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一个机器学习模型使用临床笔记来识别医生疲劳.

Chao-Chun Hsu¹, Ziad Obermeyer², Chenhao Tan³

¹University of Chicago, Chicago, IL, USA.

Nature communications

|July 1, 2025

概括

此摘要是机器生成的。

在临床笔记中检测到的医生疲劳与较差的医疗决策有关. 这种疲劳预测模型还标记了大型语言模型的问题,表明了潜在的文本扭曲.

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科学领域:

医疗信息学医疗信息学
临床决策临床决策
医疗保健中的人工智能

背景情况:

临床笔记对于医生与患者的接触至关重要,但可能反映出医生疲劳.
医生疲劳会影响临床决策和患者护理.
在临床文档中识别疲劳是一个尚未满足的需求.

研究的目的:

开发和验证一个模型,从临床笔记中识别医生疲劳.
评估医生疲劳对临床决策的影响.
评估疲劳在大型语言模型生成文本中的检测能力.

主要方法:

在129,228次急诊室 (ED) 访问中训练了一种预测模型,以识别工作高频班次的医生 (7天内≥5次).
验证了该模型在持久设置上,评估其在识别疲劳的医生和高疲劳设置 (夜班,高患者数量) 中的准确性.
分析了模型预测的疲劳与心肌梗塞诊断测试的产量之间的相关性,并评估了大型语言模型 (LLM) 生成的笔记中的疲劳信号.

主要成果:

该模型准确地识别了来自高工作量医生的笔记,并标记了来自高疲劳环境的笔记.
模型预测的疲劳增加与每标准偏差心肌梗塞检测收益率下降19%相关.
通过LLM生成的笔记显示,预测疲劳比医生写的笔记高出74%,字的可预测性增加.

结论:

通过临床笔记分析识别的医生疲劳与临床决策能力受损有关.
开发的模型可以检测医生疲劳及其对患者护理的潜在影响.
在临床文档中,LLM可能会引入微妙的文字特征,表明疲劳,因此需要进一步调查其可靠性.