{ "background": "Emergency care systems in low-resource settings are critical for reducing preventable mortality, yet robust methodological frameworks for evaluating their clinical performance are lacking. This gap impedes the development of evidence-based improvements in service delivery and patient outcomes.", "purpose and objectives": "This case study aims to methodologically evaluate the performance of hospital-based emergency care units by developing and applying a multilevel regression model to analyse clinical outcomes. The objective is to quantify the influence of system-level factors on patient survival.", "methodology": "A retrospective cohort analysis was conducted using linked clinical and administrative data from a national sample of emergency units. The primary outcome was 48-hour survival. A three-level hierarchical logistic regression model was specified: $\\text{logit}(p{ijk}) = \\beta0 + \\beta X{ijk} + u{jk} + v_k$, where patients (i) are nested within shifts (j) and hospitals (k). Uncertainty was quantified using 95% confidence intervals derived from robust standard errors.", "findings": "The analysis identified a significant association between the presence of a dedicated triage officer and improved 48-hour survival (adjusted odds ratio 1.42, 95% CI 1.15 to 1.76). System-level factors, including shift-level staffing ratios, accounted for approximately 18% of the variance in patient outcomes, highlighting their substantial influence.", "conclusion": "The methodological approach demonstrates that multilevel modelling is a powerful tool for disentangling complex, nested influences on clinical outcomes in emergency care systems. It moves beyond patient-level analysis to quantify modifiable system characteristics.", "recommendations": "Routine health information systems should be structured to capture shift-level and facility-level operational data. Future evaluations of emergency care interventions should employ multilevel analytical techniques to accurately assess their impact and inform resource allocation.", "key words": "health systems evaluation, emergency medical services, hierarchical modelling, clinical outcomes, low-resource settings", "contribution statement": "This study