Measuring efficiency in acute care hospitals: an application of data envelopment analysis.

D A Dittman, R Capettini, R C Morey
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Abstract

In this article, the authors attempted to demonstrate how DEA can be useful to hospital administrators and health care planners. They used actual data collected by the American Hospital Association through its Monitrend Data Service. Since these were national data, they are presented here for illustrative purposes only. The efficiency with which a hospital operates may well depend upon the local or regional labor market, the competition among health care providers in that market, and the demographics of the service area. The choice of variables was dictated by reasonableness and availability of data. Given the routine collection of case mix data by DRG since 1984, the use of a different set of output variables for any future studies would be quite appropriate. Additionally, if DEA were to be used, a consensus concerning relevant controllable and non-controllable input variables would need to be achieved. There are more technical caveats of which the reader should be aware. 1) The efficiency scores are all relative and are based on the performance of the other hospitals being compared; nothing can be said about the absolute efficiency of a given hospital. However, the relative ratings are conservative in that the approach "bends over backwards" to give the individual hospital the benefit of the doubt in terms of the relative importance of the various outputs and inputs utilized. The approach maintains equity in that any weights chosen for a given hospital must be feasible for all of the other hospitals. 2. The ratings assume a causal impact of the inputs on the outputs. In addition, it is possible that inclusion of additional inputs and outputs could modify the relative scores and/or help explain the differences. However, based on the factors available, any unit rated inefficient is inferior in a very real and demonstrable sense. 3. DEA is based on the generalized notion of convexity which assumes that the performance arrived at by taking any linear weighted combination of other hospitals' inputs and outputs represents a feasible and achievable technology. The general frontier surface is approximated by piecewise-linear segments with the result that observed differences in efficiency cannot be explained away as differences in economies of scale. 4. The inefficiency score and the resource conservation potentials are based on a unit's so-called contraction path, i.e., all of the controllable inputs are required to be reduced by the same factor.(ABSTRACT TRUNCATED AT 400 WORDS)

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测量急症护理医院的效率:数据包络分析的应用。
在本文中,作者试图演示DEA如何对医院管理人员和医疗保健计划人员有用。他们使用了美国医院协会通过其监测趋势数据服务收集的实际数据。由于这些都是国家数据,因此在此仅供说明之用。医院的运作效率很大程度上取决于当地或区域的劳动力市场、该市场中医疗保健提供者之间的竞争以及服务区域的人口结构。变量的选择取决于数据的合理性和可用性。鉴于DRG自1984年以来例行收集病例混合数据,在未来的任何研究中使用一组不同的输出变量将是非常适当的。此外,如果要使用DEA,就需要就相关的可控和不可控输入变量达成共识。读者应该注意更多的技术警告。1)效率得分都是相对的,是基于其他被比较医院的绩效;对于某一医院的绝对效率,没有什么可说的。然而,相对评级是保守的,因为该方法“向后倾斜”,在利用各种产出和投入的相对重要性方面,给予个别医院怀疑的好处。该方法保持公平性,因为为某一医院选择的任何权重必须对所有其他医院都是可行的。2. 评级假设输入对输出有因果影响。此外,有可能包括额外的输入和输出可以修改相对分数和/或帮助解释差异。然而,根据现有的因素,任何被评为效率低下的单位在一个非常真实和可论证的意义上都是低劣的。3.DEA基于广义的凸性概念,它假设将其他医院的投入和产出任意线性加权组合得到的绩效代表一种可行和可实现的技术。一般的边界表面由分段线性段近似,其结果是,观察到的效率差异不能用规模经济的差异来解释。4. 低效率得分和资源节约潜力基于一个单位的所谓收缩路径,即所有可控的投入都需要被同一因素减少。(摘要删节为400字)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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