Appraising healthcare systems’ efficiency in facing COVID-19 through data envelopment analysis

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.

Analyzing the Efficiency of Working Capital Management: a New Approach Based on DEA-Malmquist Technology

“…Among the numerous approaches available for assessing DMU efficiency scores, the DEA approach was chosen to evaluate the efficiency of the firms under study because of its unique characteristics. First, as Mourad et al [ 31 ], Shahwan and Habib [ 32 ], and Tone [ 33 ] argue, DEA is a versatile and powerful technique for capturing the relationship between specific outputs and inputs. Furthermore, DEA can provide critical information for continuous improvement, assisting inefficient DMUs in achieving best practices.…”

“…Second, like Cooper et al [ 37 ] and Habib and Shahwan [ 38 ] argued, DEA stands out as a benchmark technique that focuses on the best practices of DMUs rather than traditional methods that rely on measures of central tendencies. Finally, as demonstrated by Habib and Kayani [ 36 ], Mourad et al [ 31 ] and Tuskan and Stojanovic [ 35 ], DEA distinguishes itself as a non-parametric technique that does not require prior assumptions about the distribution form of data (or its residuals). Furthermore, DEA does not require any previous knowledge of the variable weights.…”

“…The DEA approach is distinguished by its ability to capture relationships between multiple outputs and inputs [ 31 – 33 ]. Additionally, DEA is a non-parametric technique that does not require prior assumptions about the distribution form of data or its residuals, and does not require any previous knowledge of the variable weights [ 34 – 36 ].…”

Second-order conic programming for data envelopment analysis models

“…The mean of that input for each DMU is given in Table1, the covariance matrix is randomly generated, and the efficiency scores are in Table2. The performances ofDMUs 1,2,3,4,6,7,8,10,11,12,14,15,18, 19,and 20 are not affected by the addition of this variable. While DMUs 9, 13, 16, and 17 performances are extremely affected by this variable, they become efficient in all cases.…”

“…Hypothetical data for 20 DMUs In this case, only deterministic variables are involved with three inputs and 2 outputs. The efficiency scores for the CCR and VRS with both orientations are given in Table2.DMUs 4,7,8,10,11,14, and 19 are efficient in all the cases. DMUs 3, 18, and 20 are almost efficient with the CRS model and efficient with VRS.…”