How Does TriageGO Work?
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Optimized Triage with Artificial Intelligence
Unlock the power of data-driven decision making in your Emergency Department, and join the ranks of leading healthcare facilities who are transforming patient triage and empowering their nurses to deliver exceptional care.
Risk vs Resources
• Resource utilization is not a sufficient proxy to severity of illness
• Outcomes-based triage assignment outperforms resource-based triage assignment
• Decrease time to care for all patient groups, while improving operations in your ED
Saved from door to admit decision1
TriageGO is a powerful and intuitive platform that leverages artificial intelligence to provide acuity-level recommendations based on real patient data.
TriageGO empowers the nurse to assign patients to the correct areas for care quickly and accurately, streamlining the triage process and improving patient outcomes.
TriageGO improves patient flow from waiting room to provider by improving the triage process. In today’s chaotic, overcrowded emergency departments, accurate and efficient triage is vital for delivering optimal patient care.
Emergency Departments that use TriageGO can observe decreased wait-times, and decreased time-to-care for their patients3
This product is only available in the United States 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. Internal Report: Time to Emergent Care. Data last analyzed in 2024. Aggregate of five hospital sites in United States.
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/
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