{ "background": "District hospital systems in sub-Saharan Africa face persistent challenges in operational efficiency and clinical yield, with a pressing need for robust, field-tested methodologies to evaluate and improve performance. Existing literature often relies on observational designs, limiting causal inference for intervention efficacy.", "purpose and objectives": "This review critically appraises the methodological framework and outcomes of a randomised field trial designed to measure and optimise yield—defined as the proportion of correctly managed cases against total presentations—within the district hospital system.", "methodology": "We conducted a systematic review of trial protocols, analytical models, and outcome reports. The core statistical model evaluated was a hierarchical logistic regression: $\\logit(P(Y{ij}=1)) = \\beta0 + \\beta1 T{ij} + \\gamma X{ij} + uj$, where $Y{ij}$ is the binary yield outcome for patient $i$ in hospital cluster $j$, $T{ij}$ the treatment assignment, and $u_j$ a random intercept for cluster $j$. Inference was based on cluster-robust standard errors.", "findings": "The methodological evaluation revealed that the cluster-randomised design effectively controlled for intra-hospital correlation. The intervention, a structured clinical audit and feedback mechanism, was associated with a significant yield improvement. A key concrete result was an estimated adjusted odds ratio of 1.82 (95% CI: 1.31 to 2.53) for correct case management in intervention clusters.", "conclusion": "The randomised field trial provides a methodologically sound template for evaluating health system interventions in similar low-resource settings. The significant positive association between the implemented mechanism and yield underscores the value of structured, data-driven feedback loops.", "recommendations": "Future research should adopt similar rigorous designs to isolate causal effects. Policy should prioritise the integration of continuous audit cycles with protected time for clinical team feedback, supported by targeted training for data capture and interpretation.", "key words": "health systems research, cluster randomised