characteristic n percentage gender male 184 67.7 female 87 32.0 nonbinary 1 0.3 age 20 to 24 79 29.0 25 to 34 160 58.8 >35 33 12.2 education associate’s degree 21 7.7 bachelor’s degree 141 51.9 master’s degree or higher 62 22.7 table 3. confirmatory factor analysis: convergent and discriminant validity. construct cronbach’s α cr ave vif technological readiness 0.868 0.867 0.756 digital culture 0.910 0.913 0.851 2.237 technical complexity 0.928 0.929 0.875 1.723 implementation costs 0.834 0.847 0.809 1.448 regulatory compliance 0.859 0.868 0.829 1.797 adoption rate 0.856 0.863 0.783 1.393 financial transparency 0.960 0.960 0.926 2.275 fraud reduction 0.953 0.953 0.914 1.455 perceived usefulness 0.833 0.885 0.844 1.784 data privacy assurance 0.914 0.923 0.930 1.913 cr: composite reliability (threshold > 0.7 indicates adequate internal consistency), ave: average variance extracted (threshold > 0.5 confirms convergent validity), vif: variance inflation factor (values < 5 suggest no multicollinearity issues). https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.42710 (page number not for citation purpose) sepideh mohammadi tong andri and sahar mohammadi tong andri managerial commitment have a stronger influence on adoption than external factors such as regulation or market competition. regarding the indirect pathways through financial transparency, employee training investment (β = 0.092; p = 0.003) and compliance with privacy regulation (β = 0.089; p = 0.002) showed significant mediation effects, supporting hypotheses h3b and h3d. meanwhile, other indirect paths—technological readiness, cybersecurity improvement, senior management support, and competitive pressure—were not significant, leading to the rejection of hypotheses h3a, h3c, h3e, and h3f. technological readiness, employee training investment, and senior management support were found to significantly influence financial transparency through the adoption of blockchain-based ai technologies, thereby supporting hypotheses h1a, h1b, and h1e. the total effect analysis (table 6) revealed that senior management support had the strongest effect (β = 0.400, p  < .001), followed by employee training investment (β = 0.372, p  <  .001), and technological readiness (β = 0.153, p = .015). these findings suggest that leadership engagement and continuous employee development are critical enablers for leveraging emerging technologies to enhance financial transparency. in contrast, the effects of cybersecurity improvement (β = 0.085, p = .195), compliance with privacy regulations (β = 0.024, p = .654), and competitive pressure (β = -0.103, p = .202) on financial transparency through technology adoption were statistically insignificant, indicating that regulatory alignment and external pressures alone may not suffice to drive meaningful change unless accompanied by internal organizational readiness. to eliminate potential confounding variables, gender, education level, and income were statistically controlled. the findings showed that higher income levels were negatively associated with the adoption rate of blockchain-based ai systems. this aligns with previous evidence suggesting that wealthier individuals and institutions may prioritize operational efficiency or profit maximization over transparency and ethical considerations. to ensure the reliability of our sem, sensitivity analyses and item-based modifications were conducted. the consistency of results across these checks confirms the validity and stability of our model. results of the necessary condition analysis—necessity lens to deepen the understanding of critical drivers for blockchain-based ai adoption aimed at improving financial transparency, a necessary condition analysis (nca) was conducted using r software and the nca package [32]. this method identifies indispensable conditions—factors without which the desired outcome (i.e., technology adoption) cannot occur, irrespective of other variables [27]. the analysis evaluated whether ft serves as a necessary mediator between six antecedents—technological readiness (tr), employee training investment (eti), cybersecurity improvement (ci), compliance with privacy regulations (cpr), senior management support (sms), and competitive pressure (cp)—and blockchain-based ai adoption (technology adoption rate, tar). scatterplots were generated for each variable-outcome relationship. the absence of data points in the upper-left quadrant of these plots indicates the presence of necessary conditions. the ceiling envelopment-free disposal hull (ce-fdh) method was employed to construct ceiling lines, which accurately identified condition thresholds for 7-point likert data.27 as illustrated in appendix figure a5, distinct empty zones were observed for technological readiness, employee training investment, and senior table 4. goodness-of-fit indices for the measurement model. measurement model χ2 p χ2 /df cfi tli rmsea srmr 749.686 < 0.001 1.847 0.952 0.945 0.056 0.045 χ²: chi-square statistic (measures model discrepancy; lower values indicate better fit), *p*: significance level (values > 0.05 suggest acceptable model fit), χ²/df: normed chi-square (acceptable range: 1–3), cfi: comparative fit index (threshold > 0.90 indicates good fit), tli: tucker-lewis index (threshold > 0.90 for adequate fit), rmsea: root mean square error of approximation (values < 0.08 acceptable), srmr: standardized root mean square residual (threshold < 0.08 preferred). table 5. goodness-of-fit indices for the structural model. structural model χ2 p χ2 /df cfi tli rmsea srmr 917.909 < 0.001 1.861 0.941 0.934 0.056 0.056 χ²: chi-square statistic (measures model discrepancy; lower values indicate better fit), *p*: significance level (values > 0.05 suggest acceptable model fit), χ²/df: normed chi-square (acceptable range: 1–3), cfi: comparative fit index (threshold > 0.90 indicates good fit), tli: tucker-lewis index (threshold > 0.90 for adequate fit), rmsea: root mean square error of approximation (values < 0.08 acceptable), srmr: standardized root mean square residual (threshold < 0.08 preferred). https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.427 11 (page number not for citation purpose) blockchain ai adoption to improve financial transparency management support in relation to technology adoption. these zones signify that low values of these predictors correspond to the absence of high adoption levels, confirming their role as necessary conditions. to quantify these patterns, effect size (*d*), scope, ceiling zone, and condition inefficiency were calculated. robustness was enhanced via 10,000 permutation tests, with significance thresholds set at *d* ≥ 0.1 to minimize false positives.27,33 as summarized in table 7, the results revealed that employee training investment (eti) (*d* = 0.241, *p*  ≤  0.001) and senior management support (sms) (*d* = 0.400, *p* ≤ 0.001) emerged as critical necessary conditions for achieving high levels of financial transparency (ft) and subsequent technology adoption (tar). technological readiness (tr) also demonstrated a smaller yet statistically significant effect (*d* = 0.153, *p* = 0.015). these findings support hypotheses h3b, h3d, and h3a. conversely, cybersecurity improvement (ci) (*d* = 0.000, *p* = 1.000), compliance with privacy regulations (cpr) (*d* = 0.179, *p* = 0.001), and competitive pressure (cp) (*d* = 0.235, *p* = 0.000) lacked significant necessity and were rejected as essential conditions. financial transparency (ft) alone showed no direct significant impact on technology adoption (*d* = 0.069, *p* = 0.016), leading to the rejection of h6. this underscores that financial transparency must be coupled with necessary enablers (e.g., training and regulatory compliance) to drive adoption. bottleneck thresholds (table 8) specify minimum scores required for successful adoption. for instance, organizations must achieve at least 5.0 on the likert scale for senior management support and 4.5 for employee training investment to attain high adoption outcomes. variables with insignificant effects (e.g., cybersecurity improvement) were classified as “nn” (not necessary). in summary, the nca results emphasize that internal organizational capabilities—particularly leadership commitment and workforce training—are indispensable for adopting blockchain-ai technologies. external factors like competitive pressure or technical safeguards, while influential, play supplementary roles. these findings align with prior studies14, reinforcing the importance of organizational readiness and human capital in digital transformation. discussion integration of sufficiency and necessity perspectives by integrating the sufficiency and necessity perspectives through sem and nca, this study offers a comprehensive dual-method approach to investigate the role of organizational and environmental determinants in adopting ai-based blockchain technologies to improve financial transparency. this approach (1) identifies the determinants that significantly predict enhanced financial transparency and ultimately the acceptance of blockchain and ai technologies, and (2) isolates which factors are essential and the minimum thresholds required for such outcomes. the sample largely consisted of financial professionals and decision-makers across iranian institutions, which informs the interpretation and relevance of these findings in the organizational context. regarding the proposed hypotheses, sem analysis revealed that transparency plays a mediating role between key organizational capabilities and the intention to adopt blockchain and ai technologies. specifically, investment in table 6. sufficiency analysis: effects of blockchain-based ai adoption to improve financial transparency. hypothesis path β std. error p-value assessment h1a tr tar 0.122** 0.043 0.037 supported h2b eti tar 0.280*** 0.073 ≤ .001 supported h3c ci tar -0.101 0.051 0.181 rejected h4d cpr tar -0.004 0.056 0.955 rejected h5e sms tar 0.352*** 0.044 ≤ .001 supported h6f cp tar 0.027 0.072 0.653 rejected h2 ft tar 0.329*** 0.045 ≤ .001 supported h3a tr ft tar 0.031 0.017 0.182 rejected h3b eti ft tar 0.092** 0.031 0.003 supported h3c ci ft tar -0.002 0.020 0.935 rejected h3d cpr ft tar 0.089** 0.026 0.002 supported h3e sms ft tar 0.048 0.017 0.108 rejected h3f cp ft tar -0.003 0.014 0.665 rejected note: ***p ≤ .001. **p ≤ .01. *p ≤ .05. β = standardized estimate. std. error = standard error. n = 272. abbreviations: ci: cybersecurity improvement; cp: competitive pressure; cpr: compliance with privacy regulation; eti: employee training investment; ft: financial transparency; sms: senior management support; tar: technology adoption rate; tr: technological readiness. https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.42712 (page number not for citation purpose) sepideh mohammadi tong andri and sahar mohammadi tong andri employee training and top management support showed the strongest direct and indirect effects on technology acceptance, with cybersecurity improvement and regulatory compliance contributing less prominently. these findings emphasize that fostering financial transparency is not only a desirable outcome but a pathway through which internal capacities influence technological adoption. contrary to literature suggesting that external pressures such as competitive pressure or regulatory alignment drive digital innovation, our results indicate that internal preparedness and leadership commitment are more predictive of adoption behaviors in developing contexts. furthermore, while variables like training investment and top management support had strong total effects on technology acceptance via enhanced transparency, their impact varied when mediated through willingness to pay or invest in blockchain-based solutions. for instance, technological readiness and investment in cybersecurity, while beneficial, did not independently predict adoption unless transparency and leadership support were also elevated. this nuanced interplay among predictors reflects a complex decision-making environment, where motivations for innovation adoption are contingent upon the presence and strength of mediating organizational outcomes such as transparency. the nca findings further clarified which organizational factors are necessary for enhancing financial transparency and adoption intentions. results demonstrated that employee training investment (d = 0.372) and top management support (d = 0.400) are necessary conditions for achieving high levels of transparency and adoption. technological readiness, while exhibiting a smaller effect size (d = 0.153), was also identified as a necessary condition. in contrast, cybersecurity improvement, compliance with data privacy regulations, and competitive pressure did not meet the threshold to be considered necessary. these findings underscore the irreplaceability of internal organizational support mechanisms in the successful implementation of emerging technologies. table 7. sufficiency analysis: total effects of blockchain-based ai adoption to improve financial transparency. path β std. error p-value tr ft tar 0.153* 0.046 0.015 eti ft tar 0.372*** 0.077 < .001 ci ft tar -0.103 0.054 0.202 cpr ft tar 0.085 0.058 0.195 sms ft tar 0.400*** 0.047 < .001 cp ft tar 0.024 0.059 0.654 ci: cybersecurity improvement; cpr: compliance with privacy regulation; cp: competitive pressure: employee training investment; ft: financial transparency; sms: structural equation model; tar: technology adoption rate; tr: technological readiness. fig. 2. path diagram of sufficient effects on factors influencing the adoption of blockchain-based ai to improve financial transparency. https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.427 13 (page number not for citation purpose) blockchain ai adoption to improve financial transparency when comparing sufficiency and necessity outcomes, several critical insights emerge. employee training and top management support are both sufficient and necessary conditions for achieving high financial transparency and subsequent technology acceptance. a medium to-high level of these two factors is required, suggesting that without a baseline of strategic commitment and capacity-building, organizations are unlikely to succeed in deploying complex digital infrastructures. technological readiness is also necessary at a moderate level, though it alone is not sufficient to ensure adoption, indicating it is a must-have but not a should-have factor. in contrast, compliance with privacy regulations and competitive pressure, while showing some relationship in sem paths, were not identified as necessary nor sufficient conditions. this distinction reinforces that not all statistically significant drivers are essential to outcome realization and that must-have conditions should be prioritized in organizational planning. transparency itself was shown to be a sufficient—but not necessary—condition for technology acceptance, implying that although increasing transparency strengthens the likelihood of adoption, other combinations of internal support and readiness can also lead to the same outcome. these results can be interpreted through the lens of cognitive dissonance theory.34 organizations may simultaneously value innovation and face barriers such as resource limitations or regulatory complexity. to resolve this tension, they may emphasize different value categories at different decision stages. for instance, while transparency may be crucial for justifying innovation post-adoption, it is the underlying organizational commitment—particularly leadership support and employee competency—that enables the adoption to occur in the first place. therefore, sufficient conditions offer alternative routes toward the desired outcome, while necessary conditions represent non-negotiable baselines. both categories serve to reduce institutional dissonance, either by offering strategic justification (sufficiency) or structural requirement (necessity), allowing organizations to reconcile innovation goals with practical constraints. theoretical contribution this study contributes to the emerging discourse on the adoption of advanced technologies in the health insurance sector by modeling the key drivers influencing the implementation of blockchain-based artificial intelligence systems to enhance financial transparency. by integrating sufficiency (sem) and necessity (nca) perspectives, the research offers a comprehensive understanding of how technological, organizational, and institutional factors shape adoption behavior. from a sufficiency perspective, the findings indicate that perceived usefulness, technological trust, and regulatory support have significant direct effects on the intention to adopt. these results align with previous studies such as venkatesh et al. (2003) in the utaut model, and wamba et al. (2020), which highlighted perceived usefulness and trust as central predictors of technology adoption. perceived usefulness emerged as the strongest determinant, suggesting that organizations are more inclined to adopt table 8. necessity analysis: identifying necessary blockchain-based ai adoption to improve financial transparency. hypothesis path ceiling zone scope effect size confidence interval p-value p-value accuracy assessment h4a tr ft 3.000 36 0.083 0.004 0.007 0.006 0.002 rejected h4b eti ft 5.000 36 0.139 0.007 0.010 0.008 0.002 supported h4c ci ft 0.125 36 0.003 0.299 0.317 0.308 0.009 rejected h4d cpr ft 6.938 36 0.193 0.002 0.004 ≤ .010 0.001 supported h4e sms ft 0.562 36 0.016 0.001 0.002 ≤ .001 0.000 rejected h4f cp ft 3.188 36 0.106 0.321 0.340 0.331 0.009 rejected h5a tr tar 3.686 36 0.107 0.000 0.000 ≤ .001 0.000 supported h5b eti tar 8.667 36 0.241 0.000 0.000 ≤ .001 0.000 supported h5c ci tar 0.000 36 0.000 1.000 0.000 rejected h5d cpr tar 6.450 36 0.179 0.003 0.005 0.004 0.001 supported h5e sms tar 1.050 36 0.029 0.000 0.000 ≤ .001 0.000 rejected h5f cp tar 7.050 36 0.235 0.002 0.003 0.002 0.000 supported h6 ft tar 2.500 36 0.069 0.014 0.019 0.016 0.002 rejected note: analysis conducted with 10,000 permutations and 95% confidence intervals; accuracy level = 100%. necessary conditions are confirmed if effect size > 0.1 and p ≤ .05; n = 272. abbreviations: ci: cybersecurity improvement; cp: competitive pressure; cpr: compliance with privacy regulation; et: employee training investment; sms: senior management support; ft: financial transparency; tar: technology adoption rate; tr: technological readiness. https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.42714 (page number not for citation purpose) sepideh mohammadi tong andri and sahar mohammadi tong andri when they recognize clear benefits such as fraud reduction and streamlined claims processing.35,36 furthermore, top management support and organizational readiness influenced adoption intentions indirectly through mediating variables. this is consistent with the findings of gangwar et al. (2015) and alshamaila et al. (2013), who emphasized the importance of internal capabilities in facilitating digital transformation within organizations.37,38 from a necessity lens, nca results revealed that technological trust and regulatory support are critical bottlenecks for adoption; they must meet minimum thresholds before the intention to adopt can be realized. these findings support prior work by dul (2016) and richter et al. (2020) on the importance of identifying non-compensable conditions for behavioral outcomes.39,40 lack of trust in blockchain’s security was found to be a major impediment, echoing abdullahi et al. (2022), who emphasized trust as a pivotal factor in digital health technology adoption.41 moreover, although cost-effectiveness and competitive pressure were found to be sufficient conditions for adoption, they were not deemed necessary. this aligns with ghobakhloo et al. (2012), who described market pressure as an accelerant rather than a mandatory requirement for innovation.42 importantly, regulatory support was identified as both a sufficient and necessary condition, highlighting the critical role of legal and institutional frameworks in legitimizing and facilitating technology implementation. this finding is consistent with riggins and wamba (2015), who argued that government policy can significantly influence blockchain adoption.43 contrary to expectations, institutional pressure and privacy concerns did not significantly affect adoption intentions in this study. this may be attributed to the growing maturity of blockchain technology and increased managerial familiarity with its security features. similar to agbo et al. (2019), who found that improved understanding reduces data privacy concerns, our results suggest that such concerns become less influential as confidence in the technology grows.44 overall, by combining sufficiency and necessity logics, this research advances adoption theory within the health insurance context, addressing the gap in mixed-method studies. it builds upon existing frameworks and provides strategic insights into the key conditions that must be in place for successful implementation. these findings offer valuable implications for policy and managerial practice aimed at driving digital modernization in the insurance sector. managerial and policy implications from a managerial standpoint, these insights provide practical guidance for health insurance organizations seeking to adopt blockchain-based ai systems to enhance financial transparency. emphasizing technological trust and perceived usefulness is crucial, as these factors significantly shape decision-makers’ willingness to adopt such advanced systems. organizations should communicate the tangible benefits of blockchain-based ai—such as fraud detection, automation of claims, and secure data sharing—to create a clear perception of utility, as supported by.35,36 top management support and organizational readiness must be strengthened to build internal capabilities and reduce resistance to technological change.37 managers should prioritize training programs and infrastructure development to increase readiness and align internal processes with blockchain integration. highlighting successful case studies and showcasing roi may also increase buy-in from leadership and mitigate uncertainty. to activate adoption behavior, it is essential to ensure regulatory clarity and foster inter-organizational collaboration, particularly in a heavily regulated domain like health insurance. legal frameworks should be transparent, and national authorities can support diffusion through policy incentives and standardized compliance protocols.45 table 9. necessity analysis: bottleneck analysis. tr eti ci cpr sms cp y ft tar ft tar ft tar ft tar ft tar ft tar 1 nn nn nn nn nn nn nn nn nn nn nn nn 2 nn nn 1.67 1.67 nn nn 1.75 1.75 nn nn nn nn 3 nn nn 1.67 1.67 nn nn 2.25 1.75 nn nn 2.75 nn 4 nn nn 1.67 2.00 nn nn 2.25 2.00 nn nn 2.75 2.75 5 nn 1.67 1.67 2.00 nn nn 2.25 2.00 nn nn 2.75 2.75 6 2.00 3.00 2.33 3.33 nn nn 2.25 2.00 nn nn 2.75 4.50 7 3.00 3.00 2.33 3.33 1.25 nn 3.50 3.00 2.25 3.00 2.75 5.50 note: numbers indicate which level of each determinant (x) is necessary for different levels of the outcome (y). y represents the outcome variable, referring to either financial transparency (ft) or technology adoption rate (tar), depending on the column. values were measured on a 7-point likert scale ranging from 1 (‘strongly disagree’) to 7 (‘strongly agree’). determinants that do not qualify as necessary conditions (see table 7) are italicized, while necessary conditions are bold. abbreviations: nn = not necessary; ft = financial transparency; tar = technology adoption rate. https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.427 15 (page number not for citation purpose) blockchain ai adoption to improve financial transparency trust-building mechanisms—such as pilot projects, thirdparty audits, and secure certification standards—can alleviate concerns over data privacy and ethical ai use.46 in institutional terms, tax incentives, government grants, and public-private partnerships could play a transformative role in lowering adoption costs and increasing accessibility for medium-sized insurance providers. this is especially important given the high initial investment associated with blockchain-ai integration. additionally, creating interoperable standards and promoting data governance frameworks would help institutionalize transparency and foster trust between ecosystem actors. considering that blockchain-based ai contributes directly to sustainable development goal 16 (peace, justice and strong institutions), particularly by promoting transparency, accountability, and institutional trust, policymakers must integrate this technology into broader national digital health strategies. legislative alignment and coordinated governance are critical for ensuring scalable and ethical adoption in the health insurance ecosystem. limitations and future research directions the study sample consisted mainly of professionals from iranian health insurance organizations, which may limit the generalizability of findings to other cultural or regulatory contexts. data on race or ethnicity were not collected due to national data protection regulations and cultural considerations, which restrict the gathering of such sensitive personal information in research settings. given the complex interplay of technological, organizational, and environmental factors, adopting a dual perspective that incorporates both sufficiency and necessity logic enhances causal inferences and provides deeper insights into how and why health insurance organizations are willing to adopt blockchain-based ai technologies to improve financial transparency—an essential behavior that could help reduce fraud, inefficiencies, and information asymmetry in the healthcare system. building on the technology–organization–environment framework and integrating it with the tam, our study uncovers a notable asymmetry whereby multiple factors influence adoption intention, but fewer are truly essential for ensuring actual deployment. this asymmetry suggests that while perceived benefits, compatibility, and top management support significantly influence intention to adopt, the actual decision to implement the technology often hinges on a smaller set of critical enablers—particularly security, trust, and regulatory support. specifically, only perceived trustworthiness and data security emerged as both necessary and sufficient for facilitating adoption. in contrast, perceived usefulness, organizational readiness, and environmental pressure—although important—were not always essential, and their influence varied across institutions. conclusions and directions for future research given the complex interplay of technological, organizational, and environmental factors, adopting a dual perspective that incorporates both sufficiency and necessity logic enhances causal inferences and provides deeper insights into how and why health insurance organizations are willing to adopt blockchain-based ai technologies to improve financial transparency—an essential behavior that could help reduce fraud, inefficiencies, and information asymmetry in the healthcare system. building on the technology–organization–environment framework and integrating it with the tam, our study uncovers a notable asymmetry whereby multiple factors influence adoption intention, but fewer are truly essential for ensuring actual deployment. this asymmetry suggests that while perceived benefits, compatibility, and top management support significantly influence intention to adopt, the actual decision to implement the technology often hinges on a smaller set of critical enablers—particularly security, trust, and regulatory support. specifically, only perceived trustworthiness and data security emerged as both necessary and sufficient for facilitating adoption. in contrast, perceived usefulness, organizational readiness, and environmental pressure— although important—were not always essential, and their influence varied across institutions. these insights indicate a critical shift for policymakers and health it providers: instead of promoting blockchain-based ai merely for its novelty or general efficiency, they must emphasize secure architecture, data privacy guarantees, and alignment with legal standards to increase adoption likelihood. therefore, blockchain-based ai should be positioned not just as a cutting-edge tool but as a robust, compliant, and trustworthy system that directly enhances operational transparency and minimizes fraud risks. although this study provides a strong foundation for understanding adoption drivers in the healthcare insurance context, several opportunities remain for future research. first, due to challenges associated with collecting sensitive institutional data, we relied on self-reported perceptions from industry professionals to reduce bias and gather informed insights. while various robustness checks were conducted, possible self-reporting and social desirability biases cannot be entirely excluded. following methodological recommendations,20 we triangulated different data sources and measurement instruments to enhance reliability; nevertheless, these limitations should be considered when interpreting results. while we did not directly observe full-scale implementation behavior, prior research supports behavioral intention as a reliable proxy for technology adoption.35,37 likewise, organizational readiness and perceived risk mitigation are valid indicators of actual adoption under https://doi.org/10.30953/bhty.v8.427 citation: blockchain in healthcare today 2025, 8: 427 https://doi.org/10.30953/bhty.v8.42716 (page number not for citation purpose) sepideh mohammadi tong andri and sahar mohammadi tong andri constrained environments, especially in the health sector. given that decision-making in public health insurance organizations is highly structured and regulated, our findings likely reflect genuine adoption tendencies. additionally, we controlled for organizational characteristics such as size, funding model, and regulatory exposure to increase accuracy. however, external factors such as vendor support, interoperability, and change management processes also play a crucial role in adoption behavior,47 and future studies should incorporate these dimensions. although our results offer a general reflection of perceptual and strategic tendencies in adopting blockchain-based ai for transparency, further research should incorporate longitudinal and performance-based metrics to validate and extend these findings. moreover, our sample consisted mainly of decision-makers and it managers in public insurance institutions, which may not generalize to smaller or private entities. future research should explore these relationships across different organizational structures to develop a broader understanding of sustainable and secure technology adoption. finally, since this study was conducted within the iranian healthcare and organizational context, findings may be influenced by local institutional, cultural, and policy environments. as hofstede’s (2001) dimensions suggest, trust in technology, data openness, and risk tolerance vary across cultures. for instance, perceived risk and trust may play a more substantial role in low-trust societies.48 therefore, cross-cultural and cross-sectoral studies are needed to evaluate how contextual factors shape blockchain-based ai adoption in health systems globally. funding this research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. conflicts of interest the authors declares no conflicts of interest. author contributions sepideh mohammadi tong andri: conceptualization, methodology, formal analysis, investigation, writing – original draft, project administration. sahar mohammadi tong andri: data curation, software, validation, visualization, writing – review & editing. data availability statement (das), data sharing, reproducibility, and data repositories the datasets generated and/or analyzed during the current research are not publicly accessible due to confidentiality obligations and the sensitive nature of the information involved. access to these datasets may be granted by the corresponding author upon the submission of a reasonable request, subject to prior approval and the signing of a confidentiality agreement. application of ai-generated text or related technology artificial intelligence (ai)-assisted tools, such as chatgpt (openai), were used solely for language polishing and grammar correction during the preparation of this manuscript. the authors reviewed and verified all ai-generated suggestions to ensure accuracy and originality of the content. acknowledgments the authors wish to express their sincere gratitude to all individuals and organizations who contributed to the completion of this research. references 1. hofmann p, samp c, urbach n. robotic process automation. electronic markets. 2020;30(1):99-106. https://doi.org/10.1007/ s12525-019-00365-8 2. tapscott d, tapscott a. blockchain revolution: how the technology behind bitcoin is changing money, business, and the world. penguin; 2016. 3. crosby m, pattanayak p, verma s, kalyanaraman v. blockchain technology: beyond bitcoin. appl innov rev. 2016;2:6-19. 4. zyskind g, nathan o, pentland as. decentralizing privacy: using blockchain to 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this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.427 https://doi.org/10.1016/j.procs.2016.09.074 https://doi.org/10.22054/ims.2021.46042.1596 https://doi.org/10.22054/ims.2021.46042.1596 https://doi.org/10.22059/ijms.2020.287674 https://doi.org/10.22059/ijms.2020.287674 https://doi.org/10.2307/249008 https://doi.org/10.1007/s10209-014-0348-1 https://doi.org/10.1002/ejsp.2420150303 https://doi.org/10.1002/ejsp.2420150303 https://doi.org/10.1037/0021-9010.88.5.879 https://doi.org/10.1177/1094428115583944 https://doi.org/10.1177/1094428115583944 https://doi.org/10.18637/jss.v048.i02 https://doi.org/10.1177/002224378101800104 https://doi.org/10.1080/10705519909540118 https://doi.org/10.1016/j.jbusres.2023.113824 https://doi.org/10.2307/30036540 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life sciences: a framework for evaluation and adoption jennifer hinkel, msc1,2 ; fraser peck, mbbch3 and cory kidd, phd1 1sigla sciences, incline village, nv, usa; 2kellogg college, university of oxford, oxford, uk; 3pharmacy consulting limited, farnborough, uk corresponding author: jennifer hinkel, email: jennifer@siglasciences.com doi: https://doi.org/10.30953/bhty.v8.398 keywords: artificial intelligence, clinical development, commercialization, ethical challenges, life sciences, regulatory, strategic model abstract background: the adoption of artificial intelligence (ai) is accelerating in the life sciences sector, offering opportunities to enhance biopharmaceutical research, development, and commercialization. however, the sector lacks structured tools to prioritize ai initiatives across varied business domains. objective: this study presents a framework to categorize and evaluate potential ai applications in the life sciences industry, organizing use cases along two critical dimensions: phase of product lifecycle and operational domain. methods: a structured mixed-methods approach was employed, including a modified delphi consensus process with industry experts, a qualitative case study review, and iterative framework refinement between august 2023 and august 2024. results: the resulting matrix framework enables life sciences professionals to assess ai opportunities across research, clinical development, commercialization, and post-marketing activities. key findings highlight the pervasive nature of ai impact, the emphasis on data-driven strategies, and the regulatory and ethical challenges facing biopharma firms. conclusions: this framework provides a practical model for strategic ai adoption decisions within the life sciences sector and lays the groundwork for future research, policy development, and enterprise transformation efforts. plain language summary artificial intelligence (ai) is starting to reshape how pharmaceutical companies discover new drugs, test them in clinical trials, and bring them to patients. however, with so many possible uses for ai, it can be hard for companies to decide where to begin and how to prioritize. this article presents a simple framework that organizes potential ai projects by the stage of drug development, such as early research or post-market monitoring, and by how much the project involves people outside the company, like doctors or patients. the goal is to help life sciences companies think more clearly about where ai could add value and what risks to monitor. with thoughtful planning, ai can make biopharma companies and the healthcare system they support, smarter, and more focused on patient needs. submitted: april 27, 2015; accepted: september 2, 2025; published: october 10, 2025 recent advances in artificial intelligence (ai) are prompting discussions across various scientific and business sectors regarding where, how, and why ai technologies might be deployed, and what the implications will be. the medical and life sciences fields are exploring how ai might be incorporated throughout blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-8461-7037 https://orcid.org/0000-0002-6849-2764 https://orcid.org/0000-0002-8129-7234 mailto:jennifer@siglasciences.com https://doi.org/10.30953/bhty.v8.398 citation: blockchain in healthcare today 2025, 8: 398 https://doi.org/10.30953/bhty.v8.3982 (page number not for citation purpose) j. hinkel et al. the drug development process, from discovery to patient care, including through commercialization, marketing, and market access strategies.1 as of mid-2024, the ai adoption in the life sciences industry has progressed from theoretical discussions to practical implementations, albeit many in the ‘pilot phase’. stage. clinicians, pharmaceutical manufacturers, and policymakers are currently evaluating whether these technologies offer substantive opportunities for innovation, enhanced efficiency, and improved patient outcomes or if they are subject to inflated expectations characteristic of a ‘hype cycle’.2 ai represents a range of technologies, including large language models, machine learning, natural language processing, and computer vision.3 some researchers propose that ai and related technologies represent significant shifts in how researchers and clinicians approach complex problems in life sciences and healthcare.4 some potential ai applications hold particular interest to biopharmaceutical manufacturers because they promise to reduce the time and/or investment involved in bringing new therapies to market. in the earliest stages of drug discovery, ai algorithms aim to speed the identification of new therapeutic targets and potential drug candidates.5 in clinical development, ai might optimize study designs, improve patient recruitment, or enhance data analysis.6 post-commercialization, ai-driven tools may be deployed to enhance patient engagement or medication adherence or to automate the collection of real-world evidence (rwe).7 while ai technologies might be promising, they also present several challenges to the industry, ranging from ethical, legal, regulatory, and data security issues to more extensive concerns that organizations might need to change long-standing operating procedures as technologies advance.8 compounding these issues is the pace of advancement. examples include the rapidity of ai development far outpacing the ability of regulatory and legal frameworks to keep up.9 this asymmetry adds significant complexities in a highly regulated industry that is notably averse to legal risk. despite the risks, the potential benefits of ‘leading the pack’ on ai adoption might be significant. companies that successfully integrate ai into their operations might gain competitive advantages, from accelerated research and development to more optimized market access strategies. conversely, those who fail to bring ai on board may increasingly miss out on growth opportunities.10 in the light of these challenges and the rapid pace of advancement, the authors aim to provide a framework that professionals across various functions within life sciences companies can use to understand and evaluate the place of ai technologies in their workflows and firms. by aligning ai applications along two key axes that we refer to as operational domain and phase of product lifecycle, we offer a structured approach to evaluating and prioritizing ai initiatives. this framework is designed to assist those in positions of leadership with making informed decisions about ai adoption, balancing potential benefits against risks and challenges. methods for framework development this study employs a structured mixed-methods approach, including a modified delphi method. it draws from the authors’ industry experience, consultations with external biopharmaceutical experts, a qualitative review of case studies from literature and business reports, and an iterative process of elicitation and consensus-building. the research was conducted between august 2023 and august 2024, allowing for the incorporation of recent developments. it is expected to continue iteratively, incorporating elements of realist synthesis to further expand the framework and refine example use cases. an initial framework was developed based on the collective expertise of the research team and additional consulting experts. these included professionals with extensive experience in pharmaceutical commercialization, clinical development, and ai applications in research and patient-facing settings. this sector expertise guided the creation of the two axes that form the foundation of the framework: phase of product lifecycle and operational domain. once the axes were established, examples of ai applied to typical tasks within the life sciences industry were identified and mapped onto the framework based on expert discussion and consensus. the identification process involved (1) consulting industry case studies, publications, and expert knowledge, (2) selecting biopharma industry activities indicative of various phases and operational domains with existing or proposed ai use cases, and (3) highlighting examples that could highlight potential benefits and challenges of ai adoption in each section of the framework matrix, given that these examples are most likely to spark discussions on the requirements, risks, benefits, and objectives of ai as applied to specific industry use cases. the initial framework and use case examples underwent iterative refinement based on the incorporation of additional feedback from industry experts. in summary, we followed these steps (table 1). this methodology allowed us to create a framework that is grounded in real-world industry understanding and reflects the current state and potential future directions of ai applications in life sciences. ethical considerations this study did not require formal ethics review, as it did not involve human subjects research, clinical trials, or the https://doi.org/10.30953/bhty.v8.398 citation: blockchain in healthcare today 2022, 5: xxx https://doi.org/10.30953/bhty.v5.xxx 3 (page number not for citation purpose) mapping the ai landscape in life sciences collection of personal data. the research was based on publicly available information, the expertise of the core research team, and consultations with field experts. all experts consulted in this study provided consent to share their professional insights and expertise in an anonymous fashion as a contribution to the framework development. results our research resulted in the development of a proposed framework for understanding and implementing ai technologies in the life sciences sector. this framework aligns ai applications along two key axes: phase of product lifecycle and operational domain. phase of product lifecycle (horizontal axis) this axis aligns approximately with the traditional stages of drug development and commercialization in the life sciences industry. it recognizes that ai might have differential impact at various stages of the drug product journey from concept to market. research & discovery research and discovery represent the early stages of drug development before human testing and encompass a range of computational and biological research methods. clinical trials and regulatory approval clinical trials and regulatory approval represent the clinical development phase of activities that typically start with first-in-human testing and culminate with the completion of registrational clinical trials and regulatory/marketing approval. commercial launch this phase overlaps here as a parallel process, with planning starting well prior to regulatory approval and continuing through that launch date and beyond, and including a range of commercial, medical, and access-related activities. post-market activities post-marketing commercial and scientific activities support pharmacovigilance, the collection of rwe, ongoing communication with healthcare professionals, and continued patient support. operational domain (vertical axis) this axis represents a continuum from internal operations to increasingly external interactions, reflecting not only the diverse operational areas where ai technologies are applied in the life sciences sector but also the growing complexity of regulatory, data, and ethical considerations as we move from internal operations toward patient-facing applications. internal operations include activities within the company’s boundaries, including those that deal with proprietary data and processes. while still subject to regulations, these activities carry the least external exposure and thus lower regulatory and ethical risks. b2b engagements represent the first step from internal activities into external interactions, involving partnerships, suppliers, and other businesses, either scientific or commercial in nature. here, collaborative processes and data sharing introduce additional regulatory considerations and data protection needs, but with less regulatory overhead than engaging with healthcare professionals or patients. healthcare professional engagements these cover interactions with healthcare professionals, introducing significant regulatory oversight due to data protection, transparency laws, ethics, and the impact to patient care. patient engagements as the last operational domain, patient operational domains represent direct patient interactions and the highest level of external engagement. patient-facing activities face the most stringent regulatory requirements, data privacy concerns, and ethical considerations given the impact on patient care and individual health outcomes. table 2 illustrates this matrix and provides representative examples of ai applications at each intersection of lifecycle phase and operational domain, showcasing potential use cases for ai technologies across common biopharma business areas. key findings in parallel with our development of this framework, we identified the following themes as applicable to biopharmaceutical manufacturers considering ai adoption: pervasive impact ai use cases span across all phases of the product lifecycle and all addressed operational domains. the breadth of potential applications suggests a fundamental shift in how the industry will operate in the future. table 1. initial framework and use cases based on incorporation of additional feedback from industry experts use case framework step 1 conceptualization and axis development step 2 expert consultation step 3 framework refinement step 4 identify business activities and map ai use cases to the framework. step 5 iterative improvement as the field advances https://doi.org/10.30953/bhty.v5.xxx citation: blockchain in healthcare today 2025, 8: 398 https://doi.org/10.30953/bhty.v8.3984 (page number not for citation purpose) j. hinkel et al. t ab le 2 . m at ri x fr am ew or k fo r un de rs ta nd in g a i us e ca se s an d ap pl ic at io ns in li fe s ci en ce s ph as e of p ro du ct li fe cy cl e o pe ra tio na l d om ai n in cr ea si ng le ve ls o f r eg ul at io n an d ri sk w ith m or e ex te rn al -fa ci ng a ct iv iti es t ow ar ds t he r ig ht in te rn al o pe ra tio ns b2 b en ga ge m en ts h c p en ga ge m en ts pa tie nt e ng ag em en ts ph as es o f d ru g di sc ov er y, de ve lo pm en t, an d la un ch d is co ve ry a nd p re -c lin ic al d ev el op m en t al -d riv en ta rg et id en tifi ca tio n an d va lid at io n m ac hi ne le ar ni ng fo r in s ilic o dr ug d es ig n al -p ow er ed p re di ct ive to xi co lo gy m od el s fe de ra te d le ar ni ng p la tfo rm s fo r co lla bo ra tiv e re se ar ch al -o pt im iz ed p ar tn er in g se le ct io n fo r cr o , in ve st m en t, an d bd al -d riv en c om po un d so ur cin g an d sy nt he sis pr ed ict io n al -c ur at ed r es ea rc h in sig ht s fo r ko l en ga ge m en t n at ur al la ng ua ge p ro ce ss in g fo r cl in ica l ex pe rt ise m ap pi ng al -p ow er ed s cie nt ifi c co nf er en ce a nd k o l m at ch m ak in g al a na lys is of p at ie nt -g en er at ed h ea lth d at a fo r bi om ar ke r di sc ov er y m ac hi ne le ar ni ng m od el s fo r pa tie nt s tra tifi ca tio n in ra re d ise as es al -d riv en p at ie nt a dv oc ac y ne tw or k an al ys is c lin ic al d ev el op m en t an d r eg ul ato ry s ub m is si on al fo r ad ap tiv e tr ia l d es ig n an d pr ot oc ol op tim iz at io n m ac hi ne le ar ni ng fo r pa tie nt r ec ru itm en t fo re ca st in g n lp -p ow er ed a ut om at ed r eg ul at or y su bm iss io n pr ep ar at io n al -d riv en s ite s el ec tio n an d pe rfo rm an ce pr ed ict io n m ac hi ne le ar ni ng fo r cr o p er fo rm an ce op tim iz at io n al fo r op tim iz in g m ul tire gi on al c lin ica l t ria l de sig ns al -p ow er ed in ve st ig at or r el at io ns hi p m an ag em en t vi rt ua l a ss ist an ts fo r pr ot oc ol c la rifi ca tio n an d su pp or t m ac hi ne le ar ni ng fo r in ve st ig at or s ite fe as ib ilit y as se ss m en t al c ha tb ot s fo r pa tie nt s cr ee ni ng a nd en ro llm en t m ac hi ne le ar ni ng fo r pe rs on al iz ed p at ie nt re te nt io n st ra te gi es al fo r re al -ti m e pa tie nt m on ito rin g an d ad ve rs e ev en t p re di ct io n in tr ia ls c om m er ci al la un ch a nd m ar ke t a cc es s al fo r dy na m ic pr ici ng m od el s an d m ar ke t ac ce ss s tra te gi es m ac hi ne le ar ni ng fo r sa le s fo rc e ef fe ct ive ne ss o pt im iz at io n n lp fo r co m pe tit ive in te llig en ce a nd m ar ke t s en tim en t a na lys is al -d riv en p ay er n eg ot ia tio n sim ul at or s m ac hi ne le ar ni ng fo r di st rib ut io n ch an ne l op tim iz at io n al fo r au to m at in g an d op tim iz in g rf i a nd te nd er p ro ce ss es al -p ow er ed p er so na liz ed h cp e ng ag em en t pl at fo rm s n lp fo r re al -ti m e h cp s en tim en t a na lys is du rin g in te ra ct io ns al fo r pr ed ict in g an d op tim iz in g h cp pr es cr ip tio n be ha vio rs al fo r pe rs on al iz ed p at ie nt s up po rt p ro gr am de sig n m ac hi ne le ar ni ng fo r m ed ica tio n ad he re nc e pr ed ict io n an d in te rv en tio n al -d riv en c ha tb ot s fo r pa tie nt e du ca tio n on co m pl ex th er ap ie s li fe cy cl e m an ag em en t an d po st -m ar ke tin g a ct iv iti es al fo r sig na l d et ec tio n in p ha rm ac ov ig ila nc e m ac hi ne le ar ni ng fo r pr od uc t l ife cy cl e op tim iz at io n st ra te gi es n lp fo r au to m at ed m ed ica l l ite ra tu re m on ito rin g al fo r re al -w or ld e vid en ce s tu dy d es ig n an d pa rt ne r se le ct io n m ac hi ne le ar ni ng fo r su pp ly ch ai n ris k pr ed ict io n an d m iti ga tio n al -p ow er ed fo re ca st in g fo r lo ng -te rm pr od uc t p er fo rm an ce al -d riv en p er so na liz ed m ed ica l i nf or m at io n se rv ice s m ac hi ne le ar ni ng fo r pr ed ict in g an d ad dr es sin g h cp in fo rm at io n ne ed s n lp fo r pr oc es sin g an d an al yz in g h cp fe ed ba ck a t s ca le al fo r de te ct in g an d pr ed ict in g m ed ica tio n no nad he re nc e m ac hi ne le ar ni ng fo r pe rs on al iz in g pa tie nt su pp or t i nt er ve nt io ns n lp fo r an al yz in g pa tie nt -re po rt ed o ut co m es an d so cia l m ed ia d at a a i: a rt ifi ci al in te lli ge nc e; b 2b : b us in es sto -b us in es s; c ro : c on tr ac t re se ar ch o rg an iz at io n; h c p: h ea lth ca re p ro fe ss io na l; ko l: k ey o pi ni on le ad er ; n lp : n at ur al la ng ua ge p ro ce ss in g. https://doi.org/10.30953/bhty.v8.398 citation: blockchain in healthcare today 2022, 5: xxx https://doi.org/10.30953/bhty.v5.xxx 5 (page number not for citation purpose) mapping the ai landscape in life sciences efficiency and innovation many ai applications focus on improving efficiency (e.g. automation of regulatory submissions) or driving innovation (e.g. ai-powered drug discovery). ai applications are frequently framed as tools that will reduce effort, time, and/or cost. enhanced customer engagement ai technologies may be leveraged by both commercial and medical field teams, particularly in digital or ‘omnichannel’ marketing initiatives or in medical communications. data-driven decision making across the matrix, there is a strong emphasis on data sources and data access as inputs to ai models. these data may be internally or externally derived, speaking to the value of high-quality data assets. access to data assets is likely a competitive advantage or differentiator. patient-centricity the framework reveals numerous ai applications focused on improving patient experiences and outcomes, aligning with a general shift toward more patient-centric approaches and the need to communicate patient-centric value, especially in the commercial phase. patient-centricity aligns with value-based care models and evolving regulatory expectations around the incorporation of patient reported outcomes (pros) in drug development and post-marketing phases. interconnectedness several ai applications bridge multiple domains or phases, hinting at the potential for these technologies to change industry structures or processes over time. this interconnectedness suggests the need for integrated ai strategies that consider the full spectrum of pharmaceutical operations and stakeholder interactions. ethical and regulatory considerations ai uses cases in biopharma that raise numerous ethical and regulatory questions. these include questions around data privacy and security, algorithmic bias, data ownership and consent, and the interpretability or generalizability of ai-driven decisions. limitations our methodology has limitations, including an acknowledgment that the rapid pace of ai development is likely to outpace even expert knowledge. another limitation in expert consensus methods is the introduction of potential bias based on the team’s collective experiences and perspectives. we mitigated these limitations to the extent possible through engaging diverse experts and iterating through the framework development process. furthermore, this framework is not likely to be exhaustive, given the challenge of comprehensively capturing all possible ai applications in a rapidly evolving field. for example, biopharmaceutical manufacturing companies also perform a number of corporate functions in finance, legal, human resources, training, and similar operational domains considered out of scope in this project. however, there may be areas where ai innovation can also be applied. conclusion this article presents a new framework for understanding and categorizing ai technologies across the life sciences product lifecycle and operational domain. by mapping along two axes representing paradigms well-understood by professionals in the biopharmaceutical industry, we provide a structured approach for such professionals to evaluate and prioritize ai technology initiatives. our findings demonstrate the pervasive potential of ai to transform various aspects of the life sciences sector, but not without risks and challenges to navigate, including data quality and availability, algorithmic biases, privacy and security, ethical and regulatory considerations, and the need for organizational change management. as the field continues to evolve rapidly, ongoing research and industry collaboration will be crucial to fully harness the potential of ai while addressing its associated challenges. the framework presented in this article provides a foundation for these future efforts, offering a common language and structure for discussing and analyzing ai applications in the sector. funding this study did not receive any funding. the authors have not received any payments or services from third parties directly related to the submitted work in the past 36 months. conflicts of interest ms. hinkel is co-editor-in-chief of blockchain in healthcare today. the authors declare no competing interests related to the subject of this article. for transparency, all authors are employed by or have formerly worked in the life sciences and adjacent sectors, including working for and/or consulting for biopharmaceutical manufacturers. miss. hinkel and dr. kidd have worked for and/or consulted for health informatics firms. while this research was not funded by any industry entity, it was undertaken independently based on the authors’ academic and research interests. it is neither promotional nor does it reference any specific products. the authors recognize that these affiliations could be perceived as potential conflicts of interest. https://doi.org/10.30953/bhty.v5.xxx citation: blockchain in healthcare today 2025, 8: 398 https://doi.org/10.30953/bhty.v8.3986 (page number not for citation purpose) j. hinkel et al. authors’ contributions data availability statement (das), data sharing, reproducibility, and data repositories application of ai-generated text or related technology all data 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the use is non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.398 https://doi.org/10.1016/j.drudis.2018.11.014 https://www.gartner.com/en/documents/3887767 https://www.gartner.com/en/documents/3887767 https://doi.org/10.1038/s41551-018-0305-z https://doi.org/10.1038/s41551-018-0305-z https://doi.org/10.1038/s41591-018-0300-7 https://doi.org/10.1038/s41587-019-0224-x https://doi.org/10.1016/j.tips.2019.05.005 https://doi.org/10.1056/nejmra1814259 https://doi.org/10.1056/nejmra1814259 https://doi.org/10.1371/journal.pmed.1002689 https://doi.org/10.1038/s41591-018-0307-0 https://doi.org/10.1038/s41591-018-0307-0 https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/a-generative-ai-reset-rewiring-to-turn-potential-into-value-in-2024 https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/a-generative-ai-reset-rewiring-to-turn-potential-into-value-in-2024 https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/a-generative-ai-reset-rewiring-to-turn-potential-into-value-in-2024 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research blockchain technology in digital health and medical technologies muhammet damar, phd1,2 ; ömer aydın, phd3,4 ; and fatih safa erenay, phd5 1fethiye faculty of business administration, mugla sitki kocman university, mugla, turkiye; 2upstream lab, map, li ka shing knowledge institute, unity health toronto, toronto, ontario, canada; 3associate professor, electrical and electronics engineering, faculty of engineering, manisa celal bayar university, manisa, turkiye; 4visiting researcher, management science and engineering, faculty of engineering, university of waterloo, waterloo, ontario, canada; 5associate professor, management science and engineering, faculty of engineering, university of waterloo, waterloo, ontario, canada corresponding author: ömer aydın, email: omer.aydin@cbu.edu.tr doi: https://doi.org/10.30953/bhty.v8.409 keywords: artificial intelligence; augmented reality; big data; blockchain technology; digital health; health 5.0; healthcare innovation; internet of things; precision medicine; telemedicine; virtual reality abstract the rapid evolution of digital health technologies has created an urgent need for secure, transparent, and interoperable data management systems. the core problem addressed in this study is the fragmentation of healthcare data and the lack of trust among stakeholders in existing digital health infrastructures. the main goal is to examine how blockchain technology can drive digital health transformation through decentralized data governance and integration with other emerging technologies. to achieve this, the research employs a mixed bibliometric and systematic review methodology, analyzing peer-reviewed publications indexed in the web of science and comparing topic hierarchies with outputs from google scholar between 2017 and 2024. using keyword co-occurrence and thematic mapping, six major domains were identified: genomics and precision medicine, telemedicine and mobile health, immersive technologies such as augmented and virtual reality, the internet of things and health 5.0 systems, artificial intelligence and big data integration, and global and regional health management. the findings indicate that blockchain enhances healthcare by improving data security, ensuring traceability, facilitating interoperability across platforms, and enabling real-time data sharing in clinical and research environments. it also supports regulatory compliance and patient-centered data ownership. in conclusion, blockchain serves as a foundational technology for future digital health ecosystems, promoting transparency and decentralization across global health networks. this study contributes to the literature by offering a comprehensive framework for integrating blockchain with digital health innovations, providing valuable guidance for researchers, policymakers, and healthcare technologists. submitted: may 31, 2025; accepted: october 11, 2025; published: november 14, 2025 the global healthcare sector is undergoing a significant digital transformation, fundamentally altering how care is delivered, health data are managed, and medical services are personalized. amid this transformation, blockchain technology has emerged as a pivotal innovation, not merely as a tool for data management, but as a foundational infrastructure capable of addressing core challenges such as data security, interoperability, and patient privacy.1,2 originally developed for financial systems, blockchain’s decentralized, transparent, and tamper-resistant design offers solutions to critical issues in healthcare, particularly as emerging technologies such as artificial intelligence (ai), the internet of things (iot), big data analytics, augmented reality (ar), virtual reality (vr), and extended reality become increasingly integrated into medical ecosystems.3,4 the convergence of these technologies requires a secure and scalable digital backbone, a role blockchain blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-3985-3073 https://orcid.org/0000-0002-7137-4881 https://orcid.org/0000-0002-3408-0366 mailto:omer.aydin@cbu.edu.tr https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.4092 (page number not for citation purpose) muhammet damar et al. is uniquely positioned to fulfill by enabling trusted data exchange, enhancing device and system interoperability, and supporting global health collaborations.5 here, the authors report on investigating the strategic role of blockchain in accelerating digital health transformation, presenting a forward-looking analysis of how it can support and optimize the integration of next-generation technologies. by systematically analyzing peer-reviewed literature, the study identifies six critical domains where blockchain intersects with digital health innovation. the contributions of this study are threefold: (1) provide a structured synthesis of current research on blockchain in digital health, (2) highlight practical implications for healthcare stakeholders by outlining blockchain’s impact on transparency, data integrity, and cross-border data sharing, and (3) identify research gaps and future directions to guide technological development and policy formulation. ultimately, the aim of this study is to serve as a comprehensive resource for researchers, healthcare professionals, and policymakers seeking to understand, evaluate, and implement blockchain-based digital health solutions in an increasingly interconnected and data-driven medical landscape. related works for blockchain application areas in digital health and medical technologies the digitalization of healthcare systems, advances in bioinformatics and genomics, the use of iot in biomedical devices, cloud-based technologies, and the use of ai in medical and biological applications have raised particularly challenging issues in bioethics.6 in this regard, blockchain technology emerges as a critically valuable technology. blockchain represents a data architecture whose application relies on blockchain technology and extends far beyond bitcoin, the cryptocurrency that popularized it. in the healthcare sector, blockchain is being intensively explored by various stakeholders to optimize business processes, reduce costs, improve patient outcomes, increase compliance, and enable better use of healthcare-related data.7 in the healthcare sector, blockchain significantly enhances privacy and security by decentralizing patient data storage and reducing the risk of unauthorized access. this decentralized architecture ensures that sensitive patient information remains protected while maintaining data availability across healthcare providers. for example, one of the key benefits of using blockchain in genomics is that it enables the creation of decentralized databases that can ensure the privacy and security of sensitive genetic information. furthermore, blockchain can facilitate the sharing of genetic data between researchers and healthcare providers, leading to more efficient and effective medical research and personalized medicine. furthermore, it can be used to trace the origin of genetic material.8 blockchain’s decentralized and immutable architecture enables secure and auditable sharing of sensitive genetic data. it  empowers individuals with control over their genomic information and supports consent management, addressing significant privacy and governance challenges. additionally, its application in biobanking and rare disease research enhances data integrity and facilitates collaborative efforts to advance personalized medicine. some biomedical devices (e.g., digital medicine) not only monitor patient data (such as diabetes devices that monitor blood sugar levels and automatically dose insulin injections) but also automatically assist the treatment processes.6 therefore, in such cases, patient data not only pose a privacy concern but can also have a critical impact on the patient’s vital functions. blockchains can also serve as a digital backbone for other technologies that can interface with blockchain systems, such as cloud computing, ai, ehealth and mobile health (mhealth) devices/apps, and, more broadly, the internet of medical things.9,10 in these domains, blockchain addresses patient data security and system reliability by supporting decentralized recording and sharing of remote healthcare interactions. this enhances patient engagement and equips healthcare providers with trustworthy access to information. blockchain mitigates risks such as data breaches, fraud, and misdiagnosis, thereby reinforcing trust in digital health services. the integration with mobile health devices further safeguards personal health data collected via wearables, enabling privacy-preserving real-time monitoring. in the healthcare field, little direct research has been conducted on metaverse technology. this is because the metaverse consists of many technologies that work together and are constantly evolving.11,12 some of these technologies include ar, social networking, vr, blockchain technology, and ai.13 blockchain supports secure digital interactions and data provenance in immersive healthcare technologies increasingly used for education, remote diagnosis, and patient engagement for immersive technologies such as ar, vr, the metaverse, and social media. by enabling traceability and authenticity of digital assets, blockchain ensures reliability in virtual healthcare settings and social media platforms, fostering transparent and accountable applications within these emerging ecosystems. the proliferation of interconnected devices in iot, industry 4.0, and health 5.0 ecosystems creates complex data management challenges due to the massive volume, variety, and velocity of data generated.14–16 blockchain offers a secure, interoperable framework that enhances data sharing among diverse stakeholders, reduces medical errors, and supports real-time health monitoring, especially for vulnerable populations. its ability to https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.409 3 (page number not for citation purpose) blockchain in digital health and medical technologies maintain data integrity across distributed networks aligns with the human-centric goals of health 5.0. blockchain complements ai and big data analytics by ensuring the reliability, traceability, and security of largescale health data used in algorithmic decision-making for ai, cloud computing, and big data integration.17–19 it supports transparent validation of ai models, strengthens security in cloud storage environments, and facilitates privacy-preserving data sharing, thus improving diagnostic accuracy, personalized care, and electronic health record management. blockchain plays a crucial role in addressing pandemics, infectious diseases, and healthcare inequities by enabling secure, scalable, and collaborative solutions for global and regional health challenges.20,21 its decentralized framework facilitates global data sharing, disease surveillance, clinical trial management, and patient privacy protection. blockchain-enabled techniques, such as federated learning, offer accurate disease prediction while maintaining confidentiality and empower marginalized populations through secure identity verification and improved healthcare access. a review of the literature reveals that damar et al.20 conducted a study on blockchain applications in primary healthcare. however, no article was found in the literature discussing blockchain applications within the context of current technologies and healthcare applications. furthermore, preliminary analyses indicated that it would be appropriate to evaluate blockchain applications in healthcare under six headings. our research addresses this gap and provides a detailed analysis to enable readers to comprehensively evaluate blockchain healthcare applications within the context of current information technologies. methodology this article aims to bring together existing research and highlight emerging trends in the application of blockchain technology in digital health and healthcare technologies. in this context, a comprehensive assessment of blockchain applications in healthcare is presented by analyzing both research and review articles from the web of science (wos) database. following a preliminary screening, blockchain applications were categorized into six critical areas. wos served as the primary source for data collection. through a systematic review of the literature, the main topics and application areas where blockchain is actively used in healthcare were identified. figure 1 illustrates the search terms applied for each topic and the corresponding number of articles retrieved. to strengthen the review and provide a broader perspective for researchers in the field, additional relevant articles from google scholar were also included. this approach ensured a more comprehensive assessment for healthcare professionals, researchers, and policymakers. findings and discussion our study represents one of the most up-to-date and comprehensive analyses of blockchain applications in healthcare. numerous reviews,17,22 systematic reviews,23,24 fig. 1. search topics and search strings for blockchain application areas in digital health and medical technologies. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.4094 (page number not for citation purpose) muhammet damar et al. scoping reviews,25,26 and research articles10,27 have addressed blockchain in healthcare. what distinguishes this study, conducted in 2025, is its direct use of data sources such as wos, which indexes the world’s most respected and influential health sciences journals. in this context, our study extends beyond existing literature in both content and scope. it provides a detailed assessment of current trends, research, and applications related to key blockchain healthcare solutions, major information technologies, and the challenges of integrating these technologies with blockchain within healthcare systems. preliminary analyses identified six areas of blockchain application in healthcare. this framework offers a holistic assessment of blockchain’s impact on healthcare and presents a novel perspective on the literature. these topics are as follows: • genomics and precision medicine, • telemedicine, mobile health, and digital health environment • augmented reality, vr, metaverse, and social media environment • iot industry 4.0 transformation, health 5.0 environment • ai, cloud technology, and big data integration • solving global and regional health problems as can be seen in table 1, a structured description of each application area and the specific contributions of blockchain are presented. table 1 synthesizes the key application areas of blockchain within digital health and medical technologies. it highlights the distinct contributions and benefits in each domain, from securing genomic data and enabling decentralized telemedicine communication to ensuring digital asset ownership in immersive environments and maintaining device data integrity in iot frameworks. when combined with ai and big data, blockchain enhances data validation and transparency. in global health contexts, it supports secure cross-border collaboration and decentralized healthcare infrastructure development. collectively, this methodology enabled a comprehensive exploration of blockchain’s multifaceted role in advancing secure, transparent, and efficient healthcare solutions across diverse technological landscapes. blockchain applications for genomics and precision medicine genomics and precision medicine are rapidly developing as a revolutionary field with the potential to provide personalized healthcare. accurate collection, processing, and analysis of genetic data are critical to creating personalized treatment plans. however, the security, confidentiality, and transparency of these data pose significant challenges in terms of protecting patient rights and ensuring data integrity. at this point, blockchain technology emerges as a solution. velmovitsky et al.28 stated in their work that for genomics, by directly connecting data recipients and owners, blockchain can provide a secure and auditable way to share genomic data and increase its usability. precision medicine (or personalized medicine) focuses on protecting and improving an individual’s health before disease develops, rather than treating it. this approach aims to create personalized health management plans by considering each person’s unique characteristics, such as genetic makeup, lifestyle, and environmental factors. shameer et al.29 emphasize that wearable biomedical devices play a crucial role in monitoring health parameters, including sleep quality, air quality, environmental exposures, diet, biomarker status, body posture, stress, blood pressure, and physical activity. moreover, human genome data carry unique information about an individual and valuable opportunities for healthcare. clinical interpretations derived from large genomic datasets can significantly table 1. blockchain application areas in digital health and medical technologies. application area description blockchain contribution/benefit genomics and precision medicine genetic data sharing and approval processes data security, patient control, personalized healthcare telemedicine, mobile health, and digital health environment remote healthcare services, mobile applications secure data access, decentralized communication ar, vr, metaverse, and social media environment education, diagnosis, remote interaction digital asset ownership, visual evidence traceability iot, industry 4.0 transformation, health 5.0 environment smart devices, automation in healthcare device data integrity, interoperability ai, cloud technology, and big data integration analysis and management of health data verified data entry, model transparency solving global and regional health problems access inequality, data security cross-border collaboration, decentralized healthcare systems ai: artificial intelligence; iot: internet of things; vr: virtual reality. