Optimizing Drone-Delivered Automated External Defibrillators
Can a drone network designed with the aid of a mathematical model reduce the time to automated external defibrillator (AED) arrival?
The authors applied a two-level (optimization and queuing) model to 53,702 out-of-hospital cardiac arrests (OHCAs) within the Toronto Regional RescuNET emergency medical system. The primary analysis quantified the drone network size required to deliver an AED 1, 2, or 3 minutes faster than historical median 911 response times for several subregions. A secondary analysis quantified the reduction in drone resources required if RescuNET was treated as one large coordinated region.
The region-specific analysis determined that 81 bases and 100 drones would be required to deliver an AED ahead of median 911 response times by 3 minutes. In the most urban region, the 90th percentile of the AED arrival time was reduced by 6 minutes and 43 seconds relative to historical 911 response times in the region. In the most rural region, the 90th percentile was reduced by 10 minutes and 34 seconds. A single coordinated drone network across all regions required 39.5% fewer bases and 30.0% fewer drones to achieve similar AED delivery times.
An optimized drone network designed with the aid of a novel mathematical model can substantially reduce the AED delivery time to an OHCA event.
Given the necessity for immediate defibrillation in cases of cardiac arrest, any intervention shortening time to defibrillation can mean the difference between survival with preserved cognitive function and death. Delivery of rapid defibrillation is particularly challenging in nonpublic and rural environments. The present study is potentially transformative, if the mathematical models tested here can be reproduced in real-life circumstances. Implementation of an effective AED drone system would likely be quite complex, adjusted to local conditions, and it would require an extensive educational campaign. Future studies will have to examine the logistical feasibility and cost-effectiveness of such a drone network.
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