Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-28T05:57:35.331Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparison of Unmanned Aerial Vehicle Technology Versus Standard Practice in Identification of Hazards at a Mass Casualty Incident Scenario by Primary Care Paramedic Students

Published online by Cambridge University Press:  31 January 2018

Trevor Jain Open the ORCID record for Trevor Jain [Opens in a new window] ,
Aaron Sibley ,
Henrik Stryhn  and
Ives Hubloue
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
Ives Hubloue
Affiliation:
Department of Emergency Medicine, Universitair Ziekenhuis Brussel, Research Group in Emergency and Disaster Medicine, Vrije Universiteit Brussel, Brussel, Belgium
*
Correspondence and reprint requests to Trevor Jain, Division of Paramedicine, University of Prince Edward Island, Duffy Science Centre #430, 550 University Ave, Charlottetown, PE, Canada C1A 4P3 (e-mail: [email protected]).
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduction

The proliferation of unmanned aerial vehicles (UAV) has the potential to change the situational awareness of incident commanders allowing greater scene safety. The aim of this study was to compare UAV technology to standard practice (SP) in hazard identification during a simulated multi-vehicle motor collision (MVC) in terms of time to identification, accuracy and the order of hazard identification.

Methods

A prospective observational cohort study was conducted with 21 students randomized into UAV or SP group, based on a MVC with 7 hazards. The UAV group remained at the UAV ground station while the SP group approached the scene. After identifying hazards the time and order was recorded.

Results

The mean time (SD, range) to identify the hazards were 3 minutes 41 seconds (1 minute 37 seconds, 1 minute 48 seconds-6 minutes 51 seconds) and 2 minutes 43 seconds (55 seconds, 1 minute 43 seconds-4 minutes 38 seconds) in UAV and SP groups corresponding to a mean difference of 58 seconds (P=0.11). A non-parametric permutation test showed a significant (P=0.04) difference in identification order.

Conclusion

Both groups had 100% accuracy in hazard identification with no statistical difference in time for hazard identification. A difference was found in the identification order of hazards. (Disaster Med Public Health Preparedness. 2018;12:631–634)

Keywords

UAVmass casualty incidentssituational awarenessEMSparamedics
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 12 , Issue 5 , October 2018 , pp. 631 - 634
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Abrahamsen, HB. A remotely piloted aircraft system in major incident management: concept and pilot, feasibility study. BMC Emerg Med. 2015;15(1):12.Google Scholar
2. Lichtman, A, Nair, M. Humanitarian uses of drones and satellite imagery analysis: the promises and perils. AMA J Ethics. 2015;17(10):931.Google Scholar
3. 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/. Published 2015. Accessed February 24, 2017.Google Scholar
4. Mardell, J, Witkowski, M, Spence, R. A comparison of image inspection modes for a visual search and rescue task. Behav Inform Technol. 2014;33(9):905-918.Google Scholar
5. Castle Rock Fire and Rescue Department, South Metro Firerescue authority plan to sue UAV’s this fall. www.emsteam.org/drones. Accessed February 24, 2017.Google Scholar
6. The Cobber, William Carey University. Medical college develops fully equipped telemedical drone. J Emerg Med Serv.Google Scholar
7. 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):e0155176.Google Scholar
8. 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):15717-15737.Google Scholar
9. Thiels, CA, Aho, JM, Zietlow, SP, Jenkins, DH. Use of unmanned aerial vehicles for medical product transport. Air Med J. 2015;34(2):104-108.Google Scholar
10. Fornace, KM, Drakeley, CJ, William, T, Espino, F, Cox, J. Mapping infectious disease landscapes: unmanned aerial vehicles and epidemiology. Trends Parasitol. 2014;30(11):514-519.Google Scholar
11. Pulver, A, Wei, R, Mann, C. Locating AED enabled medical drones to enhance cardiac arrest response times. Prehosp Emerg Care. 2016;20(3):378-389.Google Scholar
12. Kurvinen, K, Smolander, P, Pöllänen, R, Kuukankorpi, S, Kettunen, M, Lyytinen, J. Design of a radiation surveillance unit for an unmanned aerial vehicle. J Environ Radioact. 2005;81(1):1-10.Google Scholar
13. Wesson, K, Humphreys, T. Hacking drones. Sci Am. 2013;309(5):54-59.Google Scholar
14. How drones can provide eyes in the sky for EMS; 2013. www.ems1.com. Accessed February 24, 2017.Google Scholar
15. Rodriguez, PA, Geckle, WJ, Barton, JD, et al. An emergency response UAV surveillance system. In: AMIA Annual Symposium Proceedings 2006, Vol. 2006. 2006: American Medical Informatics Association:1078.Google Scholar
16. Cook, Z, Zhao, L, Lee, J, Yim, W. Unmanned aerial system for first responders. In: Ubiquitous Robots and Ambient Intelligence (URAI), 2015 12th International Conference, 2005: IEEE:306-310.Google Scholar
17. Claesson, A, Fredman, D, Svensson, L, et al. Unmanned aerial vehicles (drones) in out-of-hospital-cardiac-arrest. Scand J Trauma, Resuscitation Emerg Med. 2016;24(1):124.Google Scholar