Clinical trial matching is the task of identifying trials for which patients
may be potentially eligible. Typically, this task is labor-intensive and
requires detailed verification of patient electronic health records (EHRs)
against the stringent inclusion and exclusion criteria of clinical trials. This
process is manual, time-intensive, and challenging to scale up, resulting in
many patients missing out on potential therapeutic options. Recent advancements
in Large Language Models (LLMs) have made automating patient-trial matching
possible, as shown in multiple concurrent research studies. However, the
current approaches are confined to constrained, often synthetic datasets that
do not adequately mirror the complexities encountered in real-world medical
data. In this study, we present the first, end-to-end large-scale empirical
evaluation of clinical trial matching using real-world EHRs. Our study
showcases the capability of LLMs to accurately match patients with appropriate
clinical trials. We perform experiments with proprietary LLMs, including GPT-4
and GPT-3.5, as well as our custom fine-tuned model called OncoLLM and show
that OncoLLM, despite its significantly smaller size, not only outperforms
GPT-3.5 but also matches the performance of qualified medical doctors. All
experiments were carried out on real-world EHRs that include clinical notes and
available clinical trials from a single cancer center in the United States.

利用实际世界的电子健康记录，我们进行了第一次端到端的大规模实证评估，展示了大型语言模型在准确匹配患者和适当临床试验方面的能力。