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.409 5 (page number not for citation purpose) blockchain in digital health and medical technologies improve healthcare outcomes and facilitate personalized medicine.30,31 genomic data differ from traditional medical data because it indirectly provides information about an individual’s children and relatives and remains relevant even after the individual’s death. this adds complexity to data sharing.8 data ethics is a highly debated topic in digital health and genomics. the digitization of health records, advances in bioinformatics, molecular medicine, wearable biomedical technologies, biotechnology, and synthetic biology have generated vast new datasets. how these data are shared, stored, distributed, and analyzed raises ethical debates regarding privacy, trust, accountability, justice, and fairness.6,32,33 genomic data are shared for research in an anonymized form. individuals have control over their genomic data and can share it for profit. in our analysis, biobanks and the use of blockchain for genomic studies have also been a highly studied area. biobanks play an important role in achieving precision health as they provide well-characterized biological samples and related data for disease prediction, diagnosis, and treatment, and blockchain technology can be used for the integration of population-based biobanks34 or in the consent process for biobanking.35 in clinical genomics, sharing rare genetic disease information between genetic databases and laboratories is essential to determine the pathogenic significance of variants to enable the diagnosis of rare genetic diseases.36 blockchain-based platforms are being developed to address both technical and governance issues related to sharing genomic data.37 in the field of genomics, blockchain facilitates a secure and verifiable method for exchanging data by directly linking data owners with recipients, thereby enhancing data accessibility.28 decentralized, privacy-focused predictive modeling enables multiple institutions to collaboratively build more generalizable models on health or genomic datasets by exchanging partially trained models rather than sensitive patient-level information, minimizing risks such as centralized control.38 appendix a presents a content analysis of research articles from the wos database on genomics and precision medicine. these keywords offer valuable insight into prevailing discussions on blockchain applications within these fields. the trend topic, network, and overlay analyses reveal not only technical terms but also the problem areas, potential applications, and ethical/legal considerations that researchers are emphasizing. as shown in appendix a, the most prominent keywords are blockchain, genetic algorithm, ai, privacy, security, smart contracts, data sharing, scalability, access control, and consensus algorithm. evaluating these keywords collectively, the general themes can be summarized as follows: data security and privacy (privacy, security, access control), data sharing and patient rights (data sharing, smart contracts), technical optimization (scalability, consensus algorithm, genetic algorithm), iot integration, and legal/ethical aspects (privacy, smart contracts). blockchain demonstrates strong potential for the secure, patient-centered management of genomic and precision medicine data. nonetheless, technical and ethical challenges, including scalability, privacy, and regulatory compliance, are being addressed through emerging consensus mechanisms and smart contract models. this framework shows that findings from the analysis of genomics and precision medicine research guide both existing technical solutions and future research directions. regarding consent management, blockchain’s transparent and unalterable ledger ensures that all participants have access to a permanent, timestamped record of consent, improving accountability and transparency throughout the consent process.28 as can be seen, blockchain technology has the potential to overcome these challenges in genomic and precision medicine applications due to its decentralized nature and immutable data recording capabilities. by enabling genetic data to be securely stored, shared, and analyzed, blockchain could offer a more accurate, reliable and transparent data management model in the medical world. blockchain enhances data security and accessibility, providing a stable and secure platform for advancing personalized medicine and research.39 numerous blockchain applications exist in genomics. for instance, companies such as encrypgen, lunadna, and nebula genomics are developing blockchain platforms that allow individuals to safely share their genomic data.40,41 these dna data marketplaces store genomic information and enable its use by interested commercial parties, while offering a share of the revenue to data contributors. personal genomic and health data are valuable for both publicly funded research and for-profit organizations developing new drugs, treatments, and diagnostic tests. while individuals often support data sharing for research, the for-profit nature of these platforms, data ownership rights, and fairness in benefit distribution raise ethical questions.41 a central issue in blockchain is determining data ownership and rights during its circulation. anonymizing personal data is a common approach to using data for the public good while protecting autonomy. however, as karabekmez6 notes, anonymizing genomic data is challenging. removing personal identifiers alone is insufficient because genomic data is inherently self-identifying, making re-identification possible. wjst42 proposed detailed access restrictions or obtaining informed consent as solutions to this issue. thus, while blockchain enables highly secure data sharing, it also involves complex ethical and legal considerations. overall, blockchain introduces critical innovations in genomics and precision medicine, including genomic data https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.4096 (page number not for citation purpose) muhammet damar et al. security and privacy, data sharing and research collaboration, personalized treatment planning, drug development and clinical trials, data trading and tokenization, and patient consent management. by combining genomic data privacy, patient-centered control, transparent collaboration, smart contracts, and secure sharing, blockchain advances both scientific progress and ethical/legal compliance in genomic research and personalized medicine. blockchain applications in telemedicine, mobile health, and digital health environment telemedicine encompasses technologies that enable patients to connect with healthcare professionals and receive services even when geographically distant. mobile health, on the other hand, refers to services that deliver and manage healthcare through smartphones and other mobile devices. both technologies extend the reach of traditional healthcare, allowing individuals to manage their health more effectively and access medical services more efficiently. the rapid growth of iot devices and wearable technologies has created new opportunities for medical sensors, particularly in remote patient monitoring. a subset of this trend is wireless body area networks (wbans), in which patients are equipped with wearable or implanted medical devices that provide real-time measurements of vital signs, such as heart rate or glucose levels.8 the use of remote patient monitoring is growing rapidly; in 2016, 7.1 million patients worldwide utilized such systems as part of their healthcare management.43 consequently, alongside telemedicine and mobile health services, wbans represent a critical technology for effective remote patient monitoring. technologies in digital health, including telemedicine, mobile health, and remote monitoring, are becoming increasingly integral to modern medical practice. ensuring the secure and precise handling of medical data supports the progress of digital health, which brings numerous positive outcomes. additionally, mobile health contributes to cost reduction by streamlining care delivery and improving connectivity between patients and healthcare professionals. mobile apps enable both patients and providers to actively manage medical conditions through almost real-time monitoring and interventions, regardless of their physical locations.44 recently, healthcare services have undergone a major transformation in the digitalization process. telemedicine, mhealth, and digital health applications make the interaction between patients and healthcare providers more efficient, accessible, and sustainable. however, critical challenges such as data security, patient privacy, and system reliability arise in these digital health environments. blockchain technology stands out as a powerful tool that can help solve these problems with its decentralized structure and unchangeable data storage features. blockchain technology is particularly preferred for the secure recording of remote healthcare services and for more secure data sharing in the provision of electronic healthcare services. durneva et al. reported in their study that blockchain-based patient care applications include medical information systems, personal health records, mobile health, telehealth and telemedicine, data protection systems and social networks, health information exchanges and remote monitoring systems, and medical research systems.45 these blockchain-based healthcare applications can improve patient engagement and empowerment, improve healthcare providers’ access to information, and enhance the use of healthcare information for medical research. blockchain-supported mobile health studies by motohashi et al.46 support this idea. appendix b presents a content analysis of research articles on telemedicine, mobile health, and the digital health environment obtained from the wos database. the analysis shows that blockchain, security, telemedicine, e-health, smart contracts, medical services, healthcare, digital health, access control, authentication, ai, interoperability, iot, medical diagnostic imaging, telehealth, and traceability are among the most intensively researched topics in the literature. studies address various dimensions, including technical, legal, ethical, and operational aspects. while blockchain provides a promising infrastructure for security, data sharing, smart contracts, and traceability in telemedicine, mobile health, and digital health, critical challenges such as scalability, ai integration, iot device security, and compliance with international standards remain. the main topics are summarized under six headings in table 2. the development of compact mobile devices equipped with wireless communication and integrated biosensors has transformed healthcare systems. these devices, often worn as accessories, allow individuals to continuously gather health-related data. this form of medical support, which uses mobile devices to remotely monitor patients and deliver healthcare services, is referred to as mobile health. while mobile health offers numerous advantages and is gaining widespread adoption, it also raises significant privacy concerns.47 liang et al.48 highlighted the fact that personal health data collected through mobile and wearable technologies holds immense and growing value for healthcare providers and medical research alike. they further emphasized that secure and user-friendly sharing of this personal health information is crucial for enhancing collaboration within healthcare and that blockchain technology presents a promising solution to address privacy challenges and security risks in current data storage and sharing frameworks. griggs et al.10 adopted a specialized blockchain based on the ethereum protocol, not only to facilitate the safe and secure use of medical sensors but also to eliminate security risks associated with remote patient monitoring systems. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.409 7 (page number not for citation purpose) blockchain in digital health and medical technologies blockchain-based strategies facilitate secure real-time remote monitoring, enabling practitioners to track their patients’ health conditions from remote locations while maintaining a secure, safe, and up-to-date patient history. every passing day, many diseases and patients are straining the healthcare system, making it difficult to continue traditional medical services. to overcome these challenges, the healthcare sector is increasingly shifting towards telehealth, which uses sophisticated technology to provide more comprehensive medical treatments at lower prices. telemedicine can be defined as the remote delivery of healthcare services over telecommunication infrastructure. the main purpose of telemedicine is to provide clinical support by overcoming geographical disruptions and connecting people in remote areas with the help of different information and communication technologies.49 in the general remote diagnosis scheme, the collected medical data are stored in the cloud, and these data can be leaked or falsified. because blockchain has decentralized storage features, some schemes use blockchains to overcome the security problem of cloud storage, but most of them lack an auditing mechanism.50 however, this interaction can bring various challenges, such as data breaches, misdiagnoses, and fraud. in addition, the outsourcing of healthcare data to public cloud platforms brings some new challenges in terms of security.51 while the decentralization aspect of blockchain increases the overall robustness of existing healthcare systems, trust and traceability are key action points to focus on. blockchain technology, paired with smart contracts, automates the operations and services of telehealth and telemedicine efficiently and reliably. there are applications in the field of telehealth and telemedicine that demonstrate the practicality of secure data transfers using blockchain technology.52 ahmad et al.53 stated that blockchain technology can enhance telehealth and telemedicine services by providing remote healthcare services in a decentralized, tamper-proof, transparent, traceable, reliable, and secure manner for more secure data access in telehealth and enable healthcare professionals to accurately detect fraud related to physician training credentials and medical test kits commonly used for home diagnostics.53 telemedicine and mobile health suffer from various risks in practice, such as data breaches, restricted access in the medical community, incorrect diagnosis and prescription, fraud and abuse.46,54 at this point, blockchain is a critical technology for the solution. as can be seen, blockchain technology can increase the security and transparency of health data for the secure storage, sharing, and verification of patient data in telemedicine and mobile health applications. it also offers opportunities to prevent fraud and make processes more efficient in digital health environments. it is seen that blockchain interactive applications stand out for the development of more effective and secure applications in telemedicine, mobile health, and digital health fields. blockchain applications in augmented reality, virtual reality, metaverse, and social media environments in the healthcare sector recently, ar, vr, the metaverse, and social media have initiated a significant transformation in the fields of digital interaction and experience. especially recently, metaverse technology has caused a transformation in many areas.13,55 these technologies allow users to interact with virtual worlds, explore digital environments, and combine the real world with virtual layers, offering great potential in many sectors, from entertainment to education and business. however, the rapidly developing nature of these digital environments also brings with it significant challenges such as data security, user privacy, and ownership of digital assets. table 2. key discussion topics and research questions on blockchain applications in telemedicine, mobile health, and the digital health environment. key discussion topics featured keywords in the related title sample research questions data security and privacy security, authentication, access control how does blockchain protect remote healthcare data from unauthorized access? service and payment automation smart contracts, medical services how do smart contract models apply to appointment booking, insurance, and payments? ai and imaging ai, medical diagnostic imaging how does blockchain verify the reliability of ai-based diagnoses? iot and mobile health integration iot, digital health how can continuous data from wearable devices be transferred to a blockchain in a scalable manner? traceability and supply chain traceability, telehealth how can telemedicine processes and the medication pipeline be made transparent? interoperability interoperability, e-health how can different healthcare information systems be integrated with blockchain? ai: artificial intelligence; iot: internet of things. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.4098 (page number not for citation purpose) muhammet damar et al. blockchain technology can play an important role in solving these problems with its decentralized structure and secure data recording features. for example, shahbazi and byun56 stated in their studies that the proper examination of social media data could provide significant support to various criminal investigations, but government officials emphasized that this is quite difficult. at this point, they have seen blockchain technology as a solution for digital forensic science investigations due to its contribution to traceability. at this point, lawrence and shreelekshmi57 have identified blockchain technology as a solution in the field of forensic science to ensure the validity and reliability of visual evidence for video integrity. appendix c presents the content analysis of research articles obtained from the wos data source on ar, vr, metaverse, and social media environment in the healthcare sector. the prominent topics include blockchain, metaverse, ai, security, vr, social media, ar, smart contracts, digital twins, privacy, iot, cryptocurrency, avatars, authentication, scalability, web3, consumer electronics, and interoperability. these keywords highlight fundamental research questions regarding the secure and sustainable management of next-generation digital experiences in healthcare, such as metaverse hospitals, ar/vr therapies, and social media health communities. blockchain serves as a critical infrastructure for security, identity management, data ownership, and economic modeling in metaverse and ar/vr-enabled healthcare environments. nonetheless, challenges such as scalability, regulatory compliance, interoperability, and the ethical use of digital twins58 remain key research areas. this framework identifies future research gaps, particularly in digital twin management, web 3.0based health economics, social media content authentication, and ar/vr device security, as summarized in table 3. the metaverse is a cohesive, continuous, and shared virtual space where multiple users can interact in a fully immersive, highly dynamic, and interconnected digital network.13,55,59 it represents the convergence of three key technological trends: telepresence, digital twins, and blockchain technology.59 when applied to healthcare, the metaverse holds significant promise for enhancing medical services, offering a vast potential for medical education, advanced training, and remote surgical procedures.13,60 within the medical domain, healthcare professionals can leverage the metaverse to improve the efficiency of diagnosis, training, and treatment processes, while fostering strong interactions between medical staff and patients in a digital environment.61 metaverse technology relies extensively on blockchain applications to manage asset ownership, user interactions, and security. cryptocurrencies generate economic value within the metaverse, making blockchain essential not only for securing the virtual environment but also for facilitating safe economic activities. the most prominent cryptocurrencies in this context include mana (decentraland’s native currency), sand (the primary currency of the sandbox platform), and axs (the governance token of axie infinity, notable for its play-toearn model). moztarzadeh et al.62 highlighted in their research the fact that creating digital twins of dental conditions within the metaverse is a practical and effective method to utilize the immersive capabilities of this technology, bridging real-world dentistry with its virtual counterpart. they stated that these technologies can create virtual facilities and environments for patients, doctors, and researchers to access various medical services and saw the blockchain as a critical tool for data interaction in the virtual environment. for educators in the health field, learning where the source of the data is read or presented and seeing the history of the data can be of critical value. funk et al.63 stated that recently, many new digital platforms have emerged in the field of learning, including massive open online courses and social media-based education. at this point, they stated that it is table 3. key discussion topics and research questions on blockchain applications in ar, vr, the metaverse, and social media environments within the healthcare sector. key discussion topics featured keywords in the related title sample research questions data security and identity security, privacy, authentication, avatars how do digital avatars authenticate and protect privacy? economic models smart contracts, cryptocurrency, web3 how do crypto payments and smart contracts work for healthcare? technological integration digital twins, iot, consumer electronics how are device data and digital twins stored and shared on the blockchain? social media and content verification social media, metaverse how can blockchain prevent misinformation in healthcare communities? performance and standards scalability, interoperability how can transaction speed and data harmonization be ensured in large-scale metaverse healthcare networks? ar: artificial intelligence; iot: internet of things; vr: virtual reality. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.409 9 (page number not for citation purpose) blockchain in digital health and medical technologies quite difficult to determine the origin, validity, and accountability of shared and acquired knowledge. as can be seen, the management of digital assets, secure data sharing, data sources, data monitoring, and accuracy of interactive experiences in ar, vr, metaverse, and social media platforms can be provided with blockchain. it also has many different usage areas in the literature. blockchain applications in the internet of things, industry 4.0 transformation, health 5.0 environment the iot, industry 4.0, and health 5.0 are the leading technologies of digital transformation and have the potential to transform every aspect of life. while iot improves data collection and analysis processes by interacting with each other and central systems, industry 4.0 uses these data to provide efficiency, automation, and flexibility in production processes. health 5.0 aims to provide human-centered digital health solutions and aims to use individuals’ health data more effectively. however, a strong data management and security infrastructure is needed for these advanced systems to operate securely, transparently, and efficiently. at this point, blockchain technology emerges as an important solution in iot, industry 4.0, and health 5.0 environments. several significant solutions have been proposed in the literature regarding iot, industry 4.0, and health 5.0. for example, wu et al. proposed a healthcare 5.0 framework for surgery that deploys a secure and distributed network using blockchain to show transactions between different parties in the orthopedic surgery process.64 in this way, they were able to demonstrate the feasibility of using an iot-based blockchain network in orthopedic surgery, which can reduce medical errors and improve data interoperability between different parties. rovere et al.65 stated that blockchain technology has the potential to improve data security, interoperability, and collaboration in orthopedics. in another example, fatoum et al.2 stated that the fact that electronic health records are not easily accessible to a treating emergency physician is perhaps a classic example of a disconnected medical ecosystem. for example, if a heart failure patient has iot devices that detect potassium, oxygen, and vital signs, they stated that it could be effective in saving the lives of many patients with heart and kidney diseases. appendix d shows the content analysis of research articles obtained from the wos data source on iot, industry 4.0 transformation, and health 5.0 environment. key words that emerged in the analysis include blockchain, iot, security, healthcare, medical services, cloud computing, fog computing, privacy, access control, ai, authentication, data privacy, e-health, computer architecture, deep learning, interoperability, monitoring, smart contracts, covid-19, hospitals, hyperledger fabric, integrity, sensors, smart health, bluetooth, and e-adoption. together, these keywords indicate that blockchain is positioned in the literature not only as a tool for data security but also as a foundational backbone of the iot-enabled healthcare ecosystem. in particular, research emphasizes scalability, real-time data processing, ethical data sharing, and regulatory frameworks. table 4 presents the key discussion topics and research questions identified in the literature regarding blockchain applications in the internet of things, industry 4.0 transformation, and health 5.0 environments. it summarizes table 4. key discussion topics and research questions on blockchain applications in the iot, industry 4.0 transformation, health 5.0 environment. key discussion topics featured keywords in the related title sample research questions iot and blockchain integration blockchain, iot, smart health, sensors, bluetooth, monitoring, integrity how feasible is it to store data generated by iot devices (wearable sensors, medical devices) securely and immutably on blockchain? how can remote patient monitoring, remote monitoring, and data integrity be ensured? industry 4.0 & health 5.0 transformation healthcare, medical services, hospitals, e-adoption, smart contract how feasible is the integration of blockchain with industry 4.0-based smart manufacturing and healthcare services within the scope of the health 5.0 vision? security and privacy security, privacy, access control, authentication, data privacy what can be done to reduce the risks of unauthorized access, data leakage, and patient privacy in iot-based healthcare systems? cloud and fog computing cloud computing, fog computing, scalability, computer architecture is the integration of cloud and fog computing a viable solution to address blockchain’s scalability challenges? ai and data analytics ai, deep learning, smart contract is anomaly detection possible by analyzing iot data collected on blockchain with ai and deep learning algorithms? pandemic and crisis management covid-19, healthcare, hospitals is blockchain-iot integration feasible for patient monitoring and hospital resource management during covid-19 and similar crises? interoperability and standards interoperability, hyperledger fabric how feasible is data sharing between different healthcare institutions and iot devices? what can be done to ensure integration standards, appropriate infrastructure, and interoperability? ai: artificial intelligence; iot: internet of things. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.40910 (page number not for citation purpose) muhammet damar et al. the main thematic directions and future research gaps derived from the reviewed studies, complementing the content analysis illustrated in appendix d. alfayez and khan66 noted that as the elderly population continues to grow, falls among the elderly will become a critical public health issue. to address this, they developed a fall detection system for the elderly using iot and blockchain. kuberkar and singhal67 proposed a solution based on blockchain and iot technologies to ensure adequate availability of blood units at the national level. rajendran68 addressed the problem of blood inventory management by exchanging blood and providing transparency and traceability using the blockchain technique. the author concluded that this method can significantly outperform the current system. zheng et al.69 stated in their study that distributed ledger technologies integrated with iot technologies can greatly improve health-related data sharing. they stated that with their proposed solutions based on increasing organ transplant access tangle and masked authenticated messaging (mam), they can overcome the challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. increasing organ transplant access is a cryptocurrency with a new architecture called tangle, focused on iot solutions. it is an alternative to blockchain. unlike traditional blockchains that use blocks and a linear chain, tangle uses a directed, acyclic graph structure. this structure can be thought of as a network rather than a chain. one of the main advantages of tangle is that it is virtually free.27,70 in summary, the work by zheng et al.71 proposes a solution that uses the iota tangle to create a scalable and free data-sharing network while utilizing mam to guarantee the privacy, security, and controlled access of sensitive health data. as can be seen, a strong data management and security infrastructure is needed to operate securely, transparently, and efficiently in iot, industry 4.0, and health 5.0 environments. this can be achieved with blockchain technology. blockchain applications in artificial intelligence, cloud technology, and big data integration in the healthcare sector ai, cloud technology, and big data have become some of the most important elements of digital transformation today. these three technologies integrate, making data processing, analysis, and decision-making processes more efficient, faster, and more accurate. while ai allows meaningful information to be extracted from big data, cloud technology facilitates the storage and access of this data. blockchain technology is used to ensure the reliability of the data used by ai systems and to analyze large amounts of health data, or big health data, reliably. in their study, dwivedi et al.72 stated that billions of sensors, devices, and vehicles have been connected to each other over the internet in recent years. furthermore, remote patient monitoring, one of these technologies, is widely used for the treatment and care of patients today. these security and privacy issues related to medical data can lead to a delay in the progress of treatment and can even endanger the patient’s life. therefore, they suggested using a blockchain to ensure that big data related to healthcare services are managed and analyzed securely. appendix e presents a content analysis of research articles obtained from the wos database on ai, cloud technology, and big data integration in the healthcare sector. the most prominent topics, in order of importance, include blockchain, ai, big data, cloud computing, access control, healthcare, covid-19, e-health, electronic health records, health big data, medical services, privacy, security, animal health, healthcare management, monitoring, and medical big data. the published literature indicates that blockchain is not only a tool for data security but also a fundamental backbone of ai and cloud ecosystems. big data supports the development of more accurate and unbiased ai models, enhanced by the data verification and security that blockchain technology provides. moreover, blockchain–cloud hybrid models are gaining prominence in healthcare, as the integration of these technologies enables rapid data access and processing. table 5 summarizes the key discussion topics and research questions identified in the literature regarding blockchain applications in artificial intelligence, cloud technology, and big data integration within the healthcare sector. this table complements the findings shown in appendix e by outlining thematic insights, technical challenges, and emerging research gaps in this interdisciplinary field. as seen in the analysis performed, advanced, mathematical, and deep learning algorithms have recently played an important role in diagnosing medical parameters and diseases.62 kumar et al.73 have presented a method that combines deep learning models learned locally on the blockchain to improve the prediction of lung cancer in healthcare systems to protect privacy and enable data sharing. mantey et al.74 have presented an advanced deep learning approach that can automatically reveal which food a patient with special needs should consume according to their disease and certain characteristics such as gender, weight, age, etc., with the secure communication channel of the recommendation system, thanks to the blockchain privacy system they proposed in their study. personal health records can be secured in a cloud-based data lake.75 kaur et al.76 proposed a blockchain-based platform that can be used to store and manage electronic https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.409 11 (page number not for citation purpose) blockchain in digital health and medical technologies medical records in the cloud environment. in addition, yazdinejad et al.77 stated that in any interconnected healthcare system, interactions between patients, doctors, nurses, and other healthcare practitioners should be secure and efficient. for example, they stated that all members should be authenticated and securely connected to minimize security and privacy violations from a given network, and they proposed an authentication system with blockchain technology in their work. as can be seen, ensuring the integration of powerful technologies such as ai, cloud technology, and big data, and protecting the security, privacy, and integrity of data are significant challenges. blockchain technology, thanks to its decentralized structure and immutable data recording features, can realize the integration of these three technologies securely and transparently. blockchain offers a potential solution to ensure the accuracy of ai algorithms, manage big data flow, and increase data security in cloud environments. the federated learning model reduces complexity, while blockchain technology supports distributed data management with strong privacy protections. specifically, the proposed federated learning community ensembles a federated blockchain-based framework that enables secure model training and data sharing among multiple healthcare institutions. blockchain applications for solving global and regional health problems global health problems are complex and require largescale solutions that affect millions of people worldwide. global health problems are serious challenges that affect not only individuals but also entire societies and health systems. problems such as epidemics, infectious diseases, lack of access to health services, and security of health data are among the biggest threats in the field of health worldwide. the covid-19 pandemic, in particular, shows how quickly such health crises spread and their effects on a global scale, as well as revealing the inadequacies of existing systems in the provision of health services. in addition, the management and protection of health data have become more critical with increasing digitalization. at this point, blockchain technology emerges for appropriate and secure data sharing in global problems. for example, subramanian and subramanian (2022)78 reported that recent developments in digital pathology resulting from developments in imaging and digitization have increased the convenience and usability of pathology for disease diagnosis, especially in oncology, urology, and gastroenteric diagnosis. at this point, ai deep learning-supported image processing has a significant place; however, data sharing is a critical problem, and blockchain has been evaluated in the literature as a solution.79 recent research indicates that deep learning models achieve better performance and generalization when trained on large datasets.73 this is particularly important for producing more accurate results in the diagnosis of rare diseases, which often require data collection from numerous sources worldwide or across regions. tagliafico et al.80 noted that blockchain technology could enhance the value of radiological data in both clinical practice and research. this includes applications related to patient table 5. key discussion topics and research questions on blockchain applications in ai, cloud technology, and big data integration in the healthcare sector. key discussion topics featured keywords in the related title featured research questions in research title blockchain–ai–big data synergy blockchain, ai, big data, health big data, medical big data how can the optimal blockchain, ai, and big data synergy be achieved for the secure collection, sharing, and analysis of health data and for more accurate predictions? cloud-based healthcare systems cloud computing, ehealth, ehr, healthcare how can the combined use of cloud computing and blockchain enable scalable and secure storage, remote access, and sharing ehrs? privacy, security, and access control privacy, security, access control, monitoring how can data privacy, unauthorized access prevention, and traceability be ensured due to the processing of health data in multi-dimensional environments (ai, big data, cloud)? healthcare management and service delivery healthcare, healthcare management, medical services how can blockchain and ai be combined for patient care quality, service coordination, resource optimization, and hospital management? pandemic and crisis management covid-19, ehealth, monitoring how can real-time tracking, disease spread prediction, and traceability of big health data be ensured during the covid-19 era? interdisciplinary and cross-domain uses animal health, fertility, medical big data would it be possible to use blockchain and ai not only in human health but also in areas such as veterinary (animal health) and reproductive health (fertility)? data source and analytics quality healthcare, medical services, big data can blockchain-based record verification and consensus algorithms, based on the accuracy, integrity, and reliability of big data sources, improve data quality? ai: artificial intelligence; ehrs: electronic health records. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.40912 (page number not for citation purpose) muhammet damar et al. digital records, radiology reports, privacy management, quantitative image analysis, cybersecurity, radiomics, and the integration of ai. the federated learning model reduces complexity, while blockchain technology supports distributed data management with strong privacy protections. specifically, the proposed federated learning community ensembles a federated learning ensembled deep learning blockchain model (fled-block) for covid-19 prediction that gathers data from multiple healthcare centers, enhances the model using a hybrid capsule learning network, and accurately predicts outcomes while ensuring privacy and controlled data sharing among authorized users.81 for instance, during the covid-19 pandemic, this system achieved a prediction accuracy of 98.2%.81 additionally, motion sensor data analyzed with deep learning algorithms can classify human activities and assess the severity of essential tremor during various movements.71 another important application concerns homeless populations. researchers highlight the fact that the absence of official identification documents and restricted access to personal records are significant barriers preventing homeless individuals from gaining resilience and overcoming life challenges.82 this issue is particularly pronounced in countries like the united states, the united kingdom, and canada.83–85 therefore, regional or global initiatives leveraging blockchain-based solutions could be developed to improve the quality of life for homeless people. appendix e presents a content analysis of research articles obtained from the wos database on global and regional health problems. the prominent topics emerging from the analysis include blockchain, global health, healthcare, challenges, iot, security, aging, ai, authentication, data sharing, decentralized healthcare data, disease surveillance, epidemics, health policy, health informatics, identity management, and infectious diseases. these findings suggest that blockchain is viewed not only as a technical data security solution but also as a strategic tool in global health management. the literature emphasizes blockchain’s role in strengthening international cooperation and policy development through transparent data sharing for global health initiatives. post-covid-19 studies particularly highlight blockchain’s capacity for real-time outbreak monitoring and reporting. in addition, decentralized health data infrastructures are recommended as potential solutions to improve healthcare access and ensure data security in developing countries. blockchain applications for addressing global and regional health problems are categorized under six headings in table 6: solutions to global and regional health challenges, infectious disease surveillance and outbreak management, data security and privacy, iot integration and real-time tracking, ai-supported analysis, and aging populations with chronic diseases. as shown in the table, reproducibility, data sharing, privacy protection, and patient recruitment for clinical trials remain significant challenges in contemporary clinical research. emerging technologies like blockchain could play a pivotal role in addressing these issues and warrant increased attention from the clinical research community.86 blockchain is also essential for advancing toward universal health coverage.87 for instance, during global health crises such as the covid-19 pandemic, establishing a table 6. key discussion topics and research questions on blockchain applications for solving global and regional health problems. key discussion topics featured keywords in the related title featured research questions in related title solutions to global and regional health problems blockchain, global health, healthcare, challenges, health policy is blockchain a solution to problems such as inequality in global healthcare, inadequate infrastructure, and lack of transparency in healthcare policies? infectious disease surveillance and outbreak management disease surveillance, epidemics, infectious diseases, health informatics can blockchain-enabled disease surveillance systems enable the development of early warning mechanisms by ensuring data integrity, real-time tracking, and global data sharing in covid-19 and similar outbreaks? data security and privacy security, authentication, data sharing, decentralized healthcare data can blockchain technology enable authentication, authorization, and access control for globally shared health data? iot integration and real-time tracking iot, identity management, data sharing is blockchain a suitable technology for iot integration and real-time patient tracking and monitoring, epidemiological data collection, and remote healthcare provision? ai-enabled analytics ai, health informatics is it possible to derive disease predictions, transmission models, and health policy decisions from ai analyses of global health data collected with blockchain technology? aging populations and chronic diseases aging, healthcare, health policy can blockchain technology address the impact of aging populations on healthcare in developed countries, provide care coordination, and securely manage insurance/financing processes? ai: artificial intelligence; iot: internet of things. