Building the Foundations for Model-Ready Data

Healthcare data is fragmented — models can’t fix what infrastructure hasn’t solved.

Healthcare data is distributed, context-dependent, and shaped by clinical workflows. Even state-of-the-art models struggle if the underlying data is inconsistent, delayed, or locked in silos.

Cognome’s Architecture: Designed for Complexity

Cognome’s architecture is built for this reality. From the outset, we designed our systems to handle:

What Makes Our Infrastructure Model-Ready

• Entity Resolution: Reconciles patient, encounter, and practitioner identities across disparate hospital systems.
• Real-Time Ingestion: Supports HL7, FHIR, imaging, and unstructured clinical notes as live feeds.
• Clinical NLP Pipelines: Detects phenotypes, applies negation logic, and respects temporal context in narrative text.
• Lineage Tracking: Maintains full traceability from ingestion to analysis and model output.
• Governance Enforcement: Applies HIPAA, IRB roles, and data use agreements automatically at every step.

Why It Matters

By addressing these at the infrastructure level, we enable models to learn from complete, contextualized, and trustworthy data — not just what’s easy to extract.

Case Study: Montefiore’s Unified Cohort Engine

At Montefiore, researchers needed to build cohorts using a mix of:

Sources of Complexity

• Structured EHR Data: Standard encounters, labs, meds, etc.
• Clinical Notes: Free-text documentation and progress notes.
• External Study Cohorts: Tools like REDCap.
• Biospecimen Registries: With independent governance and identifiers.

The Transformation with Cognome

Before Cognome, this process required multiple teams, manual scripting, and delays. Now, researchers use a self-service interface where:

• De-identification: Is applied on-the-fly with full auditability.
• Querying: Spans structured and unstructured modalities without needing schema knowledge.
• Trust: Is built in—governance and lineage enforcement are automatic, not optional.

The impact? What once took weeks now takes under an hour.

Why Better Engineering = Better AI

Cohorts like these aren’t just for reporting — they form the training sets for AI models. Their quality determines what the models learn. We improve that quality by design.

How We Improve Training Data

• Cross-Silo Integration: Merges EHR, imaging, and clinical notes into one dataset.
• Event Detection: Uses NLP and rules to detect and normalize clinical concepts.
• Temporal Integrity: Respects the real-world timing and ordering of events.
• Provenance Tracking: Preserves a history of source data and transformation steps.

What Good Engineering Looks Like in Practice

Here’s how a typical setup compares to Cognome’s deployment:

Final Word: It’s Not Just More Data — It’s the Right Data

Where Real Progress Starts

We believe real progress in healthcare AI doesn’t start with the model. It starts with the decisions engineers make when wiring the system.

The Foundation for Every AI Insight

From cohort creation to LLM outputs to trial enrollment — every insight rests on a foundation of high-integrity, context-aware, auditable data. We build the systems that make that foundation possible.