医疗保健物流革命：无人机技术在远程和紧急护理血袋交付中的战略作用

IF 3.2 3区工程技术 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Journal of Transport & Health Pub Date : 2025-04-13 DOI:10.1016/j.jth.2025.102053

Tejinder Singh Lakhwani, Yerasani Sinjana, Anuj Pal Kapoor

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引用次数: 0

摘要

及时提供血液制品对紧急医疗护理至关重要，包括创伤反应、手术和孕产妇保健干预。然而，由于地理障碍、交通拥堵和基础设施限制，现有的医疗物流系统面临严重的延迟。目的研究基于人工智能驱动的无人机医疗物流框架，优化应急血液运输。该模型集成了无人机飞伴旅行商问题（FSTSPD），实现了动态条件下的实时路线优化。该框架通过涉及德里40家医院和4个血库的实际案例研究进行评估。方法采用混合方法将Zipline、Matternet和Wingcopter的实际操作数据与优化建模相结合。FSTSPD算法基于交通拥堵、空域约束和紧急需求模式动态重新计算路线。本案例研究应用基于gis的映射来模拟城市医疗保健物流，并根据公开的城市交通数据集模拟医院需求率和拥堵状况。结果整合人工智能驱动的路径优化使输送效率提高15%，高峰时段平均血液输送时间从90 min减少到20 min。应急响应成功率从80%提高到95%，显示出在重症监护情况下的有效性。该系统还将冷链符合性从92%提高到99%，最大限度地减少了血液浪费。此外，可持续发展效益包括能源效率提高42%，碳排放减少67%，支持环保医疗物流解决方案。尽管无人机为医疗物流带来了变革，但在空域整合、基础设施准备和监管审批方面仍存在挑战。本研究为应对这些挑战提供了可行的策略，为政策制定者、医疗保健提供者和物流开发人员提供了见解。这项研究通过展示可扩展性、效率和环境效益，确立了无人机作为优化全球医疗保健物流的可行解决方案。

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Revolutionizing healthcare logistics: The strategic role of drone technology in blood bag deliveries for remote and emergency care

Introduction

Timely delivery of blood products is essential for emergency medical care, including trauma response, surgeries, and maternal health interventions. However, existing healthcare logistics systems face critical delays due to geographic barriers, traffic congestion, and infrastructure limitations.

Objective

This study presents an AI-driven drone-based healthcare logistics framework to optimize emergency blood transportation. The proposed model integrates the Flying Sidekick Traveling Salesman Problem with Drones (FSTSPD) for real-time route optimization under dynamic conditions. The framework is evaluated through a real-world case study involving 40 Delhi hospitals and 4 blood depots.

Methods

A mixed-methods approach combines real-world operational data from Zipline, Matternet, and Wingcopter with optimization modeling. The FSTSPD algorithm dynamically recalculates routes based on traffic congestion, airspace constraints, and emergency demand patterns. The case study applies GIS-based mapping to model urban healthcare logistics, with hospital demand rates and congestion conditions simulated based on publicly available urban mobility datasets.

Results

The integration of AI-driven route optimization led to a 15 % improvement in delivery efficiency, reducing average blood transport time from 90 min to 20 min during peak hours. Emergency response success rates increased from 80 % to 95 %, demonstrating effectiveness in critical care scenarios. The system also enhanced cold-chain compliance from 92 % to 99 %, minimizing blood wastage. Additionally, sustainability benefits included a 42 % increase in energy efficiency and a 67 % reduction in carbon emissions, supporting environmentally friendly medical logistics solutions.

Conclusions

While drones offer a transformative shift in medical logistics, challenges remain in airspace integration, infrastructure readiness, and regulatory approvals. This study provides actionable strategies for addressing these challenges, offering insights for policymakers, healthcare providers, and logistics developers. This research establishes drones as a viable solution for optimizing healthcare logistics worldwide by demonstrating scalability, efficiency, and environmental benefits.

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