Locating AED Enabled Medical Drones to Enhance Cardiac Arrest Response Times

Abstract Background: Out-of-hospital cardiac arrest (OOHCA) is prevalent in the United States. Each year between 180,000 and 400,000 people die due to cardiac arrest. The automated external defibrillator (AED) has greatly enhanced survival rates for OOHCA. However, one of the important components of successful cardiac arrest treatment is emergency medical services (EMS) response time (i.e., the time from EMS “wheels rolling” until arrival at the OOHCA scene). Unmanned Aerial Vehicles (UAV) have regularly been used for remote sensing and aerial imagery collection, but there are new opportunities to use drones for medical emergencies. Objective: The purpose of this study is to develop a geographic approach to the placement of a network of medical drones, equipped with an automated external defibrillator, designed to minimize travel time to victims of out-of-hospital cardiac arrest. Our goal was to have one drone on scene within one minute for at least 90% of demand for AED shock therapy, while minimizing implementation costs. Methods: In our study, the current estimated travel times were evaluated in Salt Lake County using geographical information systems (GIS) and compared to the estimated travel times of a network of AED enabled medical drones. We employed a location model, the Maximum Coverage Location Problem (MCLP), to determine the best configuration of drones to increase service coverage within one minute. Results: We found that, using traditional vehicles, only 4.3% of the demand can be reached (travel time) within one minute utilizing current EMS agency locations, while 96.4% of demand can be reached within five minutes using current EMS vehicles and facility locations. Analyses show that using existing EMS stations to launch drones resulted in 80.1% of cardiac arrest demand being reached within one minute Allowing new sites to launch drones resulted in 90.3% of demand being reached within one minute. Finally, using existing EMS and new sites resulted in 90.3% of demand being reached while greatly reducing estimated overall costs. Conclusion: Although there are still many factors to consider, drone networks show potential to greatly reduce life-saving equipment travel times for victims of cardiac arrest.

[1]  R. Lozano,et al.  Quadrotor's trajectory tracking control using monocular vision navigation , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[2]  J van der Lei,et al.  The incidence of sudden cardiac death in the general population. , 2004, Journal of clinical epidemiology.

[3]  G B Hall,et al.  Assessment of ambulance response performance using a geographic information system. , 1999, Social science & medicine.

[4]  Horst A. Eiselt,et al.  Location analysis: A synthesis and survey , 2005, Eur. J. Oper. Res..

[5]  Samuel J. Ratick,et al.  Application and extension of the Moore and ReVelle Hierarchical Maximal Covering Model , 2009 .

[6]  George A Wells,et al.  Optimal defibrillation response intervals for maximum out-of-hospital cardiac arrest survival rates. , 2003, Annals of emergency medicine.

[7]  Sandeep Vijan,et al.  Public use of automated external defibrillators. , 2003, The New England journal of medicine.

[8]  Jon Nicholl,et al.  Role of ambulance response times in the survival of patients with out-of-hospital cardiac arrest , 2010, Emergency Medicine Journal.

[9]  P. J. Wang,et al.  Improving survival from sudden cardiac arrest: the role of the automated external defibrillator. , 2001, JAMA.

[10]  Richard L. Church,et al.  SYMPOSIUM ON LOCATION PROBLEMS: IN MEMORY OF LEON COOPER , 1984 .

[11]  M. Eisenberg,et al.  SENSITIVITY, ACCURACY, AND SAFETY OF AN AUTOMATIC EXTERNAL DEFIBRILLATOR Report of a Field Evaluation , 1984, The Lancet.

[12]  M. E. Mejail,et al.  A simple visual navigation system for an UAV , 2012, International Multi-Conference on Systems, Sygnals & Devices.

[13]  M. DeGarmo.  Issues Concerning Integration of Unmanned Aerial Vehicles in Civil Airspace November 2004 , 2004 .

[14]  Andreas Zell,et al.  Framework for Autonomous On-board Navigation with the AR.Drone , 2013, 2013 International Conference on Unmanned Aircraft Systems (ICUAS).

[15]  H. Wellens,et al.  Out-of-hospital cardiac arrest in the 1990's: a population-based study in the Maastricht area on incidence, characteristics and survival. , 1997, Journal of the American College of Cardiology.

[16]  Alan T. Murray.  Optimising the spatial location of urban fire stations , 2013 .

[17]  Bülent Çatay,et al.  A multi-period double coverage approach for locating the emergency medical service stations in Istanbul , 2011, J. Oper. Res. Soc..

[18]  Erhan Erkut,et al.  Ambulance location for maximum survival , 2008 .

[19]  Daoqin Tong,et al.  Coverage optimization in continuous space facility siting , 2007, Int. J. Geogr. Inf. Sci..

[20]  Anja Vogler.  Business Site Selection Location Analysis And Gis , 2016 .

[21]  S. Galea,et al.  Explaining racial disparities in incidence of and survival from out-of-hospital cardiac arrest. , 2007, American journal of epidemiology.

[22]  C. Albert,et al.  Epidemiology and genetics of sudden cardiac death. , 2012, Circulation.

[23]  Donald H Jenkins,et al.  Use of unmanned aerial vehicles for medical product transport. , 2015, Air medical journal.

[24]  Richard L. Church,et al.  The maximal covering location problem , 1974 .

[25]  R. MacDonald,et al.  Use of Geographic Information Systems to Determine New Helipad Locations and Improve Timely Response While Mitigating Risk of Helicopter Emergency Medical Services Operations , 2010, Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors.