This is like giving ER triage nurses a smart calculator that looks at a patient’s vital signs and symptoms and helps decide how urgent their case is, so the sickest people are seen first and fewer patients are mis-prioritized.
Manual triage using the Emergency Severity Index (ESI) is variable and error-prone, especially in crowded emergency departments. AI assistance aims to improve the accuracy and consistency of triage decisions, reduce under‑ and over‑triage, and support nurse workload and patient flow.
Clinical performance evidence and validation data from real-world ED encounters; integration into hospital EHR/triage workflows; regulatory approvals and trust from nursing leadership and clinicians.
Classical-ML (Scikit/XGBoost)
Structured SQL
Medium (Integration logic)
Integration with heterogeneous hospital EHR systems and ensuring real-time inference with high availability and robust clinical validation across sites.
Early Majority
Focus specifically on AI-assisted Emergency Severity Index triage accuracy and nursing outcomes, backed by a systematic review of clinical effectiveness rather than generic ED decision support or broad symptom-checker tools.