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医院数据包络分析(DEA)效率测量的投入和产出选择方法:系统评价。

Approach in inputs & outputs selection of Data Envelopment Analysis (DEA) efficiency measurement in hospitals: A systematic review.

机构信息

Medical Development Division, Ministry of Health Malaysia, Putrajaya, Malaysia.

Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.

出版信息

PLoS One. 2024 Aug 14;19(8):e0293694. doi: 10.1371/journal.pone.0293694. eCollection 2024.

Abstract

The efficiency and productivity evaluation process commonly employs Data Envelopment Analysis (DEA) as a performance tool in numerous fields, such as the healthcare industry (hospitals). Therefore, this review examined various hospital-based DEA articles involving input and output variable selection approaches and the recent DEA developments. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilised to extract 89 English articles containing empirical data between 2014 and 2022 from various databases (Web of Science, Scopus, PubMed, ScienceDirect, Springer Link, and Google Scholar). Furthermore, the DEA model parameters were determined using information from previous studies, while the approaches were identified narratively. This review grouped the approaches into four sections: literature review, data availability, systematic method, and expert judgement. An independent single strategy or a combination with other methods was then applied to these approaches. Consequently, the focus of this review on various methodologies employed in hospitals could limit its findings. Alternative approaches or techniques could be utilised to determine the input and output variables for a DEA analysis in a distinct area or based on different perspectives. The DEA application trend was also significantly similar to that of previous studies. Meanwhile, insufficient data was observed to support the usability of any DEA model in terms of fitting all model parameters. Therefore, several recommendations and methodological principles for DEA were proposed after analysing the existing literature.

摘要

效率和生产力评估过程通常采用数据包络分析(DEA)作为一种性能工具,应用于许多领域,如医疗保健行业(医院)。因此,本综述检查了各种基于医院的 DEA 文章,涉及输入和输出变量选择方法以及最近的 DEA 发展。采用系统评价和荟萃分析的 Preferred Reporting Items(PRISMA)方法,从各种数据库(Web of Science、Scopus、PubMed、ScienceDirect、Springer Link 和 Google Scholar)中提取了 2014 年至 2022 年间包含实证数据的 89 篇英文文章。此外,使用来自先前研究的信息确定了 DEA 模型参数,而方法则以叙述方式确定。本综述将这些方法分为四组:文献综述、数据可用性、系统方法和专家判断。然后,对这些方法应用了独立的单一策略或与其他方法相结合的策略。因此,本综述对医院中使用的各种方法的关注可能会限制其发现。可以在不同领域或基于不同视角,使用替代方法或技术来确定 DEA 分析的输入和输出变量。DEA 的应用趋势也与之前的研究非常相似。同时,观察到缺乏足够的数据来支持任何 DEA 模型在拟合所有模型参数方面的可用性。因此,在分析现有文献后,提出了一些关于 DEA 的建议和方法原则。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87a3/11324144/ba0bd6c3d1ed/pone.0293694.g001.jpg

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