前列腺癌收件箱消息的回顾性比较分析：封闭域大语言模型与临床团队的回复对比
Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical Teams
AI总结：
本文提出基于GPT-4的前列腺癌放疗专用大语言模型RadOnc-GPT，集成EHR后在158条收件箱消息上评估，其在清晰度和同理心上略优于临床团队，并能显著节省医护时间，有望减轻工作负担并降低医疗成本。
摘要：
收件箱消息交互在医患沟通中起着至关重要的作用，贯穿患者诊疗旅程的所有阶段（诊疗前、诊疗中和诊疗后）。然而，回复这些患者咨询已成为医疗工作流程中的重大负担，耗费临床护理团队大量时间。为解决这一问题，我们引入了 RadOnc-GPT，这是一个由 GPT-4 驱动的专用大语言模型（LLM），其设计重点在于前列腺癌的放射治疗，并采用了先进的提示工程，专门用于辅助生成回复。我们将 RadOnc-GPT 与患者电子健康记录（EHR）进行了集成，数据来源既包括全院范围的 EHR 数据库，也包括一个内部的放射肿瘤学专用数据库。RadOnc-GPT 在 158 条历史记录的收件箱消息交互上进行了评估。我们使用定量自然语言处理（NLP）分析以及由临床医生和护士参与的两项评分研究来评估 RadOnc-GPT 的回复。研究结果表明，RadOnc-GPT 在“清晰度”和“同理心”方面略优于临床护理团队，同时在“完整性”和“正确性”方面取得了相当的得分。据估计，从阅读咨询到发送回复，RadOnc-GPT 可为护士每条消息节省 5.2 分钟，为临床医生节省 2.4 分钟。采用 RadOnc-GPT 生成收件箱消息草稿，有潜力通过产出高质量、及时的回复来减轻临床护理团队的工作负担并降低医疗成本。
英文摘要：
In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a patient's care journey. However, responding to these patients' inquiries has become a significant burden on healthcare workflows, consuming considerable time for clinical care teams. To address this, we introduce RadOnc-GPT, a specialized Large Language Model (LLM) powered by GPT-4 that has been designed with a focus on radiotherapeutic treatment of prostate cancer with advanced prompt engineering, and specifically designed to assist in generating responses. We integrated RadOnc-GPT with patient electronic health records (EHR) from both the hospital-wide EHR database and an internal, radiation-oncology-specific database. RadOnc-GPT was evaluated on 158 previously recorded in-basket message interactions. Quantitative natural language processing (NLP) analysis and two grading studies with clinicians and nurses were used to assess RadOnc-GPT's responses. Our findings indicate that RadOnc-GPT slightly outperformed the clinical care team in "Clarity" and "Empathy," while achieving comparable scores in "Completeness" and "Correctness." RadOnc-GPT is estimated to save 5.2 minutes per message for nurses and 2.4 minutes for clinicians, from reading the inquiry to sending the response. Employing RadOnc-GPT for in-basket message draft generation has the potential to alleviate the workload of clinical care teams and reduce healthcare costs by producing high-quality, timely responses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
