A DEA and SFA analysis on public emergency department efficiency in Portugal and the role of frequent users

Abstract

his Work Project carries out a comparative analysis of the efficiency of 15 emergency departments (EDs) within the Portuguese National Health Service, using Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The work takes on a Problem-based learning (PBL) approach, in which complex real-world problems—like the efficiency of healthcare services—are used as vehicles for learning and collaborative student work. The dataset used, covering 2021 to 2023, was collected by EDs and submitted to the Tribunal de Contas (Portuguese Court of Auditors) for an upcoming audit. The analysis highlights Decision-Making Units (DMUs) 8 and 10 as best performers, showcasing adequate resource management, staffing levels and time allocation. In contrast, DMUs 7 and 14 consistently underperform, exhibiting inefficiencies in staffing ratios and prolonged patient wait times. A seasonality analysis revealed that efficiency drops in summer, due to staffing shortages and operational challenges, while a cost-analysis identified DMU 13 as the more cost-effective unit. Further analyses are conducted to examine factors influencing ED efficiency, such as non urgent users, frequent users, and those who leave without being seen. These findings trace a clear path for targeted policy interventions, where underperforming units can benefit from adopting best practices observed in efficient EDs.