Canada's fragmented healthcare infrastructure leaves patients without access to their own medical histories across providers. EHR vendors operate in siloed ecosystems with little incentive for interoperability, making cross-institutional care slow, expensive, and incomplete. Meanwhile, rare disease clinical trials struggle to recruit eligible participants, stalling treatments that could save lives.

Challenge

Solution!

Building on prior publications in clinical NLP, we designed & prototyped researching and evaluating pipeline architectures for cross-vendor EHR integration. We investigated how NLP + NER can extract and standardize clinical terminology, then experimented with LLM + RAG approaches to perform semantic inference across inconsistently formatted records; mapping variant expressions like "SOB," "interstitial fibrosis," and "lung scarring" to unified clinical concepts.

We further explored federated node architectures as a mechanism for satisfying multi-jurisdictional privacy regulations (PIPEDA, GDPR) without centralizing PHI data. Our research facilitated a full system design with an opt-in, consent-driven trial matching engine, demonstrating how anonymized semantic match scores can connect patients to global clinical trials while preserving data sovereignty.

Skills Literature review, Rapid prototyping, System architecture design

Deliverables & Results

  1. Full system design submission for a cross-vendor EHR integration platform with NLP + NER + RAG pipeline architecture for clinical terminology standardization

  2. Privacy-compliant federated data node model supporting multi-jurisdictional deployment

  3. Stakeholder analysis covering hospitals, patients, researchers, and legislative considerations.