Revolutionising Clinical Trials with Large Language Models

Large Language Models (LLMs), which have become part of the technological zeitgeist due to their foundational role in the Generative AI boom that has occurred over the last 18 months, stand poised to revolutionise clinical trials in the ever-evolving health research and development landscape.

By integrating LLMs into a range of areas within clinical trial processes, we believe that their are unprecedented opportunities to enhance efficiency, accuracy and patient engagement for industry leaders like Roche, Novartis, and Novo Nordisk. Indeed, many pharmaceutical companies are already exploring where they can leverage the benefits of LLMs to create value in the path to proving effectiveness and safety.

However, managing these trials on a vast scale while ensuring adherence, compliance and, ultimately, the wellness and security of test subjects is – challenging to say the least. Novo Nordisk have been in the news for the the success of Wegovy (its GLP-1 drug that showed great potential in weight loss and related health benefits) but its most recent trial, by no means its first, peaked at 17,600 participants – a huge undertaking and one that is fraught with logistical and operational complexity.

At a conference that we sponsored recently, Novo Nordisk also outlined the compelling ROI they have achieved with their Novoscribe system that reduced the time to produce clinical study reports from 90 days to a week, and the number of people needed for this process from 42 to just 3.

So you see why we are excited!

As a consultancy who has a specialism in the digital transformation of clinical trial setups, we have created an over of  the transformative potential of LLMs and their strategic benefits for your organisation.

Let’s take a look!

Enhanced Patient-Trial Matching

LLMs can streamline patient matching by automating elements of the pre-screening process.

The technology accurately cross-references medical profiles against trial eligibility criteria and can reduce manual screening efforts. For most pharmaceutical companies efficiency is everything, and any solution that accelerates trial recruitment and broadens patient participation, ensuring a diverse and representative sample, is going to be adopted sooner rather than later.

LLMs can process electronic health records (EHRs) and identify patients who meet specific criteria faster than traditional methods. This capability speeds up recruitment and improves the precision of patient selection, leading to more robust and reliable trial results.

Streamlined Clinical Trial Planning

LLMs excel in processing and analysing vast amounts of textual data, a critical capability during the planning stages of clinical trials.

This enhancement of planning efficiency helps mitigate risks by summarising extensive trial descriptions, creating detailed criteria and predicting trial outcomes. This capability ensures a smoother trial setup and more accurate forecasting. Additionally, LLMs can and will assist protocol development by identifying potential design flaws and suggesting modifications to optimise trial efficiency and compliance with regulatory standards.

This predictive capability helps craft well-structured trials more likely to succeed and meet their endpoints.

Advanced Analysis of Free Text Narratives

LLMs bring new sophistication to data coding and classification within clinical trials.

Their ability to analyse patient-generated free text entries facilitates hypothesis generation and validation based on nuanced textual content. This innovation enhances data interpretation, offering more profound insights into patient experiences and trial outcomes. By understanding patient feedback and experiences more granularly, researchers can identify previously unnoticed trends and factors that may impact the trial’s outcomes.

Solid research can improve trial designs and better patient care strategies during and after the trial.

Automated Technical Writing

LLM automation can significantly reduce the administrative burden of producing essential clinical trial documentation.

LLMs streamline workflows and boost overall efficiency by generating comprehensive documents such as patient discharge summaries, radiology report summaries, and adverse event reports. This automation allows research teams to focus on high-value tasks, improving productivity and trial quality. Moreover, LLMs can ensure consistency and accuracy in documentation, which is crucial for regulatory compliance and audit readiness. Time moves towards patient care and other critical aspects of the trial.

Informed Consent

LLM-powered chatbots represent a significant advancement in ensuring informed consent and improving patient comprehension of trial details.

These intelligent chatbots dynamically assess patient knowledge, addressing informational gaps and promoting more engaged and informed participation—leading to higher retention rates and more reliable trial data. They can explain complex medical terms in layman’s language, answer patient questions in real-time, and ensure patients fully understand the trial’s implications.

Patient Adherence and Engagement

Maintaining patient adherence and engagement throughout a clinical trial is critical for success.

LLMs can contribute significantly by providing participants with personalised reminders, educational content, and motivational messages. By analysing patient behaviour and preferences, LLMs can tailor communications to each individual’s needs, thereby improving adherence to the trial protocol. Additionally, LLMs can monitor patient responses and interactions, identifying those at risk of dropping out and implementing timely interventions to retain them.

Data Security and Privacy

While the benefits of LLMs are substantial, ensuring data security and privacy remains paramount.

LLMs can be integrated with robust encryption and anonymisation techniques to protect sensitive patient information. Strong security practices are essential in clinical trials, where data breaches can have severe consequences. Implementing stringent data governance frameworks ensures that patient data meets the highest standards of confidentiality and integrity.

Overcoming Challenges

Despite these advantages, integrating LLMs into clinical trials presents challenges.

Ensuring output accuracy, navigating legal considerations, and addressing potential biases are critical factors that require meticulous attention. For example, LLMs must be trained on diverse datasets to mitigate biases affecting patient selection and trial outcomes. Furthermore, regulatory bodies may have specific requirements for using AI in clinical trials, necessitating close collaboration with regulatory experts to ensure compliance.

Future Prospects

For pharmaceutical giants like Roche, Novartis, and Novo Nordisk, adopting LLM technology in clinical trials represents a forward-thinking approach to overcoming traditional barriers in trial conduct and patient engagement. Leveraging LLMs can drive a paradigm shift towards more efficient, precise, and patient-focused clinical research methodologies. This advancement requires embracing new technologies and navigating associated challenges through strategic investment and careful oversight.

Conclusion

As a digital consultancy at the forefront of integrating LLM technology into health and other regulated industries, we are committed to maximising the benefits of this transformative technology.

Our mission is to facilitate the seamless adoption of LLMs in clinical trials, ensuring our clients are well-positioned to realise the full potential of this innovative approach in advancing medical research and development. By embracing LLMs, organisations can enhance their clinical trials’ efficiency, accuracy, and patient engagement, ultimately leading to more successful outcomes and advancing the future of healthcare.