Forecasting Yield Improvement in Kenyan Community Health Centres Using Time-Series Models: A Methodological Assessment

Community health centers (CHCs) in Kenya play a crucial role in healthcare delivery, yet their operational efficiency is often underutilized due to challenges such as resource allocation and service demand variability. A systematic review and application of autoregressive integrated moving average (ARIMA) models were applied to historical data from selected CHCs, focusing on monthly patient consultations as the primary indicator. The study aimed at identifying patterns in yield improvement over time and determining the reliability of ARIMA forecasts. The analysis revealed a significant trend towards increased patient consultations during peak seasons with an average increase of 20% compared to off-peak periods, indicating a robust seasonal component in CHC service demand. The ARIMA model demonstrated high predictive accuracy, with forecast errors within ±5% confidence intervals. ARIMA models offer a promising method for forecasting yield improvement in Kenyan CHCs, providing healthcare managers with actionable insights to optimise resource allocation and improve operational efficiency. Healthcare authorities should consider implementing ARIMA forecasts as part of their strategic planning processes. Regular model re-evaluation and adjustments are recommended based on emerging trends and feedback from field operations. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.