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©2022 American Medical Writers Association. All rights reserved.  
ISSN 2163-5315

AMWAJournal.org     19

The panel members respond to questions that have been 

raised during sessions on different developments in using 

technology in medical writing. Medical writing is a pro-

fession dedicated to transforming data and analyses into 

useful and digestible information, whether that informa-

tion involves regulatory applications or documents for the 

public and, specifically, patients. Decisions about treat-

ments or granting of approvals depend on the distillation 

being accurate, clear, and understandable. Using available 

technology well can support this goal in that it is a means 

for shifting a writer’s focus from what can be accomplished 

by artificial intelligence (AI), machine learning, and func-

tions to the creation of content.

What are the drivers for using aspects of automation in 
medical writing, what gaps has it filled?

Helle Gawrylewski: In the pharmaceutical industry and in 

health care, automation in the writing process has spotty 

adoption depending on the size and digital sophistication 

of a company. Automations have been used effectively in 

writing by templates in which sections are prepopulated 

based on text from other documents. The protocol might 

be populated with text from the investigator’s brochure 

(IB) or protocol concept document. Some companies 

have developed or acquired systems that can be used to 

accomplish this type of text before population and reuse. 

Microsoft Word itself has some slick automated capabil-

ities that may not be fully used, like text tagging for reuse 

in other parts of a document. Another gap filled is the writ-

ing of routine text in documents like the safety narratives in 

clinical study reports (CSRs). Narratives are required in the 

CSR but are onerous to write, especially in cases in which 

there are many variables or many study participants with 

adverse events as in an oncology study. US Food and Drug 

Administration (FDA) reviewers have not been fond of 

safety narratives being totally written by automation, so this 

is not as common as it might be. But hybrid narratives, in 

which the data appear in brief tables and the discussion and 

assessment are written by a medical writer, can be efficient 

and accurate and medically useful. Safety narratives written 

entirely by AI require a large data set to teach the algorithms 

to produce adequate text.

 The writing process also has benefitted from automa-

tion in review tools and quality control (QC). It’s useful for 

the applicable style manual to be digitally available and 

automatically applied for document checking. Routine 

checks can be more efficient this way and a time-saver 

for the writer. Tools for the review cycle have also been 

used because it’s tedious to send out sequential versions 

for document review when this can be done by a tool like 

Please Review and others, in which all comments can be 

seen by the team, tracked, and ultimately incorporated. 

Technology improves the process immensely and has had 

a positive impact not only on efficiency but also quality.

 Other parts of an eCTD (electronic common technical 

document) have also benefitted by making the integrated 

summaries of safety and effectiveness (ISS and ISE) linked 

to the individual reports for a population, and the literature 

summaries can be captured by AI technology. I’m not sure 

how many companies take advantage of AI in this respect, 

but Nimita can perhaps address this more fully.

 Other options for the use of AI and deep learning can 

be technical summaries of results for registries, and these 

can be populated when a CSR is written, as can the FDA 

Study Snapshots for safety by demographic characteris-

tics that are required at approval. It’s possible to populate 

the requirements automatically as an application is being 

built.

 Scientific writing in another language, also referred to 

as translation or localization, benefits from at least some 

aspects of machine translation. Companies that use trans-

lation memories, machine learning, or advanced deep 

learning methods (also known as deep structured learning, 

with multiple layers between the input and output layers) 

can produce complex documents in many languages 

quickly, required for Lay Summaries in the European 

Union (EU) portal (implemented in January 2022). This 

Helle Gawrylewski, MA1 and Nimita Limaye, PhD2/ 1Former Senior Director, Global Regulatory Writing,  
Johnson & Johnson, New Hope, PA; 2Vice President, Research, IDC Health Insights, Needham, MA

Technology to Further Medical Writing: Status and Future Vision

THEME ARTICLE

http://www.amwajournal.org


AMWAJournal.org     20Technology to Further Medical Writing: Status and Future Vision

type of automation requires standardization of concepts 

and terms so that coding can be used for digital exchange. 

Groups like the Clinical Data Interchange Standards 

Consortium (CDISC) and the Medical Dictionary for 

Regulatory Activities (MedDRA) code research terms and 

adverse events so they can be easily exchanged.

 All of these uses require standardization of terms and 

definitions. A concept that assists in reusing information: 

text must also be considered data. Written content is data, 

and a document is just a compilation of data elements. 

Computer systems can be designed to use natural language 

processing (NLP) to understand written text. Machine 

learning and deep learning keep advancing, making these 

tools a substantial efficiency gain for any organization.

It’s also a boon to medical writers who can use the tools to 

summarize large amounts of data to ensure that all applica-

ble resources are considered.

