SBIR Phase I: Drone-Delivered AEDs for Treatment of Out-of-Hospital Cardiac Arrest

Quantworks, Inc., Chapel Hill NC

Investigators

Abstract

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to reduce emergency medical services (EMS) response times and improve patient outcomes for out-of-hospital cardiac arrests (OHCA) through the creation of a 911-integrated drone service. Sudden cardiac arrest is responsible for more than 450,000 deaths annually and is the third leading cause of death in the United States. Of these deaths, ~350,000 occur outside of the hospital. Survival for OHCA in the U.S. is around 8%-10%, and despite considerable effort this statistic has changed little over the past three decades. The solution to improving OHCA outcomes is early and effective bystander cardiopulmonary resuscitation (e.g., chest compressions) and prompt defibrillation using an automated external defibrillator (AED). In locations with publicly-available AEDs, survival has increased 40%-70%. However, ~70% of OHCAs events occur in private residence, and public-access AEDs are not available for these patients. Drones can deliver AEDs to the scene of a cardiac arrest more quickly than a ground ambulance team, enabling early defibrillation by bystanders with the potential for substantive improvements in survival rates. The impact on EMS response times will likely be greatest for patients currently not well served by EMS systems such as individuals living in poor and minority neighborhoods. EMS drones can also address other health problems such as delivering naloxone for opioid overdoses and blood products for the pre-clinical treatment of hemorrhagic shock. This SBIR Phase I project addresses critical barriers to creating a 911-integrated drone service. First, it develops algorithms to optimize the spatial distribution of drones given performance parameters. Second, it integrates the emergency care drone response directly into the 911 dispatch process. The system uses state-of-the-art prediction and data integration solutions to provide real-time launch decision support and asset tracking capabilities to the 911 dispatcher. The result is a drone response system that is accurate, fast, and scalable. The project focuses on delivering automated external defibrillators to victims of out-of-hospital cardiac arrests. The decision support system and underlying algorithms developed in this work can be applied to other use cases integrating drone services into the 911 response. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.