大型语言模型,以更有效地报告医院质量测量措施
Aaron Boussina1, Rishivardhan Krishnamoorthy1, Kimberly Quintero2
1Division of Biomedical Informatics, University of California, San Diego, San Diego.
大型语言模型 (LLM) 准确地从电子健康记录中抽象出复杂的医院质量指标. 这种方法有望提高医疗保健系统的数据抽象效率和可靠性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 提高质量 提高质量
背景情况:
- 医院质量测量对于学习卫生系统至关重要,但面临的挑战是成本,统计能力和互评级可靠性.
- 大型语言模型 (LLM) 显示了自动化医疗保健任务的潜力,包括从临床记录中大规模抽象数据.
研究的目的:
- 评估基于LLM的系统的性能,以抽象严重败血症和败血症冲击管理捆绑 (SEP-1) 质量测量.
- 评估LLM驱动的数据抽象的准确性和可靠性,与使用电子健康记录数据的手动方法相比.
主要方法:
- 开发了一个LLM系统,可以摄取快速医疗互操作性资源 (FHIR) 数据并输出SEP-1抽象.
- 该LLM系统在加利福尼亚大学圣地亚哥健康的100个手动SEP-1抽象上进行了测试.
- 计算了LLM系统和手动抽象器之间的一致性,专家审查了不一致的情况.
主要成果:
- 该LLM系统在SEP-1测量类别分配上与手动抽象器达成90%的协议 (κ=0.82).
- 专家对不一致案例的审查显示,四个是原本手册抽象中的错误.
- 这表明LLM系统在抽象复杂的质量指标方面具有很高的准确性.
结论:
- 基于LLM的系统显示出使用可互操作的EHR数据精确可靠地抽象复杂的医院质量测量数据的潜力.
- 这项技术可能提供一种可扩展的解决方案,以提高医疗保健中质量测量报告的效率和一致性.
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