用于预测护理单位人员需求的逆传播神经网络模型的开发和验证:一个横截面研究
Xinyue Pang1, Jia Pan1, Zhi Chen1
1The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
International journal of nursing studies
|September 19, 2025
概括
这项研究开发了一个人工智能模型来预测护士人员需求,提高效率和患者安全. 该模型优化了护士对需求的比例,提高了临床单位的劳动力管理.
科学领域:
- 医疗保健管理的管理
- 医疗保健中的人工智能
- 护理劳动力优化护理劳动力优化
背景情况:
- 战略护士人员配置对于患者安全和劳动力效率至关重要.
- 现有的框架需要加强,以使人员与患者需求保持一致.
- 优化护士人员配置需要将预测分析与运营效率相结合.
研究的目的:
- 开发基于深度学习的预测模型,以优化护士人员配置.
- 将效率评估指标与护士人员预测建模相结合.
- 通过智能人员解决方案,提高患者安全和劳动力效率.
主要方法:
- 一项横截面研究在一家高等公立医院的55个护理单位进行.
- 一个反向传播神经网络模型是使用从优化人员单位的数据开发出来的.
- 该模型使用床位占用,成本和护理质量等变量预测护士人员需求,并与低于最佳单位进行验证.
主要成果:
- 护士人员预测模型表现出高准确度 (R平方=0.97),偏差最小.
- 实施模拟显示了技术效率的提高 (91.67%) 和优化护士到需求比率 (100%).
- 该模型有效地平衡了人员精确性与运营灵活性,支持资源优化.
结论:
- 为了预测护士人员数量,提出了一种混合模型,将运营效率和人员调整联系起来.
- 该模型有助于管理策略,如员工重新分配,优化资源利用.
- 建议进一步进行多中心验证,以便在数据驱动的护理劳动力管理中得到更广泛的应用.
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