Utilizing ChatGPT to Enhance Clinical Trial Enrollment
This addresses the challenge of timely patient enrollment in clinical trials for medical researchers and healthcare providers, though it is incremental as it builds on existing EHR and retrieval methods.
The study tackled the problem of identifying eligible patients for clinical trials by using ChatGPT to extract information from unstructured clinical notes and generate search queries, resulting in improved retrieval performance over existing approaches and even outperforming human-generated queries.
Clinical trials are a critical component of evaluating the effectiveness of new medical interventions and driving advancements in medical research. Therefore, timely enrollment of patients is crucial to prevent delays or premature termination of trials. In this context, Electronic Health Records (EHRs) have emerged as a valuable tool for identifying and enrolling eligible participants. In this study, we propose an automated approach that leverages ChatGPT, a large language model, to extract patient-related information from unstructured clinical notes and generate search queries for retrieving potentially eligible clinical trials. Our empirical evaluation, conducted on two benchmark retrieval collections, shows improved retrieval performance compared to existing approaches when several general-purposed and task-specific prompts are used. Notably, ChatGPT-generated queries also outperform human-generated queries in terms of retrieval performance. These findings highlight the potential use of ChatGPT to enhance clinical trial enrollment while ensuring the quality of medical service and minimizing direct risks to patients.