Data quality as a product feature
Quarantine, triage, source watchlists, promotion review and last-known-good snapshots keep quality visible instead of treating it as a back-office concern.
Capabilities in Action · Data & Analytics
From source quality to trusted analytics.
FHIR Health Equity Pipeline is a local-first synthetic healthcare data platform that demonstrates how multi-source FHIR-shaped data can be ingested, validated, quarantined, modeled and surfaced as stakeholder-ready analytics.
347FHIR resourcesSynthetic records
loaded in the current demo.
60Synthetic patientsNo real patient
data is used.
39dbt testsAcross bronze, silver and
gold models.
11dbt modelsCurrent local walking
skeleton.
The problem
FHIR-shaped data still needs governance before it deserves trust.
Healthcare organizations can receive data from multiple EHR, practice-management and integration feeds. Even when the payloads are FHIR-shaped, completeness, coding, timing and operational quality can vary by source.
This reference implementation asks whether synthetic multi-source FHIR data can be governed, tested, modeled and converted into trustworthy analytics for care gaps, health equity and pipeline reliability.
Engineering pattern
Validate before promotion. Preserve the evidence when something fails.
The pipeline routes synthetic FHIR records through source adapters, validation and quarantine, DuckDB bronze landing, dbt transformations, quality checks and gold analytical marts. Failed records remain visible for stewardship rather than disappearing from the decision trail.
1Ingest
Synthetic FHIR-shaped records from simulated sources.
2Validate
Route, tag, quarantine and emit quality evidence.
3Model
DuckDB bronze plus dbt bronze, silver and gold.
4Test
Identity, reference, accepted-value and plausibility gates.
5Surface
Care-gap, reliability and masked analytical views.
What this demonstrates
Patterns that transfer to client data-platform work.
Analytics with lineage
Bronze/silver/gold modeling connects raw source evidence to dashboard-ready care-gap and reliability marts.
Operational observability
Pipeline monitoring and OpenTelemetry-style traces expose run fidelity, error paths and operational behavior.
FHIR evolution
A mapping workbench demonstrates R4/R4B/R5 schema lookup, migration simulation and forced-conversion decision notes.
Evidence boundary
What the project does—and does not—claim.
- All records are synthetic; there is no real patient data.
- Simulated source feeds are not real EHR integrations.
- Masking demonstrates design patterns; it does not establish HIPAA compliance.
- The v1 demo runs locally with DuckDB rather than production cloud infrastructure.
- Cloud documentation describes scaling patterns, not production use of Spark, Kafka, Snowflake, Databricks or BigQuery.
- Production use would require formal source contracts, FHIR validation, security, orchestration, compliance, observability and business validation.
Visual walkthrough
From system health to detailed analytical review.
The working reference UI is the evidence. The supporting graphic below summarizes the same architecture at a glance.
Relevant service
Need a data platform where quality and lineage are visible?
Use this project as a concrete starting point for conversations about healthcare data engineering, analytics, interoperability patterns and governance.