Scaling Clinical Trial Matching Using Large Language Models: A Case
  Study in Oncology
Authors: name: Cliff Wong, name: Sheng Zheng, name: Yu Gu, name: Christine Moung, name: Jacob Abel, name: Naoto Usuyama, name: Roshanthi Weerasinghe, name: Brian Piening, name: Tristan Naumann, name: Carlo Bifulco, name: Hoifung Poon

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.