A regional healthcare network's clinical AI deployments were blocked by fragmented data infrastructure and missing governance. We unblocked both.
Sector
Healthcare
Size
Regional network (~3,200 employees)
Region
North America
Engagement Type
Data Strategy & Governance
The AI was ready. The data wasn't. Two clinical initiatives stalled before they started.
A regional healthcare network operating six hospitals had identified two high-priority clinical AI initiatives — a sepsis prediction model and a readmission risk tool. Clinical informatics had built proof-of-concept models showing strong results. But the compliance and legal teams had blocked deployment pending a data governance framework that didn't exist.
The network's clinical data was distributed across four EHR systems, three data warehouses, and numerous departmental databases — with no unified governance, inconsistent coding standards, and significant gaps in data quality documentation.
Mapped the complete data infrastructure across all facilities. Identified critical data quality gaps affecting both target AI initiatives. Documented governance requirements from compliance, legal, and clinical leadership.
Designed a health data governance framework covering data ownership, stewardship roles, quality standards, consent management, and de-identification protocols. Built specifically to satisfy HIPAA requirements while maintaining the data utility the AI models required.
Built a prioritized remediation plan — 90 days of targeted data quality work — that would bring both clinical AI initiatives to deployment-ready status. Stayed through the implementation to ensure the plan was executed correctly.