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Clinical trial matching is a key process in health delivery and discovery. Inpractice, it is plagued by overwhelming unstructured data and unscalable manualprocessing. In this paper, we conduct a systematic study on scaling clinicaltrial matching using large language models (LLMs), with oncology as the focusarea. Our study is grounded in a clinical trial matching system currently intest deployment at a large U.S. health network. Initial findings are promising:out of box, cutting-edge LLMs, such as GPT-4, can already structure elaborateeligibility criteria of clinical trials and extract complex matching logic(e.g., nested AND/OR/NOT). While still far from perfect, LLMs substantiallyoutperform prior strong baselines and may serve as a preliminary solution tohelp triage patient-trial candidates with humans in the loop. Our study alsoreveals a few significant growth areas for applying LLMs to end-to-end clinicaltrial matching, such as context limitation and accuracy, especially instructuring patient information from longitudinal medical records.

Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology