Databricks · Healthcare & Life Sciences
Pharma RWE platform on Databricks scales to 6 therapeutic areas
38% cut in study data prep · Unity Catalog as the compliance control point
At a glance
Key metrics
38%
reduction in study data prep
6
therapeutic areas live
OMOP
common data model across studies
Challenge
The situation
Per-therapeutic-area silos made cross-study analysis slow and governance inconsistent. Every study paid the compliance-review tax from scratch.
Approach
How we delivered
- 01 Consolidated RWE on Databricks Lakehouse with Unity Catalog as the PHI governance control point.
- 02 Standardized on OMOP to make cohorts reusable.
- 03 Packaged cohort-builder patterns as reusable, audit-friendly notebooks.
Architecture
Solution architecture
Claims, EHR, registry, and genomics data in Delta Lake. Transformations in dbt-on-Databricks. Vector Search over unstructured notes. Cohort-builder notebooks with approval gates; MLflow tracking every run.
Architecture diagram placeholder
Data flow across source systems, platform, governance, and activation layers.
Outcomes
Measured results
- 38% reduction in study data prep.
- 6 therapeutic areas onboarded in 12 months.
- Compliance narrative approved at first internal-audit review.
Technology
Tech stack
Platform
- Databricks on Azure
- Unity Catalog
- Delta Lake
Tools
- dbt
- MLflow
- Databricks Vector Search
Standards
- OMOP CDM
- FHIR
“Unity Catalog made governance a platform feature, not a project tax.”
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