在卫生系统中调整效率分析:在COVID-19大流行期间对数据包裹分析应用进行范围审查
Athanasios Mitakos1, Panagiotis Mpogiatzidis1
1Department of Midwifery, School of Health Sciences, University of Western Macedonia, 50200 Ptolemaida, Greece; dmw00017@uowm.gr.
Journal of market access & health policy
|October 28, 2024
概括
数据包裹分析 (DEA) 适应在COVID-19大流行期间评估医疗保健效率. 研究表明,不断发展的应用程序包括不必要的输出和先进的技术,以获得更好的洞察力.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 运营研究 运营研究
- 计量经济学 计量经济学
背景情况:
- 在全球范围内,COVID-19大流行给医疗保健系统带来了前所未有的挑战.
- 评估和提高医疗保健效率成为管理资源和患者结果的关键.
- 数据包围分析 (DEA) 是一种强大的方法来评估复杂系统中的效率.
研究的目的:
- 在COVID-19大流行期间系统审查和综合DEA在评估医疗保健效率中的应用.
- 在此背景下识别DEA方法的趋势,挑战和进步.
- 为未来的医疗保健政策和战略制定提供见解.
主要方法:
- 采用了一个范围审查方法,搜索诸如 Web of Science 和 Scopus 等数据库.
- 包括的研究是DEA的主要应用,用于评估大流行期间的医疗保健效率.
- 专题分析用于综合发现,考虑决策单位 (DMU) 定义的变化,不良输出,外部因素和技术集成.
主要成果:
- 从供应链到国家卫生系统,DEA应用于各种各样的单位.
- 在DEA模型中,将不良输出 (例如死亡率) 纳入DEA模型的数量显著增加.
- 外部因素和先进的计算方法,包括机器学习,越来越多地被整合到DEA应用中.
结论:
- 在COVID-19危机期间,DEA已被证明是医疗保健效率分析的适应性和不断发展的工具.
- 疫情驱动的DEA应用变得更加全面,整合了社会和健康结果.
- 这些发现强调了动态效率分析在全球卫生紧急情况期间为战略提供信息的价值.
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