The 50TB on-premise environment, managing millions of patient records and clinical documents, failed to process massive HL7 message queues quickly. This architectural lag meant care centers faced delayed lab results, restricting physician workflows.
Maintaining complex PL/SQL stored procedures and triggers tightly coupled to the Java/.NET application created extreme overhead. High enterprise licensing fees and rigid database schemas prevented using modern population health tools, and limited real-time reporting.
We used the AWS Schema Conversion Tool (SCT) to meticulously translate heavily embedded PL/SQL procedures and complex Oracle database triggers into modern, compatible database logic.
We deployed AWS Database Migration Service (DMS) to synchronize historical data and stream ongoing healthcare transactions, guaranteeing zero data loss during the final production cutover.
The new architecture features end-to-end encrypted storage on Amazon S3, strict access control policies, and exact audit logging, guaranteeing all Protected Health Information (PHI) meets stringent healthcare regulatory standards.
We separated the monolithic application layer from the database backend, allowing developers to build independent services and quickly deploy new clinical features without relying on rigid database logic.
The migration creates a unified data flow routing complex healthcare datasets into Amazon Redshift, allowing the system to process real-time clinical dashboards and future AI workloads.
100% removal of expensive Oracle licensing and on-premise hardware costs
Zero downtime achieved during the massive historical data cutover and synchronization
60% faster development cycles for advanced population health analytics and AI tools