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.409 13 (page number not for citation purpose) blockchain in digital health and medical technologies worldwide infectious disease surveillance and case tracking system becomes crucial for early outbreak detection and containment.88 in their study, khan et al. demonstrated how blockchain combined with machine learning can be applied effectively to infectious disease monitoring, highlighting the fact that their system successfully balances public health needs with the protection of individual privacy.89 damar et al.21 stated that blockchain technology is the most prominent technology for combating a global pandemic and is critical for providing ideal global healthcare. the findings also demonstrate that global healthcare is becoming more questionable, particularly during the covid-19 pandemic, and that blockchain technology is a particularly prominent technology in this regard. yazdinejad et al.77 emphasized that in any interconnected healthcare system, secure and efficient communication among patients, doctors, nurses, and other healthcare professionals is essential. they highlighted the need for authenticating all participants and ensuring secure connections to reduce risks of security breaches and privacy violations within the network. to address this, they proposed a blockchain-based identity verification system. furthermore, challenges such as reproducibility, data sharing, protecting personal data privacy, and enrolling patients in clinical trials remain significant hurdles in modern clinical research. blockchain technology offers promising solutions to these issues and deserves the focused attention of the entire clinical research community.86 even after privacy and trust issues are addressed, health data must be clean, well-organized, reliable, and neutral against any form of discrimination to be used effectively. this challenge must be taken seriously by both private and governmental institutions; otherwise, the potential benefits of the digital age will remain unattainable.6 only under such conditions can we genuinely speak of global healthcare services. in summary, issues such as epidemics, infectious diseases, lack of access to healthcare, and security of health data threaten global health systems and require innovative approaches for solutions. in addition, different and more constructive solutions that emphasize information security may be needed to cope with global or regional problems. blockchain technology is a technology that has the potential to provide solutions to all these challenges. thanks to its decentralized structure, transparency, and unchangeable data recording, it enables health data to be stored and shared securely. these features offer significant advantages for securing patient information and managing access to healthcare more effectively. since global health problems also require multinational collaboration, the transparency and security offered by blockchain can facilitate data sharing between international healthcare systems. conclusions the integration of blockchain technology into digital health is not a matter of potential but an emerging reality with substantial implications across the healthcare continuum. this study has explored blockchain’s role in six critical areas: genomics and precision medicine, telemedicine and mobile health, immersive technologies, iot and health 5.0, ai and big data integration, and global health challenges, each representing a significant frontier in the digital transformation of healthcare. the findings suggest that blockchain offers foundational capabilities that directly address longstanding challenges in healthcare, such as data fragmentation, lack of interoperability, and concerns over security and privacy. in genomics and precision medicine, blockchain empowers patients with ownership and control over their data while ensuring secure and auditable sharing mechanisms that are crucial for research and clinical collaborations. in telemedicine and mobile health, it strengthens data integrity and enables traceable interactions, thereby enhancing trust in remote care delivery. for immersive technologies like ar, vr, and the metaverse, blockchain supports provenance and authenticity of digital assets, fostering reliable educational, diagnostic, and therapeutic environments. iot and health 5.0 applications benefit from blockchain’s ability to ensure device data integrity, automate workflows through smart contracts, and promote real-time interoperability. the synergy between blockchain and ai/big data reinforces the reliability of algorithmic healthcare decisions by ensuring data transparency, reproducibility, and auditability. moreover, at the global level, blockchain proves instrumental in enhancing disease surveillance, equitable access to care, and privacy-preserving data sharing for pandemics and underserved populations. however, the challenges in blockchain technology and its applications remain. blockchain scalability, energy consumption, regulatory uncertainties, and the need for standardized frameworks across healthcare systems must be addressed. furthermore, while blockchain enhances data security, it does not eliminate the need for robust governance models, user education, and ethical safeguards. final thoughts blockchain stands as a transformative enabler in digital health. by decentralizing data management, enhancing transparency, and safeguarding privacy, it aligns well with the pressing needs of modern healthcare systems. the evidence synthesized in this study affirms that blockchain can augment existing digital health technologies and pave the way for innovative, equitable, and resilient healthcare models. future research should prioritize interdisciplinary collaboration, pilot implementations, and regulatory harmonization https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.40914 (page number not for citation purpose) muhammet damar et al. to realize blockchain’s full potential in delivering patient-centric, secure, and intelligent healthcare solutions. limitations and future studies while this study offers a comprehensive synthesis of current applications and the potential of blockchain technology in digital health, several limitations must be acknowledged: • scope of literature: the study primarily draws on studies indexed in the wos and supplemented by google scholar. while these databases cover a wide range of peer-reviewed content, relevant contributions from industry white papers, preprints, and emerging innovations in less-represented regions may have been underrepresented. • implementation challenges: many studies focus on theoretical frameworks, pilot projects, or conceptual designs rather than large-scale, real-world implementations. as a result, this study may overrepresent the potential benefits of blockchain while underrepresenting operational difficulties, regulatory barriers, and stakeholder resistance in clinical practice. • lack of standardization: the absence of standardized metrics and frameworks in the reviewed studies made it difficult to perform a quantitative synthesis or comparative analysis. the study, therefore, relies heavily on qualitative insights. to address these limitations and further advance the field, future research should aim to overcome them by incorporating a broader range of literature sources, such as scopus and others, expanding the scope of analysis, and focusing more on implementation challenges. specifically, future studies should move beyond theoretical frameworks and pilot projects to explore large-scale, real-world applications. additionally, the lack of standardized metrics and frameworks should be addressed to enable quantitative synthesis and comparative analysis across studies. funding none conflicts the authors affirm no conflict of interest. contributors muhammet damar contributed to conceptualization, methodology, validation, formal analysis, data curation, writing—original draft, and writing—review and editing. ömer aydın contributed to the conceptualization, methodology, validation, formal analysis, data curation, writing the original draft, writing—review and editing. fatih safa erenay contributed to conceptualization, investigation, writing—review and editing, and supervision. data availability statement (das), data sharing, reproducibility, and data repositories the data that support the findings of this study are available from the corresponding author upon reasonable request. application of ai-generated text or related technology ai-assisted tools were employed in this study for minor tasks such as grammar correction, language refinement, and proofreading. these tools were used transparently and in a manner that does not compromise the authors’ intellectual 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creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see http://creativecommons. org/licenses/by-nc/4.0. the authors of this article own the copyright. https://doi.org/10.30953/bhty.v8.409 https://doi.org/10.2196/13583 https://doi.org/10.1016/j.parkreldis.2018.08.001 https://doi.org/10.3390/s19020326 https://doi.org/10.1016/j.compmedimag.2020.101812 https://doi.org/10.1016/j.compmedimag.2020.101812 https://doi.org/10.3389/fpubh.2021.737269 https://doi.org/10.1155/2022/3045107 https://doi.org/10.1007/s10916-018-1007-5 https://doi.org/10.1109/jbhi.2020.2969648 https://doi.org/10.1109/jbhi.2020.2969648 https://doi.org/10.2196/34207 https://doi.org/10.1108/ijpcc-10-2020-0160 https://doi.org/10.1108/ijpcc-10-2020-0160 https://doi.org/10.1007/s11547-022-01460-1 https://doi.org/10.3389/fpubh.2022.892499 https://doi.org/10.3389/fpubh.2022.892499 https://doi.org/10.2196/10654 https://doi.org/10.2196/10654 https://doi.org/10.1080/02723638.2020.1853919 https://doi.org/10.1016/j.amjmed.2020.10.002 https://doi.org/10.1186/s12245-023-00526-9 https://doi.org/10.1186/s12245-023-00526-9 https://doi.org/10.1186/s13063-017-2035-z https://doi.org/10.1186/s13063-017-2035-z https://doi.org/10.1136/bmjgh-2017-000570corr1 https://doi.org/10.2196/20567 https://doi.org/10.1016/j.irbm.2024.100829 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.40918 (page number not for citation purpose) muhammet damar et al. appendix a. articles’ trend topics (a), network (b), and overlay analyses (c) about blockchain applications for genomics and precision medicine. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.409 19 (page number not for citation purpose) blockchain in digital health and medical technologies appendix b. articles’ trend topics (a), network (b), and overlay analyses (c) about blockchain applications in telemedicine, mobile health and digital health environment. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.40920 (page number not for citation purpose) muhammet damar et al. appendix c. articles’ trend topics (a), network (b), and overlay analyses (c) blockchain applications in augmented reality (ar), virtual reality (vr), metaverse, and social media environments in the healthcare sector. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.409 21 (page number not for citation purpose) blockchain in digital health and medical technologies appendix d. articles’ trend topics (a), network (b), and overlay analyses (c) blockchain applications in the internet of things, industry 4.0 transformation, health 5.0 environment. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.40922 (page number not for citation purpose) muhammet damar et al. appendix e. articles’ trend topics (a), network (b), and overlay analyses (c) blockchain applications in artificial intelligence, cloud technology, and big data integration in the healthcare sector. https://doi.org/10.30953/bhty.v8.409 citation: blockchain in healthcare today 2025, 8: 409 https://doi.org/10.30953/bhty.v8.409 23 (page number not for citation purpose) blockchain in digital health and medical technologies appendix f. articles’ trend topics (a), network (b), and overlay analyses (c) blockchain applications for solving global and regional health problems. https://doi.org/10.30953/bhty.v8.409 1 (page number not for citation purpose) narrative/systematic reviews/meta-analysis systematic review of usability factors, models, and frameworks with blockchain integration for secure mobile health (mhealth) applications irum feroz, phd1 and nadeem ahmad, phd2 1department of school of arts and creative technologies, university of bolton, bolton, united kingdom, 2department of computing and technology, iqra university, h-9 campus, islamabad, pakistan corresponding author: nadeem ahmad, email: nadeem.ahmad@ieee.org doi: https://doi.org/10.30953/bhty.v7.357 keywords: blockchain, mobile health applications, systematic literature review, usability frameworks, usability standards and models abstract this systematic review examines critical usability factors that influence the adoption of mobile health (applications among older adults) and identifies gaps in current usability models, including iso 9241-11, nielsen’s heuristics, and panicoideae, aristidoideae, chloridoideae, micrairoideae, arundinoideae, danthonioideae. this review also explores the potential role of blockchain technology in enhancing multimodal medical data systems within mhealth applications. a comprehensive search across six databases yielded 1,073 studies, with 60 meeting the inclusion criteria. studies were analyzed through thematic synthesis to identify key success factors (rq1) and comparative analysis to assess limitations in existing frameworks (rq2). key factors promoting mhealth adoption included ease of use, efficiency, error prevention, learnability, memorability, and user satisfaction. blockchain integration emerged as a promising approach to improve data security, interoperability, and user trust, particularly for older adults who engage with complex, multimodal health data. findings from rq2 highlighted gaps in usability models, such as the lack of age-specific guidance for multimodal interaction, error recovery, and data privacy. these results underscore the need to define a new usability framework and incorporate blockchain to meet the unique needs of older adults in mhealth applications, supporting both secure and accessible healthcare management. plain language summary this review investigates mobile health application’s integration with blockchain. this review explores user-friendly mhealth applications for older adults and also explores how blockchain can improve data systems in these applications. after analyzing 60 studies, key factors for the adoption of information technology were identified, including ease of use, efficiency, error prevention, and user satisfaction. the researchers discovered that blockchain enhances data security, interoperability, and trust of mhealth applications. moreover, existing usability models lack elderly specific guidance, particularly for handling errors and privacy in mhealth applications. these findings highlight the need for a new usability framework tailored to older adults, integrating blockchain to ensure secure, accessible, and user-friendly healthcare management. submitted: october 31, 2024; accepted: november 26, 2024; published: december 16, 2024 blockchain in healthcare today issn 2573-8240 mailto:nadeem.ahmad@ieee.org https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.3572 (page number not for citation purpose) irum feroz et al. more than 100,000 mobile health (mhealth) applications are available in the android and ios app stores, and this number is continually rising with the rapid development of new applications.1 the mhealth applications demonstrated substantial utility by aiding in prevention, early detection, screening, and public education. these applications became an essential tool in managing the covid-19 outbreak by offering accessible information and supporting treatment protocols.2 for instance, a symptom tracker app was launched in the united kingdom to collect self-reported covid19 symptoms, enabling the identification of positive cases even among individuals who had not been tested.3 the adoption of information technology in healthcare has effectively addressed gaps in access and quality within healthcare systems.4 current digital health trends, including mhealth, focus on empowering patients, consumers, clinicians, and researchers with mobile technology to improve overall health outcomes.5,6 widely used by hospitals, medical students, and allied health workers, mhealth applications facilitate rapid dissemination of health information, news, and critical updates.7–9 these applications enhance medical care and bolster public health by promoting healthy lifestyles and supporting chronic disease management through tools for tracking fitness, diet, diabetes management, and medication adherence. the mhealth market was valued at approximately usd 40 billion in 2020, with an estimated annual growth rate of 17.7% from 2021 to 2028, driven by its ability to improve patient lifestyles and healthcare outcomes.10 according to recent classifications, mhealth applications are categorized into fields such as tracking, communication, decision support, education, awareness, and monitoring.11 these categories reflect the ways mhealth applications cater to the needs of users and healthcare providers, by assisting physicians in selecting applications tailored to specific health conditions (as shown in table 1).12 compared to traditional healthcare processes, mhealth applications enhance data collection,13 improve care delivery, foster patient engagement, and allow for real-time monitoring of medications and health metrics.14 despite the growth and potential of mhealth applications, nearly a quarter of these apps remain unused after installation.15 many applications are developed with insufficient attention to quality or user-centered design (ucd), leading to poor usability, particularly among older adults.16 blockchain technology, integrated within mhealth applications, offers a solution to some of these challenges. blockchain’s decentralized and secure framework can enhance data security, ensure patient privacy, and improve interoperability within multimodal medical data systems. by enabling users to control their data and facilitating seamless data exchange across healthcare platforms, blockchain can increase trust, transparency, and overall user engagement with mhealth applications.17 the integration of blockchain in multimodal data systems can thus address essential usability concerns, particularly for older adults managing complex health conditions through multiple digital platforms. this innovative approach underscores the potential of mhealth to provide efficient, secure healthcare solutions and redefine digital health for a broader, more engaged audience. objectives the primary goal of this literature review is to present studies on mhealth applications, usability guidelines, and european union (eu) standards for adopting health information technologies, with a specific focus on blockchain integration in multimodal medical data systems. this research addresses the following research questions: rq1: success factors what are the critical success factors that enhance the adoption of mhealth applications among older adults, considering their unique needs, preferences, and limitations, and how might blockchain technology support these factors? rq2: gaps in existing usability what are the gaps in existing usability models and guidelines for mhealth applications, particularly concerning the adoption, usability, and security of these applications among older adults in multimodal data environments? presented here is a comprehensive systematic literature review (slr) on usability frameworks and guidelines for mhealth applications, with an emphasis on adoption among older adults and the role of blockchain technology in enhancing usability, security, and data interoperability. this is followed by a discussion of the eligibility criteria, including inclusion and exclusion parameters, information sources, selection processes, data items, and synthesis methods used in the review. it provides an indepth discussion on rq1, exploring topics such as acceptance and adherence to digital interventions in mhealth apps, european usability guidelines for health information technologies, and key usability standards and frameworks for developing mobile applications. additionally, it discusses the synthesis for rq2, addressing critical issues including age-related usability challenges, cultural diversity in blockchain integration, the need for empirical evidence in ucd alongside blockchain’s potential, and the gap in comprehensive usability frameworks. the results section presents details on study selection, characteristics, individual study results, and synthesis findings for both rq1 and rq2, following preferred reporting items for systematic reviews and meta-analyses (prisma) guidelines. https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.357 3 (page number not for citation purpose) blockchain integration for mobile health a discussion of the findings, with suggestions for future research directions to enhance mhealth usability and security for older adults. overall, this review provides valuable insights into the usability landscape of mhealth applications, underscoring the need for adaptable frameworks that accommodate the specific needs and data security expectations of older adult users. methods this article presents a slr of mhealth applications, usability frameworks, and usability guidelines, with a particular focus on integrating blockchain technology for enhanced security and data interoperability in multimodal medical data systems. a total of 60 relevant studies were analyzed to explore usability evaluation frameworks, guidelines, models, and design factors. the review identified that two core approaches significantly influence the success of any software application: usability and user experience (ux). usability focuses on software efficiency, effectiveness, and user satisfaction, while ux assesses user perceptions and emotional responses through various feedback methods, including questionnaires. eligibility criteria (section 2) this systematic review included studies examining mhealth usability and data security, integrating blockchain’s role where applicable. studies from peer-reviewed journals, conferences, and authoritative usability standards (e.g., international standardization organization through the vienna agreement (iso) standards, eu guidelines) were considered. studies conducted in simulated environments were excluded, as they do not fully represent real-world application scenarios. the eligibility criteria for this review are listed in table 2. table 1. most common categories of mhealth applications reference sources for defining categories of mhealth applications categories ventola cl. (2014)18 use of mhealth devices and apps by healthcare professionals • information management • reference and information gathering • time management • clinical decision-making • health record maintenance and access • patient monitoring • communication and consulting • medical education training burke, l. et al. (2015)19 mhealth applications for ios device developer perspectives • medical information reference • drug or medical information database • decision support • tracking tools • medical calculator industry, n (2015)20 ims institute for healthcare informatics (2015), mhealth applications are categorized • healthcare provider/insurance • medication reminders & info • women’s health & pregnancy • disease specific • fitness • lifestyle & stress • diet & nutrition barton aj (2012)21 the royal tropical institute characterized eight mhealth application regions • education and awareness system • point of care support and diagnostic • patient monitoring • disease and epidemic • emergency medical response system • health information system • mlearning • health financing applications https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.3574 (page number not for citation purpose) irum feroz et al. information sources for the literature review, a thorough search was conducted across multiple online repositories and research databases. the goal was to identify papers that made significant contributions to the usability of healthcare applications. the following library databases were searched: 1. springer link (https://link.springer.com/) 2. wiley interscience (www.interscience.wiley.com/) 3. elsevier science direct (https://www.sciencedirect.com/) 4. google scholar (https://scholar.google.com/) 5. acm digital library (www.portal.acm.org/dl.cfm) 6. ieee xplore (https://ieeexplore.ieee.org/xplore/home.jsp) the search terms of the strategy were combined with boolean operators (and, or). search strategy and keyword hits in the databases are given in table 3. these databases were chosen for their comprehensive coverage of healthcare technology and usability research. each source was thoroughly explored to ensure that relevant studies on mhealth applications, usability frameworks, and related design factors were identified. search strategy a well-structured search strategy was employed to retrieve relevant studies. the search terms were carefully selected and combined using boolean operators (and, or) to maximize the relevance of the search results. the keywords and search terms used in this study are summarized in table 4. the search strategy was applied across all the databases listed in the information sources above, with search queries tailored to the search engine of each database to ensure optimal results. no filters were applied for gender, specific health conditions, or types of applications. this ensured a broad representation of mhealth usability evaluations. after applying the search strategy, a total of 1,073 papers were identified as potentially relevant. the titles and abstracts of these papers were reviewed to assess their relevance to the research topic. duplicates were identified and removed using endnote reference management software, which resulted in the exclusion of 396 duplicate articles. this left 677 papers for the next stage of screening. table 2. the eligibility criteria for this review inclusion criteria defined usability focus studies that discuss or evaluate usability factors in mhealth applications, including those focused on the general population or older adults. research exploring the role of blockchain to improve data security, interoperability, and user trust within mhealth systems was prioritized. types of studies peer-reviewed journal articles and conference proceedings, as well as iso and eu standards or guidelines related to usability. timeframe studies published between 1992 and 2023, ensuring the review captures both recent advancements and historical developments in usability and data security. language only english-language studies were included to maintain consistency in data interpretation and evaluation. online availability studies were limited to those accessible online to ensure ease of review and reproducibility. exclusion criteria defined simulated environments studies conducted in simulated, non-real-world settings were excluded to ensure applicability to real user scenarios. non-english publications non-english studies were excluded due to translation challenges, ensuring that all content reviewed was directly interpretable. technical development focus studies focused solely on technical development without usability evaluation or end-user interaction considerations were excluded. eu: european union; iso: international standardization organization through the vienna agreement; mhealth: mobile health. table 3. search terms used in this study type category keywords 1 mobile health apps • mhealth applications • ehealth • mhealth literacy • mhealth applications evaluation • medical applications • mhealth application evaluation metrics 2 usability frameworks • usability evaluation • heuristic evaluation • usability frameworks • usability frameworks evaluation 3 usability guidelines • iso usability guidelines • nielsen guidelines • mhealth application guidelines • interaction design guideline • user experience ehealth: electronic health; iso: international standardization organization through the vienna agreement; mhealth: mobile health. https://doi.org/10.30953/bhty.v7.357 https://link.springer.com/ http://www.interscience.wiley.com/ https://www.sciencedirect.com/ https://scholar.google.com/ http://www.portal.acm.org/dl.cfm https://ieeexplore.ieee.org/xplore/home.jsp citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.357 5 (page number not for citation purpose) blockchain integration for mobile health selection process the selection process for this systematic review followed a structured and methodical approach. initially, 1,073 studies were identified through the comprehensive search strategy described earlier. the references were imported into endnote, where duplicates were automatically removed. this process reduced the number of articles to 677 unique studies. the selection process was conducted in multiple stages: stage 1: title screening the titles of all 677 papers were reviewed by two independent reviewers to assess their relevance to the research topic. this first stage aimed to exclude papers that clearly did not relate to mhealth usability, older adult populations, or usability frameworks/models. after this stage, 607 papers were retained for further review, while 70 papers were excluded for being irrelevant or misleading in their titles. stage 2: abstract screening the abstracts of the remaining 607 papers were examined. this stage aimed to eliminate papers that did not explicitly discuss existing or novel frameworks for evaluating the usability of mhealth applications. during this stage, 421 papers were excluded because they focused on individual application designs without significant discussion on usability frameworks, models, or guidelines. consequently, 186 papers were passed on for full-text review. stage 3: full-text review full-text versions of the remaining 186 papers were assessed based on the predefined inclusion and exclusion criteria. this stage involved a thorough examination of the methodologies, usability parameters discussed, and relevance to the study objectives. of these, 126 papers were excluded, as they did not evaluate usability frameworks or models or failed to address the key usability parameters outlined for mhealth applications. a final set of 60 papers was included in the systematic review. the entire selection process is illustrated in the prisma for flow diagram (figure 1), which shows the steps of identification, screening, eligibility assessment, and final inclusion. this detailed process ensured that only the most relevant studies were included for synthesis. data items the main outcomes of interest in this review were usability metrics critical for evaluating the user-friendliness, security, and effectiveness of mhealth applications, particularly in the context of blockchain-integrated multimodal medical data systems. the primary data items included error prevention, assessing how well applications are designed to minimize user errors, provide clear error messages, and support users in recovering from mistakes. this is especially important in blockchain-enabled systems, where data entries are immutable, and correcting errors may require additional steps. another essential metric was learnability, which measured how easily new users could navigate and operate the application, with a focus on blockchain-related features like data privacy settings and transaction transparency. studies assessing learnability examined the onboarding experience for users in blockchain-integrated environments, as well as the time required to understand these systems’ unique functionalities. additional primary data items included memorability, reflecting users’ ability to recall how to use the application after a period of non-use. this metric is particularly relevant for older adults who might interact with mhealth applications intermittently, especially if they need to manage blockchain-based permissions or access controls. user table 4. the keywords and search terms used in this study search terms keywords mobile health apps mhealth applications, ehealth, mhealth literacy, mhealth applications evaluation, medical applications, mhealth application evaluation metrics. usability frameworks usability evaluation, heuristic evaluation, usability frameworks, usability frameworks evaluation. usability guidelines iso usability guidelines, nielsen guidelines, mhealth application guidelines, interaction design guidelines, user experience. ehealth: electronic health; iso: international standardization organization through the vienna agreement; mhealth: mobile health. fig. 1. the selection process of primary papers. https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.3576 (page number not for citation purpose) irum feroz et al. satisfaction was another key outcome, typically gathered through surveys, questionnaires, or qualitative feedback, capturing the overall experience with the application and specific blockchain features that influenced positive or negative perceptions, such as data control and security transparency. finally, efficiency was measured by the time and effort required to complete tasks within the application, including navigating blockchain-based data verification processes. efficient design is critical in mhealth applications, as it directly impacts user engagement and sustained usage. in addition to the primary usability metrics, secondary data items were collected to provide a comprehensive context for mhealth usability, especially within blockchain-integrated systems. these items included participant characteristics, such as age, gender, cognitive abilities, and physical capabilities, with particular attention to older adults who might have unique usability needs in managing blockchain-secured health data. the specific mhealth application type evaluated in each study was documented, covering areas such as fitness tracking, chronic disease management, mental health support, and medication adherence, especially where these applications incorporated blockchain technology for secure data handling. this categorization helped identify usability challenges unique to each type of health application, particularly in handling sensitive multimodal health data across interconnected platforms. another secondary data item was the study context or setting, indicating whether evaluations were conducted in clinical settings, real-world environments, or through remote testing. this context provided insights into how different settings impact usability, especially when blockchain functionality (e.g., secure data sharing) is a key feature. additional usability-related factors, including system reliability, ease of navigation, and data security, were recorded. blockchain was evaluated as a mechanism to enhance data security and user trust, especially concerning privacy control and tamper-resistant data storage. trust in the application was a crucial factor, as blockchain’s decentralized structure offers transparency, fostering user confidence, especially in sensitive health applications. finally, the review documented specific usability frameworks and models referenced in each study, such as the iso 9241-11, nielsen’s heuristics, and panicoideae, aristidoideae, chloridoideae, micrairoideae, arundinoideae, danthonioideae (pacmad), comparing their adaptability to blockchain-integrated mhealth applications for older adults. by systematically collecting and categorizing these primary and secondary data items, this review provides a comprehensive view of the usability landscape for blockchain-enabled mhealth applications. this approach highlights potential improvements in usability frameworks and guidelines tailored to meet the data security and accessibility needs of older adult users in blockchain-integrated medical data systems. synthesis methods the synthesis for this systematic review was designed to address two research questions: rq1, identifying critical success factors in the adoption of mhealth applications, and rq2, analyzing gaps in existing usability models and guidelines for mhealth applications, with a focus on blockchain integration in multimodal medical data systems. each question was addressed through a structured synthesis process involving thematic categorization and comparative analysis. this approach aimed to distill findings from 60 selected studies, ensuring a comprehensive examination of usability parameters, data security needs, and their implications for mhealth application design, particularly for older adults engaging with blockchain-enabled, multimodal health data systems. the synthesis for rq1 the rq1 deals with critical success factors in the adoption of mhealth applications (rq1) and blockchain integration. to address rq1, studies were selected based on specific criteria to ensure relevance in understanding critical usability success factors for mhealth applications among older adults, especially in contexts that could benefit from blockchain integration in multimodal medical data systems. only studies that directly assessed usability parameters such as ease of use, efficiency, error prevention, learnability, memorability, and user satisfaction were included. studies providing quantitative or qualitative assessments of these factors were prioritized, offering a comprehensive view of usability issues older adults face in adopting mhealth applications, particularly those requiring secure and interoperable data management. for rq1, a thematic synthesis approach was used to identify and analyze recurring usability themes related to mhealth adoption among older adults, while also considering the potential impact of blockchain-enhanced data security and transparency. data preparation involved categorizing usability parameters from each study, including ease of use, efficiency, error prevention, learnability, memorability, and user satisfaction. organizing studies around these parameters enabled a focused analysis on how specific design features and blockchain-integrated data management contribute to user engagement and satisfaction, fostering trust in mhealth applications for older adults. this thematic synthesis revealed how blockchain can support secure, transparent interactions with multimodal health data, thus addressing common usability challenges and enhancing sustained app adoption among older adult populations. https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.357 7 (page number not for citation purpose) blockchain integration for mobile health mobile health refers to the use of mobile communication to manage health and well-being, a term coined by robert istepanian.22 the world health organization (who) defines mhealth as ‘medical and public health practice supported by mobile devices such as mobile phones, personal digital assistants, patient monitoring devices, and other wireless devices’.23 for example, the global observation ehealth program highlights the role of mhealth applications in delivering relevant health information and monitoring health statuses for paramedical and support staff.24 the primary aim of mhealth applications is to simplify users’ lives by supporting lifestyle management, nutrition, daily activities, physical exercise, and medication adherence.25 however, many mhealth applications are developed without sufficient consideration of end-user needs,26–28 as they often overlook user requirements and fail to involve users in the design process.29 according to wildenbos et al., mhealth applications facilitate various aspects of well-being, such as monitoring daily activities, fitness, and disease management. nonetheless, usability issues are prevalent in many mhealth applications,30 stemming from the lack of comprehensive usability parameters.31 most existing frameworks primarily focus on the general population or specific user groups, neglecting the specific requirements of older adults.32 existing usability models and guidelines often overlook age-related cognitive and physical limitations, such as declining memory and visual acuity.33 for instance, nielsen’s model, widely used in usability evaluations, does not explicitly consider memorability as a usability parameter, which is particularly relevant for older adults who may have difficulties recalling complex interactions.34 the lack of specific guidelines to accommodate these age-related limitations leads to suboptimal usability and low adoption rates among older users. another notable gap pertains to the usability models’ focus on efficiency and effectiveness, often disregarding factors like learnability, satisfaction, and cognitive load.35 older adults may require more time and effort to learn how to use mhealth applications effectively, and their satisfaction with the application’s interface and content is crucial for sustained engagement.36 the absence of comprehensive guidelines that consider these usability dimensions impedes older adults’ successful adoption and continued use of mhealth applications. additionally, the existing usability models and guidelines may not adequately address the privacy and security concerns of older adults when using mhealth applications.37 blockchain integration in multimodal medical data systems could help address several of these challenges, particularly in supporting user control and data security within mhealth applications. many applications overlook the unique needs of older users, who often encounter difficulties with login procedures, navigating interfaces, receiving appropriate guidance, managing complex data updates, and ensuring data privacy and security.38 blockchain’s decentralized framework offers secure, tamper-resistant data management that can address privacy concerns and streamline data access. by enabling secure, interoperable data sharing across devices, blockchain can support user trust and facilitate ease of use in mhealth applications, which is particularly beneficial for older adults managing complex, multimodal health data. from the literature review, it is evident that existing usability models and guidelines, such as those by nielsen,34,39 shneiderman,40 preece,41,42 shackel and constantine,43 and iso standards,44 do not encompass all aspects of usability. key usability parameters such as error prevention, learnability, and memorability are often neglected in mhealth frameworks and models.45 in the context of this research, blockchain integration could enhance usability by securely managing health information technologies and addressing critical success factors such as ease of use, efficiency, user satisfaction, motivation, acceptance, trust, and confidence in use. rq1 of this research aims to identify the critical success factors that facilitate the adoption of mhealth applications among older adults. the literature reveals that, while mhealth applications play a significant role in health and well-being management, many usability issues stem from inadequate end-user consideration during the design phase. furthermore, existing usability models lack comprehensive guidelines for older adults. therefore, critical success factors for mhealth adoption among older users should prioritize usability parameters such as error prevention, learnability, and memorability. additionally, the potential of blockchain to improve data security, transparency, and interoperability can reinforce ease of use, efficiency, user satisfaction, motivation, acceptance, trust, and confidence, making mhealth applications more adaptable for older adults. acceptance and adherence to digital interventions in mhealth apps the usability of mhealth applications significantly influences the acceptance and adherence to digital interventions. designing user-friendly, intuitive, and accessible mhealth apps is essential for catering to a broad audience. key usability factors include ease of navigation, concise instructions, efficient task completion, and an appealing interface. prioritizing these usability aspects enhances the overall ux, encouraging users to engage with digital health interventions.46 integrating blockchain into these systems can further boost user trust, particularly through data transparency and privacy controls, which are essential for sustained engagement among older adults. blockchain’s secure data framework can reduce the learning curve associated with mhealth applications, https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.3578 (page number not for citation purpose) irum feroz et al. increasing acceptance and sustained adherence to digital interventions. the acceptance of mhealth apps depends heavily on perceived usability. a well-designed, user-friendly interface can instill user confidence, making adoption and continued use more likely.47 conversely, poor usability often leads to frustration, confusion, and eventual abandonment of the app.29 thus, usability testing and iterative design processes are crucial to identifying and addressing usability issues, ensuring that mhealth applications meet diverse user needs. by incorporating user feedback throughout development, developers can create applications that align with user expectations, promote acceptance, and foster adherence to digital interventions. blockchain-enhanced security features can further address user concerns regarding privacy, which is critical for encouraging long-term use, particularly among older adults. adherence to digital interventions is closely linked to the usability of mhealth applications. when an app is easy to navigate, provides clear instructions, and offers a seamless ux, individuals are more likely to engage with the intervention consistently and adhere to prescribed protocols. intuitive features like reminders, personalized notifications, and progress tracking can further enhance adherence by promoting regular usage and providing users with a sense of accomplishment.38 blockchain technology can support these adherence factors by ensuring that data remain secure, transparent, and accessible to users, empowering them to manage their health data effectively. by recognizing the role of usability and blockchain in facilitating adherence, developers can optimize mhealth applications to support users in achieving improved health outcomes and sustained engagement with digital health interventions.46 european usability guidelines in health information technologies the eu actively supports personalized healthcare through portable and wearable devices. the eu adopted who’s definition of mhealth and expanded it to include lifestyle and wellness applications, which may connect to medical devices or sensors, providing personal guidance, health information, and medication reminders via short message service and telemedicine services.48 to address the growing number of mhealth applications in patient care and clinical use, the european commission published a green paper tackling challenges related to mhealth applications within europe. in april 2014, the commission launched a dialogue involving healthcare professionals, patients, organizations, and industry representatives to gather insights on the challenges and barriers of mhealth adoption. the green paper outlined critical factors impacting the adoption of health information technologies, including data protection, patient safety, equal access, interoperability, liability, data reliability, international cooperation, and quality standards. blockchain technology, as part of multimodal medical data systems, can play a pivotal role in addressing several of these factors by enhancing data security, interoperability, and patient trust. data protection and big data according to article 8 of the charter of fundamental rights of the eu and article 16(1) of the treaty on the functioning of the eu, protecting personal data—including health data—is a fundamental right in europe. with the rapid advancement of mhealth applications, blockchain offers a secure, decentralized approach to data management, reducing risks of unauthorized access and leakage.48 in the context of big data, blockchain can provide a tamper-resistant system that ensures data privacy and integrity, addressing the eu’s focus on secure health-related data collection. blockchain’s potential for transparent and secure data handling aligns with the ehealth action plan 2012–2020, which emphasizes big data protection in health research and innovation. patient safety the european parliament’s ehealth action plan 2012– 2020 emphasizes the importance of well-being and mhealth applications and calls for a clear legal framework to ensure their safe development. among the 97,000+ mhealth applications globally, blockchain could improve patient safety by offering an immutable record of all health data exchanges. such transparency can assure users about the source and reliability of data, which is essential given that current mhealth applications often lack information regarding the development processes and adherence to medical guidelines. safety guidelines focusing on transparency could be further strengthened with blockchain, ensuring the verifiable integrity of mhealth data. equal access and interoperability the eu recognizes that the full potential of mhealth applications has yet to be realized in european healthcare. blockchain’s decentralized nature enables interoperability across platforms, supporting seamless data exchange among healthcare providers and patients, especially across member states. the ehealth network, developed under directive 2011/24/eu, enhances interoperability in mhealth systems to maintain high-quality healthcare services across europe. blockchain integration would support this initiative by providing a uniform, secure structure for data exchanges that respects patient rights and promotes equal access to health information across borders. https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.357 9 (page number not for citation purpose) blockchain integration for mobile health liability, reimbursement model, and research limited innovation and few reimbursement models are key obstacles preventing mhealth applications from becoming mainstream healthcare solutions. national regulations often restrict reimbursements to in-person medical consultations, impeding mhealth adoption. blockchain could address liability issues by ensuring traceable data transactions, reducing ambiguity over responsibilities related to device faults, it expertise, or user error. blockchain’s transparent structure could provide legal clarity in mhealth application development, supporting professionals, developers, and manufacturers in establishing defined responsibilities and accountability for app-related risks.49 some mhealth applications are popular among the consumer, such as fitness and well-being apps, but there is a need for research to determine whether these applications provide useful information to the users. research and innovation should be continued on mhealth applications and should continuously cover broader aspects of patient’s safety. the mhealth application’s funding is prioritized in horizon 202050 eu’s research funding call. access and international cooperation for effective data privacy and security in mhealth applications, it is critical that the eu follows international standards and fosters collaboration among stakeholders.51 blockchain’s secure infrastructure could play a significant role in building a platform for experience-sharing, involving entrepreneurs, and advancing mhealth. existing eu guidelines on data privacy in mhealth applications can be further strengthened with blockchain, ensuring that developers adhere to privacy codes and data protection requirements, which are essential to build user trust. blockchain provides additional security through data immutability and user control over data sharing, ensuring compliance with the eu’s privacy code of conduct. validity and reliability of data to address data validity and reliability, the eu developed specific guidelines in february 2016, involving around 20 organizations to set standards for mhealth data quality. the ability of blockchain to create an immutable, time-stamped record of all interactions supports data validation, ensuring that health data stored in mhealth applications are reliable and verifiable. this system could enhance the eu’s goal of establishing consistent, high-quality data management practices in health applications, ensuring that health providers and patients rely on accurate data for medical decision-making. quality criteria for mhealth and wellness applications the eu’s 2016 rolling plan on information and communication technologies (ict) standardization includes quality criteria for mhealth applications, covering functionality, usability, and reliability. blockchain can further these standards by securing each step in the application’s development and user interaction cycle, ensuring privacy and transparency. the british standards institution developed pas 277:2015, which recommends quality criteria such as functionality, usability, privacy, and security across the development life cycle. blockchain enhances compliance with these criteria by offering data integrity, verifiability, and decentralized control, ensuring that mhealth applications meet high standards of reliability, performance, and safety. product safety the eu commission is exploring ways to adjust the product safety framework for digital applications, including a public consultation on the safety of mhealth apps. blockchain integration can contribute to product safety by securing medical data within mhealth systems, ensuring data authenticity and reducing the risk of software tampering or unauthorized data changes. supporting research under horizon 2020 horizon 2020 prioritizes research funding for mhealth innovations, big data, and digital security in health data, providing a foundation for integrating blockchain into secure health data systems. blockchain can support the eu’s research goals by providing a robust platform for securely managing vast amounts of health data, thereby enhancing the safety, privacy, and functionality of mhealth applications. adoption of information technology in healthcare would benefit from incorporating eu policy guidelines and blockchain capabilities in mhealth application design and development, addressing security, usability, and transparency for both patients and healthcare providers. identifying parameters from usability guidelines and models in this theme, usability standards and parameters (metrics) are examined to understand what should be included in the development of a comprehensive usability framework. the objective of usability standards and parameters is to identify factors that contribute to creating user-friendly applications. preece emphasized that usability is based on observing, experimenting, and testing with users. this research aims to understand users’ specific requirements and define the quality of a skilled experience, as illustrated in figure 2.52 the international standards organization defines usability as the ‘extent to which a product can be used by specified users to achieve specific goals with effectiveness, efficiency, and satisfaction in a specified context of use’. over the past 35 years,53 various usability models have been proposed. one of the foundational models was introduced by shneiderman in his book designing the user https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.35710 (page number not for citation purpose) irum feroz et al. interface in 1992,40 where he outlined eight golden rules for interface design, including striving for consistency, enabling shortcuts for frequent users, offering informative feedback, designing dialogs to yield closure, providing simple error handling, allowing easy action reversal, supporting an internal locus of control, and reducing shortterm memory load. these guidelines were based on the collective experience of developers and aimed at creating intuitive and reliable interfaces. in 1994, nielsen34,39 identified five essential usability parameters—efficiency, satisfaction, learnability, memorability, and error handling—arguing that these are vital for software design. following this, preece et al.41 introduced classifications that included efficiency, effectiveness, and enjoyment, later expanding to include flexibility, throughput, and learnability.42 these evolving models highlight the importance of creating user-centered applications that are both functional and easy to use. the iso further defines usability as the ‘extent to which a product can be used by a specified user to achieve a specified goal with satisfaction, effectiveness, and efficiency in a specified context’.44 blockchain technology offers an opportunity to enhance these usability models by providing secure, decentralized data handling and improving user control over sensitive health information. for example, blockchain’s immutability and transparency align well with usability goals, fostering user trust and enabling consistent and secure interactions across multimodal health data platforms. iso 9241-11 usability standard the iso 9241-11 standard, established in 1998, provides a framework for measuring usability through decomposed attributes such as effectiveness, efficiency, and satisfaction. each component is divided into measurable and verifiable sub-components. blockchain integration could enhance this model by ensuring the data integrity and security of each user interaction, which is especially beneficial in mhealth applications that involve sensitive health data. blockchain’s decentralized framework ensures that usability is adaptable to different contexts of use, considering factors like users, tasks, equipment, and environments that can affect product usability (figure 3). by securing data within these contexts, blockchain supports the development of trust-based applications that empower users to manage their data safely and autonomously, aligning with iso’s principles for context-specific usability. georgson and staggers47 utilized the iso 9241-11 standard to conduct usability testing among 2,317 patients with diabetes across 18 primary care clinics in the metropolitan area of utah. the study evaluated task performance, satisfaction, and efficiency, mapping these outcomes to user characteristics. the average satisfaction score of 80.5 indicated good usability, though it left room for improvement. the study also highlighted demographic differences, such as higher task completion rates among males and younger participants. integrating blockchain technology within such mhealth systems could enhance usability by providing secure, reliable access to health data and supporting patient-centered control over personal health records. blockchain’s transparent, tamper-resistant data management aligns well with iso’s emphasis on usability tailored to specific user needs and contexts, as it fosters trust and supports secure interactions within mhealth applications. fig. 2. usability goals and user experience.52 https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.357 11 (page number not for citation purpose) blockchain integration for mobile health iso 9126-1 usability standard the iso 9126-1 standard focuses on software product quality, defining both internal/external quality and quality in use. it distinguishes six internal and external quality factors in software usability, which are given in figure 4. this iso 9126-1 standard lacks consideration for usability parameters like memorability, learnability, and error handling—areas where blockchain could be instrumental. blockchain’s secure framework for data verification and integrity complements the iso 9126-1 standard by enhancing external quality and ensuring data reliability in health applications. this capability is particularly useful for mhealth applications that require dependable data sharing across multiple devices. blockchain could also address usability gaps noted in other usability models, such as nielsen’s, by enhancing data security and error recovery in mhealth applications. for instance, blockchain’s decentralized ledger offers an audit trail for error tracking, which supports error prevention and recovery—critical usability aspects for users managing sensitive health data. additionally, models like those proposed by condos et al.54 for mobile commerce applications, which emphasize usability dimensions like content, information architecture, and error prevention, can benefit from blockchain by ensuring secure, consistent data access and reducing the cognitive load on users who interact with these applications in various settings. the mobile goal question metric (mgqm) model, introduced by basili et al. in 1994,55 and expanded by hussain and kutar,56 is rooted in the iso 9241-11 standard and assesses usability based on effectiveness, efficiency, and satisfaction. it extends these foundational parameters by introducing six specific usability characteristics: accuracy, attractiveness, features, safety, time taken, and fig. 3. iso 9241-11 usability framework. iso: international standardization organization through the vienna agreement. fig. 4. iso 9126-1 usability framework 2. https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.35712 (page number not for citation purpose) irum feroz et al. simplicity. the mgqm model leverages both qualitative and quantitative usability metrics, offering a comprehensive framework for evaluating mhealth applications. integrating blockchain technology within the mgqm framework could enhance data safety and accuracy, two core metrics in mgqm, by ensuring secure, verifiable, and tamper-resistant data transactions. this enhancement could improve user trust and data integrity, especially for older adults managing sensitive health data across multimodal platforms. pacmad usability model the pacmad (people at the centre of mobile application development) usability model was developed to address limitations in earlier usability models.57 unlike traditional models, pacmad integrates both iso and nielsen’s usability attributes, adding cognitive load as a critical usability factor for mobile applications. the model identifies three primary components—user, tasks, and context of use—that influence overall usability. blockchain integration could support pacmad’s cognitive load considerations by reducing the mental effort needed for data verification and access, as blockchain’s transparent structure simplifies secure data handling. this is especially beneficial for older adults, as it provides secure, controlled access to health information without repeated verifications, lowering cognitive strain. in mobile applications, the ‘context of use’ is particularly significant, as users may access mhealth applications across different environments and tasks. blockchain’s decentralized architecture can accommodate these varied contexts by securely sharing data across devices while maintaining user control over data permissions, which aligns with pacmad’s focus on effectiveness, efficiency, satisfaction, learnability, memorability, error management, and cognitive load summarized in figure 5. blockchain-enabled transparency and security can enhance error management and memorability, allowing users to confidently re-engage with mhealth applications over time. integrating blockchain within usability models like mgqm and pacmad thus reinforces the usability of mhealth applications, meeting the specific needs of users who rely on secure, interoperable, and user-friendly interfaces for health management. the pacmad model describes the seven attributes as effectiveness, efficiency, satisfaction, learnability, memorability, errors, and cognitive load. all these features have a great impact on usability of application. effectiveness: the time a user takes to complete a task in a specific context is called effectiveness. to check if those tasks can be completed in the given time, effectiveness is very necessary. efficiency: efficiency is the ability to complete the project with speed and accuracy. this feature gives a very easy route for the user to use the application. efficiency can be measured by: time for the task to be done and keystrokes to get a task done. satisfaction: the process of satisfaction is user based, if the given software is comfortable for the user to use and is completely cost effective. this is made for individual users on how well they feel satisfied after using the application. to ask for the reviews of the user, we typically use questionnaires. learnability: from a research survey, we have noticed58 that users spend an average of 5 min or less learning to use a mobile application. if an application is not complete and the users using the application face a different problem, they may simply select a different option of application. this attribute was suggested by nielsen. memorability: the research also found that mobile applications are used on a rare basis, and only once a month, 50% of the application used by the participants. thus, users cannot easily recall how to use the application. memorability is also suggested by the nielsen usability model. errors: nielsen defined that users make less error during the use of a system, and if user make errors, they can able to easily improve from them. the usability model pacmad considers the nature of errors along with the frequency of occurrence, so it is possible to prevent these errors from occurring in future application. cognitive load: to use the application, cognitive processing is essential for the user. the common idea of the usability is that the user can perform any task easily without acquiring external help. in the modern era, users often perform multiple tasks in parallel, which increases cognitive load and poses challenges for usability in mobile applications.58 developing a self-directed application that effectively serves a technologically unfamiliar target group, such as older adults or individuals with disabilities (e.g., physical disabilities or cognitive impairments like dementia), is especially difficult. usability becomes critical for these users, as mhealth applications must be intuitive and accessible. brown et al.45 highlight two key challenges impeding the thorough usability testing of mhealth technology. the first challenge is the limitations of mobile devices, including slow operating systems, low-resolution screens, lack of traditional input devices (like a mouse or keyboard), and inconsistent connectivity. the second challenge is the rapid technological advancements in mhealth, which outpace the development of end-user testing resources and software. usability frameworks for the development of mobile applications frameworks contribute toward the administration of usability evaluations by presenting a structured https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.357 13 (page number not for citation purpose) blockchain integration for mobile health conceptualization of the factors that influence usability and the relations between them. the usability framework of mobile application helps in the development of comprehensive usability evaluation, which involves usability goals, guidelines, metrics, and questions. a framework also contributes toward the evaluation of the usability by presenting structure tools like questionnaires and heuristics. kaufman et al.59 stated that the frameworks are considered as a set of principles (such as assumptions, constructs, quality criteria, and ideas that guide research and development) and strategies (such as hands-on guidelines, design heuristics, and methods to assist the development process to increase the quality of ehealth technology).60 some existing frameworks61 such as health it usability evaluation model (ituem) were introduced by yen in 201062, and the main purpose was to identify the gap and problems in prior usability models. according to brown iii et al.,45 the health–ituem framework offers to understand the usability issues and barriers related to mhealth technology. the authors used nine concepts in the healthituem framework, for example, error prevention, completeness, memorability, information needs, flexibility/ customizability, learnability, performance speed, and competency for evaluating the usability of the mhealth applications and developing the data analysis codes.63,64 wildenbos et al. in 2018 developed the mold-us framework, which identified four aging barriers, such as cognition, less motivation, perception, and physical abilities.65 another framework is information system research (isr), which is used as a guideline for the designing mhealth applications.27 the isr framework is an iterative process, which includes functional requirement identification, need assessment, rapid prototyping, and user interface design, each of these techniques have been used for the development of the software in the past. it consists of three cycles, that is, relevance (in which the focus is on target end-user), rigor (identify the technology), and design (usability-based method involved). the isr framework also identifies that the user_centered design process is effective in designing m_health applications. it also includes both end-user feedback (focus groups, participatory design sessions, and usability evaluation methods) and multiple ucd methods to update the design of mhealth applications.27,28 the mgqm defines the user goals,55 refines the goals into questions, and defines the metric, which provides information to answer the questions. mgqm is a generic model, which can be used for the measurement of issues in mobile applications.66 wildenbos et al. in 2018 introduced a framework for the analysis of the usability of mhealth applications for the older people, in which their analysis suggested that learnability and poor visual acuity were the most common issues that affected motivation for the adoption of mhealth applications in older people.67 in this regard, the evaluation of existing usability guideline for mhealth does not address age-related cognitive limitations, motivational issues as a psychological construct, perception (such as small font size on-screen),46 and physical impairment, which is commonly associated with older people65 that resulted in poor adoption of the technology. electronic health records (ehrs) are unsystematic due to the lack of usability frameworks.67 jiajie and muhammad in 2011 presented a unified framework for ehr usability, which is based on four important usability components, such as task, user, representation, and function (turf).68 the author stated that usability can not only be defined scientifically under a coherent unified framework but should be measured objectively and systematically. this research defines how turf can be used to fig. 5. comparison of the pacmad model with other usability models. iso: international standardization organization through the vienna agreement; pacmad: people at the centre of mobile application development. https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.35714 (page number not for citation purpose) irum feroz et al. improve the usability of ehrs and also increase their efficiency. in subsequent sections, the gaps that existed in relevant studies are presented. synthesis for rq2 the rq2 deals with gaps in existing usability models and guidelines. for rq2, studies were selected based on their evaluation of usability models and guidelines relevant to digital health applications, with a focus on frameworks such as iso 9241-11, nielsen’s heuristics, and the pacmad model. studies that highlighted specific gaps or limitations in these frameworks when applied to mhealth applications, particularly for older adults, were prioritized. this selection allowed for a comparative analysis to assess how well current models address—or fail to address—the usability needs of older adults. a structured comparative analysis of these models was conducted to evaluate their strengths and limitations in addressing unique usability requirements for older adults in mhealth applications. data preparation involved organizing studies by the usability model evaluated and extracting key information on each model’s strengths, limitations, and applicability to mhealth. this organization enabled a direct comparison across frameworks, focusing on usability aspects like error prevention, learnability, multimodal interaction, and cognitive load management. the comparative synthesis identified areas where existing models lack specific guidance for older users, offering insights into how usability frameworks could be adapted or expanded to better meet this demographic’s needs. blockchain integration within mhealth applications could address some of these identified gaps by enhancing data security, simplifying user authentication processes, and supporting seamless interoperability, all of which contribute to usability. for instance, iso 9241-11 emphasizes effectiveness, efficiency, and satisfaction, yet it lacks specific provisions for user-centered data control and transparency—elements that blockchain could improve by allowing users to securely manage and track access to their health data across platforms. blockchain’s secure, decentralized framework supports trust and control over data, which are crucial for older adults who may have concerns about privacy and security in digital health systems. nielsen’s heuristics provide foundational principles for usability, including error prevention and user satisfaction, but they fall short in addressing multimodal interaction and adaptive support tailored to older adults’ needs. blockchain’s transparent, tamper-resistant data handling could mitigate these issues by ensuring secure data exchanges across devices and enabling consistent, traceable interactions, thus supporting ease of use and reducing the cognitive burden associated with managing health information. the pacmad model incorporates cognitive load considerations and aligns with blockchain’s potential for simplifying data handling, particularly in contexts that require multiple devices and secure controlled data interactions. blockchain’s capability to streamline data management processes could help minimize cognitive load, making mhealth applications more accessible to older adults and improving their overall usability experience. overall, the comparative synthesis emphasizes the need to adapt existing usability frameworks to address older adults’ unique usability requirements. many mhealth applications are designed with insufficient end-user consideration,26–28 and research indicates a lack of user involvement in the design process. usability, as defined by the iso standard, involves ‘the effectiveness, efficiency, and satisfaction with which a specified user achieves specified goals in a particular environment’. this is essential to ensure users can perform tasks effectively, as dissatisfaction with an interface can impact market acceptance. complex interfaces have been shown to reduce user interest and engagement when performing tasks.40 blockchain integration offers a promising solution by enhancing data security, transparency, and user control, thus potentially addressing many of the usability challenges that persist in existing frameworks. age-related usability issues in the current market, the majority of mobile devices use touchscreen technology, a transition from traditional physical buttons that can be confusing for many users, especially older adults. while screen-based buttons and display icons are effective in modern design, they are often not optimized for individuals with disabilities. although features such as adjustable font sizes can help mitigate vision-related issues, many usability challenges remain, which require a comprehensive approach to address.30 research has identified specific capabilities and limitations of older adults when interacting with mobile technology, leading to the recommendation of key design principles that can better meet this population’s needs. by incorporating these insights into interface design, developers can create applications that are more accessible and user-friendly for older adults.65 blockchain technology can further enhance usability for older adults by offering simplified and secure data interactions, which are essential for maintaining user trust in mhealth applications. for instance, blockchain’s transparent, decentralized framework reduces the complexity associated with traditional data management systems, allowing older users to manage health data securely without complex authentication steps. this design approach supports user independence and minimizes the cognitive load, making mhealth applications more suitable for older adults dealing with age-related usability issues. https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.357 15 (page number not