Comparison of Unmanned Aerial Vehicle Technology-Assisted Triage versus Standard Practice in Triaging Casualties by Paramedic Students in a Mass-Casualty Incident Scenario

2018 ◽  
Vol 33 (4) ◽  
pp. 375-380 ◽  
Author(s):  
Trevor Jain ◽  
Aaron Sibley ◽  
Henrik Stryhn ◽  
Ives Hubloue
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Prince Edward Island ◽  
Motor Vehicle ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Evacuation Time ◽  
Standard Practice ◽  
Time To Completion ◽  
Significant Difference ◽  
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

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

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S83-S83 ◽  
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.

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

2018 ◽  
Vol 12 (5) ◽  
pp. 631-634 ◽  
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)

First Responder Accuracy Using SALT during Mass-casualty Incident Simulation

2016 ◽  
Vol 31 (2) ◽  
pp. 150-154 ◽  
Author(s):  
Christopher W.C. Lee ◽  
Shelley L. McLeod ◽  
Kristine Van Aarsen ◽  
Michelle Klingel ◽  
Jeffrey M. Franc ◽  
...  
Keyword(s):  
Primary Care ◽  
Motor Vehicle ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Medical Attention ◽  
First Responder ◽  
Publication Type ◽  
Fire Science ◽  
Significant Difference ◽  
Second Year

AbstractIntroductionDuring mass-casualty incidents (MCIs), patient volume often overwhelms available Emergency Medical Services (EMS) personnel. First responders are expected to triage, treat, and transport patients in a timely fashion. If other responders could triage accurately, prehospital EMS resources could be focused more directly on patients that require immediate medical attention and transport.HypothesisTriage accuracy, error patterns, and time to triage completion are similar between second-year primary care paramedic (PCP) and fire science (FS) students participating in a simulated MCI using the Sort, Assess, Life-saving interventions, Treatment/Transport (SALT) triage algorithm.MethodsAll students in the second-year PCP program and FS program at two separate community colleges were invited to participate in this study. Immediately following a 30-minute didactic session on SALT, participants were given a standardized briefing and asked to triage an eight-victim, mock MCI using SALT. The scenario consisted of a four-car motor vehicle collision with each victim portrayed by volunteer actors given appropriate moulage and symptom coaching for their pattern of injury. The total number and acuity of victims were unknown to participants prior to arrival to the mock scenario.ResultsThirty-eight PCP and 29 FS students completed the simulation. Overall triage accuracy was 79.9% for PCP and 72.0% for FS (∆ 7.9%; 95% CI, 1.2-14.7) students. No significant difference was found between the groups regarding types of triage errors. Over-triage, under-triage, and critical errors occurred in 10.2%, 7.6%, and 2.3% of PCP triage assignments, respectively. Fire science students had a similar pattern with 15.2% over-triaged, 8.7% under-triaged, and 4.3% critical errors. The median [IQR] time to triage completion for PCPs and FSs were 142.1 [52.6] seconds and 159.0 [40.5] seconds, respectively (P=.19; Mann-Whitney Test).ConclusionsPrimary care paramedics performed MCI triage more accurately than FS students after brief SALT training, but no difference was found regarding types of error or time to triage completion. The clinical importance of this difference in triage accuracy likely is minimal, suggesting that fire services personnel could be considered for MCI triage depending on the availability of prehospital medical resources and appropriate training.LeeCWC, McLeodSL, Van AarsenK, KlingelM, FrancJM, PeddleMB. First responder accuracy using SALT during mass-casualty incident simulation. Prehosp Disaster Med. 2016;31(2):150–154.

