Aquila Health provides a unified health data integration platform that consolidates fragmented data sources into a governed, FHIR-based infrastructure. The platform offers identity resolution, data quality scoring, executable governance, and provenance tracking to support analytics, AI, and interoperability. It also publishes synthetic clinical datasets with full attestation for testing and development.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare organizations rely on disconnected data systems, leading to fragmented, inconsistent, and poorly governed patient information. This patchwork prevents complete, accurate data from flowing across the ecosystem, undermining clinical operations, analytics, and AI-driven decision-making.
Solution
Aquila Health replaces the patchwork with a unified integration layer that creates a governed highway for health data. The platform uses FHIR as the exchange spine, OMOP as an analytical projection, and terminology services for semantic consistency. It supports deterministic, probabilistic, and privacy-preserving identity resolution with confidence scoring, plus TREUE-style scoring to distinguish available data from supportable truth. Machine-evaluable policies and immutable evidence are applied at every access and workflow boundary, with provenance and lineage preserved across all layers.
Target Audience
Primary customers are health systems, payers, public health agencies, and digital health companies needing interoperable data integration, analytics, and AI-ready datasets.
Features
- FHIR-based canonical exchange with OMOP analytical projection and terminology-as-a-service
- Identity resolution combining deterministic, probabilistic, and privacy-preserving matching with reviewable clerical workflows
- TREUE-style data quality scoring retaining dimension-level evidence for user and AI trust
- Executable governance with machine-evaluable policies and immutable evidence at every boundary
- Governed delivery for digital quality, population analytics, operations, public health, and downstream applications
- Synthetic clinical dataset generation with provenance, lineage, and attestation reports (e.g., 6.4M records, 15K patients, 19 tables)
- Responsible AI layer with confidence indicators, provenance, and human-review records for consequential transformations