This study focuses on evaluating district hospital systems in Uganda, specifically investigating how a time-series forecasting model can be used to measure system reliability. A mixed methods approach was employed, combining quantitative data from health records with qualitative insights from interviews and focus groups conducted among local stakeholders. The time-series forecasting model utilised an autoregressive integrated moving average (ARIMA) to predict system performance trends. The ARIMA model showed a directionally positive forecast for hospital efficiency improvements over the next five years, with a prediction interval suggesting a 95% confidence that these forecasts will remain within ±10% of actual outcomes. The mixed methods study demonstrated the effectiveness of using time-series forecasting models in evaluating district hospitals' reliability and performance in Uganda. The ARIMA model provided reliable predictions for system improvement. Further research should explore the scalability and adaptability of this method to other healthcare systems, with a focus on identifying key factors influencing hospital efficiency. district hospitals, time-series forecasting, reliability assessment, autoregressive integrated moving average (ARIMA), stakeholder engagement Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.
Kizza et al. (Thu,) studied this question.