Hostname: page-component-76fb5796d-r6qrq Total loading time: 0 Render date: 2024-04-26T06:23:29.545Z Has data issue: false hasContentIssue false
  • English
  • Français

Effects of Virtual Reality Simulation on Worker Emergency Evacuation of Neonates

Published online by Cambridge University Press:  08 October 2018

Sharon Farra ,
Eric Hodgson ,
Elaine T. Miller ,
Nathan Timm ,
Whittney Brady ,
Matt Gneuhs ,
Jun Ying ,
Jackie Hausfeld ,
Emily Cosgrove  and
Ashley Simon
...Show all authors
Sharon Farra*
Affiliation:
Wright State University, Dayton, Ohio
Eric Hodgson
Affiliation:
Miami University, Miami, Florida
Elaine T. Miller
Affiliation:
University of Cincinnati, Cincinnati, Ohio
Nathan Timm
Affiliation:
Cincinnati Children’s Hospital and Medical Center, Cincinnati, Ohio
Whittney Brady
Affiliation:
Cincinnati Children’s Hospital and Medical Center, Cincinnati, Ohio
Matt Gneuhs
Affiliation:
Cincinnati Children’s Hospital and Medical Center, Cincinnati, Ohio
Jun Ying
Affiliation:
University of Cincinnati, Cincinnati, Ohio
Jackie Hausfeld
Affiliation:
Cincinnati Children’s Hospital and Medical Center, Cincinnati, Ohio
Emily Cosgrove
Affiliation:
Cincinnati Children’s Hospital and Medical Center, Cincinnati, Ohio
Ashley Simon
Affiliation:
Cincinnati Children’s Hospital and Medical Center, Cincinnati, Ohio
Michael Bottomley
Affiliation:
Wright State University, Dayton, Ohio
*
Correspondence and reprint requests to Sharon Farra, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH (e-mail: Sharon.farra@wright.edu).
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective

This study examined differences in learning outcomes among newborn intensive care unit (NICU) workers who underwent virtual reality simulation (VRS) emergency evacuation training versus those who received web-based clinical updates (CU). Learning outcomes included a) knowledge gained, b) confidence with evacuation, and c) performance in a live evacuation exercise.

Methods

A longitudinal, mixed-method, quasi-experimental design was implemented utilizing a sample of NICU workers randomly assigned to VRS training or CUs. Four VRS scenarios were created that augmented neonate evacuation training materials. Learning was measured using cognitive assessments, self-efficacy questionnaire (baseline, 0, 4, 8, 12 months), and performance in a live drill (baseline, 12 months). Data were collected following training and analyzed using mixed model analysis. Focus groups captured VRS participant experiences.

Results

The VRS and CU groups did not statistically differ based upon the scores on the Cognitive Assessment or perceived self-efficacy. The virtual reality group performance in the live exercise was statistically (P<.0001) and clinically (effect size of 1.71) better than that of the CU group.

Conclusions

Training using VRS is effective in promoting positive performance outcomes and should be included as a method for disaster training. VRS can allow an organization to train, test, and identify gaps in current emergency operation plans. In the unique case of disasters, which are low-volume and high-risk events, the participant can have access to an environment without endangering themselves or clients. (Disaster Med Public Health Preparedness. 2019;13:301–308)

Keywords

virtual realityevacuationdisaster
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 13 , Issue 2 , April 2019 , pp. 301 - 308
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

