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Optimizing a Drone Network to Respond to Opioid Overdoses

INTRODUCTION: Effective out-of-hospital administration of naloxone in opioid overdoses is dependent on timely arrival of naloxone. Delays in emergency medical services (EMS) response time could potentially be overcome with drones to deliver naloxone efficiently to the scene for bystander use. Our ob...

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Autores principales: Cox, Daniel J., Ye, Jinny J., Zhang, Chixiang, Van Vleet, Lee, Nickenig Vissoci, João R., Buckland, Daniel M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Department of Emergency Medicine, University of California, Irvine School of Medicine 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10527828/
https://www.ncbi.nlm.nih.gov/pubmed/37788021
http://dx.doi.org/10.5811/westjem.59609
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author Cox, Daniel J.
Ye, Jinny J.
Zhang, Chixiang
Van Vleet, Lee
Nickenig Vissoci, João R.
Buckland, Daniel M.
author_facet Cox, Daniel J.
Ye, Jinny J.
Zhang, Chixiang
Van Vleet, Lee
Nickenig Vissoci, João R.
Buckland, Daniel M.
author_sort Cox, Daniel J.
collection PubMed
description INTRODUCTION: Effective out-of-hospital administration of naloxone in opioid overdoses is dependent on timely arrival of naloxone. Delays in emergency medical services (EMS) response time could potentially be overcome with drones to deliver naloxone efficiently to the scene for bystander use. Our objective was to evaluate a mathematical optimization simulation for geographical placement of drone bases in reducing response time to opioid overdose. METHODS: Using retrospective data from a single EMS system from January 2016–February 2019, we created a geospatial drone-network model based on current technological specifications and potential base locations. Genetic optimization was then used to maximize county coverage by drones and the number of overdoses covered per drone base. From this model, we identified base locations that minimize response time and the number of drone bases required. RESULTS: In a drone network model with 2,327 opioid overdoses, as the number of modeled drone bases increased the calculated response time decreased. In a geospatially optimized drone network with four drone bases, response time compared to ambulance arrival was reduced by 4 minutes 38 seconds and covered 64.2% of the county. CONCLUSION: In our analysis we found that in a mathematical model for geospatial optimization, implementing four drone bases could reduce response time of 9–1–1 calls for opioid overdoses. Therefore, drones could theoretically improve time to naloxone delivery.
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spelling pubmed-105278282023-09-28 Optimizing a Drone Network to Respond to Opioid Overdoses Cox, Daniel J. Ye, Jinny J. Zhang, Chixiang Van Vleet, Lee Nickenig Vissoci, João R. Buckland, Daniel M. West J Emerg Med Behavioral Health INTRODUCTION: Effective out-of-hospital administration of naloxone in opioid overdoses is dependent on timely arrival of naloxone. Delays in emergency medical services (EMS) response time could potentially be overcome with drones to deliver naloxone efficiently to the scene for bystander use. Our objective was to evaluate a mathematical optimization simulation for geographical placement of drone bases in reducing response time to opioid overdose. METHODS: Using retrospective data from a single EMS system from January 2016–February 2019, we created a geospatial drone-network model based on current technological specifications and potential base locations. Genetic optimization was then used to maximize county coverage by drones and the number of overdoses covered per drone base. From this model, we identified base locations that minimize response time and the number of drone bases required. RESULTS: In a drone network model with 2,327 opioid overdoses, as the number of modeled drone bases increased the calculated response time decreased. In a geospatially optimized drone network with four drone bases, response time compared to ambulance arrival was reduced by 4 minutes 38 seconds and covered 64.2% of the county. CONCLUSION: In our analysis we found that in a mathematical model for geospatial optimization, implementing four drone bases could reduce response time of 9–1–1 calls for opioid overdoses. Therefore, drones could theoretically improve time to naloxone delivery. Department of Emergency Medicine, University of California, Irvine School of Medicine 2023-09 2023-08-30 /pmc/articles/PMC10527828/ /pubmed/37788021 http://dx.doi.org/10.5811/westjem.59609 Text en © 2023 Cox et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) License. See: http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/)
spellingShingle Behavioral Health
Cox, Daniel J.
Ye, Jinny J.
Zhang, Chixiang
Van Vleet, Lee
Nickenig Vissoci, João R.
Buckland, Daniel M.
Optimizing a Drone Network to Respond to Opioid Overdoses
title Optimizing a Drone Network to Respond to Opioid Overdoses
title_full Optimizing a Drone Network to Respond to Opioid Overdoses
title_fullStr Optimizing a Drone Network to Respond to Opioid Overdoses
title_full_unstemmed Optimizing a Drone Network to Respond to Opioid Overdoses
title_short Optimizing a Drone Network to Respond to Opioid Overdoses
title_sort optimizing a drone network to respond to opioid overdoses
topic Behavioral Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10527828/
https://www.ncbi.nlm.nih.gov/pubmed/37788021
http://dx.doi.org/10.5811/westjem.59609
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