Input–Output Specification in Hospital Data Envelopment Analysis: What Choices Drive Efficiency Scores?
Systematic review
Keywords:
Data Envelopment Analysis, Hospital Efficiency, Technical Efficiency, Inputs and Outputs, Model Orientation, Returns to Scale, Case-Mix Adjustment, Quality of CareAbstract
Frontier efficiency methods are widely used to measure healthcare productivity, but results vary greatly because of methodological heterogeneity. There is limited consensus on input-output variable selection, and differences in DEA orientation, returns-to-scale assumptions, case-mix adjustment, and quality incorporation complicate cross-study interpretation. Methods: Following PRISMA guidance, we screened 509 records on public hospital efficiency and included 60 empirical hospital DEA studies. Extracted data covered inputs, outputs, model orientation, returns to scale, DEA variants, case-mix adjustment, quality measures, and sensitivity analyses. Results: DEA was the dominant frontier method. Reported efficiency scores were highly conditional on model specification, commonly ranging from 73% to 97% across studies. In the authors' extraction, most studies used input-oriented models (36/60, 60.0%) and either VRS or CRS/VRS comparisons (42/60, 70.0%). Core inputs were staff (56/60, 93.3%) and beds (50/60, 83.3%), while core outputs were inpatient volume (54/60, 90.0%) and outpatient volume (48/60, 80.0%). Case-mix adjustment was present in 21 studies (35.0%). Quality was directly incorporated into the DEA model in only 5 studies (8.3%) and considered either directly or in parallel in 15 studies (25.0%). Conclusion: Frontier efficiency analysis is useful for benchmarking but is highly sensitive to specification choices. Robust input-output selection, justified orientation and scale assumptions, and explicit case-mix and quality safeguards are essential for valid comparisons and responsible policy use.
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