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Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework
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 inefficienc...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9474503/ https://www.ncbi.nlm.nih.gov/pubmed/35192085 http://dx.doi.org/10.1007/s10729-022-09590-8 |
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author | Zarrin, Mansour Schoenfelder, Jan Brunner, Jens O. |
author_facet | Zarrin, Mansour Schoenfelder, Jan Brunner, Jens O. |
author_sort | Zarrin, Mansour |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9474503 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-94745032022-09-16 Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework Zarrin, Mansour Schoenfelder, Jan Brunner, Jens O. Health Care Manag Sci Article 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. Springer US 2022-02-22 2022 /pmc/articles/PMC9474503/ /pubmed/35192085 http://dx.doi.org/10.1007/s10729-022-09590-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zarrin, Mansour Schoenfelder, Jan Brunner, Jens O. Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework |
title | Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework |
title_full | Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework |
title_fullStr | Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework |
title_full_unstemmed | Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework |
title_short | Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework |
title_sort | homogeneity and best practice analyses in hospital performance management: an analytical framework |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9474503/ https://www.ncbi.nlm.nih.gov/pubmed/35192085 http://dx.doi.org/10.1007/s10729-022-09590-8 |
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