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基于分类回归树的效率数据包络分析模型在医院绩效评价中的应用。

An efficiency data envelopment analysis model reinforced by classification and regression tree for hospital performance evaluation.

机构信息

Department of Information Management, Kainan University, Taoyuan, Taiwan.

出版信息

J Med Syst. 2011 Oct;35(5):1075-83. doi: 10.1007/s10916-010-9598-5. Epub 2010 Sep 28.

Abstract

As changes in the medical environment and policies on national health insurance coverage have triggered tremendous impacts on the business performance and financial management of medical institutions, effective management becomes increasingly crucial for hospitals to enhance competitiveness and to strive for sustainable development. The study accordingly aims at evaluating hospital operational efficiency for better resource allocation and cost effectiveness. Several data envelopment analysis (DEA)-based models were first compared, and the DEA-artificial neural network (ANN) model was identified as more capable than the DEA and DEA-assurance region (AR) models of measuring operational efficiency and recognizing the best-performing hospital. The classification and regression tree (CART) efficiency model was then utilized to extract rules for improving resource allocation of medical institutions.

摘要

随着医疗环境的变化和国家医疗保险政策的变化,对医疗机构的业务绩效和财务管理产生了巨大影响,因此,有效的管理对于医院提高竞争力和争取可持续发展变得越来越重要。本研究旨在评估医院的运营效率,以实现更好的资源配置和成本效益。首先比较了几种基于数据包络分析(DEA)的模型,结果表明,与 DEA 和 DEA 保证区域(AR)模型相比,DEA-人工神经网络(ANN)模型在衡量运营效率和识别运营效率最高的医院方面更具优势。然后利用分类和回归树(CART)效率模型提取医疗机构资源配置的改进规则。

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