The key to transforming medical writing efficiency

Generative AI promises to positively disrupt medical writing in Pharma, across use cases linked to regulatory documentation and safety report summaries.

But as companies have looked to build capabilities in-house, they have found there’s more to leveraging GenAI effectively than simply recruiting the relevant technology skills.

The pharma industry is an ideal candidate for Generative AI (GenAI)-based process transformation, because of its highly regulated and painstakingly templated repeat activities including licence application and maintenance and safety report writing (medical writing).

Even if GenAI-based ‘bots’ could just distil the right content and craft a first draft of these typically hefty documents (for attentive checking and amendment by skilled humans), this step alone would substantially relieve the strain on overstretched professionals.

However, GenAI technology needs a lot of moulding and crafting to be of reliable use – even with the benefit of large language models (vast data sets) to build system knowledge.

Even with the advanced, intuitive natural language processing and deep learning capabilities, AI models still need to be guided in what to look for, how to repurpose information and data correctly, and what ‘good’ looks like.

This requires a unique combination of AI proficiency and hands-on experience of pharma medical writing.

In the context of pharmaceutical medical writing, AI skills need to be combined with life sciences industry fluency in specialist language and vocabularies, required templates and the nuanced demands of each market.

That includes in-depth knowledge of what will be accepted by regulatory agencies, and how that differs from region to region, and country to country, and a strong feel for specific medical writing best practices linked to each use case.

It also takes vast reams of successful example content to ‘teach’ a GenAI model what’s needed, and the ideal output to aim for.

For pharma companies to bring their own capabilities up to speed, and stay ahead, seems an impossible feat.

In a survey conducted recently with the Regulatory Affairs Professionals Society (RAPS), medical writing emerged as being a critical area requiring support, due primarily to the rising pressure on regulatory professionals’ time.

Over half (57%) of surveyed companies were planning to invest in technology to improve medical writing over the year ahead, almost on a par with eCTD v4.0 spending, the two categories dominating immediate Regulatory IT spending plans.

The primary medical writing needs identified by Regulatory professionals, in terms of requiring additional support, were clinical study protocol/report writing, and drafting of regulatory documents.

More than half of respondents in the 2024 study identified a need to harness AI in data extraction (56%) and information summarisation (53%), where just 9-10 percent are using AI for those purposes today – specifically within the context of medical writing.

12% said they were actively in the process of incorporating AI into automated report generation from multiple sources, which is the ultimate opportunity on offer.

Despite these ambitions, more than half (53%) of survey respondents in pharma conceded that their organisations did not possess sufficient knowledge internally to implement AI technologies themselves.

Other potential barriers to AI uptake revolve around confidentiality and the scope for inadvertent breaches, linked in part to how the technology is ‘prompted’ to call up information.

Because the technology is so complex below surface level, there are concerns that applying public cloud-based GenAI models could compromise internal data security.

All of these concerns are readily addressable in an appropriate ‘closed’ processing environment, which has been purposefully tailored to life sciences use cases. This ensures that all data interactions remain within a secure and controlled ‘space’, for data confidentiality and integrity purposes.

Other considerations include traceability and auditability: the ability to see where extracted data has come from or from which source content summarisation has been achieved (for instance, clear links in the final report to original documents).

This is essential to build confidence and trust in solutions, so that they are seen to add significant value and save time; over-reliance on painstaking checks could undermine the return on investment.

Knowing that there are solutions that can be validated for priority pharma medical writing use cases will be an important facilitator for companies with a growing need for smarter support, as medical writing workloads continue to soar rather than diminish over time.

In looking for the value from an effective GenAI medical writing automation capability, pharma companies need to look at and beyond efficiency gains associated with smart data extraction, information summarisation and narrative authoring.

Further gains will come from improvements to consistency, and to leaner, tighter output once specified regulatory documents are being drafted according to training (from extensive exposure to approved documents) on what ‘good’ looks like.

Investing in R&D or partnership with external technology solution providers on a variety of use cases promises to make better use of scientific experts’ time, as use of their time shifts from initial drafting to the strategic thinking in collaboration with clinical development professionals.

In a more strategic context, in early clinical evaluation, Gen-AI based tools could help summarise the vast wealth of existing information on the internet to hone the focus of planned research in order to avoid wasted time investment.

AI-assisted search across multiple sources can also summarise headline findings, while also highlighting what emerges as the best path to follow.

Although pharma companies themselves may lack sufficient R&D resources internally to play around with possibilities, experimentation is a powerful way forward in determining where GenAI offers maximum value in transforming medical writing.

It will be important, then, to find a way to at least co-design a pilot solution (for example with an appropriate technology or service partner), that can be tested with example documents – to determine how powerful and accurate the technology can be and how quickly it learns and adapts, to hone its output.

By trying out the technology, companies will get more of a feel for what’s possible, and the scale of difference this could make to everyday regulatory workflow.

Punya Abbhi is Chief Operating Officer at Celegence. Go to celegence.com