Case Study: Cloud Data Architecture & Migration for Healthcare Network

Healthcare

Cloud Data Architecture & Migration for Healthcare Network

The Challenge: Fragmented On-Premise Infrastructure Blocking Clinical Analytics

A regional healthcare network operating 12 hospitals and 80+ ambulatory clinics had inherited five separate on-premise data warehouses through a decade of acquisitions — running across Oracle, Teradata, and SQL Server. Each warehouse had evolved independently with its own data models, ETL processes, and reporting tools, making cross-system analysis nearly impossible without manual reconciliation. Reporting lag averaged 30 days, meaning clinical leadership was making value-based care decisions on month-old data. Annual infrastructure costs exceeded $8M, with 60% consumed by license fees for platforms the organization was actively seeking to migrate away from. Critically, the network had signed several value-based care contracts with payers that required near-real-time quality metric reporting — a capability the existing infrastructure fundamentally could not support. A failed attempt at in-house modernization two years prior had left the data team demoralized and leadership skeptical.

Our Solution: AWS Lakehouse with Common Clinical Data Model

Lydatum led the full architecture design and migration execution over nine months, building a modern cloud data platform on AWS that served as the unified analytical foundation for the entire network:

• Target Architecture Design: We designed a lakehouse architecture using Amazon S3 as the raw and curated data lake, AWS Glue for serverless ETL orchestration, and Amazon Redshift Serverless as the analytical warehouse layer. dbt handled all transformation logic with full lineage documentation, enabling transparent, testable data pipelines across all five source systems.
• Clinical Data Standardization: We implemented a common clinical data model aligned to the OMOP CDM and FHIR R4 standards, creating a shared semantic layer across all 12 hospitals. This enabled true cross-network analytics on patient outcomes, quality measures, and population health for the first time. The FHIR layer also supported direct integration with payer reporting APIs, addressing the value-based care contract requirements.
• Migration Execution: Fifteen terabytes of historical clinical and operational data was migrated across five platforms over six phased cutovers, with zero data loss and validated record-level reconciliation at each stage. Legacy systems were decommissioned progressively as each migration wave completed.
• Data Governance: AWS Lake Formation provided fine-grained access control aligned to HIPAA requirements. We integrated Collibra as the enterprise data catalog, enabling clinical informaticists and analysts to discover, understand, and trust data assets without routing requests through engineering.

The Impact: Real-Time Clinical Intelligence at a Fraction of the Cost

Within 60 days of final cutover, the network's quality reporting team was delivering value-based care metrics to payers in real time — a capability that had previously been deemed technically infeasible. Clinical leadership gained access to cross-network dashboards updated hourly rather than monthly.

Annual data infrastructure costs dropped from $8M to $4.4M. The OMOP-aligned common data model unlocked the network's first multi-site clinical research program, enabling retrospective cohort studies across all 12 hospitals. The platform became the foundation for three subsequent ML initiatives including predictive readmission modeling and sepsis early warning.

Services Delivered: Cloud Data Strategy & Migration, Data Architecture & Modeling, Data Governance & Compliance, Data Pipeline & Integration

Technologies Used: AWS S3, Amazon Redshift Serverless, AWS Glue, dbt, Collibra, AWS Lake Formation, OMOP CDM, FHIR R4