Over the past decade, FHIR has become the dominant standard for exchanging clinical and administrative healthcare data. EHR platforms, national infrastructures, digital health applications, and research initiatives increasingly expose data in FHIR format. Interoperability has made structured, semantically rich data accessible across systems and organizations.

But interoperability is only the first step.

The next frontier is interoperable analytics and intelligence — the ability to run analytics, Clinical Decision Support (CDS), and Business Intelligence directly on standardized FHIR data in a consistent and portable way. Instead of building isolated analytical silos, we can leverage shared data models and shared logic to enable intelligence that works across platforms and institutions.

This session provides a technology-focused overview of how this becomes possible.

We will examine:

• FHIR as a semantic data model and what kinds of analytical and clinical use cases it already enables

• SQL on FHIR as a bridge between FHIR and traditional analytics, reporting, and BI ecosystems

• CQL (Clinical Quality Language) as a formal language for expressing computable clinical logic over FHIR data

• How these technologies complement each other in analytics, CDS, quality measurement, and management scenarios

• Architectural patterns for transforming exchange-oriented FHIR data into scalable analytical pipelines

• Practical limitations and trade-offs when using FHIR data for intelligence

The focus is not theoretical. We will discuss concrete capabilities: what can be implemented today, how these technologies are used in real systems, and where the ecosystem is evolving.

As FHIR adoption grows, the question is no longer whether we can exchange data, but whether we can turn that data into actionable insight. This session explores how FHIR, SQL on FHIR, and CQL together form the foundation for scalable, interoperable Business and Clinical Intelligence.