TriageGO

Artificial Intelligence for the Emergency Department

TriageGO is a clinical decision support tool, embedded into the EHR, that supports the triage nurse by applying artificial intelligence to recommend an acuity-level.

There is no change in workflow at intake. TriageGO utilizes the patients' demographics, chief complaint, vital signs, and medical history (if available), and evaluates the risk of:

1. Critical Care Outcome
2. Emergent Procedure
3. Hospitalization

Schedule a Demo

Saving Valuable Time

How is TriageGO helping emergency departments today? The results speak for themselves.

35 Minutes

Saved from door to admit decision¹

61-82 Minutes

Decrease time-to-emergent-care for high-risk patients²

More Reliable Detection

Of high-acuity patients at risk of a critical care outcome¹

Peer-Reviewed Journals

Educational Resources

“TriageGO has helped nurses triage more than 1.4 million
patient visits”

Explore TriageGO Features

Click on the image below to learn more

A Decision Support Tool That Helps Your Staff Improve Triage Accuracy TriageGO provides a consistent, risk-driven triage alternative to subjective and variable ESI.

Count on more consistent and precise clinical documentation at triage to help you:

Smooth and increase patient flow to high-efficiency, fast-track pathways with reliable identification of low-risk patients ^1,3
Reduce “three-iage” with fewer ambiguous Level 3s³
Reduce arrival-to-disposition decision time to unlock capacity ¹

Video Empowering your Emergency Department with AI-driven CDS

Dr. Phillip Levy, Professor of Emergency Medicine at Wayne State University and Fellow of the American College of Emergency Physicians, discusses how Clinical Decision Support tools, powered by Artificial Intelligence and Machine Learning, provide valuable guidance to clinicians in the ER.

Watch now

TriageGO
Artificial Intelligence for the Emergency Department

Speak to a Representative about TriageGO

Thank you for your request.
A Beckman Coulter representative will contact you shortly.

This product may not be available in your country or region at this time. Please contact your Beckman Coulter sales representative or distributor for more information.

Sources:
1. Levin S, Toerper M, Hinson J, Gardner H, Henry S, McKenzie C, Whalen M, Hamrock E, Barnes S, Martinez D, Kelen G. Machine-Learning Based Electronic Triage: A Prospective Evaluation. Ann Emerg Med. 72(4), S116. https://www.annemergmed.com/article/S0196-0644(18)31035-7/fulltext

2. Data last analyzed at Johns Hopkins Health System on 2-4-2021 (Unpublished)

3. Levin S, Toerper M, Hamrock E, Hinson J, Barnes S, Gardner H, Dugas A, Linton B, Kirsch T, Kelen G. Machine Learning-Based Triage More Accurately Differentiates Patients with Respect to Clinical Outcomes Compared to the Emergency Severity Index. Ann Emerg Med. 71(5):565-574, 2018. https://pubmed.ncbi.nlm.nih.gov/28888332/

4. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb268-ED-Costs-2017.jsp

5. Smalley CM, Meldon SW, Simon EL, Muir MR, Delgado F, Fertel BS. Emergency Department Patients Who Leave Before Treatment Is Complete. West J Emerg Med. 2021 Feb 26;22(2):148-155. doi: 10.5811/westjem.2020.11.48427. PMID: 33856294; PMCID: PMC7972384.

Information addressed to health professionals. The products presented on this site are for professional use only, and, where applicable, comply with the requirements of the IVD Directive 98/79 / EC. Instructions for use must be consulted before using these products.

© 2022 Beckman Coulter, Inc. All rights reserved. Beckman Coulter, the stylized logo, and the Beckman Coulter product and service marks mentioned herein are trademarks or registered trademarks of Beckman Coulter, Inc. in the United States and other countries. All other trademarks are the property of their respective owners.

U.S. Government Users Restricted Rights - As per GSA ADP Schedule Contract with Beckman Coulter, Inc., the use, duplication or disclosure is restricted as follows: Beckman Coulter hereby authorizes user to copy those documents published on the World Wide Web by Beckman Coulter, for noncommercial use, and only within the user's organization. Except as expressly provided above, nothing contained herein shall be construed as granting any license or right under any Beckman Coulter copyright. Nothing contained herein shall be construed as granting by implication, estoppel or otherwise any license or right under any patent or trademark of Beckman Coulter or any third party.

TriageGO

Saving Valuable Time

Peer-Reviewed Journals

TriageGO Prospective Evaluation

TriageGO Evaluation at Johns Hopkins

Evaluation of ESI & Mistriage

TriageGO Retrospective Analysis

TriageGO Retrospective Comparison

Under-triage Affects Critical-care Patients

Vulnerabilities in Triage Process can Compromise Care

Evaluation of ESI Triage Distribution in Practice

Current Triage Practices Worldwide

The Effect of ED Crowding on Patient Outcomes

Educational Resources

TriageGO at Yale New Haven Health System

TriageGO Helps Johns Hopkins ED

Frequently Asked Questions

ACEP23 Expert Theater Panel Discussion

Implementing TriageGO at Yale New Haven

Blowing Up Triage Using AI and Machine Learning

Infographic: Clear the way to a more efficient ED

Empowering your ED with AI-driven CDS

Rooting out Sepsis using AI in the ED

Evolution of CDS to ID/Treat Sepsis

“TriageGO has helped nurses triage more than 1.4 million
patient visits”

Explore TriageGO Features

A Decision Support Tool That Helps Your Staff Improve Triage Accuracy TriageGO provides a consistent, risk-driven triage alternative to subjective and variable ESI.

Video Empowering your Emergency Department with AI-driven CDS

TriageGO
Artificial Intelligence for the Emergency Department

Speak to a Representative about TriageGO

Choose your country/region and language

Country or Region

Language

TriageGO

Saving Valuable Time

Peer-Reviewed Journals

TriageGO Prospective Evaluation

TriageGO Evaluation at Johns Hopkins

Evaluation of ESI & Mistriage

TriageGO Retrospective Analysis

TriageGO Retrospective Comparison

Under-triage Affects Critical-care Patients

Vulnerabilities in Triage Process can Compromise Care

Evaluation of ESI Triage Distribution in Practice

Current Triage Practices Worldwide

The Effect of ED Crowding on Patient Outcomes

Educational Resources

TriageGO at Yale New Haven Health System

TriageGO Helps Johns Hopkins ED

Frequently Asked Questions

ACEP23 Expert Theater Panel Discussion

Implementing TriageGO at Yale New Haven

Blowing Up Triage Using AI and Machine Learning

Infographic: Clear the way to a more efficient ED

Empowering your ED with AI-driven CDS

Rooting out Sepsis using AI in the ED

Evolution of CDS to ID/Treat Sepsis

“TriageGO has helped nurses triage more than 1.4 million patient visits”

Explore TriageGO Features

A Decision Support Tool That Helps Your Staff Improve Triage Accuracy TriageGO provides a consistent, risk-driven triage alternative to subjective and variable ESI.

Video Empowering your Emergency Department with AI-driven CDS

TriageGO Artificial Intelligence for the Emergency Department

Speak to a Representative about TriageGO

“TriageGO has helped nurses triage more than 1.4 million
patient visits”

TriageGO
Artificial Intelligence for the Emergency Department