Adapting Efficiency Analysis in Health Systems: A Scoping Review of Data Envelopment Analysis Applications During the COVID-19 Pandemic.

Q2 Medicine
Journal of market access & health policy Pub Date : 2024-10-22 eCollection Date: 2024-12-01 DOI:10.3390/jmahp12040024
Athanasios Mitakos, Panagiotis Mpogiatzidis
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引用次数: 0

Abstract

Objective: To synthesize the current evidence base concerning the application of Data Envelopment Analysis (DEA) in healthcare efficiency during the COVID-19 pandemic using a scoping review of 13 primary studies. Methods: We consulted databases including Web of Science (WoS) and Scopus, as well as manual search entries up to September 2022. Included studies were primary applications of DEA for assessing healthcare efficiency during the COVID-19 pandemic. Key findings derived from thematic analysis of repeating pattern observations were extracted and tabulated for further synthesis, taking into consideration the variations in DMU definitions, the inclusion of undesirable outputs, the influence of external factors, and the infusion of advanced technologies in DEA. Results: The review observed a diverse application of DMUs, ranging from healthcare supply chains to entire national health systems. There was an evident shift towards incorporating undesirable outputs, such as mortality rates, in the DEA models amidst the pandemic. The influence of external and non-discretionary factors became more pronounced in DEA applications, highlighting the interconnected nature of global health challenges. Notably, several studies integrated advanced computational methods, including machine learning, into traditional DEA, paving the way for enhanced analytical capabilities. Conclusions: DEA, as an efficiency analysis tool, has exhibited adaptability and evolution in its application in the context of the COVID-19 healthcare crisis. By recognizing the multifaceted challenges posed by the pandemic, DEA applications have grown more comprehensive, integrating broader societal and health outcomes. This review provides pivotal insights that can inform policy and healthcare strategies, underscoring the importance of dynamic and comprehensive efficiency analysis methodologies during global health emergencies.

在卫生系统中采用效率分析:COVID-19 大流行期间数据包络分析应用范围审查》。
目的通过对 13 项主要研究进行范围界定,总结目前有关在 COVID-19 大流行期间将数据包络分析法 (DEA) 应用于提高医疗效率的证据基础。研究方法我们查阅了包括 Web of Science (WoS) 和 Scopus 在内的数据库,以及截至 2022 年 9 月的人工搜索条目。纳入的研究主要是在 COVID-19 大流行期间应用 DEA 评估医疗效率。考虑到 DMU 定义的差异、不良产出的纳入、外部因素的影响以及 DEA 中先进技术的注入,对重复模式观察的主题分析得出的主要结论进行了提取并制成表格,以便进一步综合。结果:审查发现,DMU 的应用多种多样,从医疗保健供应链到整个国家医疗保健系统,不一而足。在大流行病期间,DEA 模型明显转向将死亡率等不良产出纳入其中。在 DEA 应用中,外部因素和非自由裁量因素的影响变得更加明显,突出了全球卫生挑战的相互关联性。值得注意的是,一些研究将包括机器学习在内的先进计算方法融入传统的 DEA 中,为增强分析能力铺平了道路。结论:作为一种效率分析工具,DEA 在 COVID-19 医疗危机的应用中表现出了适应性和演变性。由于认识到大流行病所带来的多方面挑战,DEA 的应用已变得更加全面,整合了更广泛的社会和健康成果。本综述提供了可为政策和医疗保健战略提供参考的重要见解,强调了在全球卫生紧急情况下动态和综合效率分析方法的重要性。
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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来源期刊
CiteScore
4.90
自引率
0.00%
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0
审稿时长
14 weeks
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