Drones are becoming popular as a complementary system for \ac{ems}. Although several pilot studies and flight trials have shown the feasibility of drone-assisted \ac{aed} delivery, running a full-scale operational network remains challenging due to high capital expenditure and environmental uncertainties. In this paper, we formulate a reliability-informed Bayesian learning framework for designing drone-assisted \ac{aed} delivery networks under environmental and operational uncertainty. We propose our objective function based on the survival probability of \ac{ohca} patients to identify the ideal locations of drone stations. Moreover, we consider the coverage of existing \ac{ems} infrastructure to improve the response reliability in remote areas. We illustrate our proposed method using geographically referenced cardiac arrest data from Scotland. The result shows how environmental variability and spatial demand patterns influence optimal drone station placement across urban and rural regions. In addition, we assess the robustness of the network and evaluate its economic viability using a cost-effectiveness analysis based on expected \ac{qaly}. The findings suggest that drone-assisted \ac{aed} delivery is expected to be cost-effective and has the potential to significantly improve the emergency response coverage in rural and urban areas with longer ambulance response times.

翻译：无人机正日益成为紧急医疗服务（EMS）的补充系统。尽管多项试点研究和飞行试验已证明无人机辅助自动体外除颤器（AED）运送的可行性，但由于高昂的资本支出和环境不确定性，运行大规模运营网络仍面临挑战。本文提出了一种基于可靠性的贝叶斯学习框架，用于在设计无人机辅助AED运送网络时考虑环境和运营不确定性。我们基于院外心脏骤停（OHCA）患者的存活概率构建目标函数，以确定无人机站点的理想位置。此外，我们考虑现有EMS基础设施的覆盖范围，以提高偏远地区的响应可靠性。我们利用苏格兰的地理参考心脏骤停数据对所提方法进行了验证。结果表明，环境变异性与空间需求模式如何影响城市和农村地区的最佳无人机站点布局。此外，我们评估了网络的鲁棒性，并基于预期质量调整生命年（QALY）的成本效益分析评价其经济可行性。研究结果表明，无人机辅助AED运送预计具有成本效益，并有望显著改善救护车响应时间较长的农村和城市地区的紧急救援覆盖范围。