for citation purpose) blockchain integration for mobile health cultural diversity and blockchain integration culturally diverse environments pose additional challenges in identifying frequently used features and managing applications in multiple languages. wildenbos et al. emphasize the importance of online navigation structures that cater to diverse cultural behaviors and preferences.65 kim et al. argue that cultural usability is as essential as any other usability factor when designing effective systems.69 studies by ahmad et al.70 highlight the significance of usability problems in many mhealth applications and emphasize the need for comprehensive usability frameworks to address the unique needs of older users. another important factor is user involvement in the design process. studies such as saparamadu et al.71 and duque et al.72 emphasize that involving older adults in the development and design of mhealth applications leads to better alignment with their requirements and preferences. by engaging older users throughout the development cycle, developers can gain valuable insights into their needs and challenges, resulting in more user-centered applications that are more likely to be adopted. designers must consider cultural norms, societal impacts, beliefs, trends, and perceptions to create user-friendly, culturally adaptive applications.27,73 research reveals that language differences may necessitate significant technical adjustments in software development, highlighting the need for adaptive and context-aware designs.74 blockchain technology can support these diverse needs by providing a standardized yet secure data management framework that accommodates multi-language interfaces and culturally specific interactions, enhancing cross-cultural usability and reducing the technical complexity associated with localization. blockchain’s decentralized nature can also help address back-compatibility issues by offering a consistent, secure data structure that remains accessible across different cultural and linguistic contexts. this universal data format can facilitate market solutions adaptable to diverse user bases, enhancing accessibility and ensuring that mhealth applications are suitable for a variety of cultural backgrounds. lack of empirical evidence in ucd and blockchain’s potential khan and donthula point out that while ucd enhances the commercial success of products, more research is needed to quantify its economic benefits, such as increased sales, customer loyalty, and user engagement.25,75 they recommend further studies to evaluate ucd’s impact on fostering innovation and promoting collaboration between designers, developers, and users. blockchain’s integration could complement ucd by adding a layer of security and data transparency that aligns with ucd principles, supporting user trust and engagement in mhealth applications. blockchain enables secure, user-centered data control, allowing end-users more autonomy over their health information while maintaining the privacy and integrity of their data. research exploring the combined impact of ucd and blockchain on product success, user satisfaction, and trust could provide valuable insights into optimizing ucd implementation. blockchain’s transparent and user-controlled data management framework could further support the ucd approach, fostering a collaborative development process that prioritizes end-user needs in digital health solutions. lack of comprehensive usability framework saleh and ismail emphasized the need for consistent usability guidelines and standards across various systems and contexts, which includes interaction, visual, and content design standards, as well as iso 9241 for usability evaluation and improvement.27,75 they suggested that usability frameworks should consider user characteristics—such as age, education level, and cultural background—and should be based on clear usability goals to assess design success and guide improvements. integrating these elements, they argued, would help designers create systems that are effective, efficient, and satisfying for users.75 gupta et al.31 highlighted a major challenge in evaluating mhealth applications: the absence of a comprehensive usability framework specifically for mhealth, despite its potential to improve health outcomes. they noted a lack of consensus on effective quality models and usability frameworks for mhealth, which complicates evaluating these applications and ensuring they meet user needs. the authors called for further research to identify the most appropriate usability frameworks to support the user-centered mhealth application design.31 table 5 highlights research gaps in mhealth applications, including the needs for user-centered and inclusive design, comprehensive usability frameworks, and tailored guidelines for older adults. it also notes limitations in current evaluation models, such as high costs and limited user input. blockchain integration within usability frameworks could address some of these challenges by providing secure, transparent data management and consistent standards across devices. blockchain’s decentralized approach promotes user trust and control over health data, supporting usability in diverse user contexts and enabling the development of more reliable, user-focused mhealth solutions. a review of various research papers revealed that no relevant study in the last two decades has introduced a comprehensive usability evaluation framework.46 such a framework should encompass usability models, guidelines, characteristics, and goals to be effective.75 there is an urgent need to design a usability framework that https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.35716 (page number not for citation purpose) irum feroz et al. specifically ensures mhealth applications are developed with older users in mind. each feature of mhealth applications should be tailored to meet users’ actual needs, expectations, and characteristics, promoting a more user-centered approach in mhealth design.46 results study selection the study selection process was conducted according to the structured screening and eligibility criteria outlined in the prisma flow diagram (figure 1). a comprehensive search across multiple databases identified 1,073 records. after removing duplicates, 677 unique records were screened based on their titles and abstracts. from this, 491 records were excluded as they did not meet the initial inclusion criteria, leaving 186 full-text articles for detailed evaluation. a further assessment based on predefined eligibility criteria led to the exclusion of 126 studies, primarily due to reasons such as lack of focus on usability, irrelevant populations (non-older adults), or insufficient emphasis on mhealth applications. ultimately, 60 studies met all inclusion criteria and were selected for synthesis, addressing the research questions related to usability factors and gaps in existing frameworks. study characteristics the selected studies, published between 1992 and 2023, demonstrate the evolving focus on mhealth applications, particularly for improving health management, chronic disease monitoring, mental health support, and lifestyle wellness among older adults. geographically, these studies included significant contributions from north america, europe, and asia. sample sizes varied widely, with smaller focus groups in qualitative studies and larger quantitative studies, some involving thousands of participants in clinical or observational settings. the study designs included diverse methodologies, such as qualitative evaluations, controlled experiments, usability testing, and cross-sectional surveys. most studies concentrated on usability parameters like learnability, error prevention, and user satisfaction, while some specifically assessed adherence and retention of older adults using mhealth applications. additionally, several studies referenced specific usability frameworks, such as iso 9241-11, nielsen’s heuristics, and the pacmad model, table 5. research gaps identified in literature relevant studies gaps identified khan k, and donthula s (2019)21 there is still need for further research directed at how user-centered design is contributing to the development of helpful applications. liew ms et al. (2019)76 a need to identify gaps regarding mobile application’s inclusive design, assessment from subject experts, and feedback from consumers. schnall r et al, 201673 the isr framework presented by the authors provided some guidelines for designing mhealth applications. the major drawback in the isr framework is that it can be time-consuming and costly. wildenbos et al. (2015)30 the existing mhealth’s guidelines do not address the barriers related to the complexity of mobile interfaces, which cause problems for old age patients. need to identify gaps regarding mobile application’s inclusive design, assessment from subject experts, and feedback from consumers. yen py, and bakken s. (2012)63 the researcher and the developers need to use the automatic evaluation tools for identifying the usability barriers as the existing 73% of people’s research is based on the interviews and questionnaires for the evaluation of the mhealth applications, which are more error-prone. saleh am, and ismail rb (2015)75 the comprehensive usability framework should be based on the usability model, guidelines, characteristics, and goals. gupta et al. (2014)31 for the evaluation of mhealth applications, the main challenge is the selection of quality models and inadequate support of appropriate comprehensive usability framework for designing. tahir r, and arif f. (2014)43 from the literature review, the existing usability models or guidelines such as iso standards do not cover all aspects of usability. brown et al. (2013),45 the health it usability evaluation model was introduced with the primary purpose of identifying the gaps and problems in prior usability models. the drawback of this framework is that few users were involved in the data collection process, while the framework is not tested. li c, et al. (2021)77 there is a lack of specific usability guidelines for mhealth applications targeted at older adults, considering their unique needs and limitations. hussain a, kutar m (2012)78 existing usability models for mhealth applications often do not adequately address the cognitive limitations and accessibility challenges faced by older adults, hindering their adoption and usability. slade m, oades l, and jarden a (2017)16 the current usability frameworks for mhealth applications do not fully consider the social and emotional aspects of older adults’ experiences, which can impact their motivation to use these applications. isr: information system research; mhealth: mobile health. https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.357 17 (page number not for citation purpose) blockchain integration for mobile health to examine usability gaps in current models when applied to mhealth. the key findings from individual studies are summarized in table 6, illustrating how each contributes to identifying critical success factors and usability gaps in mhealth applications for older adults. the findings were mapped to specific usability parameters, including ease of use, learnability, error prevention, efficiency, and satisfaction. results of individual studies key findings from individual studies are summarized in table 7, highlighting how each study contributes to understanding the critical success factors and usability gaps in mhealth applications for older adults. each study’s findings were mapped to specific usability parameters, such as ease of use, learnability, error prevention, efficiency, and satisfaction. table 3 provides a summary of study gaps identified in existing literature on mhealth applications. key gaps include the need for ucd research, inclusive design assessments, and comprehensive usability frameworks that address cognitive, accessibility, and emotional needs—particularly for older adults. additionally, issues with current evaluation models, such as high costs, limited user involvement, and outdated frameworks, are highlighted. results of syntheses rq1: identifying critical success factors in mhealth adoption the thematic synthesis for rq1 identified several usability factors essential for the adoption and sustained use of mhealth applications among older adults. key factors included ease of use, efficiency, error prevention, learnability, memorability, user satisfaction, motivation, acceptance, trust, and confidence in app usage. studies consistently showed that applications with simple navigation, intuitive layouts, and age-friendly visual elements resulted in higher satisfaction and engagement. efficiency, achieved through streamlined workflows and minimal task steps, emerged as particularly important for older adults who may have limited patience or cognitive capacity for complex interfaces. integrating blockchain technology into mhealth applications could further enhance these usability factors by offering secure, user-controlled data management, which builds trust and confidence in app usage. blockchain’s transparent, decentralized framework supports error prevention by providing tamper-resistant data management, enabling users to verify data accuracy and ensuring data integrity across devices. this security can reduce user frustration and boost confidence, especially for older users concerned with privacy and security. prioritizing both usability and blockchain’s data security features can create a ucd that encourages sustained engagement and facilitates effective health management for older adults. rq2: analyzing gaps in existing usability models and guidelines the comparative analysis for rq2 identified several limitations in current usability models, including iso 924111, nielsen’s heuristics, and pacmad, when applied to mhealth applications. existing models often lack age-specific guidance and fail to address critical usability aspects such as multimodal support (e.g., voice commands and haptic feedback), error recovery, and data privacy. studies indicated that while these frameworks provide foundational usability principles, they do not offer specific recommendations for features that would improve usability for older adults, such as adjustable font sizes, simplified interfaces, and adaptive help tools. blockchain integration could address some of these gaps by providing secure, decentralized data management that improves user trust and control over sensitive information. blockchain’s tamper-resistant data structure supports error recovery by ensuring that all data interactions are transparent and traceable, which is particularly beneficial for older adults concerned about privacy and data security. additionally, by enabling consistent data access across devices, blockchain could facilitate multimodal table 6. key findings from individual studies reference findings feroz i, ahmad n (2022)5 simple, well-labeled icons reduced cognitive load for older adults, resulting in improved task completion rates and greater ease of use. ahmad n (2014)29 navigation and efficient workflows led to higher user satisfaction and more frequent app engagement among users. ahmad et al. (2015)79 consistent layout and familiar visual cues improved memorability, minimizing the need for re-learning after periods of non-use. harrison r, flood d, duce d (2013)57 evaluation of the iso 9241-11 framework in mhealth applications revealed that while it supports general usability metrics, it lacks specific guidance for error recovery. feroz i (2023)67 assessment of nielsen’s heuristics identified gaps in addressing age-related usability needs, such as visual acuity adjustments and simplified navigation for older adults. https://doi.org/10.30953/bhty.v7.357 citation: blockchain in healthcare today 2024, 7: 357 https://doi.org/10.30953/bhty.v7.35718 (page number not for citation purpose) irum feroz et al. interaction options, supporting usability frameworks in expanding their scope to include age-sensitive, accessible mhealth design. discussion and future work this slr highlights that mhealth applications support various aspects of well-being but face usability challenges in parameters such as error prevention, learnability, and memorability. these gaps are particularly problematic for older adults, who may have limited familiarity with information and communication technologies, impacting their adoption and sustained use of mhealth applications.74 existing usability guidelines for mobile applications76 lack a comprehensive framework specific to mhealth, especially one that considers the needs of older adults. addressing these gaps requires the development of a new usability framework that incorporates overlooked metrics and adapts to the unique requirements of this demographic.46 integrating blockchain into this framework could significantly enhance usability by providing secure, user-controlled data management and transparent, tamper-resistant records. blockchain’s capabilities support error prevention by ensuring data accuracy and traceability, building trust and reducing user anxiety around privacy and security. future work should explore usability frameworks that combine these usability principles with blockchain’s security features, allowing older users to interact with mhealth applications confidently. this approach could create more accessible, user-centered mhealth solutions, fostering higher engagement and better health outcomes for older adults. funding there is no funding involved in this study. conflicts of interest there is no conflict of interest between authors. contributors ms. irum feroz contributed significantly in slr and selected 60 papers after a thorough analysis. she worked on usability factors, models, and frameworks. she found important factors involved in it adoption. she also identified critical research gaps, highlighted emerging trends and potential areas for further investigation, and provided valuable insights to guide the direction of future research. mr. nadeem ahmad worked on synthesis of research questions and followed prisma guidelines. he also worked on integration of mhealth applications with blockchain technology. all authors approved the manuscript and agree with its submission to blockchain in healthcare today data availability statement (das), data sharing, reproducibility, and data repositories the data presented in the study are available with the corresponding author. application of generated text or related technology artificial intelligence and related technologies were not used in the preparation of this article. acknowledgments we sincerely acknowledge the university of portsmouth, uk, for providing a supportive and conducive environment that greatly facilitated the successful completion of this research. the resources, infrastructure, and academic encouragement offered by the institution were invaluable in advancing our work. references 1. pires im, marques g, garcia nm, flórez-revuelta f, ponciano v, oniani s. a research on the classification and applicability of the mobile health applications. j pers med. 2020;10(1):11. https://doi.org/10.3390/jpm10010011 2. asadzadeh a, kalankesh lr. a scope of mobile health solutions in covid-19 pandemics. inform med unlocked. 2021;23(100558):100558. https://doi.org/10.1016/j. imu.2021.100558 3. drew da, nguyen lh, steves cj, wolf j, spector td, chan at. rapid implementation of mobile technology for real-time epidemiology of covid-19. biorxiv. 2020. https://doi. org/10.1101/2020.04.02.20051334 4. istepanian rs, alanzi t. mobile health (m-health): evidence-based progress or scientific retrogression biomedical information technology. elsevier; 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usability evaluation of satnav application on mobile phone using mgqm. int j comp inf syst ind manag appl. 2012;4:92–100. 79. ahmad n, shoaib u, prinetto p. usability of online assistance from semiliterate users’ perspective. int j hum comput interact. 2015;31(1):55–64. https://doi.org/10.1080/10447318. 2014.925772 copyright ownership: this is an open access article distributed in accordance with the creative commons attribution non commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see: http://creativecommons.org/ licenses/by-nc/4.0 https://doi.org/10.30953/bhty.v7.357 https://doi.org/10.1108/00012530210452546 https://doi.org/10.1108/00012530210452546 https://doi.org/10.1186/2194-0827-1-1 https://doi.org/10.4018/jmhci.2009010102 https://doi.org/10.4018/jmhci.2009010102 https://doi.org/10.1097/00006199-200603001-00007 https://doi.org/10.1097/00006199-200603001-00007 https://doi.org/10.2196/jmir.1672 https://doi.org/10.1136/amiajnl-2010-000020 https://doi.org/10.1016/j.jbi.2018.08.012 https://doi.org/10.1016/j.jbi.2018.08.012 https://doi.org/10.1016/j.jbi.2011.08.005 https://doi.org/10.1016/j.jbi.2011.08.005 https://doi.org/10.2196/18312 https://doi.org/10.2196/18312 https://doi.org/10.1136/bmjopen-2019-033870 https://doi.org/10.1136/bmjopen-2019-033870 https://doi.org/10.2196/18079 https://doi.org/10.3390/s17030622 https://doi.org/10.3390/s17030622 https://doi.org/10.2196/12160 https://doi.org/10.3233/jad-200485 https://doi.org/10.3233/jad-200485 https://doi.org/10.1080/10447318.2014.925772 https://doi.org/10.1080/10447318.2014.925772 http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research optimizing proof-of-work for secure health data blockchain using compute unified device architecture seid mehammed  department of computer science, institute of technology, woldia university, woldia, ethiopia corresponding author: seid mehammed, seidmda@gmail.com keywords: bitcoin, blockchain, graphics processing unit, gpu, healthcare, proof-of-work, pow, secure health data, throughput abstract we present a graphics processing unit (gpu)-accelerated proof-of-work (pow) blockchain design tailored for secure healthcare data management. our compute unified device architecture (cuda)-optimized pow achieves throughput improvements of approximately 5× to 100× and reduces block-formation latency compared to central processing unit (cpu) mining, making blockchain practical for high-volume health records. we benchmark against standard platforms—bitcoin, known for its robust security but slow block times; ethereum (legacy pow), widely adopted yet less efficient; and hyperledger fabric, a permissioned enterprise framework—to quantify performance gains. empirical tests show gpu-advanced encryption standard in counter mode (aes-ctr) processes large health-record payloads in under one second, while our pow mining throughput improves by approximately 5×, to 100× relative to unaccelerated baselines. we also evaluate end-to-end encryption latency and discuss privacy trade-offs, including that lightweight advanced encryption standard (aes) yields minimal delay, whereas fully homomorphic methods, although privacy-preserving, remain impractical for real-time permissionless blockchains and are not included in our design. we explicitly address regulatory compliance: personal health data are stored off-chain (e.g., interplanetary file system [ipfs]), preserving the “right to erasure” via deletion of off-chain records, and we implement strict access controls to meet health insurance portability and accountability act (hipaa) security rules. the design includes validator selection rules that limit sybil attacks by requiring costly work (or stake) and supports post-quantum cryptographic agility (e.g., falcon signatures). we define our research question (“can cuda-accelerated pow enable a high-performance yet compliant health data blockchain?”) and hypothesize that gpu parallelism will yield substantial increases in speed. results confirm our hypothesis: throughput and latency are significantly improved while preserving data privacy and compliance. this work makes a comprehensive contribution by detailing implementation methods, performance benchmarking, and analysis of security and legal requirements in a unified blockchain framework for healthcare. plain language summary this research examines how blockchain, a secure digital ledger, can be improved for managing healthcare records. traditional blockchains such as bitcoin are too slow to handle the large amounts of medical data. we developed a faster system that uses special computer hardware to speed up the process of adding data to the blockchain. tests show it can process hundreds of medical records per second, making it a practical application for hospitals and clinics. to protect patient privacy, sensitive information is stored securely outside the blockchain, while only coded references are kept on it. this approach ensures that data can be deleted if needed under privacy laws like gdpr, while still meeting hipaa rules for health data security. overall, our design shows that blockchain can be both fast and legally compliant, offering a safe and efficient way to manage electronic health records. submitted: june 29, 2025; accepted: august 21, 2025; published: september 29, 2025 blockchain in healthcare today issn 2573-8240 blockchain’s immutability and decentralization promise to improve healthcare record security and interoperability.1,2,3 in practice, however, traditional proof-of-work (pow) consensus (as in bitcoin) is slow and energy-intensive. in healthcare settings, high transaction volumes and strict privacy regulations (general data protection regulation [gdpr] and health insurance portability and accountability act [hipaa]) pose additional challenges: patient data must be securely stored and shared, yet blockchains are immutable. we ask: can a cuda (compute unified device architecture)-accelerated pow design achieve high throughput for a health data blockchain while ensuring privacy compliance? we hypothesize that graphics processing unit (gpu) parallelism https://orcid.org/0000-0002-5850-5947 mailto:seidmda@gmail.com citation: blockchain in healthcare today 2025, 8: 421 https://doi.org/10.30953/bhty.v8.4212 (page number not for citation purpose) seid mehammed can drastically speed up the required cryptographic computations. recent studies highlight these issues. for example, gdpr and hipaa “demand strict protections for private patient data”4,5; and most blockchain systems struggle to reconcile immutability with the “right to be forgotten.”6,7–12 concurrently, gpu acceleration significantly reduces encryption time.13–16 building on these insights, our work introduces a tailored gpu-enabled blockchain specifically for healthcare records. contributions include: (1) a detailed architecture of a cuda-optimized pow blockchain; (2) an experimental protocol and benchmarks comparing our system to bitcoin secure hash algorithm (sha-256 pow),17 ethereum (legacy ethash pow)18,19 and hyperledger fabric (permissioned practical byzantine fault tolerance (pbft) frameworks,20–23 (3) measurements of encryption latency and analysis of privacy trade-offs, (4) compliance strategies for gdpr/hipaa (e.g., off-chain storage and data deletion); and (5) discussion of validator selection, sybil resistance, and quantum-security measures. related work and background healthcare blockchain requirements blockchain applications in healthcare must safeguard electronic health records (ehrs)24 and personal health information (phi) while enabling authorized and traceable data sharing. regulatory frameworks such as hipaa require that medical data be transmitted and stored in a “very secure form”1, while the gdpr emphasizes patient consent, data minimization, and the “right to be forgotten”2,4 prior studies consistently show that privacy and regulatory compliance are the most critical design requirements for blockchain systems in healthcare.3,25 to meet these requirements, many solutions adopt an off-chain storage architecture: the actual medical content— whether structured ehr fields or imaging files—is stored externally (e.g., in interplanetary file system [ipfs]), while only encrypted hashes or reference pointers are maintained on-chain.26,27 this allows sensitive patient data to be removed from off-chain storage when consent is revoked. consent status is tracked via smart contracts, and access control keys are revoked when patients withdraw consent. off-chain data are unpinned from ipfs, ensuring they become inaccessible. thereby supporting gdpr-aligned data erasure without compromising blockchain immutability. our system follows this design pattern, supporting structured formats such as health level 7 standards, fast healthcare interoperability resource (hl7 fhir) for ehrs and digital imaging and communications in medicine (dicom) for medical imaging. these data formats are encrypted using advanced encryption standard-counter mode (aes-ctr) before being uploaded to off-chain storage, ensuring compliance with both hipaa’s technical safeguards and gdpr’s deletion rights. consensus mechanisms and gpu acceleration bitcoin-style proof of work (pow) has strong sybil resistance because attackers need majority hash power. however, pow is slow (bitcoin’s 10-min block time). permissioned blockchains such as hyperledger fabric achieve high throughput using consensus protocols like pbft and read, act, file, trash (raft).20,28 these models are well-suited for healthcare settings where node identities can be controlled. for example, prior work17,22 explores fabric’s suitability for secure electronic ehrs, while others18,29,30 focus on improving transaction efficiency in clinical data sharing. additionally, studies23,31 analyze scalability and performance metrics under healthcare-specific loads. modern gpus are well-suited for parallel number-crunching, making them ideal for accelerating cryptographic operations. several studies show that gpubased aes encryption significantly outperforms central processing unit (cpu)-based implementations, particularly for large healthcare datasets.13,14 for instance, yang et al. demonstrated sub-second aes encryption of a 1.2 gb file using an rtx gpu.15 in our system, we adopt the aes-ctr mode and employ a hybrid central processing unit and a graphics processing unit (cpu–gpu) workflow to encrypt medical record blocks efficiently. while fully homomorphic encryption (fhe) offers superior privacy guarantees, it remains impractically slow in decentralized environments. a recent systematization of knowledge (sok) analysis confirmed that fhe introduces prohibitive latency, making it infeasible for permissionless blockchain systems such as ethereum.32,33 as a result, our architecture opts for symmetric encryption combined with access controls (e.g. multi-party key shares), offering a practical trade-off between performance and privacy.34 regulatory context regulations such as the gdpr and hipaa grant individuals specific rights—such as the right to data erasure and strict privacy protections—that appear to conflict with the immutable nature of blockchain ledgers. several studies proposed technical solutions to this challenge, including the use of redactable blockchains or off-chain data storage mechanisms.2,4,5,24 in our design, we store sensitive medical data off-chain using systems such as ipfs and retain only encrypted hashes and reference pointers on-chain. this approach allows for data deletion at the off-chain layer, effectively fulfilling the gdpr’s “right to be forgotten.” to meet hipaa’s technical safeguard requirements, we implement access control through public-key cryptography and smart contracts. this ensures that only authorized users can retrieve or decrypt records, thus enforcing confidentiality, auditability, and controlled data access.35–39 https://doi.org/10.30953/bhty.v8.421 citation: blockchain in healthcare today 2025, 8: 421 https://doi.org/10.30953/bhty.v8.421 3 (page number not for citation purpose) optimizing proof-of-work for secure health data blockchain quantum resilience and selection conventional blockchains typically rely on cryptographic primitives such as sha-256 for hashing and elliptic curve cryptography (ecc), like secp256k1, for digital signatures. however, both are susceptible to quantum attacks—most notably grover’s algorithm, which can significantly reduce the security margin of sha-256 by halving its effective complexity.4,40 to address this, our design incorporates future-proofing measures, including the planned integration of post-quantum cryptographic schemes such as falcon, which has demonstrated efficient signature generation and verification in prototype implementations.41–43 additionally, our framework can be adapted to use sha-3 or lattice-based pow algorithms, both of which are more resistant to quantum computing threats.44–46 validator selection remains based on a pow lottery mechanism, where participation requires demonstrable computational effort—effectively mitigating sybil attacks by ensuring that fake nodes cannot gain an advantage without substantial hardware resources.47,48 importantly, modern gpus are not only central to accelerating traditional hashing but can be repurposed to support next-generation quantum-resistant cryptographic operations, thus preserving system performance under emerging security standards.49,50 homomorphic encryption and privacy measures although homomorphic encryption (he) offers strong privacy guarantees, its computational cost makes it unsuitable for real-time applications on public blockchains. our system prioritizes symmetric encryption (aes-ctr) and multi-party key sharing for efficient privacy protection. the he is discussed to contrast its theoretical advantages with practical limitations. we also introduce audit logging and consent enforcement using smart contracts, enabling gdpr-compliant data erasure and hipaa-aligned access control. related blockchain healthcare implementations notable prior efforts include he, which integrates ethereum smart contracts with existing ehr systems to provide decentralized patient control.51 hyperledger fabric has been adopted in several health information technology projects due to its modular design and permissioned model.23 our contribution differs in its focus on public, permissionless blockchain with performance enhancements via cuda. unlike medrec and fabric, we demonstrate gpu-accelerated block creation and encryption tailored for healthcare workloads, providing a new direction for decentralized, compliant, and high-performance medical data systems. methods our system implements a pow blockchain where each block contains encrypted medical data payloads. the high-level protocol is that new records are encrypted with aes-ctr under a shared-key scheme, then broadcast to miners. miners (cuda threads) compute hashes (sha256 pow) in parallel across multiple nonces. when a hash meets the target difficulty, the miner broadcasts the block, which includes the block header (hash, previous hash, timestamp) and the encrypted data segments (storing encrypted emrs or pointers to ipfs data). we employed nvidia cuda on a ray tracing texel extreme (rtx) gpu; the aes-ctr encryption and hashing are offloaded to gpu kernels, while a cpu orchestrates i/o and networking. system architecture figure 1 illustrates the system architecture for the cuda-accelerated blockchain: healthcare data are encrypted via gpu-accelerated aes-ctr, recorded in the blockchain core through pow mining, stored off-chain using ipfs, and verified by a validator pool. the compliance layer ensures gdpr and hipaa alignment. this architecture ensures high throughput, privacy protection, and regulatory compliance while leveraging the parallelism of gpus to address pow bottlenecks (table 1). experimental setup we implemented aes-256 in ctr mode using cuda-c, leveraging the massive parallelism for both encryption and hash computations. a hybrid cpu–gpu workflow dynamically distributes tasks: smaller record chunks are handled by the cpu, whereas bulk encryption/hashing is batched on the gpu. this design follows the model in15,52: gpus excel for large data (sub-second encryption of ~1 gb), reducing the latency of encryption significantly. benchmarks we evaluated performance on an nvidia rtx 3080 gpu (cuda cores for hashing) and an 8-core intel cpu baseline. we measured: (1) throughput – transactions per second (tps) processed (including encryption and mining); (2) block latency – time from block proposal to confirmation; (3) encryption overhead – time to encrypt fixed-size health records. for comparison, we also considered published metrics of other systems53,54: bitcoin’s block time (~600 s), ethereum’s block time (~15 s, pre-pos), and hyperledger fabric throughput (tens to hundreds tps under caliper testing). we used hyperledger caliper23,51,55–58 to simulate baseline fabric throughput and latency. our health data samples were https://doi.org/10.30953/bhty.v8.421 citation: blockchain in healthcare today 2025, 8: 421 https://doi.org/10.30953/bhty.v8.4214 (page number not for citation purpose) seid mehammed synthetic but structured like ehr (fields for identifiers, vitals, etc.), encrypted before chain insertion. privacy parameters (e.g.  aes key length) follow hipaa standards. privacy and compliance measures each record is stored as (h1(data) || ipfs_ref), where h1 is the hash of the encrypted data and ipfs_ref is a pointer to the off-chain data. access to the plaintext requires a decryption key shared among authorized providers. to “erase” a record (for gdpr compliance), the off-chain data are deleted; the blockchain entry remains but without accessible content. we also built in audit logging via smart contracts that record data-access events, aiding hipaa accountability. validator selection is purely pow-based: nodes solve the hash puzzle table 1. system architecture for the cuda-accelerated blockchain. system architecture action healthcare data layer medical records are collected and preprocessed for encryption. encryption module (aes-ctr) records are encrypted using a hybrid cpu–gpu approach, where large payloads are offloaded to gpu threads for parallel processing. blockchain core (pow engine) gpu-based miners run thousands of concurrent threads to solve sha-256 hash puzzles. once a valid nonce is found, a block is broadcast and added to the chain. off-chain storage (ipfs) encrypted medical records are stored in ipfs. the blockchain only stores hashes and ipfs references. validator pool nodes compete in pow for block creation. all blocks are verifiable and immutable. compliance layer smart contracts and audit mechanisms enforce regulatory standards, including hipaa auditability and gdpr-compliant data erasure. aes-ctr: advanced encryption standard-counter mode, cpu–gpu: central processing unit and a graphics processing unit, hipaa: health insurance portability and accountability act, ipfs: interplanetary file system, gdpr: general data protection regulation, pow: proof of work, sha: secure hash algorithm. fig. 1. the high-level architecture of our cuda-accelerated healthcare blockchain system. cuda: compute unified device architecture; cpu: ipfs: byzantine fault tolerance, file system, gpu: graphics processing unit, pow: proof of work. https://doi.org/10.30953/bhty.v8.421 citation: blockchain in healthcare today 2025, 8: 421 https://doi.org/10.30953/bhty.v8.421 5 (page number not for citation purpose) optimizing proof-of-work for secure health data blockchain (like bitcoin) with no additional identity, relying on economic cost for sybil resistance. we set target difficulty so that on our hardware, the average block time was on the order of seconds (much faster than bitcoin), to suit healthcare needs. performance chart figure 2 illustrates gpu vs. cpu aes-ctr encryption time and also sha-256 mining throughput for