Liu Chao, Chuang Yen-Ching, Qin Lifen, Ren Lijie, Chien Ching-Wen, Tung Tao-Hsin
Shenzhen Dapeng New District Medical and Health Group, Shenzhen, China.
Evidence-Based Medicine Center, Taizhou Hospital of Zhejiang Province, Linhai, China.
BMJ Public Health. 2025 Sep 4;3(2):e000777. doi: 10.1136/bmjph-2023-000777. eCollection 2025.
The aim of this study is to analyse the factors affecting medical burnout in hospitals, identify the characteristics of staff experiencing high levels of burnout and devise a practical and sustainable prediction mechanism.
A survey was conducted to access the current situation, followed by a regression analysis using data from the Maslach Burnout Inventory General Survey, demographic information related to healthcare personnel and employee job satisfaction metrics from the hospitals under study. Subsequently, four predictive models-logistic regression, K-nearest neighbour, decision tree and random forest (RF)-were employed to predict the degree of healthcare burnout.
The investigation revealed that 61.2% of the medical staff in the hospitals under study exhibited at least one symptom of burnout, with 9.8% experiencing high levels of burnout. Elevated rates of high burnout were observed in the 30-39 age group, among physicians and surgeons, and among those with 0-5 years of experience. In terms of predictive methods, the RF model demonstrated suitability for predicting burnout among medical staff, achieving a prediction accuracy of approximately 80%.
A significant correlation was found between job satisfaction and burnout levels. Physicians and surgeons with less than a decade of professional experience are more prone to high levels of burnout. The RF model proved effective for predicting the burnout level among medical staff, consistently achieving an accuracy rate close to 80%. These findings can serve as valuable insights for hospital administrators in their effort to prevent and mitigate burnout among medical staff.
本研究旨在分析影响医院医护人员职业倦怠的因素，确定高度倦怠的工作人员的特征，并设计一种切实可行且可持续的预测机制。
进行了一项调查以了解现状，随后使用来自马氏职业倦怠量表一般调查的数据、与医护人员相关的人口统计学信息以及所研究医院的员工工作满意度指标进行回归分析。随后，采用四种预测模型——逻辑回归、K近邻、决策树和随机森林（RF）——来预测医护人员的倦怠程度。
调查显示，在所研究医院中，61.2%的医护人员至少表现出一种倦怠症状，9.8%的人处于高度倦怠状态。在30 - 39岁年龄组、内科医生和外科医生以及工作经验为0 - 5年的人员中，高度倦怠的发生率较高。在预测方法方面，RF模型显示出适用于预测医护人员的倦怠情况，预测准确率约为80%。
发现工作满意度与倦怠程度之间存在显著相关性。专业经验不足十年的内科医生和外科医生更容易出现高度倦怠。RF模型被证明对预测医护人员的倦怠水平有效，准确率始终接近80%。这些发现可为医院管理人员预防和减轻医护人员的倦怠提供有价值的见解。
