Jamal Chu1, K H Benjamin Leung1, Paul Snobelen2, Gordon Nevils2, Ian R Drennan3, Sheldon Cheskes4, Timothy C Y Chan5. 1. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada. 2. Peel Regional Paramedic Services, Brampton, ON, Canada. 3. Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Sunnybrook Centre for Prehospital Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada. 4. Sunnybrook Centre for Prehospital Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Division of Emergency Medicine, Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada. 5. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada. Electronic address: tcychan@mie.utoronto.ca.
Abstract
BACKGROUND: Drone-delivered defibrillators have the potential to significantly reduce response time for out-of-hospital cardiac arrest (OHCA). However, optimal policies for the dispatch of such drones are not yet known. We sought to develop dispatch rules for a network of defibrillator-carrying drones. METHODS: We identified all suspected OHCAs in Peel Region, Ontario, Canada from Jan. 2015 to Dec. 2019. We developed drone dispatch rules based on the difference between a predicted ambulance response time to a calculated drone response time for each OHCA. Ambulance response times were predicted using linear regression and neural network models, while drone response times were calculated using drone specifications from recent pilot studies and the literature. We evaluated the dispatch rules based on response time performance and dispatch decisions, comparing them to two baseline policies of never dispatching and always dispatching drones. RESULTS: A total of 3573 suspected OHCAs were included in the study with median and mean historical ambulance response times of 5.8 and 6.2 min. All machine learning-based dispatch rules significantly reduced the median response time to 3.9 min and mean response time to 4.1-4.2 min (all P < 0.001) and were non-inferior to universally dispatching drones (all P < 0.001) while reducing the number of drone flights by up to 30%. Dispatch rules with more drone flights achieved higher sensitivity but lower specificity and accuracy. CONCLUSION: Machine learning-based dispatch rules for drone-delivered defibrillators can achieve similar response time reductions as universal drone dispatch while substantially reducing the number of trips.
BACKGROUND: Drone-delivered defibrillators have the potential to significantly reduce response time for out-of-hospital cardiac arrest (OHCA). However, optimal policies for the dispatch of such drones are not yet known. We sought to develop dispatch rules for a network of defibrillator-carrying drones. METHODS: We identified all suspected OHCAs in Peel Region, Ontario, Canada from Jan. 2015 to Dec. 2019. We developed drone dispatch rules based on the difference between a predicted ambulance response time to a calculated drone response time for each OHCA. Ambulance response times were predicted using linear regression and neural network models, while drone response times were calculated using drone specifications from recent pilot studies and the literature. We evaluated the dispatch rules based on response time performance and dispatch decisions, comparing them to two baseline policies of never dispatching and always dispatching drones. RESULTS: A total of 3573 suspected OHCAs were included in the study with median and mean historical ambulance response times of 5.8 and 6.2 min. All machine learning-based dispatch rules significantly reduced the median response time to 3.9 min and mean response time to 4.1-4.2 min (all P < 0.001) and were non-inferior to universally dispatching drones (all P < 0.001) while reducing the number of drone flights by up to 30%. Dispatch rules with more drone flights achieved higher sensitivity but lower specificity and accuracy. CONCLUSION: Machine learning-based dispatch rules for drone-delivered defibrillators can achieve similar response time reductions as universal drone dispatch while substantially reducing the number of trips.
Authors: Joseph Chun Liang Lim; Nicole Loh; Hsin Hui Lam; Jin Wee Lee; Nan Liu; Jun Wei Yeo; Andrew Fu Wah Ho Journal: J Clin Med Date: 2022-09-28 Impact factor: 4.964