为患者提供个性化前列腺癌教育,使用EHR-Integrated LLM代理
Yuexing Hao1,2,3, Jason Holmes4, Mark R Waddle5
1Cornell University, Ithaca, NY, USA. yh727@cornell.edu.
NPJ digital medicine
|December 18, 2025
概括
一个新的大型语言模型 (LLM) 代理,MedEduChat,改善了前列腺癌患者的教育和健康信心. 临床医生发现人工智能工具是正确的,完整的和安全的,用于患者支持.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 临床医生的工作量往往限制了及时的患者教育和在癌症护理中的个性化支持.
- 前列腺癌患者需要可访问和准确的信息,以改善治疗的坚持和结果.
研究的目的:
- 开发和评估MedEduChat,一个与电子健康记录 (EHR) 集成的大型语言模型 (LLM) 代理.
- 评估MedEduChat对前列腺癌患者教育,参与和健康信心的影响.
- 通过临床医生评估LLM代理的正确性,完整性,安全性和个性化.
主要方法:
- 一项质量改善研究,涉及15名非转移性前列腺癌患者和3名临床医生.
- 将MedEduChat LLM代理与诊所的电子健康记录系统集成.
- 通过患者交互日志,可用性得分 (UMUX) 和临床医生对交互史的评估收集数据.
主要成果:
- MedEduChat获得了高的可用性评分 (UMUX = 83.7/100).
- 患者的健康信心显著改善 (健康信心评分从9.9上升到13.9).
- 临床医生将MedEduChat评为高度正确的 (2.9/3),完整的 (2.7/3),安全的 (2.7/3),以及适度个性化的 (2.3/3).
结论:
- 像MedEduChat这样的LLM代理商显示出增强患者参与癌症护理的巨大潜力.
- 将LLM与EHR整合起来,可以有效地增加患者的教育和支持,解决临床医生的工作负载限制.
- MedEduChat展示了一种有前途的方法来提高前列腺癌患者健康信息的质量和可访问性.
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