一个基于人工智能的概率预测模型,用于预测医院单位的护理劳动力需求.
概括
准确的护士人员预测对于高质量的患者护理至关重要. 一个新的DeepAR机器学习模型有效地预测了医院单位的护理需求,优化了资源配置和人员决策.
科学领域:
- 医疗保健管理的管理
- 数据科学数据科学数据科学
- 护理工作人员分析
背景情况:
- 适当的护士人员配置对于医疗保健质量至关重要,但仍然是一个挑战.
- 对于护理需求预测的先进机器学习尚未得到充分利用.
- 优化资源利用和招聘需要可靠的劳动力预测.
研究的目的:
- 提出和评估一个概率预测方法护理劳动力需求.
- 利用DeepAR算法预测多个医院单位的需求.
- 为医疗保健管理人员提供可靠的工具,以改善人员决策.
主要方法:
- 应用DeepAR,一个自回归循环神经网络,以预测护理需求.
- 分析了来自美国大型卫生系统的5年历史劳动力数据.
- 为异质时间序列开发了一个单一的全球模型,包括通过特征编码对COVID-19的影响.
主要成果:
- DeepAR模型准确地捕获了时间模式,包括趋势和突然变化.
- 大多数基本真实值都在90%的预测间隔内.
- 该模型在各种业务部门和专业组合中表现出一致的性能和强大的通用性.
结论:
- 使用DeepAR提出的概率预测方法是预测护理劳动力需求的可靠工具.
- 这种方法使得在各种医院环境中能够做出明智的员工决策.
- 它优化了资源利用,并支持积极的招聘策略.
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