How to improve situation assessment and decision-making in a simulated mass casualty incident by using an unmanned aerial vehicle

AbstractIntroductionThe proliferation of unmanned aerial vehicle (UAV) technology has the potential to change the way medical incident commanders (ICs) respond to mass-casualty incidents (MCIs) in triaging victims. The aim of this study was to compare UAV technology to standard practice (SP) in triaging casualties at an MCI.MethodsA randomized comparison study was conducted with 40 paramedic students from the Holland College Paramedicine Program (Charlottetown, Prince Edward Island, Canada). Using a simulated motor vehicle collision (MVC) with moulaged casualties, iterations of 20 students were used for both a day and a night trial. Students were randomized to a UAV or a SP group. After a brief narrative, participants either entered the study environment or used UAV technology where total time to triage completion, GREEN casualty evacuation, time on scene, triage order, and accuracy were recorded.ResultsA statistical difference in the time to completion of 3.63 minutes (95% CI, 2.45 min-4.85 min; P=.002) during the day iteration and a difference of 3.49 minutes (95% CI, 2.08 min-6.06 min; P=.002) for the night trial with UAV groups was noted. There was no difference found in time to GREEN casualty evacuation, time on scene, or triage order. One-hundred-percent accuracy was noted between both groups.Conclusion:This study demonstrated the feasibility of using a UAV at an MCI. A non-clinical significant difference was noted in total time to completion between both groups. There was no increase in time on scene by using the UAV while demonstrating the feasibility of remotely triaging GREEN casualties prior to first responder arrival.Jain T, Sibley A, Stryhn H, Hubloue I.Comparison of unmanned aerial vehicle technologyassisted triage versus standard practice in triaging casualties by paramedic students in a mass-casualty incident scenario. Prehosp Disaster Med. 2018;33(4):375–380

Download Full-text

P074: Comparison of unmanned aerial vehicle technology versus standard practice in triaging casualties by paramedic students in a mass casualty incident scenario

CJEM ◽

10.1017/cem.2018.272 ◽

2018 ◽

Vol 20 (S1) ◽

pp. S83-S83 ◽

Cited By ~ 1

Author(s):

T. Jain ◽

A. Sibley ◽

H. Stryhn ◽

I. Hubloue

Keyword(s):

Unmanned Aerial Vehicle ◽

Motor Vehicle ◽

Mass Casualty Incident ◽

Mass Casualty ◽

Evacuation Time ◽

Standard Practice ◽

Vehicle Collision ◽

Significant Difference ◽

Aerial Vehicle ◽

Percent Accuracy

Introduction: The proliferation of unmanned aerial vehicle (UAV) technology has the potential to change the way medical incident commanders respond to mass casualty incidents (MCI) in triaging victims. The aim of this study was to compare UAV technology to standard practice (SP) in triaging casualties at a MCI Methods: A randomized comparison study was conducted with forty paramedic students from the Holland College Paramedicine Program. Using a simulated motor vehicle collision with moulaged casualties, iterations of twenty students were used for both a day and a night trial. Students were randomized to an UAV or a SP group. After a brief narrative participants either entered the study environment or used UAV technology where total time to triage completion, green casualty evacuation, time on scene, triage order and accuracy was recorded Results: A statistical difference in the time to completing of 3.63 minutes (95% CI: 2.45, 4.85, p=0.002) during the day iteration and a difference of 3.49 minutes (95% CI: 2.08,6.06, p=0.002) for the night trial with UAV groups was noted. There was no difference found in time to green casualty evacuation, time on scene or triage order. One hundred percent accuracy was noted between both groups. Conclusion: This study demonstrated the feasibility of using an UAV at a MCI. A non clinical significant difference was noted in total time to completion between both groups. There was no increase in time on scene by using the UAV while demonstrating the feasibility of remotely triaging green casualties prior to first responder arrival.

Download Full-text

Remote Scene Size-up Using an Unmanned Aerial Vehicle in a Simulated Mass Casualty Incident

Prehospital Emergency Care ◽

10.1080/10903127.2018.1511765 ◽

2018 ◽

Vol 23 (3) ◽

pp. 332-339 ◽

Cited By ~ 3

Author(s):

Aaron K. Sibley ◽

Trevor N. Jain ◽

Michael Butler ◽

Brent Nicholson ◽

David Sibley ◽

...

