Improving Hospital Efficiency and Economic Performance: A DEA Approach with Undesirable Factors in Tehran Emergency Wards

Since the 1980s, Data Envelopment Analysis (DEA) has undergone remarkable advancements in both theoretical foundations and practical applications, surpassing initial expectations in the field. To optimize organizational performance, identifying and incorporating undesirable inputs and outputs is vital for improving system efficiency, minimizing waste, and enhancing resource allocation—ultimately contributing to economic efficiency. This research employs established DEA models to assess decision-making units' (DMUs) performance while explicitly accounting for undesirable factors. The results demonstrate that including undesirable inputs and outputs significantly influences the identification of the efficiency frontier, thereby affecting the comparative assessment of DMUs and providing a more accurate reflection of real-world economic constraints. Consequently, DMU efficiency and performance can be improved by reducing undesirable outputs and increasing desirable ones, pushing them toward the efficient frontier. In healthcare, this leads to better patient outcomes and more effective resource utilization. Economically, it translates to lower operational costs, improved resource allocation, and greater overall productivity in the healthcare system. To validate the proposed models, a case study was conducted using real-world data from 30 emergency wards in Tehran hospitals, comprising five desirable inputs, one undesirable input, four desirable outputs, and one undesirable output.