Evidence map›Paper›PMID 42053180›Full record

ArticleAnales del sistema sanitario de Navarra2026

[Efficiency after integrating a hospital with a 'new management model'].

Mateo Cacho Uzal, Juan Cueva Ares, Fe Lopez-Juiz, Francisco Reyes Santías

Abstract readEnglish Abstract
In one paragraph

Article in Anales del sistema sanitario de Navarra, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Mateo Cacho UzalUniversidad de Santiago de Compostela. Santiago de Compostela. España.
Juan Cueva AresUniversidad de Santiago de Compostela. Santiago de Compostela. España.
Fe Lopez-JuizMinisterio de Justicia. Tribunal Superior de Justicia de Galicia. A Coruña. España.
Francisco Reyes SantíasUniversidad de Vigo. Vigo. España.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study evaluates the efficiency of a hospital under two management models, private (2015-2018) and public (2019-2022), and examines the impact of the COVID-19 pandemic (2020-2021).

methodsHospital efficiency was evaluated using an output-oriented data envelopment analysis model with variable returns to scale. Outputs included consultations, emergency visits, surgical procedures, admissions, and inpatient stays, assuming a constant input (=1). Robustness was assessed using a parametric bootstrap (1,500 replications) to obtain bias-corrected efficiency scores and confidence intervals. An interrupted time series analysis was conducted to estimate a counterfactual scenario under continued private management and compare it with observed outcomes under public management.

resultsEfficiency under private management remained consistently close to the frontier during 2015-2018. Under public management, efficiency declined sharply during the pandemic, reaching near-zero values in 2020-2021. Bootstrap results confirmed that these reductions were statistically significant and not random. A partial recovery was observed in 2022; however, efficiency remained below counterfactual projections.

conclusionsWhile efficiency performed stably under private management, the public model was markedly affected by the pandemic, though showing signs of resilience thereafter. These findings highlight the sensitivity of hospital efficiency to external crises and governance structures, underscoring the need to strengthen resilience, digitalization, and sustainability to maintain efficiency and equity in future health system disruptions.

Indexed as

COVID-19Efficiency, OrganizationalHospital AdministrationHospitals, PrivateHospitals, PublicModels, OrganizationalHumansSpain

Identifiers

PMID42053180
PMCPMC13227081

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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.