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

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

With the need for improvements in both real-time decision support in prehospital care and the education and training of prehospital care providers, researchers have posited the utility of integrating AR into the prehospital setting. AR technologies are tools to superimpose digitally generated 3 D and 2 D visual information into a user’s environment in real time for display and guidance.

Rayan E Harari, Sara L Schulwolf, Paulo Borges, Hamid Salmani, Farhang Hosseini, Shannon K T Bailey, Brian Quach, Eric Nohelty, Sandra Park, Yash Verma, Eric Goralnick, Scott A Goldberg, Hamid Shokoohi, Roger D Dias, Andrew Eyre

JMIR XR Spatial Comput 2025;2:e66222

AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study

AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study

The Famou S and ARTICA studies used prehospital point-of-care troponin assessments, while the Hollands Midden Acute Regional Triage–cardiology study implemented a novel triage platform combining prehospital and hospital data. Of note, the decision whether a patient can stay at home or should be transported to an ED in these studies was a purely human decision by health care professionals. The accuracy of these decisions is therefore highly dependent of training and expertise.

Enrico de Koning, Yvette van der Haas, Saguna Saguna, Esmee Stoop, Jan Bosch, Saskia Beeres, Martin Schalij, Mark Boogers

JMIR Cardio 2023;7:e51375

Technologies for Interoperable Internet of Medical Things Platforms to Manage Medical Emergencies in Home and Prehospital Care: Protocol for a Scoping Review

Technologies for Interoperable Internet of Medical Things Platforms to Manage Medical Emergencies in Home and Prehospital Care: Protocol for a Scoping Review

Io MT can increase patient safety, reduce health care costs, and streamline processes and workflows in home and prehospital care [1]. In the Io MT, devices communicate over the internet to achieve a common goal [22,23]. Furthermore, combining several devices, followed by adequate data fusion, can be advantageous in terms of system accuracy [24].

Mattias Seth, Hoor Jalo, Åsa Högstedt, Otto Medin, Ulrica Björner, Bengt Arne Sjöqvist, Stefan Candefjord

JMIR Res Protoc 2022;11(9):e40243

Impact of Face-to-Face Teaching in Addition to Electronic Learning on Personal Protective Equipment Doffing Proficiency in Student Paramedics: Protocol for a Randomized Controlled Trial

Impact of Face-to-Face Teaching in Addition to Electronic Learning on Personal Protective Equipment Doffing Proficiency in Student Paramedics: Protocol for a Randomized Controlled Trial

However, this strategy is difficult to apply in the prehospital field, where health care workers must often doff PPE on site. It is therefore all the more relevant to train prehospital health care workers in the noncontaminating removal of PPE so that they can perform it adequately under all circumstances.

Loric Stuby, Ludivine Currat, Birgit Gartner, Mathieu Mayoraz, Stephan Harbarth, Laurent Suppan, Mélanie Suppan

JMIR Res Protoc 2021;10(4):e26927

Twelve-Lead Electrocardiogram Acquisition With a Patchy-Type Wireless Device in Ambulance Transport: Simulation-Based Randomized Controlled Trial

Twelve-Lead Electrocardiogram Acquisition With a Patchy-Type Wireless Device in Ambulance Transport: Simulation-Based Randomized Controlled Trial

A large retrospective cohort study performed in the United States found that before hospital arrival, only 32% of patients had received P12 ECG, and among the patients who visited the emergency department via a 911 call with a final confirmed diagnosis of ACS, almost 41% had not received prehospital electrocardiogram (ECG) [8]. Other studies have also indicated that P12 ECG data are transmitted to the hospital for only a small portion of patients.

Sunyoung Yoon, Taerim Kim, Taehwan Roh, Hansol Chang, Sung Yeon Hwang, Hee Yoon, Tae Gun Shin, Min Seob Sim, Ik Joon Jo, Won Chul Cha

JMIR Mhealth Uhealth 2021;9(4):e24142

Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study

Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study

Although multiple prediction models have been developed to predict hospital admission for ED use [3-11] to address overcrowding and patient safety [12-15], few studies have examined prediction models for prehospital use. Previously reported prehospital prediction models have been limited to patients with a specific disease or to models predicting critically ill conditions or mortality [16-23].

Toru Shirakawa, Tomohiro Sonoo, Kentaro Ogura, Ryo Fujimori, Konan Hara, Tadahiro Goto, Hideki Hashimoto, Yuji Takahashi, Hiromu Naraba, Kensuke Nakamura

JMIR Med Inform 2020;8(10):e20324

Effect of an E-Learning Module on Personal Protective Equipment Proficiency Among Prehospital Personnel: Web-Based Randomized Controlled Trial

Effect of an E-Learning Module on Personal Protective Equipment Proficiency Among Prehospital Personnel: Web-Based Randomized Controlled Trial

The purpose of this study was to evaluate whether a specifically designed gamified e-learning module [13] could improve the rate of adequate PPE choice by prehospital personnel in the context of the COVID-19 pandemic. Our hypothesis was that knowledge of PPE guidelines would be inconsistent between prehospital personnel, and that an e-learning module may increase and standardize knowledge regarding the use of PPE. This could help limit both underuse and overuse of such equipment.

Laurent Suppan, Mohamed Abbas, Loric Stuby, Philippe Cottet, Robert Larribau, Eric Golay, Anne Iten, Stephan Harbarth, Birgit Gartner, Mélanie Suppan

J Med Internet Res 2020;22(8):e21265