AI Infrastructure for Clinical Operations
45 hospitals, 300 clinics, treating 5M patients annually with 50,000 healthcare professionals
88% reduction (6 months → 3 weeks)
Decreased from 73% to 5%
35 vs 4 previously
$51M savings from improved outcomes
60% reduction
Fragmented AI initiatives across departments led to 73% model failure rate in production. No standardized deployment process meant 6-month average time from development to clinical use.
End-to-end platform on hybrid cloud (Azure + on-premise) with automated CI/CD pipelines for model deployment, comprehensive monitoring and drift detection, and HIPAA-compliant infrastructure design.
JupyterHub with 200 concurrent users, Shared feature store (Feast)
Experiment tracking (MLflow + Weights&Biases), Automated hyperparameter tuning (Optuna)
A/B testing framework, Canary deployment automation, Real-time monitoring dashboards
FHIR-compliant data pipelines, Audit trails for FDA approval
Reduced deployment time by 88%, decreased failure rate to 5%, and achieved $51M savings from improved clinical outcomes.
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