Methodological Evaluation of Rural Clinics Systems in Senegal Using Time-Series Forecasting Models

Rural clinics in Senegal face challenges in providing consistent healthcare services due to limited resources and frequent staff turnover. A systematic review was conducted using a mixed-method approach integrating quantitative data from clinic records and qualitative insights from interviews with healthcare providers. Time-series forecasting models were applied to forecast future trends in KPIs such as patient attendance and treatment outcomes. The analysis revealed an average improvement of 15% in patient attendance when using time-series forecasting models, suggesting these models can predict clinic performance effectively. Time-series forecasting models have the potential to enhance resource allocation at rural clinics by providing accurate predictions of future KPIs. Implementing these models requires collaboration between healthcare providers and data analysts to ensure model accuracy and adaptability. Training programmes for healthcare staff on data interpretation should be developed. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.