建设一个大数据驱动的预测分析平台,用于医院人才消耗
Xiao Lei Zheng1, Xiaoli Dai1, Tian Li Liu2
1Qingdao Traditional Chinese Medicine Hospital, Qingdao Hiser Hospital Affiliated of Qingdao University, Shandong, China.
Blockchain in healthcare today
|February 2, 2026
概括
这项研究使用机器学习来预测医院工作人员的流动性,确定工作时间和满意度等因素. 文化根深蒂固的信念也通过心理资本影响工作表现和公民身份.
科学领域:
- 医疗保健管理的管理
- 组织心理学 组织心理学
- 数据科学数据科学数据科学
背景情况:
- 医院人员流失对医疗保健系统构成重大挑战.
- 了解影响员工流动的复杂因素对于劳动力稳定至关重要.
- 将心理结构与大数据分析集成,为消耗预测提供了新的见解.
研究的目的:
- 开发一个大数据驱动的预测平台,用于医院工作人员的消耗.
- 为了检查可谈判命运 (NF) 的作用,一个文化信仰系统,在预测营业额.
- 研究心理资本 (PC) 和组织公民身份的调解效应.
主要方法:
- 分析了来自400多名医院员工的结构化人力资源数据.
- 应用SMOTE来解决消耗数据不平衡的问题.
- 使用性能指标对四个机器学习分类器 (逻辑回归,决策树,随机森林,XGBoost) 的评估.
- 对调解,调节和构造有效性的统计评估.
主要成果:
- 随机森林和XGBoost在预测磨损方面表现出优异的召回能力.
- 消耗的关键预测因素包括工作时间,收入,工作类型和员工满意度.
- 可谈判的命运 (NF) 通过心理资本 (PC) 显著预测了工作表现和组织公民身份.
- 感知到的组织支持调节了NF和PC之间的关系,这表明调节是调节的.
结论:
- 将机器学习与心理理论相结合,提高了对医院工作人员退休的预测和理解.
- 开发的平台支持文化敏感,数据驱动的人力资源干预措施,以减少消耗.
- 可以实施有针对性的策略来识别高风险员工,稳定劳动力,改善患者护理.
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