基于清晰和模糊的集群技术以及概率模糊效率估计的公立医院集群
1Health Care Management, Faculty of Economics and Administrative Sciences (FEAS), Department of Health Care Management, Hacettepe University, D Block 4th Floor Beytepe 06800 Ankara, Turkey.
Informatics for health & social care
|September 17, 2025
概括
这项研究使用集群和模糊数据包围分析 (DEA) 创建了同质的医院组. 一个教学医院被认为是高效的,是其他医院的参考,突出了战略分组的洞察力.
科学领域:
- 运营研究 运营研究
- 医疗保健管理的管理
- 数据科学数据科学数据科学
背景情况:
- 数据包裹分析 (DEA) 应用程序通常假定单位均性.
- 之前对DEA的分组方法缺乏全面的方法.
- 聚类技术有可能在DEA中创建可比的单元.
研究的目的:
- 使用清晰模糊的集群开发同质的医院组.
- 应用模糊可能性DEA方法进行效率评估.
- 为了确定高效的医院和战略分组洞察力.
主要方法:
- 应用K-means,模糊c-means和自我组织的地图集群.
- 调整了用于集群技术的超参数.
- 使用了模糊的可能性DEA模型,使用了原始和双重方法.
主要成果:
- 确定了五家医院在最高绩效组.
- 一个东南教学大学医院使用模糊的DEA被发现是高效的.
- 原始效率得分随着α参数 (0到1) 的增加而增加.
结论:
- 战略分组和模糊效率估计提供了宝贵的见解.
- 突出了DEA的潜在陷,特别是同质组组的创建.
- 该研究为医院绩效评估提供了一个强大的框架.
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