The novel additive star-NDEA model for hospital performance evaluation

Abstract

Background: In 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. Methods: In 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, Results: This 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. Conclusion: The 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. © The Author(s) 2025.