电子健康记录的可验证总结使用大型语言模型来支持图表审查
medRxiv : the preprint server for health sciences
|June 12, 2025
概括
大型语言模型 (LLM) 可以从电子健康记录 (EHR) 中生成基于问题的入院摘要,潜在地提高临床医生的效率并减少心力衰竭患者的认知负担.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床决策支持 临床决策支持
背景情况:
- 电子健康记录 (EHR) 中的信息过载给临床医生带来了重大挑战,阻碍了有效的数据合成,并可能延迟患者的护理.
- 心力衰竭是住院的主要原因,需要有效的方法来管理复杂的患者信息.
研究的目的:
- 评估大型语言模型 (LLM) 在从心力衰竭患者的纵向临床笔记中生成基于问题的入院摘要的有效性.
- 评估LLM生成的摘要对临床医生的效率,准确性和工作量的影响,与传统的图表审查相比.
主要方法:
- 开发一种提取然后抽象的方法,使用特定疾病的"摘要捆绑"来优先考虑来自EHR的临床相关信息.
- 混合方法评估涉及医生回答患者特定的临床问题,使用LLM生成的摘要与标准图表审查.
主要成果:
- 频繁使用LLM生成的摘要显著加快了填写问卷的速度 (p = 0.002),尽管整体时间没有显著减少.
- 摘要的使用在回答临床问题时保持了高准确度 (88.0%的摘要 vs 86.4%没有).
- 医生表示很有可能在实践中使用摘要,预计节省时间,对摘要格式的偏好各不相同.
结论:
- 大型语言模型在为电子健康记录生成有效的纵向摘要方面表现有前途,有助于临床医生管理信息过载.
- 通过LLM生成的摘要有可能提高临床医生的效率,减少工作量,并支持在重症监护机构的知情决策.
- 可定制的摘要格式对于与个人临床医生的工作流程保持一致,并最大限度地利用LLM摘要工具的好处至关重要.
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