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arXiv · 2308.02180

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

Cliff Wong·Sheng Zhang·Yu Gu·Christine Moung·Jacob Abel·Naoto Usuyama·Roshanthi Weerasinghe·Brian Piening·Tristan Naumann·Carlo Bifulco·Hoifung Poon
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Abstract

Clinical trial matching is a key process in health delivery and discovery. In practice, it is plagued by overwhelming unstructured data and unscalable manual processing. In this paper, we conduct a systematic study on scaling clinical trial matching using large language models (LLMs), with oncology as the focus area. Our study is grounded in a clinical trial matching system currently in test 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 elaborate eligibility criteria of clinical trials and extract complex matching logic (e.g., nested AND/OR/NOT). While still far from perfect, LLMs substantially outperform prior strong baselines and may serve as a preliminary solution to help triage patient-trial candidates with humans in the loop. Our study also reveals a few significant growth areas for applying LLMs to end-to-end clinical trial matching, such as context limitation and accuracy, especially in structuring patient information from longitudinal medical records.

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Cliff Wong, Sheng Zhang, Yu Gu, Christine Moung, Jacob Abel, Naoto Usuyama, Roshanthi Weerasinghe, Brian Piening, Tristan Naumann, Carlo Bifulco, Hoifung Poon. 2023-08-18. Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology. https://arxiv.org/abs/2308.02180

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