LLMonFHIR:一个医生验证的,基于大型语言模型的移动应用程序,用于查询患者电子健康数据
Paul Schmiedmayer1, Adrit Rao1, Philipp Zagar1
1Stanford Mussallem Center for Biodesign, Stanford University, Stanford, California, USA.
JACC. Advances
|May 15, 2025
概括
LLMonFHIR是一款新的移动应用程序,它使用人工智能来帮助患者以多种语言和复杂度理解他们的健康记录. 这个数字健康解决方案旨在克服访问电子健康记录 (EHR) 的障碍.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 联邦立法要求通过快速医疗互操作资源API (FHIR) 实现电子健康记录 (EHR) 的互操作性,以提高医疗质量和增强患者权力.
- 持续存在的障碍,包括功能有限,英语语言和健康素养,阻碍了患者公平获得电子健康记录.
- 患有慢性心血管疾病的个人在处理个人健康信息方面面临着独特的挑战.
研究的目的:
- 开发和评估数字健康解决方案,消除患者与个人健康信息互动的障碍.
- 专注于管理慢性心血管疾病的个人.
- 改善不同患者群体对电子健康记录 (EHR) 的访问和理解.
主要方法:
- 开发LLMonFHIR,这是一个使用大型语言模型 (LLM) 的开源移动应用程序.
- LLMonFHIR使用户能够在不同复杂程度和多种语言中与健康记录进行交互.
- 试点评估涉及医生评估LLMonFHIR反应的准确性,可理解性和相关性,使用合成患者数据集.
主要成果:
- 医生评估了210个LLMonFHIR反应,在准确性 (5/5),可理解性 (5/5),相关性 (5/5) 方面获得了高的中位数得分.
- 确定的挑战包括总结健康状况和检索实验室结果,并指出反应的变化和遗漏.
- 需要精确的数据预处理,以确保一致和完整的信息检索.
结论:
- LLMonFHIR展示了在赋予功能有限,英语熟练或健康素养有限的个人的潜力.
- 该应用程序的多语言和多复杂性响应生成,再加上双向文本到语音,可以提高患者获得EHR的好处.
- 这项技术可以弥合医疗素养方面的差距,并促进公平获取个人健康信息.
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