Emergency departments face long wait times, inefficient triage and systemic bias, disproportionately affecting non-white, Hispanic and non-English speaking patients. Identifying critical cases early while preventing unnecessary escalations remains a persistent challenge.
Current triage systems struggle to balance accuracy with efficiency, leading to delays in life-saving care and disparities in high-severity patient designations. Without an effective solution, vulnerable populations remain at higher risk, while ED operations become increasingly strained.
Join leaders from Johns Hopkins School of Medicine and Yale New Haven Hospital as they unveil research proving how an AI-driven triage tool enhances both equity and efficiency. Published in Annals of Emergency Medicine and New England Journal of Medicine AI, their findings reveal:
- Greater accuracy in high-severity illness detection, especially for marginalized groups
- Safe reclassification of 18.7% of mid-acuity patients, ensuring better resource allocation
- 33% reduction in time to initial care, improving overall ED efficiency
- 17.3% decrease in time-to-care (median 58 minutes) for critically ill patients
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