基于基于需求和基于能力的员工规划的常规数据,针对案例复杂性的预测器选择
Reto Bürgin1, Renate Ranegger1, Dieter Baumberger1
1Department of Research and Development, LEP AG, 9000 St. Gallen, Switzerland.
Studies in health technology and informatics
|July 25, 2024
概括
护理管理面临的人员短缺的挑战. 这项研究确定了预测病例复杂性的关键患者因素,以优化人员规划和资源配置,以改善护理服务.
科学领域:
- 医疗保健管理的管理
- 护理信息学 护理信息学
- 临床操作 临床操作
背景情况:
- 护理人员短缺和日益增长的护理需求需要有效的资源配置和任务分配.
- 使用常规患者数据进行等级组合分析,可以为护理管理决策提供信息.
- 病例的复杂性是关键的决定因素,它将护理干预,工作量和适当的人员水平联系起来.
研究的目的:
- 从例行患者文档中识别病例复杂性的预测因素.
- 开发一个模型来预测患者的临床复杂性水平.
- 支持基于需求和能力的护理人员规划.
主要方法:
- 来自瑞士一家医院 (n = 3,373 个病例) 一年例行病人的文件分析.
- 应用加权累积后勤回归模型来预测患者的临床复杂性.
- 确定案例复杂性的重要预测因素.
主要成果:
- 确定的主要预测因素包括性别,年龄,入院前居住地,入院类型,自我护理指数,肺炎风险和护理干预次数.
- 开发的模型在人员规划应用中表现出有限但适当的准确性.
- 这些发现为基于需求和能力的员工规划提供了基础.
结论:
- 该研究成功地确定了护理中病例复杂性的重要预测因素.
- 预测模型虽然有限,但在人员规划中为护理管理提供了实际支持.
- 建议对医院内数据进行进一步校准和临床环境测试,以改进和实施模型.
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