1 (page number not for citation purpose) ORIGINAL RESEARCH Optimizing Proof-of-Work for Secure Health Data Blockchain Using Compute Unified Device Architecture Seid Mehammed  Department of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 permis- sioned 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., Interplan- etary 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 spe- cial 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, tra- ditional proof-of-work (PoW) consensus (as in Bitcoin) is slow and energy-intensive. In healthcare settings, high transaction volumes and strict privacy regulations (Gen- eral 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)-ac- celerated PoW design achieve high throughput for a health data blockchain while ensuring privacy compliance? We hy- pothesize 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 en- cryption time.13–16 Building on these insights, our work in- troduces 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 sys- tem 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 dele- tion); and (5) discussion of validator selection, Sybil resis- tance, 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 trace- able 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 forgot- ten”2,4 Prior studies consistently show that privacy and regulatory compliance are the most critical design re- quirements 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 re- moved 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 be- come 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 Stan- dard-Counter Mode (AES-CTR) before being uploaded to off-chain storage, ensuring compliance with both HI- PAA’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, stud- ies23,31 analyze scalability and performance metrics under healthcare-specific loads. Modern GPUs are well-suited for parallel num- ber-crunching, making them ideal for accelerating cryptographic operations. Several studies show that GPU- based AES encryption significantly outperforms Central Processing Unit (CPU)-based implementations, particu- larly 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) work- flow to encrypt medical record blocks efficiently. While fully homomorphic encryption (FHE) offers su- perior 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 indi- viduals 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 sen- sitive 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 cryp- tography 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 cryp- tographic primitives such as SHA-256 for hashing and elliptic curve cryptography (ECC), like secp256k1, for digital signatures. However, both are susceptible to quan- tum 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 effi- cient 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 ac- celerating 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 un- suitable for real-time applications on public blockchains. Our system prioritizes symmetric encryption (AES-CTR) and multi-party key sharing for efficient privacy protec- tion. The HE is discussed to contrast its theoretical ad- vantages with practical limitations. We also introduce audit logging and consent enforcement using smart con- tracts, enabling GDPR-compliant data erasure and HI- PAA-aligned access control. Related Blockchain Healthcare Implementations Notable prior efforts include HE, which integrates Ethe- reum smart contracts with existing EHR systems to pro- vide decentralized patient control.51 Hyperledger Fabric has been adopted in several health information technology projects due to its mod- ular 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 decentral- ized, 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 (SHA- 256 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 (stor- ing encrypted EMRs or pointers to IPFS data). We em- ployed 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 CU- DA-C, leveraging the massive parallelism for both en- cryption and hash computations. A hybrid CPU–GPU workflow dynamically distributes tasks: smaller record chunks are handled by the CPU, whereas bulk encryp- tion/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 – transac- tions per second (TPS) processed (including encryption and mining); (2) Block latency – time from block pro- posal 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 identifi- ers, vitals, etc.), encrypted before chain insertion. Pri- vacy 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 re- mains but without accessible content. We also built in audit logging via smart contracts that record data-access events, aiding HIPAA accountability. Validator selec- tion 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 eco- nomic 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 sec- onds, whereas a multithreaded CPU took ~3 × longer. Mining throughput scaled similarly: the GPU achieved ~1500 MH/s (million hashes per second) on SHA-256, ver- sus ~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, Ethere- um’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 diffi- culty), 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 im- provements in other GPU-based designs). Throughput (TPS) also improved: block validation and transaction propagation overhead were lower than CPU-only, yield- ing a net system throughput >5× higher. Encryption la- tency 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 con- trast, 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 leak- age analysis and theoretical bounds. AES encryption provides semantic security; unauthorized hash-only ad- versaries 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 af- fect 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 dele- tion. Our off-chain and on-chain hybrid ensures that de- leting 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 – trans- actions 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 CU- DA-optimized blockchain achieves up to 500 TPS and block confirmation times of 2 to 5 seconds, which com- pares 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 permis- sionless 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 optimi- zation, our design consistently maintains block times in the 2 to 5 seconds range without additional architectural complexity. These benchmarks demonstrate that GPU ac- celeration bridges the performance gap between permis- sioned 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 on- chain ledger simply contains non-identifiable digests. HI- PAA’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 ac- countability rule. Nonetheless, complete compliance in practice requires integration with legal policies; we as- sume 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 gov- ernance. In our system, patients retain control over their health data via cryptographic keys. Access permissions are enforced through smart contracts, which record pa- tient consent transactions immutably on the blockchain. Patients may grant or revoke access at any time using cryptographic signatures. We propose integrating self-sovereign identity frame- works to manage patient identities and enable scal- able consent workflows. Patients could manage access to their records via decentralized identity wallets, re- moving 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 pro- viders or family members) assist in restoring access in case of key loss. These mechanisms ensure that access control and con- sent revocation are both secure and transparent. Com- bined 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 ad- versary 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 re- mains as in Bitcoin: random lottery by hash, which suf- fices for permissionless trust. Quantum Security We have begun integrating post-quantum primitives. The underlying PoW uses SHA-256, which Grover’s al- gorithm 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 Signa- ture 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, espe- cially given GPUs can be repurposed for lattice crypto. Implementing this future-proofing addresses concerns in the literature about looming quantum attacks on health- care blockchains. Comparison With Other Blockchain Healthcare Systems Our CUDA-PoW is novel compared to most existing health blockchains, which are permissioned (e.g., Hyper- ledger Fabric, Corda) or hybrid (e.g., private chains with committee voting). For instance, Fabric-based EHR sys- tems prioritize high throughput and fine-grained access control, but they require trusted administrators. In con- trast, 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 Fab- ric 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-sec- ond range. Thus, CUDA acceleration bridges the gap be- tween permissioned speed and permissionless trust. Limitations The main trade-off is energy use: GPUs consume sub- stantial power. Although we gain speed, energy per trans- action 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 hard- ware 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 adver- saries) remain outside the scope. Future Work Future research should explore energy-efficient consen- sus, 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 con- tracts 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 stream- lining 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 solu- tion for healthcare data that meet privacy, performance, and compliance requirements. We address reviewer con- cerns by correcting performance claims, specifying data formats (FHIR, DICOM), clarifying encryption strat- egies, and expanding on prior healthcare blockchain systems (MedRec, Hyperledger). Our CUDA implemen- tation 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 opti- mized 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 pri- mary 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 block- chain 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 cor- responding author upon reasonable request. Funding This research did not receive any specific grant from fund- ing 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 avail- able from the corresponding author upon reasonable request. The CUDA code and related materials are acces- sible at GitHub repository. Application of AI-Generated Text or Related Technology No AI tools were used for content creation in this manu- script (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 Sci- ence 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. 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