Evidence map›Paper›PMID 41331571›Full record

ArticleBMC health services research2025

The novel additive star-NDEA model for hospital performance evaluation.

Shirin Alsadat Hadian, Reza Rezayatmand, Saeedeh Ketabi, Ahmad Reza Pourghaderi, Nasrin Shaarbafchizadeh

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Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shirin Alsadat HadianHealth Management and Economics Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.ORCID http://orcid.org/0000-0002-1443-1990
Reza RezayatmandHealth Management and Economics Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. reza.rezayatmand@gmail.com.ORCID http://orcid.org/0000-0002-9907-3597
Saeedeh KetabiDepartment of Management, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran.ORCID http://orcid.org/0000-0002-6778-5645
Ahmad Reza PourghaderiSchool of Public Health and Preventive Medicine, Monash University, Victoria, Australia.ORCID http://orcid.org/0000-0003-2682-2160
Nasrin ShaarbafchizadehHospital Management Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0001-7104-2214

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn the context of healthcare systems, evaluating hospital performance usign appropriate quantitative models is essential. This paper introduces a novel approach for assessing hospital performance in uncertain environments.

methodsIn this study, we applied an additive star network data envelopment analysis (star-NDEA) model to evaluate the interdepartmental efficiency of 26 hospitals whithin Isfahan University of Medical Sciences (MUI) using visual analytis tools. The analysis, based on data from 2019 to 2022, utilized 44 indicators (12 inputs, 17 outputs, 14 links), which were carefully selected as model variables. The relevance and suitability of these indicators were assessed through a systematic two-stage process,

resultsThis study indicated that the additive star-NDEA model identifies the specific sources of inefficiency and more accurately captures the inefficiencies of hospitals, reflecting the internal parameters of departmental-level performance evaluation. Across all the studied hospitals, the logistics department consistently had the highest efficiency score (approximately 0.9) among the eight departments evaluated over the four years. The highest efficiency scores for specific departments were recorded as follows: surgical inpatient wards in 2019 (0.64), imaging department in 2020 (0.56), clinic department in 2021 (0.59) and surgical inpatient wards again in 2022 (0.63). In contrast, the departments with the lowest efficiency scores were, the internal inpatient ward in 2019 (0.43), the clinic department in 2020 (0.33), and, the imaging department in both 2021 and 2022 (around 0.40). Overall, the efficiency scores derived from additive star-NDEA model were lower than those obtained using the classical additive DEA model.

conclusionThe model presented in this study is well-suited for assessing the efficiency of hospitals and their internal departments. These findings highlight the importance of hospitals adopting advanced quantitative models to improve to enhance performance, optimize resource utilization, plan targeted interventions, improve service delivery and ultimately increase overall technical efficiency.

Indexed as

Efficiency, OrganizationalHospital AdministrationHospitalsHumansQuality Indicators, Health CareAdditive star-NDEA modelData envalopment analysisEfficiencyHospitalPerformance evaluation

Identifiers

PMID41331571
PMCPMC12777432

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LicenceCC BY-NC-ND
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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.