Healthcare efficiency assessment using DEA analysis in the Slovak Republic

Abstract

A regional disparity is becoming increasingly important growth constraint. Policy makers need quantitative knowledge to design effective and targeted policies. In this paper, the regional efficiency of healthcare facilities in Slovakia is measured (2008–2015) using data envelopment analysis (DEA). The DEA is the dominant approach to assessing the efficiency of the healthcare system but also other economic areas. In this study, the window approach is introduced as an extension to the basic DEA models to evaluate healthcare technical efficiency in individual regions and quantify the basic regional disparities and discrepancies. The window DEA method was chosen since it leads to increased discrimination on results especially when applied to small samples and it enables year-by-year comparisons of the results. Two stable inputs (number of beds, number of medical staff), three variable inputs (number of all medical equipment, number of magnetic resonance (MR) devices, number of computed tomography (CT) devices) and two stable outputs (use of beds, average nursing time) were chosen as production variable in an output-oriented 4-year window DEA model for the assessment of technical efficiency in 8 regions. The database was made available from the National Health Information Center and the Slovak Statistical Office, as well as from the online databases Slovstat and DataCube. The aim of the paper is to quantify the impact of the non-standard Data Envelopment Analysis (DEA) variables as the use of medical technologies (MR, CT) on the results of the assessment of the efficiency of the healthcare facilities and their adequacy in the evaluation of the monitored processes. The results of the analysis have shown that there is an indirect dependence between the values of the variables over time and the results of the estimated efficiency in all regions. The regions that had low values of the variables over time achieved a high degree of efficiency and vice versa. Interesting knowledge was that the gradual addition of variables number of MR, number of CT and number of medical devices together, to the input side did not have a significant impact on the overall estimated efficiency of healthcare facilities.

Prediction and Decomposition of Efficiency Differences in Chinese Provincial Community Health Services

“…Therefore, it is important to identify efficiency measurement methods that can satisfy our needs. Data Envelopment Analysis (DEA) is one such measurement and benchmarking tool, especially in the healthcare sector [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”

Financial performance–efficiency nexus in public health services: A nonparametric evidence-based approach

“…With regard to the healthcare domain, the most widespread use of the DEA method is in the hospital setting (Kohl et al, 2019;Nistor et al, 2017;Kaitelidou et al, 2016;Gholami et al, 2015;Martini et al, 2014;Hollingsworth, 2008;Sherman, 1984). Other units included in the analysis of healthcare efficiency include complete healthcare system at the national level (Asandului et al, 2015;Asandului et al, 2014;Adang & Borm, 2007), healthcare system at the level of individual regions within a country (Stefko et al, 2018;Carrillo & Jorge, 2017;De Nicola et al, 2011;Nuti et al, 2011) or specific healthcare units, for example intensive care units, dialysis centers, nursing homes, etc. (Mitropoulos et al, 2016;Garavaglia et al, 2011;Tsekouras et al, 2010;Nunamaker, 1983).…”

Comparison of the Results of a Data Envelopment Analysis Model and Logit Model in Assessing Business Financial Health

“…One of the methods based on mathematical programming is the DEA method [48]. Compared to statistical methods, DEA is a relatively new, non-parametric method, which represents one of the possible approaches to assessing the financial health of a businesses and its risk of bankruptcy [49]. This method was first applied in 1978 by [50].…”