@Article{info:doi/10.2196/66222,
author="Harari, Rayan E
and Schulwolf, Sara L
and Borges, Paulo
and Salmani, Hamid
and Hosseini, Farhang
and Bailey, Shannon K T
and Quach, Brian
and Nohelty, Eric
and Park, Sandra
and Verma, Yash
and Goralnick, Eric
and Goldberg, Scott A
and Shokoohi, Hamid
and Dias, Roger D
and Eyre, Andrew",
title="Applications of Augmented Reality for Prehospital Emergency Care: Systematic Review of Randomized Controlled Trials",
journal="JMIR XR Spatial Comput",
year="2025",
month="Feb",
day="11",
volume="2",
pages="e66222",
keywords="prehospital emergency care; emergency medical services; randomized controlled trials; clinical decision support; training; augmented reality; emergency; care; systematic review; BLS; procedures; traumatic injury; survival; prehospital; emergency care; AR; decision-making; educational; education; EMS; database; technology; critical care; basic life support",
abstract="Background: Delivering high-quality prehospital emergency care remains challenging, especially in resource-limited settings where real-time clinical decision support is limited. Augmented reality (AR) has emerged as a promising health care technology, offering potential solutions to enhance decision-making, care processes, and emergency medical service (EMS) training. Objective: This systematic review assesses the effectiveness of AR in improving clinical decision-making, care delivery, and educational outcomes for EMS providers. Methods: We searched databases including PubMed, Cochrane CENTRAL, Web of Science, Institute of Electrical and Electronics Engineers (IEEE), Embase, PsycInfo, and Association for Computing Machinery (ACM). Studies were selected based on their focus on AR in prehospital care. A total of 14 randomized controlled trials were selected from an initial screening of 2081 manuscripts. Included studies focused on AR use by EMS personnel, examining clinical and educational impacts. Data such as study demographics, intervention type, outcomes, and methodologies were extracted using a standardized form. Primary outcomes assessed included clinical task accuracy, response times, and training efficacy. A narrative synthesis was conducted, and bias was evaluated using Cochrane's risk of bias tool. Improvements in AR-assisted interventions and their limitations were analyzed. Results: AR significantly improved clinical decision-making accuracy and EMS training outcomes, reducing response times in simulations and real-world applications. However, small sample sizes and challenges in integrating AR into workflows limit the generalizability of the findings. Conclusions: AR holds promise for transforming prehospital care by enhancing real-time decision-making and EMS training. Future research should address technological integration and scalability to fully realize AR's potential in EMS. ",
issn="2818-3045",
doi="10.2196/66222",
url="https://xr.jmir.org/2025/1/e66222",
url="https://doi.org/10.2196/66222"
}