What made this particularly difficult was scale. With data arriving from sources including Health Net, Portico, DBFS directories, and external enrichment files, the volume and variety of inputs made manual intervention not just slow, but unsustainable. The organization needed a solution that could standardize at scale, preserve auditability, and not collapse under the weight of its own complexity.
Solution
We partnered with the client to rebuild the data foundation from the ground up, re-architecting the ecosystem using Databricks-native components anchored by Brillio ADAM, its composable, tech-agnostic agentic platform designed to help enterprises adopt, scale, and govern AI with confidence.
The first change was ingestion. Data from every source, health networks, provider systems, shared drives, enrichment files, and DBFS directories, was configured for automatic ingestion into Databricks upon file upload, eliminating manual handling at the entry point.
From there, our team implemented a Medallion architecture built on Delta Live Tables. In the Bronze layer, raw data was captured and validated for structure and schema consistency. As data moved into the Silver layer, it was transformed into FHIR-compliant models, enriched, and standardized into reusable templates. ADAM’s configurable Data Quality framework was embedded here, running 43 business and quality validation rules continuously to assess accuracy, completeness, and reliability. Issues were flagged in real time, so only trusted data advanced.
Clean data from the Silver layer was then aggregated into the Gold layer, where Delta tables and materialized views supported analytics, reporting, and downstream consumption. Streaming pipelines moved data seamlessly across all three layers, reducing latency and cutting wait times for insight access.
To surface the relationship intelligence the Member 360 view required, we integrated Neo4j, a graph database that modeled and visualized connections between members, providers, and practitioners in ways that traditional relational models could not replicate. Governance and monitoring dashboards were embedded directly into the architecture, providing end-to-end visibility into data flow, quality metrics, and audit trails.
