Helios Router: An Intelligent Multi-Cloud AI Orchestration Framework for Clinical Decision Intelligence at Scale

Abstract

Healthcare organizations operating across fragmented multi-cloud environments face a structural challenge that no single AI provider has resolved: clinical AI workloads carry materially different requirements for accuracy, latency, and cost, yet most enterprise deployments route every task to a statically configured model. This paper presents MedInsightAI, a production multi-cloud clinical data intelligence platform spanning AWS and Azure, and describes the Helios Router, its intelligent AI orchestration layer. The Helios Router dynamically selects between Amazon Bedrock and Azure OpenAI for each clinical task based on real-time provider telemetry, cost-per-token, inference latency, and error rate, and executes automatic cross-provider failover with output reconciliation when a provider degrades. Integrated with an HL7v2/FHIR ingest pipeline and an event-driven backbone built on Amazon EventBridge and Amazon MSK, the platform currently serves 12 connected hospital systems (7 AWS-hosted, 5 Azure-hosted) processing over 4 million FHIR resources monthly. Production outcomes include a 70% reduction in clinician chart-review time, a 40% improvement in medical coding accuracy, and a 99.95% AI task completion rate sustained through two separate Bedrock regional degradation events. The architecture, routing logic, and empirical results are described in sufficient detail to serve as a replicable reference design for enterprise-grade clinical AI orchestration in regulated, multi-cloud healthcare settings.