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

2018 ◽  
Vol 23 (3) ◽  
pp. 332-339 ◽  
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

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

2021 ◽  
pp. 1-6
Author(s):  
Trevor Jain ◽  
Aaron Sibley ◽  
Henrik Stryhn ◽  
Adam Lund ◽  
Ives Hubloue
Keyword(s):  
Data Collection ◽  
Unmanned Aerial Vehicle ◽  
P Value ◽  
Mass Gathering ◽  
Data Collection Tool ◽  
Secondary Importance ◽  
Standard Practice ◽  
Mass Gatherings ◽  
Significant Difference ◽  
Aerial Vehicle

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.

scholarly journals Regional Variation in Critical Care Evacuation Needs for Children After a Mass Casualty Incident

2012 ◽  
Vol 6 (2) ◽  
pp. 146-149 ◽  
Author(s):  
Robert K. Kanter
Keyword(s):  
Public Health ◽  
New York ◽  
Critical Care ◽  
Metropolitan Area ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Evacuation Time ◽  
Pediatric Critical Care ◽  
Ground Ambulance ◽  
Ground Ambulances

ABSTRACTObjectives: To determine the ability of five New York statewide regions to accommodate 30 children needing critical care after a hypothetical mass casualty incident (MCI) and the duration to complete an evacuation to facilities in other regions if the surge exceeded local capacity.Methods: A quantitative model evaluated pediatric intensive care unit (PICU) vacancies for MCI patients, based on data on existing resources, historical average occupancy, and evidence on early discharges and transfers in a public health emergency. Evacuation of patients exceeding local capacity to the nearest PICU center with vacancies was modeled in discrete event chronological simulations for three scenarios in each region: pediatric critical care transport teams were considered to originate from other PICU hospitals statewide, using (1) ground ambulances or (2) helicopters, and (3) noncritical care teams were considered to originate from the local MCI region using ground ambulances. Chronology of key events was modeled.Results: Across five regions, the number of children needing evacuation would vary from 0 to 23. The New York City (NYC) metropolitan area could accommodate all patients. The region closest to NYC could evacuate all excess patients to PICU hospitals in NYC within 12 hours using statewide critical care teams traveling by ground ambulance. Helicopters and local noncritical care teams would not shorten the evacuation. For other statewide regions, evacuation of excess patients by statewide critical care teams traveling by ground ambulance would require up to nearly 26 hours. Helicopter transport would reduce evacuation time by 40%-44%, while local noncritical care teams traveling by ground would reduce evacuation time by 16%-34%.Conclusions: The present study provides a quantitative, evidence-based approach to estimate regional pediatric critical care evacuation needs after an MCI. Large metropolitan areas with many PICU beds would be better able to accommodate patients in a local MCI, and would serve as a crucial resource if an MCI occurred in a smaller community. Regions near a metropolitan area could be rapidly served by critical care transport teams traveling by ground ambulance. Regions distant from a metropolitan area might benefit from helicopter transport. Using local noncritical care transport teams would involve shorter delays and less expert care during evacuation.(Disaster Med Public Health Preparedness. 2012;6:146–149)

scholarly journals PEMANFAATAN INDEKS VEGETASI UNTUK ESTIMASI KANDUNGAN KALIUM PADA TANAMAN NANAS (Ananas comosus) MENGGUNAKAN UAV (Unmanned Aerial Vehicle) DI PT. GREAT GIANT PINEAPPLE, LAMPUNG

2020 ◽  
Vol 8 (1) ◽  
pp. 91-99
Author(s):  
Dita Khairunnisa ◽  
Mochtar Lutfi Rayes ◽  
Christanti Agustina
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Nutrient Status ◽  
Nutrient Content ◽  
Aerial Photographs ◽  
Ananas Comosus ◽  
P Value ◽  
Distribution Maps ◽  
Significant Difference ◽  
Aerial Vehicle

