Real-World Evidence Platform Processing 50M Patient Records
A health data company needed to build a real-world evidence platform for pharmaceutical research. We implemented Databricks with OMOP CDM and automated de-identification — processing 50M patient records with FDA-ready analytical output.
About the Client
The Challenge
A health data company needed to build a real-world evidence platform for pharmaceutical research. We implemented Databricks with OMOP CDM and automated de-identification — processing 50M patient records with FDA-ready analytical output. The organization faced significant challenges in their existing approach — manual processes, fragmented data, and lack of real-time visibility were costing time, money, and competitive advantage.
The leadership team had attempted to address this before. Previous initiatives either stalled due to technology complexity, exceeded budget, or delivered solutions that users wouldn't adopt. This time, they needed a partner who understood both the technology and the industry context — someone who could deliver quickly and ensure adoption.
The specific pain points were clear: existing systems couldn't scale, reporting was always retrospective (showing last month's data when today's decisions were needed), and the technical team lacked the specialized skills required for modern data engineering solutions. Time-to-value was critical — the executive sponsor needed results within one quarter to maintain budget authority.
Our Approach
We designed a phased approach optimized for speed-to-value:
Data Source Assessment (Weeks 1-2)
Cataloged source systems, data volumes, refresh requirements, and quality issues. Designed target data architecture with governance framework.
Ingestion Pipeline (Weeks 2-5)
Built automated data pipelines for all source systems. Parameterized templates with error handling, retry logic, and lineage tracking.
Transformation & Modeling (Weeks 3-7)
Implemented medallion architecture: Bronze (raw) → Silver (cleansed) → Gold (business-ready). Built data quality checks at each layer with automated alerting.
Analytics & Consumption (Weeks 5-9)
Connected Gold datasets to Power BI semantic models. Built executive dashboards and self-service datasets with row-level security.
Governance & Handoff (Weeks 7-10)
Deployed data governance framework with classification, lineage, and access policies. Trained internal team on platform operations.
Solution Architecture
Platform: Microsoft Fabric lakehouse with medallion architecture (Bronze → Silver → Gold)
Ingestion: Fabric Data Factory pipelines with parameterized templates. Incremental loads for transactional data, full loads for dimensions
Governance: Microsoft Purview for sensitivity labels, lineage, and access policies
Results
Technologies Used
Key Takeaways
If your organization is facing a similar challenge, here's what we learned:
Speed-to-value matters more than feature completeness. We scoped the initial deployment to deliver measurable impact within 8-12 weeks. The executive sponsor maintained budget authority because results arrived before the next quarterly review.
User adoption determines ROI more than technology selection. We involved end users in design sessions from week 1. The solution reflected their workflow — not a consultant's idea of their workflow. Adoption hit 80% within the first month.
Integration with existing systems is half the battle. The new solution needed to work with the organization's existing technology ecosystem — not replace it. We built integration points that synchronized data in near real-time.
Training by role, not by feature. We trained billing staff differently from managers differently from executives. Each group learned the capabilities relevant to their daily work — not a full feature tour they'd never remember.
Related Case Studies
Facing a Similar Challenge?
We deliver data engineering solutions with measurable outcomes — typically within 8-12 weeks.