Crunch time

Making sense of documentation during pivotal clinical trials

The number of documents developed, updated, maintained, summarised and stored to run clinical trials is immense.

The amount of documentation can increase based on complexity of trials or changes in regulatory requirements.

Examples of key clinical trial documents include clinical study protocols, investigator brochures, informed consent forms and statistical analysis plans to name a few.

The comprehensive nature and volume of the documentation is a challenge for the industry because it’s time-consuming to create and interpret these documents, taking time away from individuals to work more strategically.

Templates help alleviate some document creation burden, but do not typically incorporate nuances or make it easier to interpret the information.

The challenge is often compounded when stakeholders need to continuously refer to the same complex information in the same complex documents throughout a clinical trial.

Again, this translates to a lot of time wasted on re-review and less time focused on core role responsibilities.

All players in clinical trials need more effective document creation and summarisation processes to help resolve these challenges and speed up the clinical trials process.

Traditional approaches, reliant on manual drafting and labour-intensive review cycles, often prove inadequate in meeting the demands of modern clinical research.

Not only do these methods consume valuable time and resources, as mentioned above, but they also pose inherent risks of error and inconsistency.

Moreover, ensuring compliance with standards such as Good Clinical Practice and Good Documentation Practices, coupled with regulatory mandates, further complicate the documentation process by adding layers of complexity and scrutiny to every phase of a trial.

To address these challenges, stakeholders are increasingly turning to cutting-edge solutions driven by AI and cloud-based data analytics. One such solution lies in the utilisation of Large Language Models (LLMs) to help ease the administrative burden.

By leveraging LLMs, stakeholders can evolve the documentation process, automating the generation of essential documents and templates with unprecedented speed and precision.

Furthermore, LLMs can be deployed to streamline summarisation of trial documentation, facilitating rapid synthesis of complex information. Through advanced natural language processing (NLP) techniques, LLMs can distil large documents into concise synopses, providing stakeholders with actionable insights in a fraction of the time.

While human oversight remains indispensable for quality assurance and compliance purposes, the integration of LLMs into documentation workflows can significantly reduce the burden of manual re-review, freeing up valuable time and resources for core activities.

Throughout the life cycle of a clinical trial, stakeholders may have varying information needs based on their roles and responsibilities.

By tailoring document summaries to address these role-specific requirements, organisations can enhance communication and collaboration across multidisciplinary teams.

The strategic integration of AI, particularly LLMs and data analytics solutions, holds immense promise for transforming documentation processes in clinical trials.

By automating labour-intensive administrative tasks, summarising information, and decreasing the time it takes for document interpretation, stakeholders can unlock significant efficiency gains and accelerate the pace of clinical research.

As the healthcare landscape continues to evolve, embracing innovative technologies such as this will be essential for driving progress and achieving impactful outcomes in clinical development.

Brittany Shriver is Principal of Strategy and Growth, Health and Life Sciences at SAS. Go to sas.com