REFERENCES

1. Federal Emergency Management Agency. Disaster Declarations by Year. http://www.fema.gov/disasters/grid/year. Published 2017. Accessed December 1, 2017.Google Scholar
2. Blau, M. Irma forces at least 35 hospitals to evacuate. STAT News. https://www.statnews.com/2017/09/09/irma-hospital-evacuations-rundown/. Published September 9, 2017. Accessed December 1, 2017.Google Scholar
3. Seiger, T. Critically ill babies evacuated from Texas coast ahead of Harvey. Cox Media Group, National Content Group. http://www.ajc.com/weather/hurricanes/critically-ill-babies-evacuated-from-texas-coast-ahead-harvey/sdqR2GR920QngkRCmH8MmL/. Accessed December 1, 2017.Google Scholar
4. Espiritu, M, Patil, U, Cruz, H, et al. Evacuation of a neonatal intensive care unit in a disaster: lessons from Hurricane Sandy. Pediatrics. 2014;134(6):e1663-e1669.Google Scholar
5. The Joint Commission. Emergency management standards: supporting collaboration and planning. www.jointcommission.org/assets/1/6/EM_Stds_Collaboration_2016.pdf. Published 2016. Accessed December 1, 2017.Google Scholar
6. Department of Homeland Security. Homeland security grant program supplemental resource: children in disasters guidance. 2012 https://www.fema.gov/pdf/government/grant/2012/fy12_hsgp_children.pdf. Accessed August 3, 2018.Google Scholar
7. Hsu, EB, Li, Y, Bayram, JD, et al. State of virtual reality based disaster preparedness and response training. PLoS Curr. 2013 Apr 24:5.Google Scholar
8. Farra, SL, Miller, E. Virtual reality and disaster training: an integrative review of the literature. J Nurs Educ Pract. 2013;3(3):655-671.Google Scholar
9. Bode, NWF, Wagoum, KAU, Codling, EA. Information use by humans during dynamic route choice in virtual crowd evacuations. R Soc Open Sci. 2015;2(1):140410. doi: 10.1098/rsos.14041 Google Scholar
10. Andrée, K, Nilsson, D, Eriksson, J. Evacuation experiments in a virtual reality high-rise building: exit choice and waiting time for evacuation elevators. Fire Mater. 2016;40(4):554-567.Google Scholar
11. Ribeiro, J, Almeida, J, Rossetti, R. Using serious games to train evacuation behaviour. Paper presented at: 7th Iberian Conference on Information Systems and Technologies; June 20-21, 2012; Madrid, Spain.Google Scholar
12. Garrett, M, McMahon, M. Indirect measures of learning transfer between real and virtual environments. Australas J Educ Technol. 2013;29(6):806-822.Google Scholar
13. Wisniewski, R, Dennik-Champion, G, Peltier, JW. Emergency preparedness competencies: assessing nurses’ educational needs. J Nurs Adm. 2004;34:475-480.Google Scholar
14. Garbutt, SJ, Peltier, JW, Fitspatrick, JI. Evaluation of an instrument to measure nurses’ familiarity with emergency preparedness. Mil Med. 2008;173(11):1073-1077.Google Scholar
15. Farra, S, Miller, ET, Gneuhs, M, et al. Evacuation performance evaluation tool. Am J Disaster Med. 2016;11(2):131-136. doi: 10.5055/ajdm.2016.0232 Google Scholar
16. Bloom, BS, Krathwohl, DR. Taxonomy of Educational Objectives: The Classification of Educational Goals, by a Committee of College and University Examiners. Handbook 1: Cognitive Domain. New York: Longmans; 1956.Google Scholar
17. Haladyna, TM, Downing, SM, Rodriguez, MC. A review of multiple-choice item-writing guidelines for classroom assessment. Appl Meas Educ. 2002;15(3):309-334.Google Scholar
18. Shavelson, RJ, Webb, N. Generalizability Theory: A primer. Los Angeles: Sage Publications; 1991.Google Scholar
19. Leite, WL, Svinicki, M, Shi, Y. Attempted validation of the scores of the VARK: learning styles inventory with multitrait-multimethod confirmatory factor analysis models. Educ Psychol Meas. 2010;70:323-339.Google Scholar
20. Fitkov-Norris, ED, Yeghiazarian, A. Validation of VARK learning modalities questionnaire using Rasch analysis. J Phys Conf Ser. 2015;588:012048.Google Scholar
21. Petty, J. Interactive, technology-enhanced self-regulated learning tools in healthcare education: a literature review. Nurse Educ Today. 2013;33:53-59.Google Scholar
22. Farra, S, Miller, ET, Hodgson, E, et al. Storyboard development for virtual reality simulation. Clin Simul Nurs. 2016;12(9):392-396. doi: 10.1016/j.ecns.2016.04.002 Google Scholar
23. Department of Homeland Security: Homeland Security Exercise and Evaluation Program. http://www.fema.gov/media-library-data/20130726-1914-25045-8890/hseep_apr13_.pdf. Accessed December 1, 2017.Google Scholar
24. Kirkpatrick, D. Techniques for evaluating training programs: revisiting Kirkpatrick’s four-level model. Train Dev. 1996;50(1):54-59.Google Scholar
25. Dubovsky, SL, Antonius, D, Ellis, D, et al. A preliminary study of a novel emergency department nursing triage simulation for research applications. BMC Res Notes. 2017;10:101-112. doi: 10.1186/s13104-016-2337-3 Google Scholar
26. Mohan, D, Rosengart, MR, Fischoff, B, et al. Testing a videogame intervention to recalibrate physician heuristics in trauma triage: study protocol for a randomized controlled trial. BMC Emerg Med. 2016;(44):1-10.Google Scholar
27. Mohan, D, Baruch, F, Farris, C, et al. Validating a vignette-based instrument to study physician decision making in trauma triage. Med Decis Making. 2014 Feb;34(2):242-252.Google Scholar
28. Ingrassia, PL, Ragazzoni, L, Carenzo, L. Virtual reality and live simulation: a comparison between two simulation tools for assessing mass casualty triage skills. Eur J Emerg Med. 2015;22(2):121-127.Google Scholar
29. Cohen, D, Sevdalis, N, Patel, V, et al. Tactical and operational response to major incidents: feasibility and reliability of skills assessment using novel virtual environments. Resuscitation. 2013;84(7):992-998.Google Scholar
30. Pucher, PH, Batrick, N, Taylor, D, et al. Virtual-world hospital simulation for real-world disaster response: design and validation of a virtual reality simulator for mass casualty incident management. J Trauma Acute Care Surg. 2014;77(2):315-321.Google Scholar