WtrDB for Healthcare
WtrDB continuously ingests from every clinical source — EHRs, labs, imaging, claims, genomics, real-time vitals — extracts typed entities and relationships, resolves identities across systems, and governs every fact through a three-filter validation pipeline. The result: a mathematically measured, federated, queryable clinical knowledge graph.
Fragmented records. WtrDB connects the full clinical picture.
Every healthcare institution faces the same challenge: critical decisions depend on data scattered across dozens of systems — incompatible formats, contradictory records, no unified view. Medication errors from incomplete cross-system records. Duplicate tests from poor visibility. Claim denials from documentation gaps. Care coordination failures where patients fall through the cracks. These are not technology problems. They are knowledge problems.
Three steps from clinical sprawl to governed intelligence.
Ingest Everything
Clinical documents, EHR records, lab results, imaging reports, insurance claims, genomic data, real-time vital streams, HL7/FHIR feeds, pharmaceutical databases, and agent-contributed findings — all through a single governed pipeline.
Extract and Resolve
GPT-4o-mini extracts 13 typed clinical entity classes and their relationships from every ingested source. Patient and provider identities are resolved across systems using semantic intelligence and relationship context. Contradictions are detected before they enter the graph.
Govern and Measure
Every extracted fact passes through a three-filter validation pipeline. Cellular sheaf theory continuously measures graph consistency, surfacing a defensible numeric trust score for every connected piece of clinical data — the first time healthcare has had this.
Explore and rotate your WtrDB graph in 3D
Drag to rotate. Scroll to zoom. Right-click and drag to pan. Click a node to inspect its relationships.
Six healthcare workflows built on connected clinical knowledge.
Patient Identity Resolution
Resolve patient identities across EMRs, labs, imaging systems, and payer records using semantic intelligence and relationship context. Eliminate duplicate records and merge contradictory cross-system profiles into a single governed patient entity.
Clinical Trial Data Integrity
Multi-site federation with sheaf-augmented conflict tracking. Contradiction detection across trial sites. Point-in-time reconstruction for regulatory audit submissions. Mathematical structural anomaly detection that surfaces data integrity issues before they compromise trial results.
Adverse Event and Drug Interaction Intelligence
Ingest from FDA FAERS, DrugBank, DailyMed, and institutional pharmacy records. Detect drug–drug, drug–condition, and drug–genomic contraindications through graph traversal. Real-time monitoring triggers alerts when new adverse event signals match patient profiles in the graph.
Revenue Cycle and Claims Intelligence
Clinical-financial knowledge graph that maps encounters to codes to claims to denials. Structural pattern detection for claim denial root causes, split billing chains, referral loops, and revenue leakage. Payer contract federation reveals underbilled services and compliance risk.
Epidemiological Surveillance
Multi-source ingestion from hospital admissions, lab reporting, pharmacy dispensing, and syndromic surveillance networks. Outbreak detection through graph clustering. Cross-jurisdictional federation with differential mode revealing cases tracked by one jurisdiction but missed by another.
Regulatory Compliance and Audit Readiness
HIPAA-aligned access controls, OPA policy-as-code, complete audit trails, and point-in-time reconstruction built into the platform spine. Reconstruct what was known at any moment in time for any regulatory review — not from backups, from the graph itself.
Built for every team accountable to clinical data quality.
Hospital CIOs & CMIOs
Clinical data quality with a defensible number, not a narrative.
Clinical Quality Officers
Structural anomaly detection before issues become patient safety events.
Compliance & Privacy Officers
HIPAA controls, audit trails, and policy-as-code. Built in, not bolted on.
Research Leaders
Multi-site federation, conflict tracking, and regulatory-ready point-in-time reconstruction.
Revenue Cycle Leaders
Denial pattern identification and revenue leakage detection through structural analysis.
For the first time, clinical data trust has a number.
WtrDB applies cellular sheaf theory — a branch of pure mathematics — to continuously measure the consistency and conflict structure of the clinical knowledge graph. The Sheaf Laplacian computes H⁰ (connected components of agreement) and H¹ (cycles of contradiction) across every entity, relationship, and cross-source merge. Healthcare institutions receive a defensible, auditable, numeric measure of how trustworthy their connected clinical data actually is — updated continuously as new data arrives.
Ready to connect your clinical data?
Talk to the team about your data landscape, source systems, and integration requirements.