How Hyperion™ Works – Under the Hood of Real Healthcare Data Intelligence
June 27, 2025 | 5-minute read
Introduction
In the first blog of our four-part Hyperion™ blog series, we looked at the data chaos currently hindering payers - siloed sources, redundant pipelines, and batch-based analytics that delay action. In the second blog, we explained how Hyperion™ shifts this paradigm with a modular, FHIR-native foundation and a real-time, event-driven approach to data consumption.
Now, in this blog, we go under the hood - diving a bit deeper into the technical architecture of Hyperion™ and how it delivers scalable, normalized, and actionable intelligence across both FHIR and non-FHIR data.
This isn’t just about another data platform. It’s about rethinking the data layer that payers rely on every day - to eliminate duplication, reduce cost, and deliver insight when and where it matters most.
Why a New Data Architecture Was Needed
Despite exponential growth in healthcare data, most payer organizations still operate on systems that weren't designed to support it. Every new use case - be it care gap analytics, risk adjustment, or member stratification - requires new data bridges, ad-hoc transformations, and manual processes. Critical data sits in separate systems, and dashboards refresh with hours or even days of lag.
According to Gartner, over 75% of payer organizations still rely on legacy platforms that block real-time interoperability and scale1. Oracle warns that data silos compromise both member experience and timely decision-making2.
Hyperion™ addresses this head-on, not by layering more dashboards or brittle ETL pipelines, but by building a FHIR-native, modular architecture from the ground up.
From Monolithic Burden to Modular Intelligence
Traditional payer data platforms were designed for stability, not adaptability. Their monolithic structure makes even minor changes, like onboarding a new claims feed or building a custom dashboard, time-consuming and expensive. Over time, this inflexibility builds up as technical debt, forcing teams into reactive work and limiting innovation.
Hyperion™ flips that model. It’s built on a microservices-based, containerized architecture that breaks the data pipeline into independently scalable components. Deployed via Container Apps or Kubernetes, each service can scale horizontally with zero downtime, ensuring performance without bottlenecks or resource contention.
At the core of Hyperion™ lies a normalized, compute-separate and storage-separate architecture, purpose-built for native cloud scale. It stores both FHIR and non-FHIR data in a consistent, query-ready structure that aligns with payer business needs.
FHIR’s inherent design provides clean, non-redundant data in a single, structured model. Hyperion™ builds on this foundation by transforming that FHIR data and non-FHIR sources like HL7 messages or claims feeds - into normalized formats consumable via Common Data Models like PCORnet and Sentinel. This enables fast, flexible access without custom integrations.
To further streamline operations, Hyperion™ introduces the concept of Data Marts: curated, query-ready data layers that allow payer business teams to explore and segment the information they need without duplicating data or creating brittle ETL pipelines.
Whether your organization is enabling utilization management, automating risk models, or powering real-time dashboards, Hyperion™ ensures the right data is available, scalable, and ready to act.
Cloud-Native, Ecosystem-Aligned
In today’s healthcare environment, one-size-fits-all does not work. Infrastructure often spans multi-cloud environments, diverse clinical systems, and a wide array of analytics platforms.
Hyperion™ is built to align - with this ecosystem:
- FHIR Server Agnostic: Supports Azure Health Data Services, AWS HealthLake, Firely, SmileCDR, and more
- Cloud-Native: Deployable in Azure, AWS, or hybrid environments
- Destination Connectors: Seamlessly pushes curated data to Snowflake, Databricks, Microsoft Fabric, and more
- Analytics-Ready: Designed for BI tools like Power BI, Tableau, QuickSight, and DBVisualizer
With these integrations, teams don’t need to replace their stack - they just plug into better, faster, cleaner data from day one.
Streaming Data. Real-Time Insight
In most payer workflows today, analytics are still batch-driven. Data is ingested, transformed, validated - then eventually pushed into a dashboard or report, sometimes 24 hours, or even days, later depending on the complexity of transformations and availability of specialized resources.
Hyperion™ eliminates this delay by making real-time data delivery a core design principle.
As an example, data is ingested through event-driven loaders via Health Chain’s Centaur™ Data Platform, which immediately validates, deduplicates, and normalizes inputs, including both FHIR and non-FHIR sources and standardizes them into a FHIR-native format. This data can then be queried without delay, thanks to Hyperion’s analytical engine, which enables payers to create custom Data Marts effortlessly.
This continuous pipeline ensures that insights reach BI tools, triggers, and dashboards the moment the data is available - not the next morning.
As Forrester notes, real-time intelligence requires modular, interoperable platforms that replace traditional ETLs with streaming logic². Hyperion™ embodies that shift.
Driving Real Impact Across Use Cases
Hyperion™ isn’t just infrastructure - it’s enabling smarter workflows across the payer landscape.
Payer organizations are using Hyperion™ to:
- Optimize Emergency Department Utilization
→ Use real-time ADT (Admit, Discharge, Transfer) signals to alert care teams, reduce readmissions, and coordinate timely post-discharge interventions.
- Accelerate Risk Stratification
→ Leverage normalized FHIR data combined with real-world utilization to produce accurate, up-to-date risk scores and streamline risk revenue forecasting.
- Close Care Gaps Proactively
→ Trigger automated alerts or workflows when a member misses a screening, diagnosis, or follow-up milestone - driven directly from streaming data events.
All of these use cases are fueled by Hyperion™ and its’ ability to provide clean, non-redundant data in the form of one Common Data Model, while providing the flexibility for business units to create data marts tailored to their specific use case – without actually duplicating the data or having to build redundant ETL pipelines.
From Data Platform to Strategic Intelligence Layer
Legacy systems focused on moving and storing data. Hyperion™ focuses on making data usable.
With its modular, containerized architecture and real-time pipelines, Hyperion™ becomes a decision layer - not just a data platform.
Key differentiators include:
- A containerized, microservices-first architecture
- Sink connectors to Snowflake, Fabric, and Databricks
- A Medallion-style design that flows data from raw → curated → consumable
- No replication of data
- Embedded governance, reuse, and domain-specific access
- Support for both FHIR and non-FHIR data in a single pipeline
- An enterprise-level Common Canonical Data Model with flexibility
As Oracle notes, organizations that thrive will be those who turn data into operational intelligence - not just reports³. Hyperion™ delivers that future now.
Ready to See Hyperion™ in Action?
Let us show you how Hyperion™ can simplify your pipelines, accelerate insights, and turn your data into a payer intelligence engine.
📩 Contact us | 💡 Schedule a personalized walkthrough → Click here!
1 Gartner. Modernizing Healthcare Payer Core Systems. 2023
https://www.gartner.com/en/documents/4021045
2 Forrester. Healthcare Data Platforms Must Evolve or Die. 2023.
https://go.forrester.com/blogs/healthcare-data-platforms-must-evolve-or-die
3 Oracle Health. Bridging Data Gaps in Payer Ecosystems. 2022.
https://www.oracle.com/industries/healthcare/articles/connected-healthcare/
