为了简化医疗记录生成,利用大型语言模型:对医疗保健信息学的影响
Yi-Ling Chiang1,2, Kuei-Fen Yang3, Pin-Chih Su1
1Division of Clinical Informatics, Department of Digital Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.
Applied clinical informatics
|September 25, 2025
概括
大型语言模型 (LLM) 在提高医疗记录文档效率方面表现有前途. 人工智能生成的摘要与医生撰写的记录可比,有可能改善医疗保健服务.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 医疗记录文件对于临床医生来说至关重要,但耗时.
- 大型语言模型 (LLM) 为提高文档效率和质量提供了潜在的解决方案.
研究的目的:
- 与医生撰写的记录相比,评估LLM生成的医疗记录摘要的效率和质量.
- 评估人工智能辅助文档对临床工作人员工作量和医疗保健服务的影响.
主要方法:
- 一个多学科团队为LLM (Llama 3.1) 制定了指导方针,并生成了医学摘要.
- LLM生成了入院,每周和出院笔记,用于医生审查和编辑.
- 医生文档质量仪器 (PDQI-9) 用于质量评估.
主要成果:
- 对于每周汇总和放电说明的PDQI-9分数没有显著差异 (p=0.129,p=0.873).
- 招生笔记PDQI-9分数的显著差异 (p=0.004),有项目级别的变化.
- 人工智能笔记辅助功能在实施后获得了普及.
结论:
- 医疗记录总结显示出提高医疗记录总结效率和质量的巨大潜力.
- 成功的LLM整合需要持续的质量评估,培训和支持.
- 人工智能辅助的文档可以使临床工作人员为更关键的患者护理任务提供自由.
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