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Immersive Virtual Reality Simulation for Tactical Mass-Casualty Triage: An Observational Study of Usability, Realism, and Decision-Making in RAMP Training

Published online by Cambridge University Press:  29 December 2025

Kristoffer Lie Eide ,
Brad Keating ,
Asbjørn Braastad ,
Benedicte Eckhoff ,
Inger Anne Kvam ,
Solveig Pederstrand Rise ,
Nicolai Mikkelsen Skaar  and
Inger Lund-Kordahl
Kristoffer Lie Eide*
Affiliation:
Department of Organisation, Leadership and Management, University of Inland Norway, Norway
Brad Keating
Affiliation:
Mountain View Fire Rescue, USA
Asbjørn Braastad
Affiliation:
Department of Health Sciences in Gjøvik, Norwegian University of Science and Technology, Norway
Benedicte Eckhoff
Affiliation:
Department of Health Sciences in Gjøvik, Norwegian University of Science and Technology, Norway
Inger Anne Kvam
Affiliation:
Department of Health Sciences in Gjøvik, Norwegian University of Science and Technology, Norway
Solveig Pederstrand Rise
Affiliation:
Department of Health Sciences in Gjøvik, Norwegian University of Science and Technology, Norway
Nicolai Mikkelsen Skaar
Affiliation:
Department of Health Sciences in Gjøvik, Norwegian University of Science and Technology, Norway
Inger Lund-Kordahl
Affiliation:
Department of Organisation, Leadership and Management, University of Inland Norway, Norway Department of Health Sciences in Gjøvik, Norwegian University of Science and Technology, Norway
*
Corresponding author: Kristoffer Lie Eide; Email: kristofferlieeide@gmail.com
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Abstract

Introduction

Mass casualty incidents (MCIs) in high-risk environments pose major challenges for coordinated emergency response. Training is often infrequent, resource-intensive, and lacks interagency consistency. This study explores the use of Virtual Reality (VR) simulation to train responders in the RAMP triage model across emergency services.

Methods

An observational qualitative design was used. Sixteen participants from various emergency services engaged in a VR-based MCI scenario involving 26 patients and hazardous conditions. The scenario required rapid RAMP triage based on essential cues (radial pulse and the ability to follow commands). Structured interviews followed, and data were analyzed thematically.

Results

Three themes emerged: (1) Deficiencies in current training, including inconsistent MCI protocols, lack of guideline familiarity, and limited interagency practice; (2) VR as an effective, low-resource training method enabling repeatable and safe practice—RAMP triage was found intuitive and efficient, even for non-medical personnel; and (3) prerequisites for VR implementation, such as realistic design, technical infrastructure, and stakeholder involvement to support shared understanding.

Conclusion

VR-based MCI training is a feasible and effective supplement to traditional drills. It enables scalable and flexible skill-building, though it should complement and not replace live exercises.

Keywords

Virtual RealityRAMPmass casualty incidenttriageinteragency training

Information

Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 20 , 2026 , e7
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc

