Emergency care units (ECUs) in Senegal face challenges related to clinical outcomes due to varying resource availability and patient flow. A time-series forecasting model was developed using historical data from Senegalese ECUs. The model incorporates seasonal adjustments to forecast future trends in patient outcomes with an ARIMA (1, 1, 0) structure and robust standard errors providing uncertainty estimates. The model demonstrated a significant improvement (p < 0. 05) in forecasting accuracy compared to baseline methods, particularly for mortality rates over the next six months. This study validates the applicability of time-series models for enhancing ECU performance and patient care outcomes in resource-limited settings. Implementing this model can guide policy decisions aimed at optimising ECU operations to better serve Senegalese patients. Emergency Care Units, Clinical Outcomes Forecasting, Time-Series Model, ARIMA (1, 1, 0), Robust Standard Errors
Sall et al. (Wed,) studied this question.