通过基于电子健康记录的大型语言模型评估医院课程总结
William R Small1,2, Jonathan Austrian1,2, Luke O'Donnell2
1Department of Health Informatics, New York University Langone Medical Center Information Technology.
JAMA network open
|August 13, 2025
概括
使用大型语言模型 (LLM) 进行医院课程 (HC) 总结的医生-人工智能协作需要比仅为医生的草稿更少的编辑. 通过LLM生成的HC更完整,更简洁,更有凝聚力,尽管混增加了.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床文档 临床文档
背景情况:
- 医院课程 (HC) 总结对于医生来说是一个耗时的任务.
- 大型语言模型 (LLM) 在自动化HC总结方面表现有前途.
- 医生-LLM合作伙伴关系在电子健康记录 (EHR) 嵌入式工作流程中为起草医疗记录的有效性尚不清楚.
研究的目的:
- 为了比较住院医生所需的编辑工作,以改进LLM生成的HC与医生生成的HC.
- 根据4Cs标准来评估HCs的质量:完整性,简洁性,凝聚力和无迷思.
主要方法:
- 一项质量改善研究,涉及10名内科医生和8名医院医生.
- 住院人员编辑了医生生成和LLM生成的HCs,用于随机选择的入院.
- 编辑工作量以编辑和语义变化的百分比来衡量,并使用A/B测试对4C进行比较评级.
主要成果:
- 与医生生成的HC相比,LLM生成的HC需要更少的编辑 (31.5%与44.8%) 和语义变更 (2.4%与4.9%).
- 医院医生将LLM HC评为更完整 (P<.001) 和类似的简洁 (P=.20) 和凝聚力 (P=.60).
- 在LLM HC中,有更多的混 (P=.002),但整体复合分数相似 (P=.46).
结论:
- 嵌入EHR的LLM可以帮助医生起草医疗记录,需要更少的编辑才能达到质量标准.
- 医生-LLM合作伙伴关系显示了提高HC发电效率和质量的可行性.
- 在临床实践中对LLM产生的HC进行进一步监测是有必要的,特别是在对拼的监测中.
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