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Comparison of Unmanned Aerial Vehicle Technology versus Standard Practice of Scene Assessment by Paramedic Students of a Mass-Gathering Event

Published online by Cambridge University Press:  27 October 2021

Trevor Jain*
Affiliation:
Division of Paramedicine, University of Prince Edward Island, Charlottetown, PEI, Canada
Aaron Sibley
Affiliation:
Division of Paramedicine, University of Prince Edward Island, Charlottetown, PEI, Canada
Henrik Stryhn
Affiliation:
Department of Health Management, University of Prince Edward Island, Charlottetown, PEI, Canada
Adam Lund
Affiliation:
Department of Emergency Medicine, University of British Columbia, Vancouver, British Columbia, Canada
Ives Hubloue
Affiliation:
Chair, Department of Emergency Medicine, Universitair Ziekenhuis Brussel; Research Group in Emergency and Disaster Medicine, Vrije Universiteit Brussel, Jette, Belgium
*
Correspondence: Trevor Jain, MSM, CD, MD, MSc University Of Prince Edward Island Division of Paramedicine Duffy Science Centre #430 550 University Ave Charlottetown, PEIC1A 4P3Canada E-mail: Tjain@upei.ca

Abstract

Introduction:

The proliferation of unmanned aerial vehicle (UAV) technology has the potential to change the situational awareness of medical incident commanders’ (ICs’) scene assessment of mass gatherings. Mass gatherings occur frequently and the potential for injury at these events is considered higher than the general population. These events have generated mass-casualty incidents (MCIs) in the past. The aim of this study was to compare UAV technology to standard practice (SP) in scene assessment using paramedic students during a mass-gathering event (MGE).

Methods:

This study was conducted in two phases. Phase One consisted of validation of the videos and accompanying data collection tool. Phase One was completed by 11 experienced paramedics from a provincial Emergency Medical Services (EMS) service. Phase Two was a randomized comparison with 47 paramedic students from the Holland College Paramedicine Program (Charlottetown, Prince Edward Island, Canada) of the two scene assessment systems. For Phase Two, the paramedic students were randomized into a UAV or a SP group. The data collection tool consisted of two board categories: primary importance with 20 variables and secondary importance with 25 variables. After a brief narrative, participants were either shown UAV footage or the ground footage depending on their study group. After completion of the videos, study participants completed the data collection tool.

Results:

The Phase One validation showed good consensus in answers to most questions (average 79%; range 55%-100%). For Phase Two, a Fisher’s exact test was used to compare each variable from the UAV and SP groups using a P value of .05. Phase Two demonstrated a significant difference between the SP and UAV groups in four of 20 primary variables. Additionally, significant differences were found for seven out of 25 secondary variables.

Conclusion:

This study demonstrated the accurate, safe, and feasible use of a UAV as a tool for scene assessment by paramedic students at an MGE. No observed statistical difference was noted in a majority of both primary and secondary variables using a UAV for scene assessment versus SP.

Type
Research Report
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the World Association for Disaster and Emergency Medicine

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References

Cook, Z, Zhao, L, Lee, J, Yim, W. Unmanned aerial system for first responders. In Ubiquitous Robots and Ambient Intelligence (URAI) 12th International Conference. October 28, 2015; pp. 306310.CrossRefGoogle Scholar
World Health Organization. Managing health risks during mass gatherings. https://www.who.int/activities/managing-health-risks-during-mass-gatherings. Accessed June 1, 2020.Google Scholar
Serwylo, P, Arbon, P, Rumantir, G. Predicting patient presentation rates at mass gatherings using machine learning. ICIS 2011.Google Scholar
Revello, A, Marzio, A, Pugliese, FR. MGE-RS: a risk assessment scoring matrix for metropolitan mass gathering events. Med Emergency. 2012;12.Google Scholar
Jain, T, Sibley, A, Stryhn, H, Hubloue, I. Comparison of unmanned aerial vehicle technology versus standard practice in identification of hazards at a mass-casualty incident scenario by primary care paramedic students. Disaster Med Public Health Prep. 2018;12(5):631634.CrossRefGoogle Scholar
Jain, T, Sibley, A, Stryhn, H, Hubloue, I. Comparison of unmanned aerial vehicle technology-assisted triage versus standard practice in triaging casualties by paramedic students in a mass-casualty incident scenario. Prehosp Disaster Med. 2018;33(4):375380.CrossRefGoogle Scholar
Hutton, A, Zeitz, K, Brown, S, Arbon, P. Assessing the psychosocial elements of crowds at mass gatherings. Prehosp Disaster Med. 2011;26(6):414421.CrossRefGoogle ScholarPubMed
Leduc, TJ. Drones for EMS: 5 Ways to Use a UAV Today. www.EMS1.com/technology/articles/40860048-Drones-for-EMS-5-ways-to-use-a-uav-today/. Accessed February 24, 2017.Google Scholar
The Cobber, William Carey University. Medical college develops fully equipped telemedical drone. https://www.wmcarey.edu/news/2015-10-15/Carey-Medical-College-Develops-Fully-Equipped-Telemedical-Drone. Accessed June 1, 2020.Google Scholar
Wen, T, Zhang, Z, Wong, KK. Multi-objective algorithm for blood supply via unmanned aerial vehicles to the wounded in an emergency situation. PloS One. 2016;11(5).CrossRefGoogle Scholar
Sibley, AK, Jain, TN, Butler, M, et al. Remote scene size-up using an unmanned aerial vehicle in a simulated mass casualty incident. Prehosp Emerg Care. 2019;23(3):332339.CrossRefGoogle Scholar
Boccardo, P, Chiabrando, F, Dutto, F, Tonolo, FG, Lingua, A. UAV deployment exercise for mapping purposes: evaluation of emergency response applications. Sensors. 2015;15(7):15717–1537.CrossRefGoogle ScholarPubMed
Thiels, CA, Aho, JM, Zietlow, SP, Jenkins, DH. Use of unmanned aerial vehicles for medical product transport. Air Med J. 2015;34(2):104108.CrossRefGoogle ScholarPubMed
Fornace, KM, Drakeley, CJ, William, T, Espino, F, Cox, J. Mapping infectious disease landscapes: unmanned aerial vehicles and epidemiology. Trends in Parasitology. 2014;30(11):514519.CrossRefGoogle ScholarPubMed
Wesson, K, Humphreys, T. Hacking drones. Scientific American. 2013;309(5):5459.CrossRefGoogle ScholarPubMed
Mardell, J, Witkowski, M, Spence, R. A comparison of image inspection modes for a visual search and rescue task. Behav Information Tech. 2014;33(9):905918.CrossRefGoogle Scholar
Castle Rock Fire and Rescue Department. South Metro Fire rescue authority plan to use UAVs this fall. www.emsteam.org/drones. Accessed February 24, 2017.Google Scholar
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