Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The NIH 2025 Public Access Policy: Immediate access, unequal costs.

PLoS medicine·2026
Same author

The Multiethnic Cohort: A Resource for the Study of Genetic and Nongenetic Cancer Risk across Populations.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Correction: Real-World Evidence Synthesis of Digital Scribes Using Ambient Listening and Generative Artificial Intelligence for Clinician Documentation Workflows: Rapid Review.

JMIR AI·2026
Same author

Electronic health record use factors linked to efficiency and productivity: an explainable machine learning analysis.

JAMIA open·2026
Same author

Dairy products and their key nutrients as protective factors against colorectal cancer risk: The Multiethnic Cohort.

International journal of cancer·2026
Same author

Implementation of an Opioid Use Disorder (OUD) Machine-Learning Phenotype in Real-Time for the ADAPT Project.

medRxiv : the preprint server for health sciences·2025

Related Experiment Video

Updated: Feb 22, 2026

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion
08:13

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion

Published on: January 20, 2019

7.2K

Tablet-Based Patient-Centered Decision Support for Minor Head Injury in the Emergency Department: Pilot Study.

Navdeep Singh1,2, Erik Hess3, George Guo2

  • 1Medical College of Georgia, AU/UGA Medical Partnership, Athens, GA, United States.

JMIR Mhealth and Uhealth
|September 30, 2017
PubMed
Summary
This summary is machine-generated.

The Concussion or Brain Bleed app improved patient knowledge about head injuries and CT scans, with high satisfaction and usability. This supports further trials for this clinical decision support tool.

Keywords:
clinical decision supportdecision aidshead injury, minorhealth services overusemedical informaticspatient-centered outcomes researchspiral computed tomography

More Related Videos

Low-intensity Blast Wave Model for Preclinical Assessment of Closed-head Mild Traumatic Brain Injury in Rodents
06:09

Low-intensity Blast Wave Model for Preclinical Assessment of Closed-head Mild Traumatic Brain Injury in Rodents

Published on: November 6, 2020

3.1K
Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

18.0K

Related Experiment Videos

Last Updated: Feb 22, 2026

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion
08:13

Development and Implementation of a Multi-Disciplinary Technology Enhanced Care Pathway for Youth and Adults with Concussion

Published on: January 20, 2019

7.2K
Low-intensity Blast Wave Model for Preclinical Assessment of Closed-head Mild Traumatic Brain Injury in Rodents
06:09

Low-intensity Blast Wave Model for Preclinical Assessment of Closed-head Mild Traumatic Brain Injury in Rodents

Published on: November 6, 2020

3.1K
Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

18.0K

Area of Science:

  • Emergency Medicine
  • Clinical Decision Support
  • Health Informatics

Background:

  • The Concussion or Brain Bleed app is a digital tool designed for emergency departments (EDs) to aid clinicians and patients in deciding on head computed tomography (CT) scans for minor head injuries.
  • It integrates patient decision aids and clinical decision support based on the Canadian CT Head Rule (CCHR) to facilitate discussions on individualized risk and patient concerns.

Purpose of the Study:

  • To evaluate the implementation of the Concussion or Brain Bleed app in a high-volume ED.
  • To gather preliminary data on patient and clinician experience, healthcare utilization, and patient safety to inform a larger multicenter trial.

Main Methods:

  • A prospective pilot study involving adult patients (18-65 years) with minor head injuries deemed low risk by the CCHR.
  • Primary outcome: patient knowledge of injury, risks, and CT use. Secondary outcomes: patient satisfaction, decisional conflict, trust in physician, clinician acceptability, system usability, Net Promoter Score, head CT rate, and 7-day patient safety.

Main Results:

  • 41 patients and 29 clinicians participated. Patient knowledge significantly increased post-app use (3.3 to 4.7 correct answers).
  • High patient satisfaction reported regarding information clarity (85%), helpfulness (88%), and amount (88%). Clinicians found the app helpful (85%) and acceptable for future use (66%).
  • Head CT rate was 17% (7/41 patients), with no missed clinically important brain injuries at 7 days. System usability score was 85.1 (excellent).

Conclusions:

  • The Concussion or Brain Bleed app effectively enhanced patient knowledge regarding CT imaging decisions after head injury in the ED.
  • High patient satisfaction, clinician acceptance, and system usability indicate the app's potential.
  • These findings support further investigation through a larger multicenter trial to confirm the app's effectiveness.