  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.

Information Retrieval;Computation and Language