Emergency care systems in Ghana have undergone significant development since. Recognising the need for data-driven improvements and clinical outcome evaluation, this study aims to develop a robust time-series forecasting model to enhance emergency care system performance. A time-series analysis was employed, with data from to collected through structured interviews and electronic health records (EHR). The Box-Jenkins ARIMA model was applied to forecast emergency care system performance. Uncertainty in the forecasts is quantified using robust standard errors. The time-series analysis revealed a consistent upward trend in patient arrivals at emergency units, indicating an increasing demand for services over the study period. A 20% variation in forecasted outcomes was observed with 95% confidence intervals. This study establishes a reliable framework for forecasting future clinical performance of Ghana's emergency care systems based on historical data and emerging trends. The ARIMA model provides a robust tool for policymakers to anticipate system needs and allocate resources effectively. Based on the findings, it is recommended that further research be conducted to validate these results across different regions in Ghana. Additionally, continuous monitoring of patient outcomes should inform adjustments to emergency care protocols. Emergency Care Systems, Time-Series Forecasting, ARIMA Model, Ghanaian Healthcare, Clinical Outcomes Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Asare et al. (Wed,) studied this question.