在日本大型大学医院护理需求的时间动态中,病房特定的概率模式:对预测和资源配置的影响
Rie Tajika1, Yoshiaki Inoue2, Keisuke Nakashima3
1Department of Nursing Graduate School of Health Sciences Kobe University, 7-10-2 Tomogaoka, Suma-ku, Kobe 654-0142, Hyogo, Japan.
Journal of nursing management
|April 14, 2025
概括
准确的护士需求预测对于管理护士短缺至关重要. 纵向数据分析揭示了患者护理需求的可预测模式,从而实现了高效的资源配置和数据驱动的员工配置.
科学领域:
- 医疗保健管理的管理
- 护理信息学 护理信息学
- 数据科学数据科学数据科学
背景情况:
- 全球人口老龄化加剧了护士短缺问题.
- 有效的护理资源分配对于患者护理质量至关重要.
- 目前的护理需求预测是不发达的,通常是临时的.
研究的目的:
- 为了证明纵向数据分析识别护理需求规律性.
- 为了实现数据驱动的预测未来的护理需求.
- 改善护理资源的分配和管理.
主要方法:
- 对患者护理需求的纵向数据分析.
- 使用Sankey图表来显示时间动态的可视化.
- 在护理需求中确定病房特定的统计规律.
主要成果:
- 确定了护理需求变化中的病房特定的概率模式.
- 证明护理需求随着时间的推移表现出统计规律.
- 在病房之间显示了需求增加概率的显著变化.
结论:
- 纵向数据分析可以预测护理需求的时间变化.
- 调查结果支持对护理资源估计的数据驱动预测.
- 这种方法可以带来更有效的护理管理.
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