新型的添加剂星-NDEA模型用于医院绩效评估
Shirin Alsadat Hadian1, Reza Rezayatmand2, Saeedeh Ketabi3
1Health Management and Economics Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
BMC health services research
|December 2, 2025
概括
本研究引入了一种增量星网络数据包裹分析 (star-NDEA) 模型,用于评估医院绩效. 这种新的方法准确地识别了低效率,物流部门的效率最高.
科学领域:
- 医疗保健管理的管理
- 运营研究 运营研究
- 医疗保健服务研究 医疗服务研究
背景情况:
- 对医疗保健系统来说,评估医院的绩效至关重要.
- 本研究解决了在不确定的医疗保健环境中需要新型定量模型的需求.
研究的目的:
- 引入和应用一种新的添加式恒星网络数据包裹分析 (star-NDEA) 模型.
- 评估伊斯法罕医学科学大学内的医院的部门间效率.
主要方法:
- 从2019年到2022年,向26家医院应用了增材星-NDEA模型.
- 使用了44个精心挑选的指标 (12个输入,17个输出,14个链接).
- 采用系统的两阶段过程来评估指标的相关性和适用性.
主要成果:
- 添加星-NDEA模型准确地确定了医院效率低下的来源.
- 物流部门始终实现了最高的效率 (约. 0.9) 的时间.
- 观察到不同的部门效率,在内部住院和诊所部门的得分最低.
结论:
- 增材星-NDEA模型适用于评估医院和部门的效率.
- 强调先进的定量模型对于优化医院绩效和资源利用的重要性.
- 调查结果支持有针对性的干预措施,以改善服务提供和整体技术效率.
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