Methodological Evaluation of Emergency Care Systems in Kenya Using Multilevel Regression Analysis to Measure Clinical Outcomes

Emergency care systems in Kenya are crucial for managing acute health conditions efficiently. However, their effectiveness varies significantly across different regions and facilities. A multilevel regression model was employed to analyse the impact of various factors on clinical outcomes. The model includes unit-level variables such as staffing levels, equipment availability, and regional health infrastructure. The analysis revealed that having adequate staff (at least two medical doctors per unit) significantly improved patient recovery rates by 15% compared to units with insufficient staffing. This study contributes to the literature by providing robust evidence on how specific resource allocations impact clinical outcomes in emergency care settings, offering guidance for policymakers and healthcare providers. Policymakers should prioritise investments in human resources, particularly increasing the number of medical personnel in under-resourced units. Healthcare facilities can implement regular training programmes to enhance staff capabilities. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.