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Healthcare efficiency assessment using DEA analysis in the Slovak Republic

Overview of attention for article published in Health Economics Review, March 2018
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Title
Healthcare efficiency assessment using DEA analysis in the Slovak Republic
Published in
Health Economics Review, March 2018
DOI 10.1186/s13561-018-0191-9
Pubmed ID
Authors

Robert Stefko, Beata Gavurova, Kristina Kocisova

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 186 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 186 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 26 14%
Student > Ph. D. Student 25 13%
Student > Bachelor 18 10%
Student > Doctoral Student 11 6%
Researcher 10 5%
Other 27 15%
Unknown 69 37%
Readers by discipline Count As %
Economics, Econometrics and Finance 25 13%
Medicine and Dentistry 18 10%
Business, Management and Accounting 15 8%
Nursing and Health Professions 11 6%
Engineering 10 5%
Other 26 14%
Unknown 81 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 March 2018.
All research outputs
#13,582,950
of 23,026,672 outputs
Outputs from Health Economics Review
#175
of 436 outputs
Outputs of similar age
#172,528
of 332,332 outputs
Outputs of similar age from Health Economics Review
#8
of 9 outputs
Altmetric has tracked 23,026,672 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 436 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one has gotten more attention than average, scoring higher than 55% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 332,332 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one.