{ "background": "Emergency care systems in sub-Saharan Africa are underdeveloped, with a critical evidence gap regarding the impact of structured emergency care units (ECUs) on patient outcomes. Existing evaluations are largely descriptive, lacking robust comparative designs to attribute changes in clinical outcomes to system-level interventions. ", "purpose and objectives": "This protocol details a quasi-experimental study to evaluate the effect of implementing standardised ECU protocols on system performance and clinical outcomes. The primary objective is to estimate the causal impact on 30-day all-cause mortality for major trauma and acute medical conditions. ", "methodology": "A controlled interrupted time series design will be employed, comparing six intervention hospitals receiving a standardised ECU bundle (triage, staffing, protocols, equipment) with six matched control hospitals. Data will be extracted from clinical records for a pre- and post-implementation period. The primary analysis will use a segmented regression model: Yt = \0 + \1Tt + \2Xt + \3TtXt + \, where Yt is the mortality rate at time t, Tt is time, and Xt is the implementation period. Inference will be based on 95% confidence intervals for the change in level and trend (\3). ", "findings": "As a protocol, no empirical results are presented. The anticipated primary finding is a reduction in the 30-day all-cause mortality rate for targeted conditions by at least 15 percentage points in intervention ECUs relative to controls, following the implementation period. ", "conclusion": "This protocol provides a methodological framework for a robust, context-appropriate evaluation of emergency care system strengthening. The findings will contribute causal evidence on the effectiveness of integrated ECU models in resource-limited settings. ", "recommendations": "Future research should adopt similar quasi-experimental designs for health systems evaluation. Policymakers should consider the results for national emergency care policy and rollout, ensuring concurrent investment in data
Mensah et al. (Wed,) studied this question.