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The European Journal of Health Economics
Published in: The European Journal of Health Economics 6/2013

01-12-2013 | Original Paper

How efficient are Greek hospitals? A case study using a double bootstrap DEA approach

Authors: Kostas Kounetas, Fotis Papathanassopoulos

Published in: The European Journal of Health Economics | Issue 6/2013

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Abstract

The purpose of this study was to measure Greek hospital performance using different input–output combinations, and to identify the factors that influence their efficiency thus providing policy makers with valuable input for the decision-making process. Using a unique dataset, we estimated the productive efficiency of each hospital through a bootstrapped data envelopment analysis (DEA) approach. In a second stage, we explored, using a bootstrapped truncated regression, the impact of environmental factors on hospitals’ technical and scale efficiency. Our results reveal that over 80 % of the examined hospitals appear to have a technical efficiency lower than 0.8, while the majority appear to be scale efficient. Moreover, efficiency performance differed with inclusion of medical examinations as an additional variable. On the other hand, bed occupancy ratio appeared to affect both technical and scale efficiency in a rather interesting way, while the adoption of advanced medical equipment and the type of hospital improves scale and technical efficiency, correspondingly. The findings of this study on Greek hospitals’ performance are not encouraging. Furthermore, our results raise questions regarding the number of hospitals that should operate, and which type of hospital is more efficient. Finally, the results indicate the role of medical equipment in performance, confirming its misallocation in healthcare expenditure.
Appendix
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Footnotes
1
Total expenditure on health as percent of GDP was 9.7 and 10.1 for 2009 and 2008, respectively (WHO) [1].
 
2
Table 1 presents a list of studies that refers to evaluation of Greek hospitals, examining their efficiency and productivity.
 
3
We are aware that traditional DEA has proven to be an effective and versatile tool for health care efficiency measurement; a thorough literature review shows its use has spread throughout hospital performance measurement. Hollingsworth et al. [15] and Hollingsworth [16, 17] provide a comprehensive review of the literature focusing on non-parametric measurement of efficiency in health care. Furthermore, O’Neil et al. [18] provide a systematic review and taxonomy of hospital efficiency studies that utilize DEA and related techniques for efficiency measurement.
 
4
A representative example concerning the comparison of DEA and SFA for the hospital’s case can be found in Chirikos and Sear [20].
 
5
The main advantages and disadvantages of the SF and DEA approaches are discussed analytically by Coelli et al. [21].
 
6
This procedure took place in 2008, as part of a PhD thesis on health technology efficiency carried out in the Department of Economics of University of Patras.
 
7
One of the referees of this paper raised the issue of cost data, noting that the health care efficiency literature included numerous studies that examine the role of cost variables in hospital efficiency. We appreciate this valuable comment but data restrictions did not allow us to conduct an analogous examination.
 
8
Many patients proceed with preventive (or not) medical tests that sometimes led to new admissions or readmissions to the same hospital.
 
9
Model 1 uses the number of beds, doctors and nurses as input variables while number of surgeries, days of treatment and medical examinations are used as output variables. Model 3 uses number of medical examinations as a additional input variable.
 
10
The same tests were applied in the case of the different input–output combinations. However, our results did not provide any difference for both scale and technical efficiency. These results are available upon request.
 
11
Gaussian Kernel function was used in all density estimations. Our estimation using alternative functions (i.e., uniform, epanechnicov) were identified to those obtained by Gaussian. For our estimation, we used STAT 10. The optimal bandwidth provided by STAT minimizes the mean integrated squared error if a Gaussian Kernel function is used and varies between 0.01 and 0.04.
 
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Metadata
Title
How efficient are Greek hospitals? A case study using a double bootstrap DEA approach
Authors
Kostas Kounetas
Fotis Papathanassopoulos
Publication date
01-12-2013
Publisher
Springer Berlin Heidelberg
Published in
The European Journal of Health Economics / Issue 6/2013
Print ISSN: 1618-7598
Electronic ISSN: 1618-7601
DOI
https://doi.org/10.1007/s10198-012-0446-z

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