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A DEA and SFA analysis on public emergency department efficiency in Portugal and the role of frequent users
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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.
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Data Envelopment Analysis Stochastic Frontier Analysis Emergency Department Efficiency Hospital efficiency Resource allocation Technical efficiency