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.
@article{arxiv.2308.02180,
title = {Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology},
author = {Cliff Wong and Sheng Zhang and Yu Gu and Christine Moung and Jacob Abel and Naoto Usuyama and Roshanthi Weerasinghe and Brian Piening and Tristan Naumann and Carlo Bifulco and Hoifung Poon},
journal= {arXiv preprint arXiv:2308.02180},
year = {2023}
}
Comments
24 pages, 5 figures, accepted at Machine Learning for Healthcare (MLHC) 2023