<![CDATA[Patient record duplication and manual reconciliation workflows between payer and provider organizations represent a durable failure mode in healthcare data infrastructure. Single-cloud interoperability solutions address this problem only partially; they introduce standardized data exchange, but they cannot sustain AI inference continuity when a cloud provider experiences a regional outage. The Concord Framework takes a different approach: it treats multi-cloud distribution not as a redundancy add-on, but as the core architectural pattern from which both interoperability and resilience derive. Deployed across eight partner organizations in a six-month production pilot, the framework uses a cross-cloud event relay between Azure Event Grid and Amazon EventBridge as its primary integration layer, a two-model consensus architecture pairing Azure OpenAI GPT-4o with Amazon Bedrock Claude 3.5 Sonnet for high-stakes entity classification decisions, and an autonomous failover controller with a recovery time objective under four hours and a recovery point objective under fifteen minutes. Pilot outcomes include a 75% reduction in duplicate patient records, an 83% reduction in care-coordination preparation time (from 4 hours to 20 minutes), a 55% improvement in care-gap identification accuracy, and zero unclassified protected health information incidents throughout the pilot period. Partner onboarding duration was reduced from eight weeks to under two weeks. During a documented Azure OpenAI Service regional outage, the framework maintained a zero reconciliation backlog without operator intervention, and both failover events met the defined RTO and RPO targets. These results demonstrate that event-driven multi-cloud orchestration is not only feasible at production scale but also produces measurably better reliability outcomes than single-cloud approaches]]>
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