title: Scaling Clinical Trial Matching Using Large Language Models: A Case
    Study in Oncology
summary: 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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paper_authors.name: Cliff Wong
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paper_authors.name: Carlo Bifulco
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paper_authors.name: Hoifung Poon
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paper_title: Scaling Clinical Trial Matching Using Large Language Models: A Case
    Study in Oncology
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) GPT-4 eligibility criteria matching logic patient-trial candidates context limitation accuracy longitudinal medical records