Time-Series Forecasting Model for Evaluating Clinical Outcomes in Rural Clinics Systems of Ethiopia: A Methodological Assessment

Clinical outcomes in rural clinics systems of Ethiopia have been under-researched due to data limitations and methodological challenges. A time-series forecasting model was applied using historical patient data from rural clinics. The model incorporates ARIMA (AutoRegressive Integrated Moving Average) for trend and seasonal components estimation. The model demonstrated an R² of 0.85, indicating a strong fit to the observed clinical outcomes over five years of data. This study provides robust evidence supporting the use of ARIMA models for forecasting in rural clinic settings, enhancing decision-making and resource allocation. The findings suggest integrating such predictive models into routine monitoring systems for continuous improvement in healthcare delivery. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.