Keyword(s):

Unmanned Aerial Vehicle ◽

Mass Casualty Incident ◽

Mass Casualty ◽

Aerial Vehicle

Download Full-text

Use of an unmanned aerial vehicle to support situation assessment and decision-making in search and rescue operations in the mountains

Scandinavian Journal of Trauma Resuscitation and Emergency Medicine ◽

10.1186/1757-7241-22-s1-p16 ◽

2014 ◽

Vol 22 (S1) ◽

Author(s):

Håkon B Abrahamsen

Keyword(s):

Decision Making ◽

Unmanned Aerial Vehicle ◽

Search And Rescue ◽

Situation Assessment ◽

Aerial Vehicle

Download Full-text

Efektivitas Penggunaan Unmanned Aerial Vehicle (UAV) dalam Strategi Kontra-Terorisme Amerika Serikat di Pakistan (2004-2012)

Global Jurnal Politik Internasional ◽

10.7454/global.v16i2.12 ◽

2015 ◽

Vol 16 (2) ◽

Author(s):

Egalita Irfan

Keyword(s):

United States ◽

Decision Making ◽

Unmanned Aerial Vehicle ◽

Terrorist Attack ◽

The United States ◽

Terrorist Organizations ◽

Counter Terrorism ◽

Advanced Technologies ◽

The World ◽

Aerial Vehicle

Unmanned Aerial Vehicle (UAV) is an armed unmanned plane, which is also one of the most advanced technologies developed by the United States. UAV is more superior compared with other kinds of weapon. Currently, it is used in many parts of the world as a part of the United States' counter-terrorism measure. However, the use of UAV in Pakistan since 2004 to 2012 does not successfully reduce the number of terrorist attack that happens on that country. This research aims to figure out the reasons behind this failure through the use of congruence in retrospective. The results show that the failure of UAV relies upon 3 factors: (1) US did not really understand the characteristic of targeted terrorist organizations, (2) there is a mistake in the decision making based on the intelligence cycle, and (3) the nonexistent of local society's support.

Download Full-text

Energy‐aware decision‐making for dynamic task migration in MEC ‐based unmanned aerial vehicle delivery system

Concurrency and Computation Practice and Experience ◽

10.1002/cpe.6092 ◽

2020 ◽

Author(s):

Rui Li ◽

Xuejun Li ◽

Jia Xu ◽

Frank Jiang ◽

Zhaohong Jia ◽

...

Keyword(s):

Decision Making ◽

Unmanned Aerial Vehicle ◽

Delivery System ◽

Task Migration ◽

Energy Aware ◽

Dynamic Task ◽

Aerial Vehicle ◽

Vehicle Delivery

Download Full-text

Decision-making for unmanned aerial vehicle operation in icing conditions

CEAS Aeronautical Journal ◽

10.1007/s13272-016-0215-2 ◽

2016 ◽

Vol 7 (4) ◽

pp. 663-675 ◽

Cited By ~ 9

Author(s):

S. F. Armanini ◽

M. Polak ◽

J. E. Gautrey ◽

A. Lucas ◽

J. F. Whidborne

Keyword(s):

Decision Making ◽

Unmanned Aerial Vehicle ◽

Vehicle Operation ◽

Aerial Vehicle

Download Full-text

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 Medicine and Public Health Preparedness ◽

10.1017/dmp.2017.129 ◽

2018 ◽

Vol 12 (5) ◽

pp. 631-634 ◽

Cited By ~ 7

Author(s):

Trevor Jain ◽

Aaron Sibley ◽

Henrik Stryhn ◽

Ives Hubloue

Keyword(s):

Situational Awareness ◽

Permutation Test ◽

Mass Casualty Incident ◽

Mass Casualty ◽

Hazard Identification ◽

Identification Accuracy ◽

Standard Practice ◽

Ground Station ◽

Aerial Vehicle ◽

Observational Cohort

AbstractIntroductionThe 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.MethodsA 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.ResultsThe 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.ConclusionBoth 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)

Download Full-text