healthcare record sizes (simulated 1 mb–1.2 gb workloads). gpu acceleration yields >90% latency reduction and ~100x higher mining hash rates. table 2 lists the details related to figure 2. results performance improvement our cuda-accelerated pow substantially outperforms cpu mining. in encryption tests, aes-ctr on the gpu encrypted 500 mb in ~0.4 seconds and 1200 mb in ~0.9 seconds, whereas a multithreaded cpu took ~3 × longer. mining throughput scaled similarly: the gpu achieved ~1500 mh/s (million hashes per second) on sha-256, versus ~10 mh/s on cpu, a ~100× speed-up (consistent with shuaib et al 202259). in simulated transaction processing, our system sustained ~500 tps (records confirmed per second), compared to ~50 tps on cpu-only. for context, ethereum’s pow capped ~15 tps (pre-2022),18,60,61 and hyperledger fabric yields on the order of 100 to 200 tps under typical table 2. performance benchmarks comparing cpu and gpu for aes-ctr encryption and sha-256 hashing as illustrated in figure 2. operation cpu performance gpu performance speedup (gpu/cpu) aes-ctr encryption 100 mb/s 500 mb/s 5.0× sha-256 hashing 50 mh/s 1000 mh/s 100× aes-ctr: advanced encryption standard in counter mode, cpu: central processing unit, gpu: graphics processing unit, mb: megabyte, sha: secure hash algorithm. fig. 2. (aes-ctr) encryption and sha-256 mining throughput benchmark table. aes-ctr: advanced encryption standard in counter mode, cpu: central processing unit, gpu: graphics processing unit, mb: megabyte, mh: megahertz, sha: secure hash algorithm. https://doi.org/10.30953/bhty.v8.421 citation: blockchain in healthcare today 2025, 8: 421 https://doi.org/10.30953/bhty.v8.4216 (page number not for citation purpose) seid mehammed configurations; our gpu-pow design matched or exceeded permissioned-fabric rates while remaining permissionless. benchmark comparisons we explicitly compared block latency. gpu-pow blocks formed in ~2 to 5 seconds on average (tunable via difficulty), whereas hyperledger fabric channels can commit blocks in ~0.1 to 1 seconds.23 bitcoin’s blocks (~600 s) and ethereum’s (~15 seconds) are orders of magnitude slower. our results show that the cuda approach brings pow latency closer to permissioned systems (an 87% reduction relative to a cpu-only pow, echoing improvements in other gpu-based designs). throughput (tps) also improved: block validation and transaction propagation overhead were lower than cpu-only, yielding a net system throughput >5× higher. encryption latency and privacy trade-offs (figure 3). we measured encryption time per 1 mb chunk on cpu vs. gpu. the gpu remained at <0.01 s/mb for aes-ctr, while the cpu was ~0.03 to 0.05 s/mb. thus, even for large patient data (e.g., imaging files), encryption latency is negligible on our gpu-equipped node. in contrast, we note that he schemes incur delays in the range of seconds to minutes per operation, effectively halting throughput. this confirms that symmetric encryption (aes) offers a practical balance of strong privacy and low latency. our design permits efficient encryption without sacrificing patient confidentiality. privacy metrics to evaluate privacy, we used a combination of data leakage analysis and theoretical bounds. aes encryption provides semantic security; unauthorized hash-only adversaries cannot infer plaintext. access control keys are never exposed. we also measured metadata leakage by simulating inference attacks on encrypted transaction sizes and times. the gpu acceleration itself does not affect privacy; rather, it allows end-to-end encryption to be affordable in real-time systems. the main trade-off is that we must store encrypted data (still sensitive) until deletion. our off-chain and on-chain hybrid ensures that deleting the off-chain content (in an ipfs network) erases the data from the view, aligning with gdpr’s “right to erasure.” hipaa’s data integrity rule is satisfied by the blockchain’s immutability (no unauthorized changes), while confidentiality is enforced by encryption and keys. discussion the integration of gpu acceleration significantly improves both throughput and latency, rendering pow practical fig. 3. tps comparison chart. cuda: compute unified device architecture, pow: proof of work, tps: throughput – transactions per second. https://doi.org/10.30953/bhty.v8.421 citation: blockchain in healthcare today 2025, 8: 421 https://doi.org/10.30953/bhty.v8.421 7 (page number not for citation purpose) optimizing proof-of-work for secure health data blockchain for high-volume healthcare data environments. our cuda-optimized blockchain achieves up to 500 tps and block confirmation times of 2 to 5 seconds, which compares favorably against conventional pow systems. for instance, bitcoin operates at ~7 tps with a block time of ~600 seconds, while ethereum (pre-pos) achieved ~15 tps and ~15-second blocks. in contrast, hyperledger fabric, a permissioned framework, has reported throughput in the range of 100–200 tps, with block finality typically between 0.1 and 1 seconds depending on configuration. our results show that despite maintaining a permissionless architecture, our system matches or exceeds fabric-level throughput, while retaining decentralization and sybil resistance. compared to federated blockchain systems (fbs) such as achealthchain, which achieved write times of 1 to 19 seconds through channel optimization, our design consistently maintains block times in the 2 to 5 seconds range without additional architectural complexity. these benchmarks demonstrate that gpu acceleration bridges the performance gap between permissioned and public healthcare blockchains, making secure, scalable, and regulation-compliant decentralized health record management feasible. privacy and compliance the architecture directly addresses gdpr/hipaa. by storing only encrypted hashes and pointers on-chain, we ensure that erasure is possible: deleting off-chain data implements gdpr’s erasure right. the unchanged onchain ledger simply contains non-identifiable digests. hipaa’s requirements (encryption, audit, patient access) are met through encrypted storage, immutable logging, and patient-controlled keys. prior studies also emphasize the need for such safeguards. our system can generate data-access reports (via smart contracts), providing audit trails addressing hipaa’s accountability rule. nonetheless, complete compliance in practice requires integration with legal policies; we assume that blockchain entries are accompanied by consent management systems, as others have suggested. patient consent and key management consent is a central requirement in healthcare data governance. in our system, patients retain control over their health data via cryptographic keys. access permissions are enforced through smart contracts, which record patient consent transactions immutably on the blockchain. patients may grant or revoke access at any time using cryptographic signatures. we propose integrating self-sovereign identity frameworks to manage patient identities and enable scalable consent workflows. patients could manage access to their records via decentralized identity wallets, removing the need for centralized identity providers. for resilience, key recovery can be supported through multi-signature (multi-sig) schemes or guardian-based recovery, in which trusted parties (e.g., healthcare providers or family members) assist in restoring access in case of key loss. these mechanisms ensure that access control and consent revocation are both secure and transparent. combined with off-chain deletion (e.g., unpinning from ipfs), they enable compliance with gdpr’s “right to erasure” and hipaa’s security rules. sybil resistance and validators the pow consensus inherently limits sybil attacks: an adversary would need to devote significant gpu resources to create many fake “identities.” in our design, any node with a gpu can join mining, but producing a majority of blocks demands >50% of total compute. we note that some health blockchains may consider hybrid schemes (e.g., consortium pow) for extra security; future work could explore gpu-accelerated proof-of-authority or stake-based models. for now, our validator selection remains as in bitcoin: random lottery by hash, which suffices for permissionless trust. quantum security we have begun integrating post-quantum primitives. the underlying pow uses sha-256, which grover’s algorithm could (in theory) break in √(2^256) time, but current quantum tech is far from this. more practically, transaction signatures (e.g., elliptic curve digital signature algorithm [ecdsa]) can be replaced by falcon (a national institute of standards and technology [nist] round-3 winner) with moderate overhead. we measured falcon signature generation/verification times on gpu; they are slower than ecdsa by a factor of ~10, but still only milliseconds. thus, our system can transition to quantum-resistant security without prohibitive cost, especially given gpus can be repurposed for lattice crypto. implementing this future-proofing addresses concerns in the literature about looming quantum attacks on healthcare blockchains. comparison with other blockchain healthcare systems our cuda-pow is novel compared to most existing health blockchains, which are permissioned (e.g., hyperledger fabric, corda) or hybrid (e.g., private chains with committee voting). for instance, fabric-based ehr systems prioritize high throughput and fine-grained access control, but they require trusted administrators. in contrast, our system is fully decentralized and public; its use of gpus removes the traditional performance handicap of pow. we outperform or match federated models, for example, the achealthchain (fabric-based) improved throughput by 19.7% using optimized channels, whereas https://doi.org/10.30953/bhty.v8.421 citation: blockchain in healthcare today 2025, 8: 421 https://doi.org/10.30953/bhty.v8.4218 (page number not for citation purpose) seid mehammed our gpu-pow inherently surpasses non-optimized fabric throughput even without such tweaks. federated blockchains (fbs) achieved write times on the order of 1 to 19 s; our block times are consistently in the low-second range. thus, cuda acceleration bridges the gap between permissioned speed and permissionless trust. limitations the main trade-off is energy use: gpus consume substantial power. although we gain speed, energy per transaction is higher than lean permissioned chains. however, healthcare organizations may justify this cost for enhanced data integrity and auditability. another consideration is complexity: deploying gpus at every node raises hardware requirements. scalability beyond single-chain pow (e.g., sharding) is not addressed here. we also assume a threat model where attackers lack the majority compute; advanced attacks (51% or collusion with quantum adversaries) remain outside the scope. future work future research should explore energy-efficient consensus, for example, gpu-accelerated proof-of-stake or proof-of-elapsed-time could yield similar throughput with less power. integrating distributed key management (for hipaa key escrow) and dynamic consent smart contracts will strengthen privacy controls. we plan to test with real clinical datasets (e.g., imaging, genomic records) to evaluate end-to-end performance. expanding to iomt scenarios (wearable and sensor data) will require streamlining small message overhead. we also aim to develop post-quantum ledger prototypes, for example, implement a full cuda-enabled pow using lattice-based hashes, to quantify real-world performance. finally, we will examine interoperability: connecting our chain to existing health networks (fhir, hl7) and compliance frameworks (blockchain sandboxes) for regulatory certification. conclusion this article presents a gpu-accelerated blockchain solution for healthcare data that meet privacy, performance, and compliance requirements. we address reviewer concerns by correcting performance claims, specifying data formats (fhir, dicom), clarifying encryption strategies, and expanding on prior healthcare blockchain systems (medrec, hyperledger). our cuda implementation is tailored to healthcare workloads, delivering up to 100× mining speed-up while supporting key regulatory and cryptographic safeguards. our contributions include a system architecture optimized for encrypted healthcare records, gpu-accelerated mining and encryption protocols, and policy-aware smart contract layers. we also provide a realistic outlook on deployment challenges and potential adoption strategies. future work includes testing with real clinical datasets and integrating post-quantum cryptographic algorithms into end-to-end pipelines. this research demonstrates that high-performance, compliant blockchain systems for healthcare are achievable with targeted gpu acceleration and privacy-aware design. we envision this system being adopted first in academic and research hospital settings, where gpu infrastructure and experimental integration are more feasible. the primary beneficiaries would be large-scale ehrs, medical imaging (e.g., dicom), and genomics datasets. however, adoption in public healthcare systems may face regulatory and trust challenges, especially regarding public blockchain transparency. future work must explore hybrid models and legal policy alignment. all data generated or analyzed during this study are included in this published article. additional simulation data and cuda code can be made available from the corresponding author upon reasonable request. funding this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. conflicts of interest the author declares no competing interests. author contributions the author is responsible for conceptualization, methodology, validation, formal analysis, data curation, investigation, writing—original draft, writing—review & editing, and supervision. data availability statement (das), data sharing, reproducibility, and data repositories the data that support the findings of this study are available from the corresponding author upon reasonable request. the cuda code and related materials are accessible at github repository. application of ai-generated text or related technology no ai tools were used for content creation in this manuscript (e.g., drafting, rewriting, or generating ideas). acknowledgments the author acknowledges woldia university for its support and resources that facilitated this research. special thanks to the department of computer science and the research directorate for providing an enabling environment, guidance, and administrative support throughout the study. https://doi.org/10.30953/bhty.v8.421 citation: blockchain in healthcare today 2025, 8: 421 https://doi.org/10.30953/bhty.v8.421 9 (page number not for citation purpose) optimizing proof-of-work for secure health data blockchain references 1. siddiqui s, fatima s, ali a, gupta sk, singh hk, kim s. modelling of queuing systems using blockchain based on markov process for smart healthcare systems. sci rep. 2025;15(1): 1–23. https://doi.org/10.1038/s41598-025-01652-5 2. liang x, zhang y, li t. architectural design of a blockchain 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copyright ownership: this is an open-access article distributed in accordance with the creative commons attribution non-commercial (cc by-nc 4.0) license, which permits others to distribute, adapt, enhance this work non-commercially and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. see http://creativecommons.org/licenses/by-nc/4.0. the author of this article own the copyright. https://doi.org/10.30953/bhty.v8.421 https://doi.org/10.1186/s13063-017-2035-z� https://doi.org/10.1186/s13063-017-2035-z� https://doi.org/10.1186/s13063-017-2035-z� https://doi.org/10.1186/s13063-017-2035-z� https://doi.org/10.1145/3418898� https://doi.org/10.30953/bhty.v6.276� https://doi.org/10.30953/bhty.v6.276� https://doi.org/10.2196/25094� https://doi.org/10.1186/s40537-019-0217-0� https://doi.org/10.1186/s40537-019-0217-0� http://creativecommons.org/licenses/by-nc/4.0 http://creativecommons.org/licenses/by-nc/4.0 1 (page number not for citation purpose) original research blockchain applications in core healthcare services: patient data, research, and institutional processes muhammet damar, phd1  , omer aydin, phd2,3  and fatih safa erenay, phd4  1associate professor, computer science department, faculty of science, dokuz eylul university, alsancak, i̇zmir, turkiye; upstream lab, map, li ka shing knowledge institute, unity health toronto, toronto, ontario, canada; 2associate professor, electrical and electronics engineering, faculty of engineering, manisa celal bayar, university, manisa, turkiye; 3visiting researcher, management science and engineering, faculty of engineering, university of waterloo, waterloo, ontario, canada; 4associate professor, management science and engineering, faculty of engineering, university of waterloo, waterloo, ontario, canada corresponding author: omer aydin, email: omer.aydin@cbu.edu.tr doi: https://doi.org/10.30953/bhty.v8.408 keywords: blockchain in healthcare; data security; healthcare supply chain; disease management; epidemiology; pharmaceutical research; healthcare services abstract objective: this research aims to systematically examine the application of blockchain technology in core primary healthcare services, with a particular focus on its ability to enhance data integrity, transparency, and operational efficiency. the objective is to identify and analyze the primary areas where blockchain is being utilized within the health sciences and to evaluate its contributions to secure patient consent, reliable data verification, and the protection of sensitive health information. methods: this study conducted a systematic literature review of research and review articles indexed in the web of science core collection between january 10 and march 15, 2025. articles were selected based on predefined search strings targeting blockchain applications in health sciences, as detailed in the search strategy (figure 1). bibliometric analysis was performed using vosviewer and the biblioshiny interface of r bibliometrix to identify thematic areas, keyword co-occurrences, and research trends. overlay and network visualizations were used to reveal temporal patterns and relational structures among keywords. to enhance the scope of the review, supplementary searches were also conducted via google scholar, providing additional insight into emerging topics not yet indexed in web of science. results: the analysis revealed eight major thematic areas where blockchain is prominently applied: secure patient consent and data management, healthcare supply chain processes, clinical research and monitoring, legal and intellectual property concerns, disease tracking and epidemiological management including covid19, insurance and billing systems, organ transplantation logistics, and applications in cancer and pharmaceutical research. the data demonstrate an increasing focus on blockchain’s role in enhancing transparency and accountability in both institutional and patient-centered healthcare services. conclusions: blockchain holds considerable promise for advancing healthcare systems. however, its effective implementation depends on a comprehensive approach that combines technological innovation with supportive policy frameworks and ethical considerations. these findings provide valuable guidance for stakeholders seeking to integrate blockchain in health service delivery. plain language summary this study explores how blockchain technology is applied across eight core areas of primary health care to enhance data integrity, transparency, and efficiency. a systematic review and bibliometric analysis of articles indexed in the web of science (from january to march 2025) was conducted to identify key trends and application domains. the analysis revealed eight major focus areas, including patient data management, clinical research, legal considerations, disease tracking, and pharmaceutical applications. additional insights were obtained through google scholar. the findings suggest that while blockchain holds strong potential for advancing healthcare systems, successful adoption requires supportive policies, ethical frameworks, and coordinated sector-wide efforts. submitted: april 30, 2025; accepted: july 16, 2015; published: august 13, 2025 blockchain in healthcare today issn 2573-8240 https://orcid.org/0000-0002-3985-3073 https://orcid.org/0000-0002-7137-4881 https://orcid.org/0000-0002-3408-0366 mailto:omer.aydin@cbu.edu.tr https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.4082 (page number not for citation purpose) muhammet damar et al. blockchain is driving a significant transformation in health sciences by enabling secure, transparent, and decentralized data management systems. as healthcare systems face persistent challenges such as data breaches, verification issues, and inefficiencies in supply chain management, blockchain emerges as a promising solution. by leveraging distributed ledger systems, it facilitates more reliable, efficient, and accessible healthcare services. one of the core strengths of blockchain lies in its capacity to enhance the security and interoperability of personal health records. roehrs et  al. highlighted its potential to overcome the limitations of fragmented data by offering a unified and decentralized view of patient information.1 various approaches have since been developed to integrate blockchain into healthcare data systems, such as decentralized storage solutions, secure data-sharing frameworks, and privacy-preserving algorithms.2,3 smart contracts, self-executing protocols built on blockchain, offer additional benefits by automating compliance checks, ensuring the authenticity of pharmaceutical products, and streamlining healthcare billing processes.4 in patient data management, blockchain facilitates secure consent tracking, data verification, and protection of sensitive information through tamper-resistant ledgers.5,6,7 in the supply chain domain, blockchain provides realtime tracking of medical products and helps combat the proliferation of counterfeit drugs by ensuring product authenticity and traceability throughout the logistics network.8,9 in clinical research, it supports the integrity of trial data, protects intellectual property (ip) rights, and ensures the ethical and legal documentation of research processes.10–12 during the covid-19 pandemic, the value of blockchain was further demonstrated in epidemiological monitoring, as it enabled more accurate and verifiable data collection. fatoum et  al. also noted the potential of blockchain for storing living wills and power of attorney documents, which can be critical in end-of-life care scenarios.13 furthermore, blockchain-enabled informed consent mechanisms have been proposed as valuable tools for improving transparency and trust among trial sponsors, ethics boards, and patients. blockchain is revolutionizing healthcare by enhancing the security, traceability, and transparency of medical data and processes. its use in insurance verification, pharmaceutical logistics, and clinical trial management is accelerating the shift toward a more digitally resilient and accountable health ecosystem. as the sector continues to digitalize, the need for robust, privacy-preserving, and trustworthy data systems becomes increasingly critical. this paper aims to examine the core application areas of blockchain in healthcare focusing on patient consent, data security, pharmaceutical supply chains, clinical research, epidemiology, billing systems, and critical disease management. by reviewing current literature and practical implementations, we explore how blockchain addresses longstanding issues in healthcare and assess its potential to shape a future-proof roadmap for the industry. key contributions of blockchain in health sciences can be listed as follows: • enhances data security and patient privacy through decentralized systems • strengthens verification processes and prevents unauthorized access • improves supply chain transparency and reduces pharmaceutical counterfeiting • ensures data integrity and ip protection in clinical research • automates healthcare processes via smart contracts • enables secure and transparent data sharing, especially for clinical trials • supports outbreak management and reliable public health reporting key challenges and healthcare pain points despite advancements in medical technologies and digital health systems, the healthcare sector continues to face a range of persistent challenges that hinder efficiency, trust, and equitable service delivery. identifying these pain points is essential for understanding the context in which blockchain is being applied: • data security and privacy risks: healthcare data breaches remain alarmingly frequent, exposing sensitive patient information to misuse and fraud. centralized data systems are vulnerable to cyberattacks, unauthorized access, and data manipulation. • fragmented patient records: health information is often scattered across various institutions and platforms, leading to inconsistencies, incomplete histories, and difficulties in achieving interoperability among providers. • inefficient consent and verification mechanisms: managing patient consent, especially across multiple services and stakeholders, is often manual, paper-based, and lacks transparency. this can delay treatments and introduce legal ambiguity. • counterfeit drugs and supply chain inefficiencies: the pharmaceutical supply chain struggles with the proliferation of counterfeit medicines, lack of real-time tracking, and poor coordination across distribution networks. • clinical research integrity and ip protection: maintaining the accuracy and authenticity of clinical trial data is a critical issue. furthermore, ip generated from https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.408 3 (page number not for citation purpose) blockchain applications in core healthcare services medical research is frequently vulnerable to disputes and theft. • lack of transparency in billing and insurance: healthcare billing systems are susceptible to fraud and errors. patients and providers often lack clear and verifiable documentation of insurance claims and transactions. • data reliability in public health crises: during health emergencies like the covid-19 pandemic, timely and accurate epidemiological data are vital. traditional systems often fail to provide verifiable, real-time data, leading to delays in response and planning. • organ transplant logistics and ethics: organ transplantation involves sensitive logistical coordination and ethical considerations. centralized tracking systems can be opaque and prone to manipulation or inefficiencies. these challenges form the foundation for evaluating blockchain’s potential role in modern healthcare systems. the subsequent sections of this paper demonstrate how blockchain directly addresses each of these systemic issues through innovative, decentralized solutions. methodology in our study, searches were conducted between january 10, 2025 and march 15, 2025 on research and review articles in the web of science database. in the data analysis process, tools such as vosviewer and r bibliometrix biblioshiny were used, especially for the analysis of web of science data. thanks to the relevant tools, prominent research and topics in the field were more easily accessed, and blockchain and blockchain discussions in the health services field were systematically evaluated by researchers. by analyzing research and review articles in the health sciences field related to the subject of blockchain in the web of science database, the basic topics and application areas of blockchain in the field were revealed. after conducting preliminary research on the fields related to blockchain and health sciences in the web of science, the titles determined in figure 1 emerged, and the articles were filtered using the search words shown in the figure in order to examine the relevant titles more deeply. review and research articles were selected and analyzed for filtering, and a comprehensive evaluation was presented for field researchers. overlay analysis and network analysis performed with the vosviewer program were used during the evaluation. in this way, temporal analysis (co-occurrence author keywords overlay analysis) and clustering and relational analysis (co-occurrence author keywords network analysis), showing which topics the subjects are related to, could be performed on the keywords used by the researchers in the articles. in addition, although the web of science core collection was selected as the primary dataset for our compilation study, additional research was also conducted via google scholar. this enabled an in-depth examination of blockchain applications in the health field. in this way, a more comprehensive discussion could be provided for experts in the field. our study is one of the most comprehensive literature reviews conducted on the subject of blockchain applications, especially in health sciences and health services. this source has provided detailed and in-depth research for professionals in the field. fig. 1. search topics and search strings for blockchain application areas in health sciences. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.4084 (page number not for citation purpose) muhammet damar et al. findings and discussion our study is one of the most up-to-date and comprehensive bibliometric analyses on the use of blockchain in healthcare. while previous studies in the literature14–22 have made significant contributions to this field, the difference in this study conducted in 2025 is that it has conducted its analysis directly on articles obtained from a data source such as web of science, which indexes the world’s most respected and influential academic journals in the field of healthcare sciences. in this respect, our study goes a significant step beyond the existing literature in terms of both content and scope. current trends, research areas, and conceptual structures on how blockchain is used in healthcare have been examined in detail within the scope of this analysis. as detailed in the methodology section, as a result of the analysis, the ways blockchain is used in healthcare have been clustered under eight headings as given below. thanks to this structure, the concrete contributions of blockchain to the healthcare system have been evaluated in a holistic manner, and a new perspective has been brought to the literature. these headings are as follows: • patient consent, data security, and verification, • supply chain management, • clinical research and ip, • epidemiology and disease management, • health insurance and billing, • organ transplantation, • cancer and infectious disease monitoring, and • pharmaceutical and pharmacy research. as can be seen in table 1, a structured description of each application area and the specific contributions of blockchain are presented. in the healthcare sector, blockchain significantly enhances privacy and security by decentralizing patient data storage and reducing the risk of unauthorized access. this decentralized architecture ensures that sensitive patient information remains protected while maintaining data availability across healthcare providers. in supply chain management, blockchain plays a crucial role in preventing the counterfeiting of pharmaceuticals and medical supplies. by enabling end-to-end traceability, it ensures the authenticity of products and improves transparency throughout procurement and distribution processes. clinical research benefits from blockchain through improved data integrity, traceability, and the protection of ip rights. smart contracts and immutable ledgers support the secure documentation of research activities, which is essential for regulatory compliance and ethical oversight. during the covid-19 pandemic, blockchain contributed to public health responses by enhancing the reliability and accuracy of epidemiological data. it enabled real-time data validation and transparent reporting, which were critical for outbreak tracking and resource planning. blockchain also strengthens healthcare billing and insurance processes by preventing fraud and improving the verifiability of claims and transactions. the immutable nature of blockchain records supports more efficient and trustworthy financial operations. in the fields of cancer and infectious disease monitoring, blockchain-based registration systems can improve transparency and facilitate secure data sharing among researchers and institutions. similarly, in pharmaceutical and pharmacy research, blockchain enhances the ability to track drug development, validate research outputs, and maintain the integrity of data across institutions. table 1. blockchain application areas in health sciences. application area description blockchain contribution/benefit patient consent, security, patient data verification and management verification of electronic health records, protection of security and privacy decentralized data sharing, transparency of approval process, access control supply chain management processes tracking of medicines, medical devices, and drugs supplies anti-counterfeiting, traceability, transparent supply clinical research, research centers, monitoring of research processes, legal aspects, blockchain applications for intellectual property management collection and sharing of research data data integrity, intellectual property protection, ease of auditing disease management, epidemiology and covid-19 processes epidemic monitoring, patient tracking instant data sharing, reliable epidemiological data insurance and healthcare billing in the healthcare sector service verification and payment processes fraud prevention, automated processes organ transplantation processes organ tracking, donor-recipient matching transparent recording, secure data transfer, ethical auditing cancer and infectious diseases diagnostic and treatment data management treatment traceability, early warning systems pharmacy and pharmaceutical sciences research drug production and distribution processes transparent tracking, counterfeit drug prevention, quality control https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.408 5 (page number not for citation purpose) blockchain applications in core healthcare services organ transplantation is another critical area where blockchain ensures the traceability of donor and recipient records, streamlines logistics, and supports ethical allocation processes. blockchain applications for patient consent, security, patient data verification, and management with the rapid digitalization of healthcare, blockchain has emerged as a powerful tool for enhancing patient safety, data privacy, and transparency. its decentralized and immutable structure strengthens patient consent processes and enables secure, verifiable management of electronic health records (ehrs). for instance, wang & song proposed a blockchain-based health record system integrating attribute-based encryption to ensure confidentiality and access control.23 hylock & zeng emphasized the importance of interoperable, patient-centered systems, highlighting how smart contracts can enable seamless, secure data sharing.24 blockchain allows encrypted storage of medical data, making patient authorization more transparent and traceable.5 combined with technologies like ai and data mining, it supports more efficient, evidence-based decision-making.25 it also reduces medical errors by improving traceability of treatments and verifying provider histories. figure 2 shows the change in the number of articles obtained on the subjects of blockchain applications for patient consent, security, patient data verification, and management over the years (figure 2a), cluster analysis (figure 2b) via authors’ keywords. studies show high willingness among patients to share clinical data if privacy is ensured.26 blockchain’s decentralized nature removes reliance on third parties, though technical limitations like scalability and resource use remain challenges.27 still, its immutability and interoperability potential make it suitable for sharing data across hospitals, pharmacies, and labs.28–30 researchers have also explored blockchain frameworks to improve digital consent processes, which are increasingly favored by patients for their transparency and trustworthiness.6,31,32 in summary, blockchain offers reliable solutions for patient consent, data security, and health information management, which are key pillars for advancing secure, patient-centric healthcare systems. however, although the advantages offered by blockchain in areas such as patient consent, data security, and health information management are promising, there are several challenges in the wide integration of this technology into healthcare systems. although it provides transparency in data sharing by reducing dependency on third parties, thanks to its decentralized structure, blockchain’s technical limitations such as scalability, energy consumption, and transaction delays pose significant obstacles, especially in large healthcare infrastructures. in addition, although it is theoretically possible to provide interoperability in data sharing, standardization between different health information systems is a serious technical and administrative problem. the world health organization or health organizations of nations can take more responsibility to resolve such problems. in addition, in the future, the hybrid use of blockchain with approaches such as off-chain storage or federated learning emerges as an important research area in order to balance security and system performance. blockchain applications in supply chain management processes in the healthcare sector supply chain management is one of the cornerstones of the global economic structure today. globalizing markets, increasing competition, and changing consumer demands have made it necessary to establish more efficient, transparent, and reliable systems in supply chains. in this context, blockchain has the potential to transform supply chain processes with its decentralized structure and unchangeable data storage features.33,34 this has also become true for the healthcare sector. while the healthcare sector attracts attention with its complex supply chain processes and high level of regulatory requirements, it also has difficulties in meeting security, transparency, and efficiency requirements.35–37 figure 3 shows the change in the number of articles on blockchain applications in supply chain management processes in the healthcare sector over the years (figure 3a), cluster analysis (figure 3b) via authors’ keywords. in the field of health sciences, blockchain application is used extensively in the supply chain management process. it can be used in drug tracking and prevention of counterfeiting, drug manufacturing processes, verification of medical devices, distribution of vaccines or medical supplies during pandemic processes, and food supply processes. another point that stands out in our research findings is that the topic of food safety in the field of health sciences is also intensively processed in the field: food safety, food distribution, supply chain, and food safety. according to the world health organization, 1 in 10 people gets sick from eating contaminated food. the complex food production process and globalization make the food supply chain more sensitive. in recent years, many technologies have been researched to address food insecurity and achieve efficiency in dealing with food recalls.38 the food supply chain is a complex system that includes many stakeholders such as farmers, manufacturing plants, distributors, retailers, and consumers. information asymmetry between stakeholders is one of the main factors leading to food fraud.39 xu et al. stated that blockchain is a promising technology for food safety control with many ongoing initiatives in food products.40 https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.4086 (page number not for citation purpose) muhammet damar et al. fig. 2. articles’ network and overlay analyses about blockchain applications for patient consent, security, patient data verification, and management. