Abstract:
Medical equipment forms the backbone of modern healthcare delivery, yet its effective management remains a critical challenge in resource-limited settings like Ethiopia. Despite sustained investment in capital medical equipment (CME), equipment non-functionality remains a major constraint to service delivery and patient outcomes in the Ethiopian public healthcare system. Existing planning frameworks assume linear relationships between equipment availability and health outcomes, obscuring threshold effects and system fragility across hospital tiers. This study examined CME availability, effectiveness, and their nonlinear impacts on patient outcomes across different levels of care. A mixed-methods, cross-sectional study was conducted at three purposively selected public hospitals representing system maturity levels: Adama Comprehensive Specialized Hospital (tertiary), Shenen Gibe General Hospital (secondary), and Dima Primary Hospital (primary). Quantitative data were collected from 129 equipment records, institutional service logs, and 71 staff survey responses. Constrained nonlinear sigmoidal and four-parameter logistic (4PL) regression models were applied to identify threshold behaviour, saturation effects, and fragile dynamics linking equipment availability, effectiveness, and patient outcomes. Qualitative data from clinical and biomedical staff were analysed using an NVivo-guided thematic framework to contextualize quantitative results. Equipment effectiveness differed markedly across hospital tiers. The Equipment Effectiveness Index (EEI) was 0.862 at Adama Referral Hospital (saturated regime), 0.374 at Shenen Gibe General Hospital (threshold-sensitive regime), and 0.612 at Dima Primary Hospital (fragile regime). Sigmoidal modelling identified critical effectiveness thresholds, with mortality risk and patient costs responding sharply to EEI changes around 0.44 at Shenen Gibe General and 0.56 at Dima Primary. Each 10% increase in equipment availability was associated with a 6–9% reduction in mortality risk and a 25–35% decrease in patient out-of-pocket costs. Qualitative findings highlighted maintenance delays, spare-part shortages, training gaps, and partial functionality as key mediators of effectiveness. CME effectiveness follows predictable, context-dependent nonlinear patterns rather than linear trends. A tiered, phase-appropriate investment strategy is required: threshold-crossing interventions at primary hospitals, targeted effectiveness optimization at general hospitals, and efficiency-focused management at referral hospitals. These findings provide a robust framework for nonlinear health technology assessment and evidence-based resource allocation.
