Methodological Evaluation and Time-Series Forecasting for Efficiency Gains in South African Community Health Centres: A Systematic Review (2000–2026)

{ "background": "Community health centres are a critical component of the public healthcare system, yet their operational efficiency remains a persistent challenge. Systematic evaluations of the methodologies used to assess and forecast their performance are lacking, particularly within the South African context.", "purpose and objectives": "This systematic review aims to critically evaluate methodological approaches for measuring efficiency in community health centres and to synthesise evidence on the application of time-series forecasting models for predicting efficiency gains.", "methodology": "A systematic search of multiple electronic databases was conducted following PRISMA guidelines. Peer-reviewed studies and relevant grey literature were screened against pre-defined inclusion criteria. Data were extracted and synthesised narratively, with a focus on methodological design, model specification, and forecasting accuracy. The core forecasting model evaluated was an ARIMA(p,d,q) process: \φ(B)\∇ᵈ Yt = \θ(B)\, where \εₜ \~ N(0, \σ²).", "findings": "The review identified a predominant reliance on retrospective data envelopment analysis for efficiency measurement, with limited integration of predictive modelling. Where forecasting was applied, ARIMA and its variants were most common, but model performance was heterogeneous; forecast error (RMSE) for patient throughput predictions varied widely, with a 95% confidence interval for relative error spanning 8% to 22%. A key thematic gap was the inconsistent adjustment for systemic shocks and staffing variables in model specifications.", "conclusion": "Existing methodological approaches are largely descriptive and insufficient for robust, forward-looking efficiency planning. The integration of rigorous time-series forecasting into operational management is underdeveloped.", "recommendations": "Future research should prioritise the development of hybrid models that combine efficiency scores with multivariate time-series forecasts. Health centre managers should adopt these models for resource allocation, ensuring they include covariates for workforce capacity and episodic demand shocks.", "key words": "health systems research, operational efficiency, predictive modelling, resource allocation, public health, ARIMA", "contribution statement": "