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

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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 outcome 1

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

Explore TriageGO Features

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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 3s 3
  • 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

Artificial Intelligence for the Emergency Department

Speak to a Representative about TriageGO

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.

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.

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 Data last analyzed at Johns Hopkins Health System on 2-4-2021 (Unpublished)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


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.