Clinical trial matching is the task of identifying trials for which patients may be 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 also results 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 an end-to-end large-scale empirical evaluation of a clinical trial matching system and validate it using real-world EHRs. We perform comprehensive experiments with proprietary LLMs and our custom fine-tuned model called OncoLLM and show that OncoLLM outperforms GPT-3.5 and matches the performance of qualified medical doctors for clinical trial matching.

临床试验匹配是确定患者可能符合资格的试验的任务。通常，这项任务是劳动密集型的，需要根据临床试验的严格纳入和排除标准对患者电子健康记录 （EHR） 进行详细验证。这个过程也导致许多患者错过了潜在的治疗选择。大型语言模型 （LLMs使患者与试验的自动化匹配成为可能，如多项同时进行的研究所显示的那样。然而，目前的方法仅限于受限的、通常是合成的数据集，这些数据集并不能充分反映真实医疗数据中遇到的复杂性。在这项研究中，我们提出了对临床试验匹配系统的端到端大规模实证评估，并使用真实世界的 EHR 对其进行验证。我们使用专有的 LLMs 和我们名为 OncoLLM 的定制微调模型进行了全面的实验，结果表明 OncoLLM 的性能优于 GPT-3.5，并且在临床试验匹配方面与合格医生的表现相匹配。