在医疗保健中优化LLM使用:在MyChart消息中识别患者问题
Akhila Chekuri1, Armaan S Johal1,2, Matthew R Allen3
1University of California San Diego, La Jolla, CA Computer Science and Engineering.
概括
大型语言模型 (LLM) 显示出对简化患者信息的承诺,但准确识别问题是具有挑战性的. 电子健康记录中很大一部分患者的问题没有得到临床团队的回答.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床交流 临床沟通
背景情况:
- 越来越多的患者提供者电子信息需要有效的管理.
- 大型语言模型 (LLM) 提供了自动化临床消息任务的潜力.
- 有效的信息分类对于在医疗保健中成功实施LLM至关重要.
研究的目的:
- 分析LLM在患者提供者信息中解决基于知识的问题的实用性.
- 描述在现实世界的临床数据中使用LLM进行信息分类的挑战.
- 评估基于规则的语法问题检测器,用于分类消息.
主要方法:
- 分析了来自电子健康记录的400多万个患者-提供者信息.
- 在500个消息上实施和评估基于规则的语法问题检测器.
- 检测器性能与LLM问题检测能力的比较.
主要成果:
- 在非正式的临床文本中检测问题是很困难的,因为隐含的请求和各种语言.
- 语法问题检测器显示,在准确识别所有问题方面存在局限性.
- 25%的MyChart包含问题信息没有得到临床团队的回复.
结论:
- 现实世界的临床数据为自动化消息分析带来了重大挑战.
- 准确的问题检测是有效地在医疗保健中部署LLM的非微不足道但必不可少的步骤.
- 为将LLM整合到临床消息工作流程中,建议使用一个结构化的管道.
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