Аңдатпа
Introduction. The challenge of ensuring medical care accessibility in remote, sparsely populated regions remains a persistent global concern, particularly pronounced across the expansive territories of Central Asia. The effective implementation of “last-mile” delivery for essential pharmaceuticals and biomaterials demands innovative approaches unconstrained by the limitations inherent in ground-based transport infrastructure. The purpose of the study is to elaborate a model for deploying a delivery system based on unmanned aerial vehicles (UAVs) within the specific context of the East Kazakhstan Region (EKR) of the Republic of Kazakhstan. Materials and Methods. A discrete-event simulation framework was constructed, integrating a logistics network module for the EKR’s medical facilities, an economic assessment module covering both capital and operating costs, and a socio-economic impact evaluation module. Validation was performed through benchmarking against findings from comparable studies and expert judgement; sensitivity was explored via Monte Carlo simulation. Results. The optimal configuration, predicated on a public-private partnership model with an initial investment of 1.5 million USD (comprising 50 UAVs and 80 charging stations), was found to achieve financial breakeven within 3–4 years at an annual throughput of 50,000 deliveries. Calculations indicated that operational expenses could be reduced by an order of magnitude relative to conventional road transport, while medication delivery times could be shortened from 2–8 hours to 30–90 minutes. The principal impediment to implementation was identified not as technological deficiency but as regulatory constraints embedded in the Republic of Kazakhstan’s current aviation legislation. Conclusion. The investigation confirms that UAV-based delivery for the EKR represents not merely a technically viable proposition but also an economically sound strategy for addressing transport accessibility deficits, with the potential to expand healthcare coverage in remote districts by 40–60%. The developed model offers a practical decision-support tool for administrators and is adaptable to other regions sharing similar geographical characteristics.