Extended reality as a training method for medical first responders in mass casualty incidents: A protocol for a systematic review

Lucía Otero-Varela, Ana María Cintora, Salvador Espinosa, María Redondo, Miriam Uzuriaga, Myriam González, Mario García, Jessica Naldrett, Juan Alonso, Tatiana Vazquez, Alberto Blanco and María del Carmen Cardós Alonso

PLOS ONE, 2023, vol. 18, issue 3, 1-8

Abstract: Introduction/Background: Mass-casualty incidents (MCIs) and disasters require an organised and effective response from medical first responders (MFRs). As such, novel training methods have emerged to prepare and adequately train MFRs for these challenging situations. Particular focus should be placed on extended reality (XR), which encompasses virtual, augmented and mixed reality (VR, AR, and MR, respectively), and allows participants to develop high-quality skills in realistic and immersive environments. Given the rapid evolution of high-fidelity simulation technology and its advantages, XR simulation has become a promising tool for emergency medicine. Accordingly, this systematic review aims to: 1) evaluate the effectiveness of XR training methods and 2) explore the experience of MFRs undergoing such training. Methods: A comprehensive search strategy will encompass four distinct themes: MFRs, disasters/MCIs, education and simulation, and XR. Four databases (MEDLINE, EMBASE, CINAHL and LILACs) will be searched along with an in-depth examination of the grey literature and reference lists of relevant articles. MetaQAT will be used as a study quality assessment tool and integrated into Covidence as part of the data extraction form. Given the predicted high heterogeneity between studies, it may not be possible to standardise data for quantitative comparison and meta-analysis. Thus, data will be synthesised in a narrative, semi-quantitative manner. Discussion: This review will examine the existing literature on the effectiveness of XR simulation as a tool to train MFRs for MCIs, which could ultimately improve preparedness and response to disasters. Trial registration: Protocol registration: PROSPERO CRD42021275692.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0282698

DOI: 10.1371/journal.pone.0282698

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