基于机器学习的模型用于分析和准确预测与医疗保健工作者倦怠相关的因素
Chao Liu1,2, Yen-Ching Chuang3, Lifen Qin1,4
1Shenzhen Dapeng New District Medical and Health Group, Shenzhen, China.
BMJ public health
|September 9, 2025
概括
医务人员的倦怠现象很普遍,特别是在经验较少的年轻医生中. 一个随机森林模型准确地预测了倦怠,为医院管理人员提供了减轻这个问题的见解.
科学领域:
- 医疗保健管理的管理
- 职业健康 职业健康 职业健康
- 医务人员福利 医务人员福利
背景情况:
- 医疗倦怠是影响全球医疗保健专业人员的一个重大问题.
- 高燃烧率可能会对患者护理和医疗保健系统效率产生负面影响.
- 了解导致倦怠的因素和开发预测工具对于干预至关重要.
研究的目的:
- 分析在医院环境中导致医疗倦怠的因素.
- 为了确定医疗保健工作人员经历高燃烧水平的特征.
- 开发一个实用和可持续的机制来预测医疗倦怠.
主要方法:
- 为了评估医疗倦怠的现状,进行了一项调查.
- 进行回归分析,使用马斯拉赫燃烧 inventory 一般调查数据,人口统计和工作满意度指标.
- 用四种预测模型 (逻辑回归,K-最近邻居,决策树,随机森林) 来预测倦怠水平.
主要成果:
- 61.2%的医务人员表现出倦怠症状;9.8%的医务人员表现出高度倦怠.
- 在30-39岁的年龄组,医生/外科医生以及有0-5年经验的人群中观察到高燃烧率.
- 随机森林模型在预测医务人员倦怠方面取得了大约80%的准确性.
结论:
- 工作满意度与倦怠程度有显著的相关性.
- 经验不到10年的医生和外科医生面临着更高的倦怠风险.
- 随机森林模型对预测医疗倦怠非常有效,为医院管理人员提供了宝贵的见解.
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