Data Interoperability (HL7/FHIR)

Engineering Approach

When acquiring new clinics or merging hospital networks, disjointed data models create immediate clinical risk. We engineer robust data pipelines that ingest messy, legacy HL7 feeds and normalize them into clean, standardized FHIR resources, ensuring every provider has a unified 360-degree view of the patient history. Healthcare data fragmentation is the silent operational killer for scaling clinic networks. When you acquire a new practice, you inherit their EHR system, their patient database schema, and their clinical terminology — none of which aligns with your existing infrastructure. Providers can't see historical lab results from the acquired clinic. Duplicate patient records proliferate because the Master Patient Index (MPI) has no way to match 'John Smith DOB 1985-03-15' in System A with 'J. Smith DOB 03/15/1985' in System B. Billing teams can't reconcile insurance eligibility because payer IDs are stored differently across systems. The clinical risk is immediate and severe: a provider prescribes a medication that interacts with a drug from the patient's old clinic, but the interaction never fires because the medication history lives in a disconnected database. The financial risk is equally bad: duplicate patient accounts lead to claim denials, and fragmented billing data makes it impossible to track revenue cycle performance across your full network. Solving this requires data interoperability engineering — not IT support, not EHR consultants, but engineers who can build HL7 v2 parsers, FHIR transformation pipelines, and probabilistic patient matching algorithms that unify fragmented data into a single source of truth. We specialize in the messy reality of healthcare data: legacy HL7 ADT feeds that use non-standard Z-segments, proprietary EHR database schemas with no documentation, and clinical terminology that mixes SNOMED, ICD-10, LOINC, and custom codes in the same field. Our data pipelines ingest all of it, normalize it into FHIR R4 resources, and expose a unified API that your clinical applications can query without knowing which legacy system the data came from.