This is like giving ER doctors a super-fast, data-driven second opinion that watches the patient’s information in real time and quietly flags risks or suggests next steps, without replacing the doctor’s judgment.
Emergency departments are overloaded and clinicians must make high-stakes decisions quickly with incomplete information. This work focuses on building and actually deploying AI tools that help triage, risk-stratify, and guide treatment decisions in the ED, reducing missed diagnoses, overuse of tests, and delays in care.
Deep integration into ED clinical workflows, alignment with regulatory and clinical governance processes, and access to institution-specific historical patient data for model development and calibration.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Integration with hospital EHR systems, real-time data availability, and regulatory/compliance constraints (validation, monitoring, and change control of clinical models).
Early Majority
Focus on bridging the gap from algorithm development to real-world ED deployment, including workflow integration, validation in live settings, and change management, rather than just publishing predictive models in isolation.
2 use cases in this application