Department of Industrial Engineering, Universidad de los Andes, Bogotá, Colombia.
Department of Systems Engineering, Faculty of Economics, VŠB-Technical University of Ostrava, Sokolská třida 33, 702 00, Ostrava, Czech Republic.
Health Care Manag Sci. 2019 Sep;22(3):512-533. doi: 10.1007/s10729-019-09478-0. Epub 2019 Mar 1.
In recent years, most countries around the world have struggled with the consequences of budget cuts in health expenditure, obliging them to utilize their resources efficiently. In this context, performance evaluation facilitates the decision-making process in improving the efficiency of the healthcare system. However, the performance evaluation of many sectors, including the healthcare systems, is, on the one hand, a challenging issue and on the other hand a useful tool for decision- making with the aim of optimizing the use of resources. This study proposes a new methodology comprising two well-known analytical approaches: (i) data envelopment analysis (DEA) to measure the efficiencies and (ii) data science to complement the DEA model in providing insightful recommendations for strategic decision making on productivity enhancement. The suggested method is a first attempt to combine two DEA extensions: flexible measure and cross-efficiency. We develop a pair of benevolent and aggressive scenarios aiming at evaluating cross-efficiency in the presence of flexible measures. Next, we perform data mining cluster analysis to create groups of homogeneous countries. Organizing the data in similar groups facilitates identifying a set of benchmarks that perform similarly in terms of operating conditions. Comparing the benchmark set with poorly performing countries we can obtain attainable goals for performance enhancement which will assist policymakers to strategically act upon it. A case study of healthcare systems in 120 countries is taken as an example to illustrate the potential application of our new method.
近年来，世界上大多数国家都在努力应对医疗支出削减带来的后果，这迫使他们必须有效地利用资源。在这种背景下，绩效评估有助于改善医疗体系效率的决策过程。然而，包括医疗体系在内的许多部门的绩效评估，一方面是一个具有挑战性的问题，另一方面也是一个有用的决策工具，旨在优化资源利用。本研究提出了一种新的方法，该方法结合了两种知名的分析方法：（i）数据包络分析（DEA）来衡量效率，以及（ii）数据科学，以补充 DEA 模型，为提高生产力的战略决策提供有见地的建议。所提出的方法是首次尝试将两种 DEA 扩展（灵活度量和交叉效率）结合起来。我们开发了一对仁慈和激进的方案，旨在在存在灵活度量的情况下评估交叉效率。接下来，我们执行数据挖掘聚类分析，以创建同质国家组。将数据组织成类似的组有助于识别一组在操作条件方面表现相似的基准。将基准集与表现不佳的国家进行比较，可以获得可实现的绩效提升目标，这将有助于政策制定者进行战略性决策。以 120 个国家的医疗体系为例进行了案例研究，以说明我们新方法的潜在应用。
