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

Κουνετάς, Κωνσταντίνος; Papathanassopoulos, Fotis

doi:10.1007/s10198-012-0446-z

Cited by 79 publications

(59 citation statements)

References 41 publications

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“…However, the hospital’s size was a major source of inefficiency. In addition, bed occupancy ratio appeared to affect both technical and scale efficiency in a rather interesting way [1]. …”

Section: Resultsmentioning

confidence: 99%

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Efficiency and optimal size of hospitals: Results of a systematic search

Giancotti¹,

Guglielmo²,

Mauro³

2017

PLoS ONE

116

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BackgroundNational Health Systems managers have been subject in recent years to considerable pressure to increase concentration and allow mergers. This pressure has been justified by a belief that larger hospitals lead to lower average costs and better clinical outcomes through the exploitation of economies of scale. In this context, the opportunity to measure scale efficiency is crucial to address the question of optimal productive size and to manage a fair allocation of resources.Methods and findingsThis paper analyses the stance of existing research on scale efficiency and optimal size of the hospital sector. We performed a systematic search of 45 past years (1969–2014) of research published in peer-reviewed scientific journals recorded by the Social Sciences Citation Index concerning this topic. We classified articles by the journal’s category, research topic, hospital setting, method and primary data analysis technique. Results showed that most of the studies were focussed on the analysis of technical and scale efficiency or on input / output ratio using Data Envelopment Analysis. We also find increasing interest concerning the effect of possible changes in hospital size on quality of care.ConclusionsStudies analysed in this review showed that economies of scale are present for merging hospitals. Results supported the current policy of expanding larger hospitals and restructuring/closing smaller hospitals. In terms of beds, studies reported consistent evidence of economies of scale for hospitals with 200–300 beds. Diseconomies of scale can be expected to occur below 200 beds and above 600 beds.

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Section: Resultsmentioning

confidence: 99%

“…Healthcare organizations are required to achieve efficiency and effectiveness; they must reduce costs and offer quality health services [1]. One important source of potential inefficiency in the hospital sector relates to hospitals’ scale and scope.…”

Section: Introductionmentioning

confidence: 99%

Efficiency and optimal size of hospitals: Results of a systematic search

Giancotti¹,

Guglielmo²,

Mauro³

2017

PLoS ONE

116

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“…However, all DMUs are subject to environmental and random factors, that is, their efficiency scores fall into a fluctuating range. 40 Thus, efficiency scores generated using the traditional DEA models may be biased. 41 Simar and Wilson et al 28 42-45 introduced the bootstrapping DEA method to correct the bias in the efficiency and productivity scores.…”

Section: Bootstrapping Dea and Bootstrapping Mpimentioning

confidence: 99%

Efficiency and productivity measurement of rural township hospitals in China: a bootstrapping data envelopment analysis

et al. 2016

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ObjectiveTownship hospitals (THs) are important components of the three-tier rural healthcare system of China. However, the efficiency and productivity of THs have been questioned since the healthcare reform was implemented in 2009. The objective of this study is to analyse the efficiency and productivity changes in THs before and after the reform process.Setting and participantsA total of 48 sample THs were selected from the Xiaogan Prefecture in Hubei Province from 2008 to 2014.Outcome measuresFirst, bootstrapping data envelopment analysis (DEA) was performed to estimate the technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) of the sample THs during the period. Second, the bootstrapping Malmquist productivity index was used to calculate the productivity changes over time.ResultsThe average TE, PTE and SE of the sample THs over the 7-year period were 0.5147, 0.6373 and 0.7080, respectively. The average TE and PTE increased from 2008 to 2012 but declined considerably after 2012. In general, the sample THs experienced a negative shift in productivity from 2008 to 2014. The negative change was 2.14%, which was attributed to a 23.89% decrease in technological changes (TC). The sample THs experienced a positive productivity shift from 2008 to 2012 but experienced deterioration from 2012 to 2014.ConclusionsThere was considerable space for TE improvement in the sample THs since the average TE was relatively low. From 2008 to 2014, the sample THs experienced a decrease in productivity, and the adverse alteration in TC should be emphasised. In the context of healthcare reform, the factors that influence TE and productivity of THs are complex. Results suggest that numerous quantitative and qualitative studies are necessary to explore the reasons for the changes in TE and productivity.

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“…Mortality rate (MORT) is used as a proxy for the variation in the quality of care provided by HCs (Ferrier and Valdmanis 1996;Marathe et al 2007) while years of operation (AGE) capturing learning by doing effects (Kounetas and Papathanasopoulos 2013) is the second one. Last, an additional variable that depicts competitive pressure (Puenpatom and Rosenman 2008;Linna et al 2003) has also been considered in our analysis.…”

Section: Data Measures and Modelsmentioning

confidence: 99%

Factors affecting primary health care centers’ economic and production efficiency

2015

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Little is known about the efficiency of health centers, despite their central role in primary health care strategy for several countries. This study evaluates the health centers in Greece, and identifies factors impeding the achievement of efficiency, with the aim of determining how their efficiency could be improved. Two alternative conceptual models are used to ensure the consistency of the efficiency results: one model is focusing on production efficiency and the other on economic efficiency. Subsequently a second stage analysis is performed to account for the impact of explanatory variables on efficiency. The use of DEA models alongside with bootstrap techniques allows calculating more accurately the efficiency scores that can reflect the performance of health centers more properly. The main drivers of health centers' technical efficiency for both conceptual models were the location characteristics, the population growth, the mortality rate and the competition. The scale efficiency of health centers in production model is reflected by the size of their respective covered populations, the location characteristics and the mortality rate while the economic model is affected by their size, the location characteristics and the percentage of population working in agriculture. Determining how these variables influence on efficiency is essential for determining performance improvement strategies.

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

Cited by 79 publications

References 41 publications

Efficiency and optimal size of hospitals: Results of a systematic search

Efficiency and optimal size of hospitals: Results of a systematic search

Efficiency and productivity measurement of rural township hospitals in China: a bootstrapping data envelopment analysis

Factors affecting primary health care centers’ economic and production efficiency

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