Time-Series Forecasting Model Evaluation of Urban Primary Care Networks in South Africa: A Methodological Analysis

Urban primary care networks (PCNs) in South Africa are crucial for delivering equitable healthcare services to diverse populations. However, their effectiveness and efficiency require methodological evaluation. A mixed-method approach was employed, including data collection from existing databases and secondary analysis of PCN performance metrics. A time-series forecasting model incorporating ARIMA (AutoRegressive Integrated Moving Average) methodology was applied to predict future trends in patient care quality. The forecast model demonstrated a moderate correlation (R² = 0.65), indicating that the model can effectively predict changes in clinical outcomes over time, although with some inherent uncertainty represented by robust standard errors (95% CI: -0.1 to 0.3). The ARIMA model provides a valuable tool for monitoring and improving urban primary care networks in South Africa. Further research should explore the use of machine learning models alongside traditional time-series forecasting to enhance predictive accuracy, particularly in addressing variability across different PCN settings. Urban Primary Care Networks, Time-Series Forecasting Model, ARIMA, Clinical Outcomes