Evidence map›Paper›PMID 25486892›Full record

SynthesisJournal of medical systems2015

Primary care efficiency measurement using data envelopment analysis: a systematic review.

Ferruccio Pelone, Dionne Sofia Kringos, Alessandro Romaniello, Monica Archibugi, Chiara Salsiri, Walter Ricciardi

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Journal of medical systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 4 pooled it
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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

46 citing papers in PubMed, 4 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

6 authors.

Ferruccio PeloneDepartment of Public Health, Population Medicine Unit, Catholic University "Sacro Cuore", Largo "F. Vito", n.1, Rome, Italy, fpelone@gmail.com.
Dionne Sofia Kringos
Alessandro Romaniello
Monica Archibugi
Chiara Salsiri
Walter Ricciardi

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is a gap between the demand and supply of efficiency analyses within primary care (PC), despite the threatening financial sustainability of health care systems. This paper provides a systematic literature review on PC efficiency analysis using Data Envelopment Analysis (DEA). We reviewed 39 DEA applications in PC, to understand how methodological frameworks impact results and influence the information provided to decision makers. Studies were combined using qualitative narrative synthesis. This paper reports data for each efficiency analysis on the: 1) evaluation context; 2) model specifications; 3) application of methods to test the robustness of findings; 4) presentation of results. Even though a consistent number of analyses aim to support policymakers and practice managers in improving the efficiency of their PC organizations, the results indicate that DEA--at least when applied to PC--is a methodology still in progress; it needs to be further advanced to meet the complexity that characterizes the production of PC outcomes. Future studies are needed to fill some gaps in this particular domain of research, such as on the standardization of methodologies and the improvement of outcome research in PC. Most importantly, further studies should include extensive uncertainty analyses and be based on good evidence-based rationales. We suggest a number of considerations to academics and researchers to foster the utility of efficiency measurement for the decision making purposes in PC.

Indexed as

Data Interpretation, StatisticalEfficiency, OrganizationalResearch DesignBenchmarkingDecision MakingHumansPractice Patterns, Physicians'Primary Health Care

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.