Nimita Limaye: Helle has made some great points. The 

future of medical writing is really about automation with 

the human in the loop. It is about leveraging not only 

robotic process automation and AI, but also about the use 

of machine learning (ML) techniques, such as NLP (which 

turns text into structured data) and natural language gener-

ation (which turns structured data into text). The challenge 

with training ML algorithms is the availability of mas-

sive labeled data sets. Transformer-based neural network 

architectures operate in a two-stage process, unsupervised 

learning on large volumes of unlabeled datasets, and then 

supervised learning on smaller amounts of labeled data. 

These are very powerful models and can be game changers, 

but these are still early days. There has been a very inter-

esting report in the June 2022 edition of Scientific American 

about how a GPT-3 transformer was trained to write an aca-

demic paper about itself.

 Automation will bring in significant efficiencies and 

reduce not only costs, but will also reduce the monotony 

associated with authoring the often-repetitive sections 

associated with regulatory documents and will improve 

quality. One is seeing a flurry of innovation, with technol-

ogy vendors actively innovating to drive “intelligent author-

ing.” Technology in medical writing will be increasingly 

adopted by the life sciences industry, and the future is not 

about the why, it is about the how. It is about how do you 

successfully implement it at scale. The industry is still stuck 

in a “pilotitis” mode, that is, operating on running one pilot 

to see if the technology really works, which is not surprising 

because it is such a highly regulated industry.

What are the areas in which use of the technologies 
might not be the best option and what barriers still exist 
in the industry?

Helle Gawrylewski: Aspects that require expert scientific 

knowledge and assessment may be able to be produced 

by automation but at this time still require human evalua-

tion and judgment. Electronically translated text still needs 

human review because language nuances and cultural 

aspects are difficult to program, especially in many lan-

guages. A native speaker should always review and verify. 

Writers work in a global arena and should take this respon-

sibility very seriously. For safety narratives, the medical 

assessment is also better written by a qualified medical 

writer. Machine written text can take on a repetitive quality 

and be interpreted as obviously machine written and not 

properly evaluated.

Nimita Limaye: Absolutely—addressing scientific and cul-

tural nuances is critical. And I believe that writing is not just 

a science, it is an art. The sentience that a human can bring 

in can make all the difference, especially when it comes to 

developing lay summaries or building out informed con-

sent forms. In addition, interpreting findings often requires 

looking across multiple data points, possibly in different 

reports. Algorithms may not be configured to do that. This 

is where the scientific thinking that a medical writer brings 

to the table counts.

What barriers to adoption exist in the industry?

Helle Gawrylewski: Structured authoring has been difficult 

to adopt because the application initially did not support 

Word documents and the formatting was an issue. Using 

structured authoring requires staff training and often an 

authoring tool does not integrate well with other older sys-

tems. It’s easier for a new operation or initial public offering 

(IPO) to start with structured authoring than to have a large 

organization scrap all the old systems and replace them. 

Cost is definitely an issue but also technical competency of 

the staff. Even Word is not actually used to its full capacity! 

Document experts are often not writers, and many writers 

are not sophisticated technology experts! In the past writers 

have been reluctant to embrace automation because they 

think it will replace them. But the fact is that not everything 

they write is worthy of their full attention. So, offloading 

what can be offloaded allows full-time focus on the criti-

cally important document sections and elements.

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AMWAJournal.org     21Technology to Further Medical Writing: Status and Future Vision

 There are some specific phases of research or types of 

research documentation for which automation seems to be 

more useful than for others.

 In early phase 1 studies, much of the results are focused 

on data and assessments are straightforward, like blood 

levels for Cmax, AUC, and such. Wearable devices that record 

results digitally are ideal in many types of studies in which 

tracking is important and in which some participants can be 

unreliable, such as in cardiac and diabetes studies.  

 Automation of patient diaries has always been a good 

use of automation, and now it’s possible to use smartphones 

and audio recording to get quality real-time data.

Nimita Limaye: I think that the biggest barriers to adoption 

are change management and “pilotitis.” Automation cre-

ates concerns with many medical writers. Will their roles 

be replaced? No, not really. They will actually move up the 

value chain. The grunt work will be taken care of by AI/ML. 

The medical writer will need to ensure that the data are rep-

resented in the right way, are being interpreted appropri-

ately, and that the messaging is correct. It is important that 

the value of automation of medical writing is recognized. 

Secondly, implementing any technology requires invest-

ment, and returns come when the solution is implemented 

at scale. Hence, many times, companies do not see the 

returns after running a pilot, and then determine that this is 

not a good solution. That should not happen. Skill develop-

ment is also important. Not everyone is tech-savvy, and the 

ability to navigate various tools requires training. Ensuring 

transparency and regulatory compliance will be critical.

What promising developments in automation exist in the 
near future as advances in AI and deep learning technol-
ogies continue to evolve?

Helle Gawrylewski: Access to efficient and useful informa-

tion from large databases that are untapped and useless to 

regular human review, like Clintrial.gov, can have consid-

erable impact. How many people can review and get value 

from all of the studies registered and reported there? The 

information is only as useful as we can accurately search 

and summarize it using AI and other newer methods of 

deep learning. I’ve seen it done and can say that it’s excit-

ing and not used nearly as much as it could be. The same 

applies to the EU portal that will contain not only CSRs but 

entire applications and IBs.

