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Appraising healthcare systems’ efficiency in facing COVID-19 through data envelopment analysis
Date
2021-03-09
Journal Title
Journal ISSN
Volume Title
Publisher
Growing science
Abstract
The healthcare system is a vital element for any community, as it extremely affects the socio
economic development of any country. The current study aims to assess the performance of the
healthcare systems of the countries above fifty million citizens in facing the spread of the
COVID-19 pandemic since late December 2019. For this purpose, seven scenarios were adopted
via the DEA methodology with six variables, which are the number of medical practitioners
(doctors and nurses), hospital beds, Conducted Covid-19 tests, affected cases, recovered cases,
and death cases. To shed light on the relative efficiency of drivers, the Tobit analysis was used.
Besides, the study carried out various statistical tests for the DEA models' findings to validate
the choice of the variables and the obtained scores. The DEA results reveal that less than half of
the considered countries are relatively efficient. Moreover, the Tobit regression analysis showed
that the main impact on the efficiency scores was due to the number of affected and recovered
cases. Finally, the results of the tests of Spearman, Mann-Whitney U, and Kruskal-Wallis H
indicate the internal validity and robustness of the chosen DEA models. The current study
findings raise important implications, which can be helpful for decision makers regarding
continuous improvement of performance, in which the findings assert the importance of
achieving the best practices regarding relative efficiency through the linkage between the
healthcare systems’ resources, and the needed outputs.
Description
Keywords
Healthcare systems
Covid-19 pandemic
Data envelopment analysis (DEA)
Technical efficiency
Decision-making units (DMUs)
Mathematical programming
Citation
Mourad, N., Habib, A.M. and Tharwat, A. (2021) “Appraising healthcare systems’ efficiency in facing COVID-19 through data envelopment analysis,” Decision Science Letters, 10(3), pp. 301–310.