Healthcare data will grow at 35%+ CAGR through 2025¹
Most healthcare systems still rely on batch processing for high-volume data²
U.S. healthcare payer analytics market projected to reach $43.55 billion by 2034³

Healthcare moves fast. Your data platform should too. While legacy systems and siloed operations keep healthcare payers tethered to inefficiency⁴, the future demands real-time, patient-centric intelligence.

In our Hyperion™ blog series thus far, we've explored how Hyperion™ transforms payer data chaos into coordinated intelligence, examined why Hyperion™ represents a new philosophy for healthcare data workflows, and looked under the hood at how Hyperion™ delivers real healthcare data intelligence. Now, let's complete the picture with the story of how Hyperion™ evolved from HCFhir+ to meet tomorrow's healthcare data challenges and why this transformation matters for your organization.

The healthcare industry is experiencing a fundamental shift: from retrospective reporting to proactive intelligence. Organizations are now required to close care gaps in real-time, coordinate care transitions seamlessly, and make data-driven decisions on pace with patient care, otherwise the health plan’s star rating and ability to remain competitive will be compromised.

To enable Payers to unlock value from FHIR data beyond simply checking the box for interoperability compliance, Health Chain built HCFhir+ which supported analytics on FHIR data well, but lacked flexibility for modern workflows. Organizations stuck with rigid architectures miss opportunities to create competitive advantages in today's dynamic healthcare environment.

Quick Comparison:

Legacy Approach → Modern Requirements

• Batch processing → Real-time streams
• Fixed schemas → Flexible modeling
• Manual scaling → Auto-optimization

Takeaway: Healthcare organizations processing millions of records⁵ require platforms built for scale, speed, and adaptability.

Health Chain's HCFhir+ served its purpose - enabling SQL analytics on FHIR data. But as healthcare demands evolved, three critical limitations emerged:
Performance Bottlenecks

• Processing speed maxed out at thousands of records/hour
• Memory constraints with large datasets
• Limited parallel processing capabilities

Extensibility Roadblocks

• Rigid data modeling with limited flexibility
• Manual development of custom integrations
• Inflexible workflow orchestration

Scalability Ceiling

• Single-tenant architecture limitations
• Manual infrastructure management
• Cloud optimization gaps
• Growing data storage requirements lead to increased storage costs.

Hyperion™ wasn't an upgrade - it was a ground-up rebuild focused on three pillars:

1. Modular Design

• Pick-and-choose components
• Easy third-party integration with out-of-the-box sink connectors like Databricks, Snowflake and many more
• Future-proof extensibility

2. Cloud-Native Performance

• Process millions of records per hour
• Auto-scaling infrastructure
• Multi-cloud deployment options
• Rationalized data storage costs with storage separate, compute separate design

3. Configurable Intelligence

• Role-based data access
• Domain-specific customization
• Workflow automation

Real Impact: Beta organizations saw significant performance improvements, faster time-to-insight and increased usability with a Common Canonical Data Model (specific metrics vary by implementation).

"Hyperion is a normalized, compute-separated, storage-separated architecture that can scale massively in a native cloud environment to easily make normalized FHIR data available in the relational format for consumption alongside other non-FHIR data sources. FHIR provides clean, non-redundant data in a single, industry-standard data model, while Hyperion delivers easily consumable, normalized data in various Common Canonical Data Models. Hyperion also introduces the concept of Data Marts, enabling a payer’s internal business teams to curate their own data without duplicating it or building additional ETL pipelines."

1. Extensible Architecture with CDM Foundation

HCFhir+ relied on monolithic, hard-coded data flows with proprietary schemas. This created bottlenecks when organizations needed to integrate new data sources or adapt to evolving requirements.

Hyperion™ transforms this with microservices built on Common Canonical Data Model standards. Organizations can now leverage existing payer data investments while adding new data sources and domains in days, not months. Hyperion’s extensible Common Canonical Data Model provides a single, enterprise-grade data model across all Payer data domains.

2. Enhanced Performance

Sequential processing and memory bottlenecks exhausted HCFhir+, limiting throughput to thousands of records per hour.

