The Challenge
The organization had adopted FHIR as a modern standard for healthcare data exchange, but its analytics and research teams struggled to utilize the data effectively. Most of the data was stored in unstructured or nested formats, often accessible only through complex APIs. Analysts and data scientists had to manually flatten, clean, and transform the data, which slowed down research efforts and created barriers to adoption. Governance and security requirements added another layer of complexity, especially when working with PHI.
The Solution
Productive Edge implemented a Databricks-based Lakehouse architecture that ingested FHIR data and transformed it into analytics-ready Delta tables. The solution handled resource parsing, schema normalization, and linkage across FHIR resource types, including Patient, Encounter, Condition, and Observation. We established a repeatable process to automatically convert FHIR bundles into structured formats using Spark jobs. Security features, including column-level access controls and audit logs, were integrated into the system to meet HIPAA and internal compliance standards. The system was designed to support both ad hoc queries and ongoing research use cases.
The Results
With the new Lakehouse platform in place, research and operations teams gained reliable, real-time access to structured clinical data. Data preparation that previously took days can now be completed in hours or less. Multiple FHIR resource types were available for direct analysis using SQL or Python notebooks in Databricks. Teams are using the data to power research models, understand patient trends, and drive quality initiatives. Most importantly, the medical center now has an infrastructure that bridges the gap between interoperability and insight, positioning it for ongoing innovation in clinical analytics and AI.
