Methodological Evaluation and Time-Series Forecasting of Clinical Outcomes in South African Emergency Care Units: A Meta-Analysis (2000–2026)

{ "background": "Emergency care systems in South Africa face significant strain, yet a comprehensive methodological synthesis of clinical outcome forecasting models is lacking. This gap impedes the development of robust, evidence-based planning tools for healthcare delivery.", "purpose and objectives": "This meta-analysis aims to critically evaluate methodological approaches and to develop an integrated time-series forecasting model for key clinical outcomes within the nation's emergency units.", "methodology": "A systematic search identified relevant studies. Methodological quality was appraised using a modified Cochrane tool. Quantitative data were synthesised via random-effects meta-analysis. The core forecasting model is an autoregressive integrated moving average with exogenous variables (ARIMAX), specified as \∇ᵈ yt = c + \∑i=1ᵖ\ \∇ᵈ yt-i + \∑j=1q\ \εt-j + \∑k=1ᵐ\ Xk,t + \, where $Xk$ represents covariates including nurse-to-patient ratios. Uncertainty was quantified using 95% prediction intervals.", "findings": "Methodological evaluation revealed that 68% of included studies had a high risk of bias due to inadequate handling of missing data. The synthesised ARIMAX model forecasts a significant negative association between higher patient crowding and positive clinical outcomes (β = -0.23, 95% CI: -0.31 to -0.15).", "conclusion": "Current forecasting methodologies exhibit substantial limitations, but the integrated model provides a more rigorous tool for predicting clinical outcomes, directly linking operational factors to patient care trajectories.", "recommendations": "Future research must adopt more rigorous missing data protocols. Health policymakers should utilise advanced forecasting models incorporating real-time operational data for resource allocation and capacity planning.", "key words": "emergency care, forecasting, meta-analysis, clinical outcomes, health systems, South Africa, time-series", "contribution statement": "This study provides