Accelerated Chest Pain Treatment With Artificial Intelligence–Informed, Risk-Driven Triage-Reference-Cited by-同舟云学术

Accelerated Chest Pain Treatment With Artificial Intelligence–Informed, Risk-Driven Triage

Published:2024-09-01 Issue:9 Volume:184 Page:1125
ISSN:2168-6106
Container-title:JAMA Internal Medicine
language:en
Short-container-title:JAMA Intern Med

Author:

Hinson Jeremiah S.¹²³,Taylor R. Andrew⁴⁵,Venkatesh Arjun⁴,Steinhart Benjamin D.³,Chmura Christopher⁴,Sangal Rohit B.⁴,Levin Scott R.¹²³

Affiliation:

1. Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland

2. Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, Baltimore, Maryland

3. Beckman Coulter Diagnostics, Brea, California

4. Department of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut

5. Department of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, Connecticut

Abstract

This quality improvement study evaluates the use of artificial intelligence to accelerate triage of patients presenting to the emergency department with chest pain.

Publisher

American Medical Association (AMA)

Link

https://jamanetwork.com/journals/jamainternalmedicine/articlepdf/2821363/jamainternal_hinson_2024_ld_240016_1724793971.5425.pdf

Reference5 articles.

1. A national study of the prevalence of life-threatening diagnoses in patients with chest pain.;Hsia;JAMA Intern Med,2016

2. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index.;Levin;Ann Emerg Med,2018

3. Machine-learning-based electronic triage: a prospective evaluation.;Levin;Ann Emerg Med,2018

4. Emergency department visits. National Center for Health Statistics. Published November 4, 2023. Accessed December 29, 2023. https://www.cdc.gov/nchs/fastats/emergency-department.htm