Evidence map›Paper›PMID 42205917›Full record

ReviewJMIR XR and spatial computing2025

Applications of Augmented Reality for Prehospital Emergency Care: Systematic Review of Randomized Controlled Trials.

Rayan E Harari, Sara L Schulwolf, Paulo Borges, Hamid Salmani, Farhang Hosseini, Shannon K T Bailey, Brian Quach, Eric Nohelty, Sandra Park, Yash Verma and 5 more

Abstract readReview
In one paragraph

Review in JMIR XR and spatial computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Usefulness of Mixed Reality in Surgical Treatment: Delphi Study.Journal of medical Internet research · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Rayan E HarariDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.ORCID https://orcid.org/0009-0007-7494-0758
Sara L SchulwolfUniversity of Connecticut School of Medicine, Farmington, CT, United States.ORCID https://orcid.org/0000-0002-2717-732X
Paulo BorgesDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.ORCID https://orcid.org/0000-0001-5942-1166
Hamid SalmaniMetaLeaps Inc, Boston, MA, United States.ORCID https://orcid.org/0000-0003-0650-953X
Farhang HosseiniMetaLeaps Inc, Boston, MA, United States.ORCID https://orcid.org/0009-0008-0101-3416
Shannon K T BaileyCenter for Advanced Medical Learning & Simulation, University of South Florida, Tampa, FL, United States.
Brian QuachDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.
Eric NoheltyDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.
Sandra ParkDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.ORCID https://orcid.org/0000-0003-0389-804X
Yash VermaDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.ORCID https://orcid.org/0009-0000-5546-3561
Eric GoralnickDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.ORCID https://orcid.org/0000-0002-1609-7973
Scott A GoldbergDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.
Hamid ShokoohiDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.ORCID https://orcid.org/0000-0002-1280-3594
Roger D DiasDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.ORCID https://orcid.org/0000-0003-4959-5052
Andrew EyreDepartment of Emergency Medicine, Mass General Brigham, Harvard Medical School, 10 Vining Street, STRATUS Center for Medical Simulation, Boston, MA, 02453, United States, 1 617-372-1022.ORCID https://orcid.org/0000-0002-9590-1084

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ARaugmented realitybasic life supportBLScareclinical decision supportcritical caredatabasedecision-makingeducationeducationalemergencyemergency careemergency medical servicesEMSprehospitalprehospital emergency careproceduresrandomized controlled trialssurvivalsystematic reviewtechnologytrainingtraumatic injury

Identifiers

PMID42205917
PMCPMC13202509

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.