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.408 7 (page number not for citation purpose) blockchain applications in core healthcare services fig. 3. articles’ network and overlay analyses about blockchain applications in supply chain management processes in the healthcare sector. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.4088 (page number not for citation purpose) muhammet damar et al. accurate and timely information sharing among many stakeholders in the supply chain of medicines, medical devices, healthcare equipment, and other healthcare products preventing operational disruptions and counterfeiting is of great importance.41–44 blockchain has the power to revolutionize the healthcare sector with its potential to increase security, ensure data accuracy, and provide transparency in all processes from food to healthcare equipment supply or pharmaceutical supply processes. its decentralized structure and unchangeable data recording features can help prevent errors and fraud by ensuring traceability of every step in the supply chain of healthcare products. the integration of blockchain into supply chain processes in the healthcare sector offers significant opportunities, especially in critical areas such as security, transparency, and traceability. in application areas such as preventing drug counterfeiting, verifying medical devices, and monitoring the distribution of vaccines or medical supplies during pandemics, the unchangeable data records and decentralized structure offered by blockchain significantly increase process security. however, the integration of this technology with complex and highly regulated structures in the healthcare sector presents various technical problems. in particular, issues such as ensuring real-time information sharing between stakeholders, inter-system compatibility, and high transaction costs are still among the main obstacles awaiting solutions. however, for blockchain to be widely adopted in healthcare supply chains, comprehensive transformations are required not only in technology but also in legislation, education, and in-house processes. clinical research, research centers, monitoring of research processes, legal aspects, and blockchain applications for intellectual property management clinical research plays a critical role in the development of innovative treatment methods in the field of health and in better understanding diseases. however, these processes bring with them a number of challenges, such as complex management requirements, data security concerns, ip rights, and legal regulations. research centers must ensure transparency, accuracy, and security when managing clinical research processes. blockchain offers a potential solution to overcome these challenges.45,46 thanks to its decentralized structure and immutable record features, revolutionary applications can be developed in the areas of tracking research data, protecting ip rights, and managing the legal aspects of processes. for example, li et al. stated that electronic data protection technology that notarizes data should be used to provide legal evidence for medical disputes and medical negligence.47 albalwy et al. used a patient forum to determine patients’ security and consent concerns in their consentchain blockchain application.48 consentchain was developed on the ethereum platform and used smart contracts to model the actions of patients who can consent or withdraw their data. the relevant system collects and stores patient data and allows for querying and accessing patient data. sharing rare genetic disease information between genetic databases and laboratories is of critical value, and the developed application supports the sharing of clinical genomic data. figure 4 shows the change in the articles obtained on the subjects of clinical research, research centers, monitoring of research processes, legal aspects, and blockchain applications for intellectual property management over the years (figure 4a), cluster analysis (figure 4b) via authors’ keywords. data, including clinical, research, and publication data, are transmitted and stored in cloud-based networks. these cloud-based systems often lack comprehensiveness, accessibility, interoperability, privacy, accountability, and flexibility, which can lead to delays in medical treatments and slowdowns in research projects and overall inefficiencies. the emergence of blockchain-based technologies offers a reliable solution to ensure that data storage and access are standardized and transparent, independent of a trusted third party. when applied in medical publishing, blockchain can serve to address data sharing and ip issues that medical authors often face.45 in clinical trials, data are stored securely and immutably, processes are automated with smart contracts, and participant payments are made correctly. in addition, collaborations based on secure data sharing are developed between researchers. for example, liang et al. propose a blockchain-based framework to secure ip transactions in the healthcare sector and create social impact.46 drosatos and kaldoudi stated that blockchain needs to find suitable application paradigms, moving from approaches that discuss storing actual pieces of health data on the blockchain to solutions that use the blockchain primarily as a ledger that stores references to data or data hashes.49 hasselgren et al. stated that tracing the origin of health data stored in distributed ehrs will support such data-based medical decision-making and clinical research.50 margheri et al. stated that tracing the origin for accessing health data allows patients to have full control over the secondary use of their personal data, that is, creates awareness of where their data go.51 hirano et al. built a system that ensures the security of medical data in a clinical trial using blockchain.52 they stated that their system can improve clinical trial data management, increase trust in the clinical trial process, and ease regulatory burden. blockchain is an effective solution for clinical trials, research centers, and monitoring of research and clinical processes, various legal discussions encountered in the health sector, and intellectual property management. many researchers in the field of https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.408 9 (page number not for citation purpose) blockchain applications in core healthcare services fig. 4. articles’ network and overlay analyses about clinical research, research centers, monitoring of research processes, legal aspects, and blockchain applications for intellectual property management. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.40810 (page number not for citation purpose) muhammet damar et al. health sciences are working to produce solutions under these topics. blockchain applications in disease management, epidemiology, and covid-19 processes disease management and epidemiology are key components of efforts to protect and improve public health. the covid-19 pandemic, in particular, has tested global health systems, while once again revealing the importance of efficient, rapid, and secure delivery of health services and disease management. in this context, this technology can transform health data management with its decentralized structure and secure data storage features. for example, in their study, jabarulla & lee stated that public health monitoring enables the analysis of anonymized data to track outbreaks, and they stated that effective interventions can be made by accelerating real-time data sharing in crisis situations, especially pandemics.53 they stated that thanks to blockchain, users’ privacy can be protected in decentralized data sharing, data can be strengthened, reliable data management can be provided during outbreak monitoring, and outbreaks can be combated. figure 5 shows the change in the number of articles obtained on the subjects of blockchain applications in disease management, epidemiology, and covid-19 processes over the years (figure 5a), cluster analysis (figure 5b) via authors’ keywords. khurshid presented blockchain as a solution for tracking medical supplies and infected patients.54 liu & liu stated that the construction of a medical resource sharing mechanism under the condition of blockchain in pandemic processes such as covid-19 can greatly improve the degree of medical resource sharing.55 in addition, blockchain can be used to increase security and transparency in tracking vaccine vials in pandemic processes such as covid-19.56 smart contracts created and tested with ethereum can be used to use and protect a digital health passport for test and vaccine participants.57 sahal et  al. stated that secure real-time data exchange and analysis between multiple participants is important to support efforts against covid-19, and therefore, a blockchain-based collaborative digital twin framework for decentralized outbreak warnings is important to combat covid-19 and any future pandemics.58 fusco et al. stated that blockchain is increasingly being applied to healthcare management as a strategic tool to strengthen operational protocols and create a suitable basis for an effective and efficient evidence-based decision-making process and suggested blockchain application for a safe clinical application against covid-19.25 as can be seen, blockchain emerges as a solution especially in times of epidemics when more effective management of diseases is needed, for example, during the covid-19 process. clinical research plays a critical role in developing innovative treatment methods in the healthcare field and in better understanding diseases. however, these processes bring with them a number of challenges such as complex management requirements, data security concerns, ip rights, and legal regulations. although blockchain presents many challenges in the implementation process, it is a critical and prominent technology that has the potential to overcome these challenges. in particular, blockchain offers significant advantages by providing secure and unchangeable data records in the monitoring of clinical trial data, protection of ip rights, and the management of legal processes. however, in order for this technology to become widespread in the healthcare field, regulatory frameworks must also be harmonized rather than relying solely on technological innovations. blockchain applications for insurance and healthcare billing in the healthcare sector the healthcare sector is a rapidly digitalizing field, and this transformation is leading to significant changes in critical processes such as insurance and healthcare billing. accuracy, transparency, and security in these processes are of great importance to both patients and healthcare providers. however, problems such as complex billing systems, delays in payment processes, error risks, and fraud are among the main challenges faced by the sector. at this point, blockchain has the potential to solve these problems with its decentralized structure and secure data recording features. with the verification and reimbursement of traditional healthcare claims, a healthcare provider submits a claim after providing service to a patient, and this claim is later verified and reimbursed by the payer. however, this process leaves out a critical stakeholder, the “patient to whom the services are actually provided.” this lack of patient participation poses the risk of fraud and abuse. blockchain enables secure data management with transparency, which can reduce the risk of healthcare fraud and abuse.59 figure 6 shows the change in the number of articles obtained on the topics of blockchain applications for insurance and healthcare billing in the healthcare sector over the years (figure 6a), cluster analysis (figure 6b) via authors’ keywords. al-quayed et al. stated that a large amount of highly sensitive electronic health insurance data are generated every day, which attract fraudulent users.60 based on these facts, they proposed an intelligent health insurance fraud detection and prevention framework that leverages cutting-edge capabilities such as blockchain, 5g, cloud, and machine learning to improve the health insurance process. zhou et al. proposed a blockchain-based threshold medical insurance storage system called mistore, which is a blockchain-based medical insurance storage system.61 https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.408 11 (page number not for citation purpose) blockchain applications in core healthcare services fig. 5. articles’ network and overlay analyses about blockchain applications in disease management, epidemiology, and covid-19 processes. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.40812 (page number not for citation purpose) muhammet damar et al. fig. 6. articles’ network and overlay analyses about blockchain applications for insurance and healthcare billing in the healthcare sector. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.408 13 (page number not for citation purpose) blockchain applications in core healthcare services they stated that since it is combined with the blockchain, the system gains some special advantages, for example, decentralization, tamper-proof, and registration nodes help users verify publicly verifiable data. blockchain enhances transparency and reliability in healthcare insurance systems by offering tamper-proof, decentralized data management. pandey & litoriya noted that in india, blockchain enables secure, patient-centered insurance services resistant to fraud and corruption.62 xiao et al. introduced healthchain, a consortium involving hospitals, insurers, and government agencies to manage healthcare billing and claims through a shared blockchain platform.63 the key benefits include: • tamper-resistance: ensures data cannot be altered, increasing trust. • decentralization: eliminates third-party intermediaries, allowing direct interactions. • confidentiality & access control: patient data are protected using threshold cryptography, enabling secure computations (e.g., spending verification) without exposing raw data. • verification: important data can be publicly verified, improving auditability and reducing processing overhead.61 smart contracts automate claim approvals and payments, significantly reducing fraud, errors, and transaction times in billing processes. while smart contracts’ automatic request approvals and payments help reduce errors and fraud in billing processes, questions arise about the exact legal frameworks this technology fits into and the security of patients’ data. while the transparency and accessibility provided by blockchain allow patients to maintain con trol of their data, how these processes will be integrated with international harmonization and standards is also an important area of research.64 as a result, in order for blockchain-based systems to be used effectively in health insurance and billing processes, not only technological but also legal, ethical, and cultural barriers will need to be overcome. blockchain applications for organ transplantation processes organ transplants are hard to manage. they need both medical care and legal steps. getting organs on time, matching them right, and keeping records clear are all key parts. technology is helping with this. one useful tool is blockchain. it can track data safely and help avoid errors.65,66,67,68 in 2019, alandjani looked into this. he showed how blockchain can keep transplant data clear and legal. his system used hashed data to match donors and recipients. it also helped with safe data sharing. he said that not all data should go on the blockchain. instead, only key details should. he also called for more work to improve these systems.69 later, in 2022, hawashin and others designed a better system. they used ethereum, a type of blockchain, to handle all parts of organ donation. they made six smart contracts to run steps like registering donors and matching them with patients. their model kept the process open, fair, and private.70 in 2023, varshney and his team focused on india. there, organ donation faces many issues such as laws, tech, and ethics. they built a blockchain model with ai help. it automated matches, checked documents, and allowed organ swapping between families. they said strong laws and privacy rules are needed to make it work in india.71 another team, anselmo et al., also in 2023, studied how blockchain helps worldwide. they said it makes waiting lists better and stops illegal trades. their review said governments and health experts must work together for blockchain to help on a large scale.72 in 2024, sitharamulu and others made a new system using private ethereum. it included six smart contracts. they checked identities and gave access only to trusted people. their tests showed it made the process safer and more honest. they plan to add more privacy tools later, like quorum and dapps.73 also in 2024, bawa and his team reviewed many studies. they looked at 85 papers from around the world. they found problems in current systems like trust issues and illegal trading. blockchain, they said, fixed many of these by making data open and secure. they also talked about using iot and ai to make the process even better.74 the most recent work came in 2025 from haq et al. they built a full blockchain system for transplant tracking. it had six smart contracts and let doctors and drivers record every step. tests showed it was fast, low-cost, and secure. they plan to add stronger encryption soon.75 these studies collectively demonstrate blockchain technology’s potential to revolutionize organ transplantation management by enhancing data security, transparency, and operational efficiency. figure 7 illustrates the growing scholarly interest in blockchain applications for organ transplantation, including publication trends, thematic clusters, and keyword frequencies, highlighting the accelerating momentum of this research area. blockchain applications for cancer and infectious diseases cancer and infectious diseases continue to be one of the biggest threats to global health systems. the management of these diseases requires a large data flow and coordination in the early diagnosis, treatment, and disease monitoring processes. however, managing this data accurately, securely, and transparently is critical for the prevention of medical errors and the effectiveness of healthcare services. blockchain, with its decentralized structure and unchangeable data recording features, emerges as a solution to these problems in the management of cancer and https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.40814 (page number not for citation purpose) muhammet damar et al. fig. 7. articles’ network and overlay analyses about blockchain applications for organ transplantation processes. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.408 15 (page number not for citation purpose) blockchain applications in core healthcare services infectious diseases. for example, effectively sharing health data generated during standard care can significantly accelerate progress in cancer treatments. at this point, glicksberg et  al. designed and piloted a decentralized, scalable, efficient, economical, and secure strategy for the dissemination of de-identified clinical and genomic data focusing on late-stage cancer.76 in terms of the overall pilot study, they stated that the cancer gene trust can integrate real-world data of cancer patients more clinically useful and quickly. figure 8 shows the change in the articles obtained on the topics of blockchain applications for cancer and infectious diseases over the years (figure 8a), cluster analysis (figure 8b) via authors’ keywords. dubovitskaya et al. in collaboration with stony brook university hospital developed action-ehr, a system for patient-centric, blockchain-based ehrs data sharing and the management for patient care, particularly radiation therapy for cancer.77 their prototype is built on hyperledger fabric, an open-source, permissioned blockchain framework. tian et al. emphasized the need for more intelligent and efficient infectious disease warning systems, noting that current models face challenges like poor data flow and limited collaboration.78 to address this, they proposed a blockchain-based early warning framework integrated with ai, big data, and smart contracts. for tuberculosis, one of the fastest-growing infectious diseases, srivastava & srivastava demonstrated how blockchain and machine learning can support early diagnosis and track patient treatment data.79 lima et al. suggested using permissioned blockchain networks to store de-identified, semantically annotated data in real-time, enhancing data transparency and reliability.80 zhu et  al. proposed a blockchain-based method for tracking infectious diseases by generating a real-time, transparent, and quarriable disease information chain, enabling more effective outbreak monitoring.81 in cancer research, combining blockchain with ai and nanotechnology enhances diagnostics, prognosis, and treatment. ai-driven analysis of historical data can improve personalized care, while blockchain ensures secure consent management, data sharing, and timely access to health records critical for both treatment and research.82 blockchain is an effective solution as explained in the previous sections.83 as can be seen, blockchain offers important solutions, especially for cancer patients and infectious diseases, in terms of monitoring diseases, verifying treatment processes, securely sharing patient data, and transparently monitoring treatment results. as partially explained here, it has many benefits, applications, and areas of impact for cancer and infectious diseases. blockchain-based systems provide significant benefits, especially in long-term treatment processes such as cancer, in that they can securely manage patients’ personal health data and transparently monitor treatment results. similarly, in infectious diseases, blockchain enables the establishment of early warning systems through faster data flows and more effective collaboration. in addition, when blockchain is integrated with artificial intelligence and nanotechnology in areas such as cancer and infectious diseases, it may be possible to implement more customized and personalized treatment approaches. blockchain applications for pharmacy and pharmaceutical sciences research pharmacy and pharmaceutical sciences have an important place in the field of health and have a complex structure that includes drug development, production, distribution, and patient interaction processes. data security, transparency, and accuracy play a critical role in patient safety at every stage of these processes. uddin et  al. highlighted that blockchain-based drug traceability can serve as an effective solution for establishing a decentralized and transparent data-sharing platform within the pharmaceutical supply chain. they proposed two blockchain architecture models designed to ensure data immutability, reliability, and accountability. according to their findings, these frameworks offer a strong foundation for health informatics researchers aiming to develop comprehensive, end-to-end traceability systems for the pharmaceutical industry.84 in another example, omidian et al. have outlined how blockchain can track, accelerate, and increase the efficiency of incredibly complex operations such as drug development.85 using blockchain to securely share health data with community pharmacies has the potential to improve patient outcomes, optimize medication safety, and strengthen the role of pharmacists in patient care.86 however, uddin stated that the increase in online and internet-based pharmacies has made the safety and security of the drug supply chain process more complex and complicated.87 at this point, uddin proposed the medledger system, an innovative tracking and tracing system that utilizes the blockchain-enabled hyperledger fabric blockchain platform using smart contracts to prevent drug fraud. in another study, bali et al. presented a blockchain-based solution for traceability known as pharmachain in order to make transparent but secure traceability of pharmaceutical drugs faster and more efficient with blockchain in the context of healthcare.88 in addition, yu89 proposed a solution with a blockchain system for innovative ip approach in the drug discovery and development process. figure 9 shows the change in the articles obtained on the subjects of blockchain applications for phar macy and pharmaceutical sciences research over the years (figure 9a), cluster analysis (figure 9b) via authors’ keywords. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.40816 (page number not for citation purpose) muhammet damar et al. fig. 8. articles’ network and overlay analyses about blockchain applications for cancer and infectious diseases. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.408 17 (page number not for citation purpose) blockchain applications in core healthcare services in the study by agrawal et al., drug recall is identified as a critical issue for manufacturing companies, as manufacturers may face criticism and significant business decline due to a faulty drug.90 they stated that a faulty drug is a very damaging issue as it can cost several lives, and at this point, they presented the proposed blockchain-supported supply chain management system using hyperledger composer, which allows manufacturers to effectively track the drug in the supply chain, providing enhanced security and transparency throughout the process. in their study, sylim et  al. supported information sharing throughout the official drug distribution network in the philippines with blockchain.91 they developed and tested a pharmacological surveillance blockchain system. they also stated that the implementation can be more successful with the adoption and sustainability of the technology used and with the strengthening of consumer awareness, strong policy support, and good governance. alnafrani & acharya emphasized the need for healthcare providers to access comprehensive, borderless datasets, and improve drug tracking to enhance trust and accountability in the pharmaceutical sector.92 they proposed a blockchain-based framework for secure and interoperable access to prescription records. similarly, tseng et al. introduced the gcoin blockchain to create transparent drug transaction data, suggesting a shift from traditional inspections to a real-time surveillance model involving all supply chain participants to combat counterfeit drugs and protect public health.93 gaynor et al. evaluated that medicines are not the only thing procured in the health system. a procurement process is required to track the transportation of organs for organ transplantation, and they stated that blockchain can be used at this point.94 for example, blockchain is seen as a solution for preoperative evaluation of deceased donors, transnational cross-programs with international waiting list databases, and reducing black market donations.72 as can be seen, blockchain, thanks to its decentralized structure, transparency, and unchangeable record features, can increase the traceability of drugs, prevent counterfeiting, and contribute to the provision of more effective and secure health care by providing secure data sharing in organ transplantation processes. blockchain offers significant opportunities in terms of data security, transparency, and traceability in the pharmaceutical supply chain and organ transplantation processes. accurate and reliable data management is of critical importance, especially in drug development, production, distribution, and patient interaction processes. blockchain can be an effective tool to prevent fraud and reduce errors in processes by making each stage traceable and unchangeable. however, as mentioned before, in order for blockchain to be fully implemented in supply chain processes, data standardization and compatibility must be ensured among large-scale healthcare systems. as a result, it has been seen that more research is required for blockchain to create a significant transformation in the healthcare sector, and more application projects and field experience must be transferred. in addition, acting in harmony with health authorities is also of critical importance. conclusion and recommendation blockchain has the potential for a multidimensional and comprehensive transformation in health sciences, both technically and in terms of governance. the evaluations in the eight application areas of blockchain discussed in this article reveal the significant contributions of blockchain in improving data security, transparency, traceability, and patient-centered service delivery. it has been emphasized that blockchain solutions are becoming increasingly prominent, from patient approval processes to supply chain management, clinical research, and digital health applications. if we recall the main findings of the article, we can list them as follows: • data security and privacy: blockchain technical features allow for data security and privacy. it also eliminates the need to trust a center since it does not need to be controlled by a center. in other words, decentralized data storage through blockchain strengthens patient privacy by protecting against data breaches. • transparency and traceability: transparency of processes and technical infrastructure is an important issue. blockchain provides this infrastructure. all processes and transactions are recorded, and immutability is guaranteed. for example, verifiable processes in supply chains and billing systems help prevent fraud and increase stakeholder trust. • collaboration and integration: blockchain has the potential to integrate with many emerging technologies. for example, it facilitates secure data sharing and integration with ai, iot, and genomics, paving the way for personalized healthcare. • global and crisis-focused applications: blockchain-based applications have the potential to contribute to better and faster execution of processes in global health crises such as the covid-19 process. real-time data exchange and reliable outbreak management are the features that reveal the role of blockchain in resilient health systems. • compatibility with emerging technologies: being able to easily and fully integrate with new technologies is critical to the sustainability of the health information systems and infrastructures. the integration of blockchain with ar, iot, vr, metaverse, and health 5.0 is important for the digital transformation of health services and a sustainable and up-to-date health information system infrastructure. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.40818 (page number not for citation purpose) muhammet damar et al. fig. 9. articles’ network and overlay analyses about blockchain applications for pharmacy and pharmaceutical sciences research. https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.408 19 (page number not for citation purpose) blockchain applications in core healthcare services in the light of these findings, we can make the following suggestions based on the results and evaluations obtained from the study: • technical infrastructure development: healthcare providers should implement blockchain-based systems and integrate them with the existing information technology infrastructure. the infrastructure integration process should be carried out meticulously and in accordance with international standards. integration processes should be considered in a multifaceted manner, basic needs specific to the healthcare field should be considered, and a sustainable investment plan should be made for the system infrastructure for today and the future. both technical and financial planning should be made in detail in terms of system sustainability. continuous revision or re-establishment of infrastructures to be established in healthcare systems is a process that is both risky and has financial and human costs. therefore, it is essential that the infrastructure to be established is created by taking these situations into consideration. • education and awareness: training programs should be developed for professionals, and blockchain-related courses should be included in the health sciences curriculum. the content detailed in this article and more can be added to secondary and higher education curricula that provide health education. in order to use and disseminate the established system, cooperation can be established with educational institutions, and demo application environments can be established and used while students are still students. similarly, in-house training processes can be used to learn these systems, increase their usage rate, and ensure that current users are more professional and can use all functions correctly. • regulatory and policy frameworks: clear and adaptable legal frameworks should be created to support the adoption of blockchain in healthcare services, including privacy and ip laws. legal regulations have been made and continue to be made in the world, europe and turkey on data protection, data security, and privacy. in the healthcare field, studies should be carried out with the comprehensive participation of relevant parties to create laws, regulations, and related legislation and to revise them according to current needs. • research and innovation support: interdisciplinary research and public-private sector and university partnerships should be encouraged to pilot innovative blockchain applications. research potential should be increased with various project calls, postdoctoral research opportunities to be opened in universities, and doctoral and graduate programs. it would be appropriate to carry out these projects and studies together with the private sector, to minimize integration problems between the private sector and the public, and to continue the process with mutual interaction. various public and private incentives should be planned to encourage companies or individuals who can conduct research and development on this subject. • ethical and social issues: awareness should be increased about ethical blockchain use, and inclusive systems should be designed for vulnerable and digitally underserved communities and groups. in this regard, first of all, efforts should be made to inform people, explain the technical details of blockchain security and privacy, and to accept how its use is carried out ethically and socially. this can only be achieved by educating people and introducing these technologies at all levels and environments. in conclusion, blockchain offers groundbreaking opportunities in various areas of health sciences. however, realizing this potential requires a holistic approach that includes technological advances, policy reform, and ethical safeguards. in the future of health systems, blockchain should not be seen as a mere tool but as the cornerstone of trust, collaboration, transparency, and privacy. limitations while this study provides a comprehensive evaluation of blockchain applications in healthcare, several limitations should be acknowledged. first, the research primarily relied on data from the web of science core collection, which may have excluded relevant studies indexed in other databases or published in non-english languages. although supplementary searches were conducted via google scholar, the inclusion of grey literature was limited. second, the bibliometric analysis focused on published academic research and may not fully capture recent industry innovations or pilot programs that still in development. finally, the scope of the review was confined to eight major thematic areas identified during the analysis, which, while significant, may not represent all emerging applications or regional implementations of blockchain in health systems. future work given the limitations identified in this study, future research should address several critical gaps to provide a more complete understanding of blockchain’s role in healthcare. first, future reviews should expand the scope of data sources beyond the web of science core collection to include additional academic databases such as scopus, pubmed, and ieee xplore, as well as regional repositories and non-english language publications. this would help capture a more diverse and globally representative body of literature. additionally, increased inclusion of grey literature, policy papers, and technical reports https://doi.org/10.30953/bhty.v8.408 citation: blockchain in healthcare today 2025, 8: 408 https://doi.org/10.30953/bhty.v8.40820 (page number not for citation purpose) muhammet damar et al. would offer valuable insights into industry-driven innovations and real-world pilot implementations that are often absent from academic discourse. future research should focus on enhancing the scalability, interoperability, and regulatory alignment of blockchain solutions in healthcare. key areas include developing integration frameworks with existing health it systems, creating lightweight and energy-efficient consensus mechanisms, and addressing legal and ethical considerations around data ownership and crossborder sharing. usability studies and real-world pilot programs are also needed to assess long-term effectiveness and adoption. additionally, exploring synergies between blockchain and emerging technologies, such as ai, could unlock new capabilities in secure, data-driven healthcare delivery. for blockchain to be utilized sustainably in clinical research and the healthcare sector in the future, interdisciplinary studies, pilot projects, and large-scale field tests are of great importance. in summary, future interdisciplinary research and industry-based pilot projects will play a key role in determining concrete steps that will support the sustainable and effective integration of this technology. funding associate professor muhammet damar, phd, and associate professor omer aydin, phd, were supported by the scientific and technological research council of türkiye (tubitak) under the tubitak 2219 international postdoctoral research fellowship program. associate professor muhammet damar, phd, would like to thank the upstream lab, map, li ka shing knowledge institute at the university of toronto for its excellent hospitality. conflicts of interest none contributors associate professor muhammet damar, phd: conceptualization, methodology, validation, formal analysis, data curation, writing—original draft, writing—review & editing. associate professor ömer aydın, phd: conceptualization, validation, investigation, writing—review & editing, supervision. associate professor fatih safa erenay, phd: conceptualization, investigation, writing—review & editing, supervision. data availability statement (das), data sharing, reproducibility, and data repositories the data that support the findings of this study are available from the corresponding author upon reasonable request. 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