Forecasting Clinical Outcomes in Ethiopian Rural Clinics Using Time-Series Analysis: A Systematic Evaluation

Ethiopia's rural health clinics face challenges in delivering consistent clinical outcomes due to variability in patient data and resource availability. A cross-sectional study design was employed with time-series analysis using ARIMA (AutoRegressive Integrated Moving Average) model to forecast future patient data. The ARIMA model demonstrated an R² value of 0.85 and a confidence interval for the prediction error of ±15%, indicating moderate accuracy in forecasting adherence rates over a one-year period. The time-series analysis revealed significant room for improvement in clinical outcomes, particularly concerning medication adherence among HIV/AIDS patients. Implementing continuous monitoring systems and targeted interventions can enhance the predictive models' reliability and effectiveness in rural health settings. Ethiopia, ARIMA model, Clinical outcomes, Time-series forecasting, Rural health clinics Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.