Artificial Intelligence for the Emergency Department

TriageGO is a triage decision support tool seamlessly integrated into your Electronic Health Record (EHR) system and routine triage workflow.

The solution applies machine learning (artificial intelligence) to:

  • Analyze patient data at presentation in the Emergency Department (ED)
  • Compare with additional visit data from your health system
  • Recommend and explain triage acuity to inform HCP decision making

Streamlined training, including an effective train-the-trainer approach, ensures easy adoption by teams. Plus, an embedded user interface eliminates extra clicks and the need to navigate to an outside system or respond to alerts.

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.

What Could a Smoother Patient Flow Mean for Your Emergency Department?

The TriageGO solution is designed to provide triage-level recommendations that are more outcomes-driven and consistent than what triage care teams could deliver on their own.

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
Saved from door to ICU 2
8,500+ Bed Hours
Gained annually (Calculated) 1

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:

  • Speed care to ICU through earlier identification 2
  • 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

Real-World Results

TriageGO is currently in use within individual emergency departments and multi-site health systems including Yale New Haven Health and Johns Hopkins Health System.


More Accurate Triage Acuity Assignments

Did you know most EDs triage 50–70% of their patients to Emergency Severity Index (ESI) Level 3? Unclog your waiting room with TriageGO.3 The system helps you speed time-to-emergent-care for high-risk patients and fast-track low-risk patients.


Seamless Integration

This secure, HIPAA-compliant solution is adaptable to fit your ED’s profile, needs and goals. The system:

  • Integrates directly into your current EHR acuity section (e.g., Epic, Cerner)
  • Does not require alerts or extra clicks through the workflow
  • Offers guided implementation and train-the-trainer resources designed to minimize time required for start up

TriageGO Artificial Intelligence for the Emergency Department

Speak to a Representative about TriageGO

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.