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A DEA and SFA analysis on public emergency department efficiency in Portugal: the role of patients leaving without being seen
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This 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. In contrast,
DMUs 7 and 14 consistently underperform. A seasonality analysis revealed that efficiency drops
in summer, while a cost-analysis identified DMU 13 as the more cost-effective unit. These findings
trace a clear path for targeted policy interventions, where underperforming units can benefit from
adopting best practices observed in efficient EDs.
In the individual part I focused on the rate of patients of leave the ED before being seen, a metric
which provides a standardized method for assessing the performance of ED. DMUs 5 and 9 report
low rates, while DMUs 4, 7 and 12 report high rates – high-performing EDs may provide models
of best practices that can be replicated in underperforming regions to reduce their leaving without
being seen rates. The analysis finds a moderate correlation between these rates and efficiency,
supporting the idea that efficient resource utilization and optimized patient flow can reduce the
number of patients leaving prematurely.
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Data envelopment analysis Stochastic frontier analysis Emergency department efficiency Hospital efficiency Resource allocation Technical efficiency