Development of data envelopment analysis model for measuring healthcare efficiency using fuzzy randomization

This study presents a data envelopment analysis (DEA) for measuring productivity based on the SBM model, integrated with the DEMATEL-ANP hybrid method. It was used to evaluate several hospitals by calculating the number of hospital visits, a crucial metric for the proposed model, which proved its effectiveness. A random SBM model is proposed in this study to manage uncertain conditions. This study proposes a random SBM system to evaluate performance using the DEMATEL and ANP methods to identify critical criteria. Given the large number of key criteria identified for performance evaluation, a process based on these two methods is employed to determine the most important criteria. The primary objective is to identify the most effective criteria in the hospital sector, building upon previous studies that identified 16 key criteria affecting hospital efficiency. The DEMATEL/ANP method identified the number of patient visits as the most important criterion, with an initial weight of 0.237 and an average efficiency of 0.847 ± 0.156. A comparison using the deterministic SBM model revealed a bias of 7.1%. Hospital 3 achieved an efficiency of 0.642, while Hospital 8 achieved an efficiency of 0.981. After applying the uncertainty model, four hospitals demonstrated complete efficiency. The problems were solved by achieving efficiency ratios of 34% and preventing Type I errors in 27% of decision-making performance. The integration of DEMATEL-ANP further reduced this to achieve 94% efficiency. On the practical side, the results regarding resource efficiency rely on reliability indicators, improvement priorities based on standards and evidence, and the measurement of operational risks through probabilistic confidence intervals. For policymakers, evaluating healthcare system performance takes into account the degree of fluctuation in demand. The proposed system demonstrates several quantitative advantages: 15% higher discriminatory power, 23% better correlation with post-assessment clinical outcomes (r = 0.712, p < 0.01), and a reduction in negatives from 18% to 4%. Additionally, it offers 81% accuracy in uncertainty predictions, compared to 58% accuracy for deterministic models