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References

Leaning, J, Guha-Sapir, D. Natural disasters, armed conflict, and public health. N Engl J Med. 2013;369(19):1836–1842.10.1056/NEJMra1109877CrossRefGoogle ScholarPubMed
Valiño, EM, Castro, P, Castro Delgado, R. Intentional mass-casualty incidents with civil society targets in Europe: a descriptive analysis for 2000 to 2018. Emergencias. 2022;34(6):458–464.Google ScholarPubMed
Pepper, M, Archer, F, Moloney, J. Triage in complex, coordinated terrorist attacks. Prehosp Disaster Med. 2019;34(4):442–448. doi:10.1017/S1049023X1900459XCrossRefGoogle ScholarPubMed
Galant, J, Corcostegui, SP, Marrache, D, et al. Mass casualty incident: do tactical tag bracelets improve triage performance by first responders? A randomized controlled mass casualty incident simulation with a French Gendarmerie unit. Mil Med. 2023;188(7-8):e1515–e1522. doi:10.1093/milmed/usad001CrossRefGoogle ScholarPubMed
Keating, B, Masys, AJ. Innovations in active shooting emergency medical response and triage. In: Masys, A, ed. Security by Design: Innovative Perspectives on Complex Problems. Springer, Cham; 2018:55–72. doi:10.1007/978-3-319-78021-4_4CrossRefGoogle Scholar
Meredith, W, Rutledge, R, Hansen, AR, et al. Field triage of trauma patients based upon the ability to follow commands: a study in 29,573 injured patients. J Trauma. 1995;38(1):129–135. doi:10.1097/00005373-199501000-00025CrossRefGoogle Scholar
Marewski, JN, Gigerenzer, G. Good judgments do not require complex cognition. Cogn Process. 2010;11(2):103–121. doi:10.1007/s10339-009-0337-6CrossRefGoogle Scholar
Abbas, JR, Chu, MMH, Payton, A, et al. Virtual reality in simulation-based emergency skills training. Resusc Plus. 2023;16:100345.CrossRefGoogle ScholarPubMed
Farra, SL, Hodgson, E, Timm, N. Comparative cost of virtual reality training and live exercises. Comput Inform Nurs. 2019;37(9):444–454.Google ScholarPubMed
Mandal, S. Brief introduction of virtual reality & its challenges. Int J Sci Eng Res. 2013;4(4):304–309.Google Scholar
Heldring, S, Lindström, V, Jirwe, M, et al. Exploring ambulance clinicians’ clinical reasoning when training mass casualty incidents using virtual reality: a qualitative study. Scand J Trauma Resusc Emerg Med. 2024;32:90. doi:10.1186/s13049-024-01255-5CrossRefGoogle ScholarPubMed
Ericsson, KA. Deliberate practice and the acquisition of expert performance. Acad Med. 2004;79(10 Suppl):S70–S81.10.1097/00001888-200410001-00022CrossRefGoogle ScholarPubMed
Liaw, SY, Carpio, GAC, Lau, Y. Multiuser virtual worlds in healthcare education. Nurse Educ Today. 2018;65:136–149.CrossRefGoogle ScholarPubMed
Liaw, SY, Wu, LT, Soh, SL, Ringsted, C, Lau, TC, Lim, WS. Virtual reality simulation in interprofessional round training. Clin Simul Nurs. 2020;45:42–46.10.1016/j.ecns.2020.03.013CrossRefGoogle Scholar
Kip, H, Buitelaar-Huijsse, GKG, Kouijzer, MTE. From theory to implementation in practice. Glob Implement Res Appl. 2024;4:66–88.10.1007/s43477-023-00101-7CrossRefGoogle Scholar
Noy, C. Sampling knowledge: the hermeneutics of snowball sampling in qualitative research. Int J Soc Res Methodol. 2008;11(4):327–344. doi:10.1080/13645570701401305CrossRefGoogle Scholar
Guest, G, Bunce, A, Johnson, L. How many interviews are enough? An experiment with data saturation and variability. Field Methods. 2006;18(1):59–82. doi:10.1177/1525822X05279903CrossRefGoogle Scholar
Kallio, H, Pietilä, AM, Johnson, M, et al. Systematic methodological review: developing a framework for a qualitative semi-structured interview guide. J Adv Nurs. 2016;72(12):2954–2965. doi:10.1111/jan.13031CrossRefGoogle ScholarPubMed
Turner, DW. Qualitative interview design: a practical guide for novice investigators. Qual Rep. 2010;15(3):754–760.Google Scholar
Hammersley, M, Atkinson, P. Ethnography: Principles in Practice. 3rd ed. Routledge; 2007.Google Scholar
Roulston, K, Shelton, SA. Reconceptualizing bias in teaching qualitative research methods. Qual Inq. 2015;21(4):332–342. doi:10.1177/1077800414563803CrossRefGoogle Scholar
Clausen, AS. The individually focused interview: methodological quality without transcription of audio recordings. The Qualitative Report. 2012;17(37):1–17.Google Scholar
