An integrated approach to hospital efficiency using machine learning and slacks-based super-efficiency evaluation

Abstract

Healthcare systems worldwide face mounting pressures due to increasing demand and constrained resources, reinforcing the need for rigorous efficiency measurement to inform policy and managerial decision-making. Traditional Data Envelopment Analysis (DEA) models, while widely applied, are limited by their lack of predictive capability and sensitivity to sample composition. This study addresses these shortcomings by developing and applying an integrated Super-Efficiency Slacks-Based Measure DEA (SuperSBM-DEA) and machine learning (ML) framework to evaluate and predict the efficiency of Portuguese public hospitals between 2014 and 2023. The analysis reveals that 76.82% of hospital units operated inefficiently, with marked regional disparities that reflect historical differences in investment and capacity. Ten ML algorithms were trained to predict DEA efficiency scores, with XGBoost achieving the best performance ($R^2$ = 91.46%, RMSE = 0.0438, MAPE = 6.98%). The proposed SuperSBM-DEA-ML framework enables the simulation of counterfactual efficiency scenarios, offering more realistic, stepwise improvement pathways compared to rigid DEA targets. Beyond its predictive accuracy, the framework provides actionable insights for hospital managers and policymakers by supporting forward-looking, data-driven resource allocation and performance monitoring. While the study illustrates the framework’s practical potential, it emphasises that policy adoption should be accompanied by qualitative validation and stakeholder engagement to ensure contextual feasibility and acceptability.