Evidence map›Paper›PMID 27068659›Full record

ArticleHealth care management science2017

Decomposition of potential efficiency gains from hospital mergers in Greece.

Angeliki Flokou, Vassilis Aletras, Dimitris Niakas

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Article in Health care management science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Angeliki FlokouSchool of Social Sciences, Hellenic Open University, Parodos Aristotelous 18, 26 335, Patra, Greece. aggi@flokou.gr.
Vassilis AletrasSchool of Social Sciences, Hellenic Open University, Parodos Aristotelous 18, 26 335, Patra, Greece.
Dimitris NiakasSchool of Social Sciences, Hellenic Open University, Parodos Aristotelous 18, 26 335, Patra, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper evaluates the technical efficiency of 71 Greek public hospitals and examines potential efficiency gains from 13 candidate mergers among them. Efficiency assessments are performed using bootstrapped Data Envelopment Analysis (DEA) whilst merger analysis is conducted by applying the Bogetoft and Wang methodology which allows the overall potential merger gains to be decomposed into three main components of inefficiency, namely technical (or learning), scope (or harmony) and scale (or size) effects. Thus, the analysis provides important insights not only on the magnitude of the potential total efficiency gains but also on their sources. The overall analysis is conducted in the context of a complete methodological framework where methods for outlier detection, returns to scale identification, and bias corrections for DEA estimations are also applied. Mergers are analyzed under the assumptions of constant, variable and non-decreasing returns to scale in an input oriented DEA model with three inputs and three outputs. The main finding of the study indicates that almost all mergers show substantial potential room for efficiency improvement, which is mainly attributed to the pre-merger technical inefficiencies of the individual hospitals and therefore it might be possible to be achieved without the need of implementing full-scale mergers. The same -though, at a lower extent- applies to the harmony effect whilst the size effect shows marginal or even negative gains.

Indexed as

Efficiency, OrganizationalHealth Facility MergerCross-Sectional StudiesGreeceHealth Services ResearchHospitals, PublicHumansModels, StatisticalNational Health ProgramsBenchmarkingBootstrappingData envelopment analysisHospital EfficiencyMerger Analysis

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.