Rutakumwa, R, Murtagh, M, Martin, A, et al. Conducting in-depth interviews in a changing and complex environment: reflections from the field. Qual Res. 2020;20(4):593–611. doi:10.1177/1468794119884806Google Scholar
Braun, V, Clarke, V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.10.1191/1478088706qp063oaCrossRefGoogle Scholar
Ryan, K, George, D, Liu, J, et al. The use of field triage in disaster and mass casualty incidents: a survey of current practices by EMS personnel. Prehosp Emerg Care. 2018;22(4):520–526. doi:10.1080/10903127.2017.1419323CrossRefGoogle ScholarPubMed
Svensøy, JN, Nilsson, H, Rimstad, R. A qualitative study on researchers’ experiences after publishing scientific reports on major incidents, mass-casualty incidents, and disasters. Prehosp Disaster Med. 2021;36(5):536–542.10.1017/S1049023X21000911CrossRefGoogle ScholarPubMed
Norwegian Directorate of Health. National Mass Casualty Triage (SALT). 2020. https://www.helsedirektoratet.no/veiledere/masseskadetriage/Masseskadetriage-Nasjonal_veileder.pdf/_/attachment/inline/5f964973-5fb1-4075-a6a4-7c0007896184:a89a543c55ec9dea671e3ad38bb1d7a550c6078a/Masseskadetriage-Nasjonal_veileder.pdf.Google Scholar
Risavi, BL, Sabotchick, KM. Prehospital START triage performance in a rural EMS system. Prehosp Emerg Care. 2013;17(2):250–254. doi:10.3109/10903127.2012.747173Google Scholar
Gigerenzer, G, Gaissmaier, W. Heuristic decision making. Annu Rev Psychol. 2011;62:451–482. doi:10.1146/annurev-psych-120709-145346CrossRefGoogle ScholarPubMed
Kahneman, D, Klein, G. Conditions for intuitive expertise: a failure to disagree. Am Psychol. 2009;64(6):515–526. doi:10.1037/a0016755CrossRefGoogle ScholarPubMed
Kahneman, D. Fast and Slow Thinking. Allen Lane and Penguin Books; 2011.Google Scholar
Kolb, DA. Experiential Learning: Experience as the Source of Learning and Development. Prentice Hall; 1984.Google Scholar
Klein, GA. A recognition-primed decision (RPD) model of rapid decision making. In: Klein, GA, Orasanu, J, Calderwood, R, Zsambok, CE, eds. Decision Making in Action: Models and Methods. Ablex; 1993:138–147.Google Scholar
Ferrandini Price, M, Escribano Tortosa, D, Nieto Fernandez-Pacheco, A, et al. Comparative study of a simulated incident with multiple victims and immersive virtual reality. Nurse Educ Today. Dec 2018;71:48–53. doi:10.1016/j.nedt.2018.09.006CrossRefGoogle ScholarPubMed
Chumvanichaya, K, Yuksen, C, Nuanprom, P, et al. A comparison of SIEVE, SORT, and START triage training effectiveness between immersive interactive 3D learning materials using virtual reality (VR-SSST) and traditional methods in mass casualty incidents. Int J Emerg Med. 2025;18(1). doi:10.1186/s12245-025-00850-2CrossRefGoogle ScholarPubMed
Driskell, JE, Johnston, JH. Stress exposure training. J Hum Perform Extrem Environ. 1998;3(1):1–23.Google Scholar
Rose, S, Spies, C, Riegel, B. The effects of emotional arousal on simulation-based learning. Nurse Educ Today. 2019;75:118–123. doi:10.1016/j.nedt.2019.01.020Google Scholar
Brydges, R, Carnahan, H, Rose, D, et al. Coordinating progressive levels of simulation fidelity to maximize educational benefit. Acad Med. 2010;85(5):806–812. doi:10.1097/ACM.0b013e3181d7aabdCrossRefGoogle ScholarPubMed
Mathieu, JE, Heffner, TS. The influence of shared mental models on team process and performance. J Appl Psychol. 2000;85(2):273–283. doi:10.1037/0021-9010.85.2.273CrossRefGoogle Scholar
Sezgin, MG, Bektas, H. Effectiveness of interprofessional simulation-based education programs to improve teamwork and communication for students in the healthcare profession: a systematic review and meta-analysis of randomized controlled trials. Nurse Educ Today. 2023;120:105619.10.1016/j.nedt.2022.105619CrossRefGoogle ScholarPubMed
Kouijzer, MTE, Buitelaar-Huijsse, GKG. From theory to implementation in practice: a qualitative case study of the implementation of virtual reality in mental healthcare. Glob Implement Res Appl. 2024;4(1):66–88. doi:10.1007/s43477-023-00072-0Google Scholar
Mäkinen, J, Järvelä, S. Usability and engagement factors in the adoption of immersive technologies in education. Comput Educ. 2022;185:104506. doi:10.1016/j.compedu.2022.104506Google Scholar