Efficiency Enhancement in Clinical Trials: Leveraging NLP for Automated Outcome Adjudication

Music City Center 

The integration of big data and artificial intelligence (AI) is transforming clinical drug development, driving improvements in efficiency, speed, and cost-effectiveness. AI enhances clinical trials by optimizing patient recruitment, streamlining timelines, and enabling better resource allocation. Natural language processing (NLP), in particular, facilitates the extraction of critical insights from unstructured data sources, such as electronic health records, medical literature, and patient narratives. Additionally, AI supports real-time safety monitoring, allowing for proactive adverse event detection to protect participant well-being.
This presentation explores the application of NLP to automate outcome adjudication traditionally performed by physician-led clinical events committees (CEC). The manual review process requires substantial time, resources, and expertise, but NLP-driven adjudication offers a scalable, cost-effective alternative. Our goal is to develop a model that mimics the decision-making behaviors of human experts, fully automating the adjudication process while supporting CECs to save time and effort. We demonstrate using clinical trial data how this approach can enhance the efficiency of clinical trials, observational studies, and quality improvement initiatives, while addressing current limitations in automated adjudication.

LLM, CEC, Adjudication, Cardiovascular, Longformer