机器学习用于预测医疗保健工作人员的倦怠:系统审查和元分析
Huijing Shi1, Jinyang Liu2, Chaochao Yang1
1College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu City, People's Republic of China.
Contemporary nurse
|November 26, 2025
概括
机器学习模型在预测医疗保健工作者的倦怠方面表现出中等准确性. 然而,方法上的弱点和缺乏验证限制了它们的临床使用,需要改进研究设计以实现未来的进步.
科学领域:
- 职业健康 职业健康 职业健康
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 医疗保健工作者 (HCW) 倦怠是影响员工福利和患者护理的重大职业健康问题.
- 机器学习 (ML) 为早期发现和预防倦怠提供了一个潜在的解决方案.
- 目前还缺乏ML模型在这个领域的预测性能和适用性的全面综合.
研究的目的:
- 系统地评估ML模型在预测HCW燃烧的性能.
- 评估这些ML模型的方法质量和临床适用性.
- 为了确定关键的预测因素和影响ML模型HCW燃烧性能的因素.
主要方法:
- 对开发或验证用于HCW燃烧预测的ML模型的研究进行系统审查和元分析.
- 从创立到2025年2月,搜索了十个数据库,包括使用验证的倦怠评估工具的研究.
- 使用PROBAST-AI评估研究质量,通过随机效应模型将性能指标 (AUC,灵敏度,特异性) 汇集在一起.
主要成果:
- 包括22项研究,AUC总值为0.72,表明中度预测性歧视.
- 灵敏度为0.63和特异性为0.84;模型使用自我报告的数据表现更好,重点是亚太地区的护士,或使用基于MBI的评估.
- 确定了五个主要预测类别:人口/职业,心理/行为,组织/社会,生理/可穿戴,活动/工作模式. 所有研究都有高或不清楚的偏差风险.
结论:
- 机器学习模型显示出预测HCW燃烧的潜力,但受到方法学限制和异质性的阻碍.
- 缺乏外部验证和报告的弱点阻碍了可靠的临床应用.
- 未来的研究必须优先考虑严格的研究设计,透明的报告,多模式数据和有效的临床实施的伦理考虑.
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