{ "background": "Community health centres in low-resource settings face systemic inefficiencies that impede service delivery and health outcomes. Existing improvement frameworks are often ill-suited to the complex, resource-constrained operational realities of these centres.", "purpose and objectives": "This study aimed to evaluate a novel, context-adapted systems diagnostic and optimisation framework (SDOF) for improving operational efficiency in Ethiopian community health centres. The primary objective was to quantify the framework's impact on patient flow efficiency.", "methodology": "We conducted a parallel, two-arm randomised field trial. Sixty centres were randomly allocated to intervention (SDOF implementation) or control (standard practice) arms. Efficiency was measured via patient time-in-system (TIS). The primary analysis used a linear mixed-effects model: $TIS{ij} = \\beta0 + \\beta1 Groupi + \\gamma X{ij} + uj + \\epsilon{ij}$, where $uj$ is a centre-level random effect. Robust standard errors were clustered at the centre level.", "findings": "The intervention significantly reduced median patient time-in-system by 32% (95% CI: 24% to 39%) compared to control centres. Process mapping revealed that reductions were primarily achieved through reconfiguring triage and pharmacy logistics, eliminating an average of two redundant administrative steps per patient pathway.", "conclusion": "The SDOF is an effective methodological tool for identifying and rectifying systemic inefficiencies in community health centres, leading to substantial gains in operational throughput.", "recommendations": "Health policymakers should integrate systematic diagnostic tools like the SDOF into routine health systems strengthening initiatives. Further research should investigate the framework's adaptability to other frontline service delivery contexts.", "key words": "health systems, operational research, randomised trial, process efficiency, primary health care", "contribution statement": "This paper provides the first experimental evidence for a structured systems diagnostic framework tailored to community-level health facilities in a low-income setting, demonstrating a scalable methodology