Engineering an agentic replenishment mesh using Bayesian demand forecasting to eliminate $12M in annual expired drug waste while maintaining 100% HIPAA data sovereignty.
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Multi-specialty hospital pharmacy network managing high-cost, short-shelf-life biologicals and oncology meds.
ML Architect + Data Engineers + HIPAA Compliance Lead embedded within Pharmacy Operations.
Transitioning from static 'Min/Max' replenishment to agentic, patient-volume-aware forecasting.
FHIR-integrated Kafka pipelines, Bayesian forecasting models, and PHI-scrubbing de-identification layers.
The client’s inventory logic relied on legacy 'averaging' models that failed to account for shifts in seasonal patient volume and localized disease outbreaks. This 'Min/Max' trap led to consistent overstocking of high-cost biologicals, resulting in $12M of annual waste due to drug expiration.
The risk was clinical: while some stores overstocked, others faced stockouts of life-saving medications. The system required a transition to 'Predictive Replenishment' without exposing protected health information (PHI) across the procurement network.
Fixed triggers ignored real-time procedure schedules and drug expiration windows.
Agentic triggers based on localized consumption velocity and clinical timelines.
Risky CSV exports for analysis often contained non-scrubbed identifiers.
De-identified streaming metadata ensures 100% HIPAA compliance at the edge.
Excess stock identified only once drugs reached within 30 days of expiration.
ML identifies high-decay risk early, triggering cross-facility stock rebalancing.
Custom scoring logic that weights shelf-life volatility against clinical criticality to prevent life-saving drug stockouts.
Secure PHI scrubbing layer ensures that forecasting models only see anonymized clinical trends, never patient identities.
The mesh identifies surplus in oncology centers and auto-reroutes orders to community clinics before expiration windows close.
Pre-audited de-identification patterns for scrubbing PHI from streaming clinical feeds.
Production-ready Bayesian forecasting templates for complex drug decay-rate modeling.
Real-time dashboards for inventory turnover, expiration risk, and facility fill-rates.
Automated spend-limit enforcement to ensure agentic orders remain within quarterly budgets.
Agentic forecasting matched stock levels to procedure volumes, eliminating the overstock-to-disposal cycle.
Improved fill rates for life-saving meds by prioritizing critical procedures in the replenishment queue.
Immutable logs of every procurement decision, mapped back to procedure-demand forecasts.
Client Testimonial
Coretus didn't just build a forecasting tool—they engineered an agentic supply mesh that bridged our EHR data with real-world logistics. We suppressed drug waste immediately, saving millions while improving critical care readiness.
Director of Operations // Tier-1 Health System