Optimizing a Drone Network to Deliver Automated External Defibrillators

• Published in: Circulation (New York, N.Y.) N.Y.), 2017-06, Vol.135 (25), p.2454-2465
• Main Authors: Boutilier, Justin J, Brooks, Steven C, Janmohamed, Alyf, Byers, Adam, Buick, Jason E, Zhan, Cathy, Schoellig, Angela P, Cheskes, Sheldon, Morrison, Laurie J, Chan, Timothy C Y
• Format: Article
• Language: English
• Subjects: Aged; Cardiopulmonary Resuscitation - methods; Cardiopulmonary Resuscitation - standards; Cardiopulmonary Resuscitation - trends; Defibrillators - standards; Defibrillators - trends; Emergency Medical Services - methods; Emergency Medical Services - standards; Emergency Medical Services - trends; Female; Humans; Male; Middle Aged; Models, Theoretical; Ontario - epidemiology; Out-of-Hospital Cardiac Arrest - epidemiology; Out-of-Hospital Cardiac Arrest - therapy; Time-to-Treatment - standards; Time-to-Treatment - trends
• ISSN: 0009-7322; 1524-4539
• DOI: 10.1161/CIRCULATIONAHA.116.026318

BACKGROUND:Public access defibrillation programs can improve survival after out-of-hospital cardiac arrest, but automated external defibrillators (AEDs) are rarely available for bystander use at the scene. Drones are an emerging technology that can deliver an AED to the scene of an out-of-hospital cardiac arrest for bystander use. We hypothesize that a drone network designed with the aid of a mathematical model combining both optimization and queuing can reduce the time to AED arrival. METHODS:We applied our model to 53 702 out-of-hospital cardiac arrests that occurred in the 8 regions of the Toronto Regional RescuNET between January 1, 2006, and December 31, 2014. Our 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 each region independently. A secondary analysis quantified the reduction in drone resources required if RescuNET was treated as a large coordinated region. RESULTS: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. CONCLUSIONS:An optimized drone network designed with the aid of a novel mathematical model can substantially reduce the AED delivery time to an out-of-hospital cardiac arrest event.