PT Great Giant Pineapple (PT. GGP) is the largest pineapple production company in Indonesia. One of the nutrients that pineapple plants really need is potassium (K). K plays a key role in carbohydrate metabolism and transport of photosynthates from source to sink. Remote sensing technology has been developed to estimate nutrient status, one of which is using an Unmanned Aerial Vehicle (UAV). This study aims to estimate the K nutrient content in pineapple plants using vegetation indexes in the form of NDVI (Normalyzed Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and OSAVI (Optimized of Soil Adjusted Vegetation Index). The research was carried out by taking aerial photographs and samples of pineapple plants in the 5 months phase before forcing up to 2 months after forcing (F-5 to F + 2), laboratory analysis, statistical analysis, and making distribution maps. The results showed that the relationship between the vegetation index value and K plant was the strongest and most significant is in 1 month before forcing phase (F-1) with the same r value for the three indices vegetation (r=0.867). The results of the regression analysis between the NDVI, SAVI and OSAVI values with K plant were 75.17%, 75.18% and 75.17%, respectively. The calculation of the K estimate using three methods yields no different values. The validation results using paired t test (t count -0.63; t table 2.31; p-value 0.544) where the K content in the measured plants and the estimation results showed no significant difference with the measurement results.

scholarly journals Augmented Reality for Guideline Presentation in Medicine: A Crossover Simulation Trial for Technically Assisted Decision-making (Preprint)

2019 ◽  
Author(s):  
Andreas Follmann ◽  
Alexander Ruhl ◽  
Michael Gösch ◽  
Marc Felzen ◽  
Rolf Rossaint ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Mass Casualty Incident ◽  
Modern Medicine ◽  
Mass Casualty ◽  
Tablet Pc ◽  
Additional Time ◽  
Smart Glasses ◽  
Significant Difference ◽  
The Difference ◽  
Time Critical

BACKGROUND Guidelines provide instructions for diagnostics and therapy in modern medicine. Various mobile devices are used to represent the potential complex decision trees. An example of time-critical decisions is triage in case of a mass casualty incident. OBJECTIVE In this randomized controlled cross-over study, the potential of augmented reality for guideline presentation was evaluated and compared with a tablet PC as a conventional device. METHODS A specific Android app was designed for use with Smart Glasses as well as with a tablet PC for presentation of a triage algorithm as an example for a complex guideline. 40 volunteers simulated a triage based on 30 fictional patient descriptions each, with technical support from data glasses and a tablet PC in cross-over trial design. The time to come to a decision and the accuracy were recorded and compared between the different devices. RESULTS A total of 2400 assessments were performed. A significantly faster triage time has been achieved with the tablet PC (12.8 sec) compared to the smart glasses (17.5 sec; P = .001) in total. Considering the difference in triage duration between both devices, the additional time needed with the smart glasses could be reduced significantly in the course (P = .001). In accuracy of guideline decisions, there was no significant difference comparing both devices. CONCLUSIONS The presentation of a guideline on a tablet computer, as well as in the form of augmented reality, achieved good results. The implementation using smart glasses took more time due to a more complex operating concept but could be accelerated in the course of the study after adaptation. Especially in a non-time-critical working area where hands-free interfaces are meaningful, a guideline presentation with augmented reality can already be implemented.

scholarly journals WOODLAND MAPPING AT SINGLE-TREE LEVELS USING OBJECT-ORIENTED CLASSIFICATION OF UNMANNED AERIAL VEHICLE (UAV) IMAGES

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 43-49 ◽  
Author(s):  
A. Chenari ◽  
Y. Erfanifard ◽  
M. Dehghani ◽  
H. R. Pourghasemi
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Object Oriented ◽  
Classification Method ◽  
Individual Species ◽  
Fars Province ◽  
Crown Area ◽  
Single Tree ◽  
Significant Difference ◽  
Aerial Vehicle ◽  
Uav Images

Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV) digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond) and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm) gathered by real-time kinematic (RTK) method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m<sup>2</sup>) and wild almonds (3.97±1.69 m<sup>2</sup>) with no significant difference with their observed values (α=0.05). In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92) and wild almonds (accuracy of 0.90 and precision of 0.89) were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands.

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