Clinical trials are a critical component of evaluating the effectiveness of new medical interventions and driving advancements in medical research. Therefore, timely enrollment of patients is crucial to prevent delays or premature termination of trials. In this context, Electronic Health Records (EHRs) have emerged as a valuable tool for identifying and enrolling eligible participants. In this study, we propose an automated approach that leverages ChatGPT, a large language model, to extract patient-related information from unstructured clinical notes and generate search queries for retrieving potentially eligible clinical trials. Our empirical evaluation, conducted on two benchmark retrieval collections, shows improved retrieval performance compared to existing approaches when several general-purposed and task-specific prompts are used. Notably, ChatGPT-generated queries also outperform human-generated queries in terms of retrieval performance. These findings highlight the potential use of ChatGPT to enhance clinical trial enrollment while ensuring the quality of medical service and minimizing direct risks to patients.

翻译：临床试验是评估新医疗干预措施有效性和推动医学研究进展的关键环节。因此，及时招募患者对于避免试验延迟或提前终止至关重要。在此背景下，电子健康记录（EHRs）已成为识别和招募合格参与者的重要工具。本研究提出一种自动化方法，利用大型语言模型ChatGPT从非结构化临床记录中提取患者相关信息，并生成用于检索潜在合格临床试验的搜索查询。通过在两个基准检索集合上进行的实证评估，我们发现使用多种通用型及任务特定型提示词时，该方法相较于现有方法展现出更优的检索性能。值得注意的是，ChatGPT生成的查询在检索性能上也优于人工生成的查询。这些发现凸显了ChatGPT在提升临床试验入组效率方面的潜力，同时保障了医疗服务质量和最小化了患者直接风险。