FAQ
Sure. We design for both legacy HL7 v2 feeds and modern FHIR-based interoperability, since most healthcare organizations run a mix of the two: older systems still on HL7 v2, newer platforms and payer integrations increasingly requiring FHIR.
Governance is built into the data fabric architecture: access controls, audit trails, and data lineage are designed alongside the medallion data layers, so every dataset stays traceable and HIPAA-aligned.
Absolutely. That’s one of its main purposes, since clinical and operational AI models depend on clean, connected, current data. And a data fabric gives them that foundation. So use cases like predictive readmission risk, clinical decision support, or population health analytics can be built without each one requiring its own custom data pipeline.
Cost depends on the scope of your existing systems, the number of data sources being integrated, and how much of the architecture needs to be built versus reconfigured. The most accurate way to get a number is a free data architecture audit, which we use to scope engagements before quoting a price.
Timelines vary by architecture complexity and the number of systems involved. However, implementation is phased, and each stage goes live only once validated, so care delivery and reporting continue uninterrupted throughout the rollout rather than waiting for a single go-live date.
We choose the stack based on your existing architecture and requirements. Our team builds primarily on Microsoft Fabric, using OneLake for unified storage, Fabric Data Factory for ingestion, Dataflows Gen2 and Fabric Notebooks for transformation, and Power BI for reporting. For governance and lineage, we use Microsoft Purview, and for identity, security, and access control — Azure/Entra ID. Depending on your current environment, we also work with complementary platforms like Snowflake, Azure Blob Storage, and Apache Airflow for orchestration.
