Engineering an Agentic NLP Mesh to parse unstructured clinical notes and genomics data—accelerating oncology trial enrollment by 400% while maintaining absolute HIPAA-gated privacy.
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Global oncology research network managing 50+ concurrent trials across diverse EHR environments.
Bio-ML Architect + 2 Data Engineers + HIPAA Compliance Lead embedded with Clinical Ops.
Eliminating manual chart reviews to connect high-need patients with life-saving trials in real-time.
LLM-powered entity extraction, FHIR-integrated Kafka streams, and medical knowledge graphs.
The client’s oncology research arm struggled with 'Data Paralysis.' Inclusion and exclusion criteria for trials were trapped in complex PDFs, while patient data lived in unstructured clinician notes across 14 fragmented EHR systems.
Manual chart sifting took weeks per patient, leading to a 30% dropout rate as patients sought alternative treatments. The enterprise required a transition from 'Manual Sifting' to 'Computational Matching' without compromising PHI (Protected Health Information) security.
Analysts reading through thousands of clinical notes per month.
Real-time extraction of oncology biomarkers from unstructured text.
Reliance on internal staff training to prevent PHI leakage during review.
Automated PHI scrubbing and HIPAA-gated storage at the infrastructure level.
Significant latency between patient diagnosis and trial recommendation.
Near-instant matching notifications delivered via secure EHR alerts.
The system proactively alerts investigators when a new trial protocol perfectly aligns with a patient currently in the system.
Ensured all data processing occurred in transient, memory-only environments to eliminate persistent data risk.
Kafka-driven streams trigger clinical coordinator alerts the moment an EHR update meets a trial requirement.
Pre-built logic for HIPAA-compliant de-identification and PII scrubbing in medical data meshes.
Production-ready FHIR and HL7 connectors for rapid EHR interoperability setup.
Real-time monitoring for clinical recruitment funnels and investigator engagement dashboards.
Standardized NLP patterns for extracting oncology-specific biomarkers (EGFR, ALK, KRAS).
Agentic matching removed manual bottlenecks, reducing the 'Diagnosis-to-Trial' window significantly.
Automated data verification ensured only patients meeting 100% of criteria reached the lab phase.
The NLP mesh processes thousands of medical notes per second without impacting clinical workstation performance.
Client Testimonial
Coretus didn't just build a tool; they engineered a life-saving velocity engine. We are matching patients to trials 4x faster, providing treatments to those who literally cannot afford to wait.
Chief Medical Officer