Department of Health Care Operations / Health Information Management, Faculty of Business and Economics, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany.
Health Care Manag Sci. 2022 Sep;25(3):406-425. doi: 10.1007/s10729-022-09590-8. Epub 2022 Feb 22.
Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals.
使用数据包络分析（DEA）对医院进行绩效建模在文献中受到了越来越多的关注。作为传统 DEA 框架的一部分，通常假设医院在功能上相似，因此是同质的。因此，任何发现的效率低下都应该归因于投入产出的低效利用。然而，DEA 效率得分的差异可能是由于医院固有的异质性造成的。此外，尽管传统的 DEA 模型在文献中经常被用作基准工具，但缺乏预测能力。为了解决这些问题，本研究提出了一种通过结合两种互补建模方法来分析医院绩效的框架。具体来说，我们采用自组织映射人工神经网络（SOM-ANN）进行聚类分析，采用多层感知器人工神经网络（MLP-ANN）进行异质性分析和最佳实践分析。通过对包含德国 1100 多家医院的大型数据集的实施，实证证明了集成框架的适用性。该框架使决策者不仅能够预测最佳绩效，还能够探索相对效率得分的差异是否归因于医院的异质性。
