{ "background": "Community health centres in South Africa face systemic inefficiencies that constrain service delivery and health outcomes. Existing improvement approaches often lack rigorous, systems-level evaluation.", "purpose and objectives": "This study aimed to empirically evaluate a novel systems optimisation framework designed to improve operational yield, defined as the number of patients seen per clinician per day, within a randomised field trial.", "methodology": "We conducted a parallel-group, cluster-randomised field trial across 24 centres. The intervention arm implemented a four-component framework integrating lean management, task shifting, dynamic scheduling, and real-time data feedback. The primary outcome was the change in mean daily yield. Analysis used a linear mixed-effects model: $Y{ij} = \\beta0 + \\beta1 Ti + uj + \\epsilon{ij}$, where $Y{ij}$ is yield for centre $j$ at time $i$, $Ti$ is treatment assignment, $uj$ is the random intercept for centre, and $\\epsilon{ij}$ is the error term. Robust standard errors were estimated.", "findings": "Centres receiving the intervention demonstrated a significant mean yield increase of 22.3% (95% CI: 17.1 to 27.5; p<0.001) compared to control centres at follow-up. The task-shifting component was most frequently cited by staff as the critical enabling factor.", "conclusion": "The systems optimisation framework effectively improved operational yield in a real-world setting, demonstrating the value of integrated, data-driven management interventions.", "recommendations": "Policy makers should consider phased integration of similar systems frameworks, with particular emphasis on supportive supervision for task-shifting protocols. Future research should assess long-term sustainability and health outcome impacts.", "key words": "health systems research, operations research, randomised controlled trial, primary health care, health services efficiency", "contribution statement": "This paper provides the first experimental evidence for a multi-component systems optimisation framework in a low-resource