机器学习优化护理服务模式:对医院病房的实证分析
1Department of Nursing, Trakya University Faculty of Health Sciences, Edirne, Turkey.
Journal of evaluation in clinical practice
|January 21, 2025
概括
机器学习,特别是随机森林,有效地优化了护士人员和护理模式. 这种数据驱动的方法提高了医院的病人护理和运营效率.
科学领域:
- 医疗保健管理的管理
- 护理信息学 护理信息学
- 应用机器学习应用机器学习
背景情况:
- 医疗保健系统面临诸如护士短缺和患者敏度上升等挑战.
- 随着COVID-19的爆发,人们更需要灵活的护理人员配置模式.
- 需要创新的方法来优化护理服务和资源分配.
研究的目的:
- 评估机器学习 (ML) 技术,以优化护士人员配置.
- 评估医院病房护理服务模式的适当性.
- 确定ML在预测护士人员充足度和护理模式适用性方面的准确性.
主要方法:
- 一项描述性研究涉及39个医院病房的117个病房级观察.
- 使用Rush Medicus患者分类量表收集的数据.
- 分析采用了k-近邻,支持向量机,随机森林和物流回归算法.
主要成果:
- 随机森林算法在预测护士人员充分性和护理模式适当性方面取得了最高的准确性.
- 发现68.4%的病房拥有足够的护士人员.
- 26.5%的病房使用了适当的护理护理交付模式,功能护理和整体患者护理是最常见的.
结论:
- 机器学习,特别是随机森林,对于优化护士人员配置和护理交付模型是有效的.
- 功能性护理和全患者护理模型需要仔细考虑护士资格和患者需求.
- 将ML整合到医疗保健规划中可以改善患者护理,护士满意度和运营效率.
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