 Workflows can be made efficient and accurate using 

automation and AI by an authoring system that reuses, 

and is connected with, all data elements linked for easy 

searching, correcting, and replication. Providing drug labels 

globally in all native languages that are accurate (and cor-

respond with the master label), accurately translated, local-

ized, and kept up to date in a master system for tracking and 

updating. I hope this will be more common than it is now. 

I think an AI and deep learning system to render research 

into plain language to make it accessible to the public 

could be a remarkable way to counter misinformation and 

shine a light on all the great scientific research that goes 

on but is inaccessible to most. It’s said that the vaccines 

were developed so quickly that they can’t be safe—wrong! 

The platforms were used for years before for other vaccine 

development, especially the standard ones used for Ebola 

and tuberculosis, but the public finds it hard to follow or 

understand what goes on in research. And we need to mod-

ernize regulatory processes and health authority reviews, 

continue to have applications and data digitally accessible 

and reviewable globally. If we are transparent and share, 

scientific data will help us make better decisions faster and 

promote not only cures but the prevention and avoidance  

of disease.

Nimita Limaye: One will see the increasing use of real-

world data; data will be flowing in, fast and furious. It will 

be extremely challenging for medical writers to handle this 

scale and speed. This is where technology will play a valu-

able role. In addition, as global regulations keep evolving, 

dynamic document templates that embed this intelligence 

real-time will reshape the future of medical writing.

Author declaration and disclosures: The author notes no  
commercial associations that may pose a conflict of interest in 
relation to this article.

Author contact: nlimaye@idc.com

BIOSUMMARIES
Helle Gawrylewski has a MA from University of 

Pennsylvania, is a Woodrow Wilson Fellow, and is a 

former Senior Director in global regulatory and medical 

writing at Johnson & Johnson (J&J) (retired). Her experi-

ence in regulatory medical writing and global regulatory 

affairs spans more than 49 years in the pharmaceutical 

industry at Hoechst Roussel Pharma, Novo Nordisk, and 

Janssen research and development of J&J. During that 

time, Gawrylewski was directly involved with 55+ regula-

tory applications for marketing approval and in all aspects 

of product life-cycle development, ranging from early 

to full development and post-marketing medical affairs. 

http://www.amwajournal.org


AMWAJournal.org     22Technology to Further Medical Writing: Status and Future Vision

She managed and mentored staff from two to 125 and is 

a strong proponent and advocate of regulatory medical 

writing. She established linguistic services like transla-

tion and related global partnerships in medical and regu-

latory writing, leading outsourcing relationships in India 

and China and worked on the first team to submit a drug 

application electronically to the FDA. She led document 

management implementation and transparency activities 

internally while serving as a team lead at TransCelerate in 

the Clinical Trial Document Transparency group, later in 

PHUSE as a team member, and also on teams at Janssen 

that produced several European Medicines Agency Policy 

0070 submissions of transparent clinical reports. In reg-

ulatory, she established global labeling outsourcing. 

Externally, Gawrylewski was the Pharmaceutical Research 

and Manufacturers of America representative in the ICH 

E3 Q&A working group that clarified standards for study 

reports, was a member of the CDISC Glossary Team and 

was the lead for 7 years, and was DIA MW community lead 

for and a core team member for 8 years. Gawrylewski is 

dedicated to cross-industry groups designing approaches 

to common problems in clinical trials, including clear 

goals/design, auditable conduct, subsequent clear reports, 

and transparent results in plain language and well-de-

fined scientific terms shared in multiple languages. She 

is a member of the Multi-Regional Clinical Trials Plain 

Language Glossary effort, the PHUSE Transparency Term 

Harmonization Team, and contributed 2 chapters to the 

Regulatory Affairs Professionals Society’s Regulatory 

Writing: an Overview. Experience shows that such work 

allows medical knowledge to advance and ultimately to 

make a difference in patients’ lives.

Nimita Limaye, PhD, is a Vice President of Research with 

IDC Health Insights and leads Life Sciences Research and 

Development Strategy and Technology, providing research-

based advisory and consulting services as well as market 

analysis on key topics related to the life sciences industry 

with a technology lens. She is an executive business leader 

with over 25 years of experience working in the pharma-

ceutical, contact research organization, and life sciences 

technology consulting industries. She is the past chair of  

the Society for Clinical Data Management board and is the  

current chair of the global DIA medical writing community. 

She has chaired several conferences, led industry roundta-

bles, given keynotes, and has authored close to 100 publi-

cations and white papers. Limaye has led medical writing 

operations, managed strategic outsourced partnerships, 

and has conducted workshops on the outsourcing of  

medical writing.

A Career in 
Medical Communication:
Steps to Success
Learn about the skills and attributes needed 
to be a successful medical communicator 
and discover opportunities in the field.

www.amwa.org/career_steps

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