Healthcare data integration that survives an audit
From the exam room to a query
Five stages, each one commissioned as you scroll. Records enter raw and leave structured — and the ones that fail validation drop to quarantine rather than disappearing from a row count.
Clinical source
Patients, encounters and documentation, in the system of record.
EHR / EMR
- 01
Clinical source
- 02
Extraction layer
- 03
Transformation
- 04
Warehouse
- 05
Analytics & reporting
In short: ANexGen Solutions builds and operates HIPAA-aligned healthcare data integration pipelines for US providers, clinic networks and pharmacy organisations. We are a technology services and engineering company: we design, build and run the automation that moves patient, encounter and claims data across EHR and practice-management APIs (including Tebra), X12 837/835 medical claims, NCPDP pharmacy claims, HL7 v2 and FHIR R4 interfaces, into Azure SQL and cloud warehouses for analytics. All work runs under a signed Business Associate Agreement. Contact info@anexgensolutions.com.
Inside the pipeline
The same five stages, with what actually happens inside each.
Clinical source
Patient demographics, appointments, encounters and clinical documentation, held in the practice's EHR as the system of record.
EHR / EMR
Extraction layer
Tebra EHR and practice-management APIs used as the integration surface, over REST and FHIR resources, with incremental pulls keyed on change timestamps rather than full reloads.
Tebra APIs · REST · FHIR R4
Transformation
Serverless functions normalise payloads, resolve provider and facility master data, parse X12 837 and 835 claim and remittance transactions into typed records, and reject malformed inputs to a quarantine table instead of dropping them silently.
Azure Function Apps · X12 EDI
Warehouse
A dimensional model in Azure SQL with slowly-changing dimensions on patient and provider attributes, so historical reporting stays correct after a record is edited upstream.
Azure SQL
Analytics & reporting
Reconciled datasets feeding operational reporting on encounters, claims status, denial reasons and clinic utilisation — with row counts checked against the source on every load.
BI / reporting layer
What we build
EHR & practice management integration
Bi-directional integration with EHR and practice-management platforms over REST, FHIR and vendor APIs — including Tebra, which we run in production. Patient demographics, appointments, clinical notes and encounter records are extracted, normalised and reconciled rather than dumped.
Medical & pharmacy claims
X12 EDI 837 (claim submission), 835 (remittance advice), 270/271 (eligibility) and 834 (enrolment) for medical claims, and NCPDP telecommunication and SCRIPT standards for pharmacy. Rejections, denials and adjudication responses are parsed into reportable structures, not left as raw EDI.
Clinical data warehousing
Azure SQL, Azure Function Apps, AWS and Snowflake pipelines that turn transactional clinical and claims data into a queryable warehouse — with slowly-changing dimensions, incremental loads and reconciliation checks so the numbers survive an audit.
Interoperability standards
HL7 v2 message interfaces, FHIR R4 resources and CDA documents, plus the mapping layer between them. This is where most integrations quietly fail — a valid FHIR bundle that means the wrong thing is worse than no integration at all.
Compliance engineering
Business Associate Agreements before any PHI is accessed. Least-privilege role design, encryption in transit and at rest, US-region data residency where required, immutable audit logging, and documented access reviews your compliance officer can actually evidence.
De-identification & analytics
Safe Harbor de-identification and limited data sets where analytics does not require identifiers, so reporting and machine learning workloads can run without widening the PHI footprint.
Data we work with
- Patient demographics & registration
- Encounters & visit records
- Clinical notes & documentation
- Medical claims (X12 837 / 835)
- Pharmacy claims (NCPDP)
- Eligibility & benefits (270 / 271)
- Provider & facility master data
- Scheduling & appointments
- Revenue cycle & AR reporting
Unifying EHR, Encounter and Claims Data for a US Medical Practice Group
A HIPAA-aligned pipeline moving patient, encounter, claims and clinic data out of an EHR and into a queryable Azure warehouse — replacing manual exports with reconciled, auditable automation.
- Client
- A multi-location medical practice group in the United States
- Industry
- Healthcare — ambulatory care and medical billing
- Region
- United States
- Engagement
- Ongoing engagement
What we walked into
- Clinical, encounter and claims data lived in the EHR and practice-management system with no route out except manual CSV exports pulled on request.
- Reporting was therefore retrospective and inconsistent: two people running the same report a week apart got different answers, and nobody could say which was right.
- Claims and remittance data arrived as raw X12, so denials and adjudication responses were readable only by the billing team, one file at a time.
- Any analytics ambition — payer mix, denial patterns, provider throughput, clinic utilisation — was blocked on the absence of a queryable, reconciled data layer.
- Every option had to satisfy HIPAA: a Business Associate Agreement, least-privilege access, encryption in transit and at rest, and an audit trail that would survive review.
Decisions that mattered
The parts of an integration that determine whether it is still trusted a year later.
Incremental loads, not nightly full reloads
A full reload is simpler to write and worse in every other respect: it multiplies API cost, widens the window in which PHI is in flight, and hides upstream deletions. Change-timestamp-keyed incremental pulls meant the pipeline could run frequently enough for same-day reporting without hammering the source system.
Quarantine malformed records rather than skip them
Integration pipelines commonly drop rows that fail validation and log a count. In healthcare that is unacceptable — a dropped encounter is a missing clinical event and a claim that never gets billed. Failed records land in a quarantine table with the parse error attached, so nothing disappears and someone can act on it.
Reconciliation as a first-class pipeline stage
Every load compares source and destination counts and flags divergence rather than assuming success. This is the difference between a warehouse the billing team trusts and one they quietly keep spreadsheets alongside — and it is the check most integrations omit until an audit forces it.
Slowly-changing dimensions on clinical attributes
When a provider changes speciality or a patient changes plan, naive overwriting silently rewrites history and last quarter's report changes. Type-2 dimensions keep the state as it was at the time of the encounter, which is the only basis on which period-over-period reporting means anything.
De-identified data for development
Engineers built and tested against de-identified and synthetic datasets, with production PHI access limited to a narrow support path. This shrinks the PHI footprint to the smallest set of people who genuinely need it, which is both a HIPAA minimum-necessary requirement and a much easier posture to defend in a security review.
Where it landed
- Reporting moved from manual, on-request CSV exports to an automated pipeline with reconciled outputs.
- Claims and remittance data became queryable alongside clinical data, so denial patterns can be analysed in aggregate rather than file by file.
- Clinic, provider and encounter reporting runs from one modelled source, so two people asking the same question get the same answer.
- PHI access is scoped, logged and reviewable, with development running on de-identified data.
Stack
- Tebra EHR
- Tebra Practice Management API
- FHIR R4
- X12 EDI 837
- X12 EDI 835
- Python
- Azure Function Apps
- Azure SQL
- HIPAA / BAA
How we handle PHI
- A signed Business Associate Agreement before any access to protected health information.
- Least-privilege access scoped per engagement and reviewed on a schedule, not on request.
- Encryption in transit and at rest, with key management held by the client where they prefer.
- US-region data residency where the contract or state law requires it.
- Immutable audit logging of access and transformation, retained per your policy.
- De-identified or synthetic data for development, keeping the PHI footprint to the people who genuinely need it.
HIPAA has no government certification programme. Any vendor describing itself as “HIPAA certified” is describing a third-party audit, not a legal status — what binds is the Business Associate Agreement and the safeguards behind it. For the same reason we describe clients by shape rather than by name: healthcare agreements restrict publicity, and naming a covered entity alongside a description of its data flows is a disclosure we do not make without written permission.