Hyperion™ delivers distributed computing for faster query performance, using container orchestrations technology for linear scaling that grows and meets Payer data needs.

3. Payer-Optimized Configurability

One-size-fits-all data models made HCFhir+ incompatible with diverse payer workflows, forcing the use case into rigid structures that didn't match the operational reality.

Hyperion™ provides role-based views, custom data mart configuration, and pre-built data marts, delivering tailored analytics for different workflows and use cases such as Utilization Management, Care Coordination, Value Based Care, Risk Adjustment and more.

4. Resource-Aware Automation

Manual data pipeline management with no resource optimization meant constant hands-on oversight and inefficient resource allocation in HCFhir+.

Hyperion™ introduces automated workflows, intelligent resource pooling, and a significant reduction in operational overhead while maximizing current IT investments.

Hyperion™ doesn't replace your existing infrastructure - it enhances it through seamless integrations:

Natural Language Analytics (SQLAI)

• Simple natural language queries like "Show me high-risk diabetic patients in North Carolina"
• Self-service analytics for non-technical users
• Automated report generation

Enterprise Governance

• Unified RBAC across all data platforms
• Centralized audit logging and compliance
• Data lineage tracking for regulatory requirements⁶

Legacy System Bridge

• Data lake and FHIR Server connectivity
• Gradual migration pathways
• Easy integration with destination connectors like Snowflake, Databricks, etc.

Immediate Benefits

Speed: Real-time insights replace day-old reports while preserving existing data investments

Scale: Handle data growth without performance degradation, infrastructure overhaul, or snowballing storage costs

Agility: Adapt to new requirements from the business instantly using established, composable CDM frameworks

Strategic Advantages

Investment Protection: Maximizes ROI on existing payer data analytics infrastructure and CDM implementations

Competitive Edge: First-mover advantage in establishing real-time payer analytics without ripping -and -replacing entire ecosystem

Data Governance & Quality: Consumers can trust in the quality of data being consumed with detailed data lineage + the organization can trust in who is accessing the data with strict, transparent governance policies

Ready-to-Use Data: Enterprise-wide access to consumable clinical, claims, provider & administrative data from a Common Canonical Data Model.

The journey from HCFhir+ to Hyperion™ represents more than a technical upgrade - it is healthcare's data evolution in action. While legacy systems struggle with today's demands, Hyperion™ anticipates tomorrow's opportunities.

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Built for what's next, Hyperion™ helps payers lead-not follow-the future of healthcare data.

¹ Roots Analysis: "Healthcare data is anticipated to grow at an exponential rate, with a CAGR of more than 35% until 2025" - Big Data Healthcare Market Report -
https://www.rootsanalysis.com/reports/view_pdf/big-data-healthcare-market-2032/524.html

² WebMD Ignite: "Batch processing is an efficient way of processing high volumes of healthcare data" - Healthcare Data Processing Guide -
https://webmdignite.com/blog/best-way-to-manage-healthcare-big-data#:~:text=Batch%20processing%20is%20an%20efficient%20way%20of%20processing,processing%20%28CEP%29%2C%20which%20uses%20event-by-event%20processing%20and%20aggregation.

³ Nova One Advisor: "U.S. healthcare payer analytics market projected to hit around USD 43.55 billion by 2034, growing at a CAGR of 22.2%" - Healthcare Payer Analytics Market Report -
https://www.novaoneadvisor.com/report/healthcare-payer-analytics-market

⁴ Hakkoda: "Healthcare payers face legacy systems, siloed operations, and escalating costs keep organizations tethered to inefficiency" - Hakkoda – State of Data Healthcare Report 2024 -
https://hakkoda.io/state-of-data-healthcare-2024/

⁵ Roots Analysis: "A hospital can generate around 50 petabytes of patient data and operational data per day" - Healthcare Data Generation Report -
https://www.rootsanalysis.com/reports/view_pdf/big-data-healthcare-market-2032/524.html

⁶ SpringerLink: "Healthcare systems must process and analyze torrents of data in real time, but also be capable to perform long-term batch operations" - Medical Data Processing Research -
https://link.springer.com/article/10.1186/s13104-021-05810-2