ArticleBMC health services research2025
Evaluating hospital performance with additive DEA and MPI: the Isfahan University of Medical Science case study.
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 3 papers.
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3 citing papers in PubMed.
- Hospital efficiency assessment: a systematic review of DEA and hybrid DEA-machine learning approaches.Frontiers in medical technology · 2026Review
- The novel additive star-NDEA model for hospital performance evaluation.BMC health services research · 2025Article
- Performance of Greek Public Hospitals Before and After the Economic Recession and the Pandemic: Application of a Novel Cost Malmquist Index for Comparing Productivity Across Multiple Groups.Healthcare (Basel, Switzerland) · 2025Article
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5 authors.
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Abstract
backgroundHospitals are a vital pillar of the health system, and measuring their performance by an appropriate quantitative model is crucial. This study evaluated the performance of hospitals affiliated with Isfahan University of Medical Sciences. It deals with the nature of dynamics (the performance of evaluation indicators over time), examining controllable, uncontrollable, and undesirable input and output indicators.
methodsThis study evaluated the performance of 26 Isfahan University of Medical Science hospitals in terms of efficiency and productivity with hybrid Data Envelopment Analysis (DEA) models, namely, the additive classic, Malmquist productivity index (MPI), and super-efficiency models, from 2019 through 2022. Thirteen indicators (four inputs and nine outputs) were selected as model variables by brainstorming in the expert panel.
resultsThe average technical efficiency of hospitals during the four periods was 0.86, indicating an average inefficiency of 14%. Malmquist productivity index results over four periods showed hospitals operating with an average of 11% positive growth, reflecting an overall increase in productivity. Notably, some hospitals with high technical efficiency displayed lower total productivity growth rates due to fluctuations in specific indicators. On average, in the four under study years, 12 hospitals were efficient, of which 75% (9 hospitals) had performance progress (average MPI > 1). On the contrary, among the 14 inefficient hospitals during the four studied years, more than 90% of the hospitals had improved performance.
conclusionThis study introduces a multidimensional and dynamic model for evaluating hospital performance. While classic DEA models provide a statistical performance evaluation, the Malmquist Productivity Index reveals dynamic performance changes over time. These findings underscore the need for hospitals to adopt advanced quantitative models to optimize resource allocation and enhance service delivery.
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