Healthcare payers today face an unprecedented challenge: extracting meaningful insights from disparate data sources containing both structured & unstructured claims, administrative & clinical data. Traditional approaches have created a labyrinth of fragmented ETL processes, each designed for specific data sources & formats, resulting in unmaintainable systems that consume more resources than value generated.

Hyperion™ fundamentally reimagines this landscape by standardizing data processing through FHIR (Fast Healthcare Interoperability Resources), eliminating the need for multiple custom ETL pipelines and enabling a unified core data model that integrates seamlessly with existing analytics tools ¹

Challenge: Today's payer organizations grapple with data sources hosting a heterogeneous mix of structured claims data, unstructured clinical notes, provider directory data, and member records. Each data source demands its own custom ETL logic, forming an unscalable web of interdependencies. Engineers spend up to 80% of their time on data wrangling, while stakeholders wait days or weeks for insights ^{3, 4, 25}

Solution: Hyperion™ simplifies this chaos with a unified, FHIR based processing architecture. By transforming all incoming data into standardized FHIR resources, it reduces ETL development time by up to 65% and ensures consistency across all healthcare domains ^{5, 6}

Challenge: Legacy systems require separate ETL logic for each domain - claims, clinical, pharmacy, provider, etc. As new sources are added, complexity multiplies exponentially ^{7, 8}. This results in technical debt that burdens the team and introduces unnecessary data quality and governance risks.

Solution: Hyperion™ leverages FHIR’s modular schema to eliminate the ETL duplication problem. Whether ingesting from Epic, Cerner, Core Administrative tools, Labs or other third parties, Hyperion™ provides one standard transformation engine ^{9, 10, 11}

Challenge: Analysts are forced to navigate SQL scripts, spreadsheets, and siloed BI tools, each offering conflicting views of the same data. This inconsistency erodes trust and increases overhead ^{12, 13}.

Solution: Hyperion™ exposes a FHIR native core data model, accessible via REST APIs, SQL on FHIR, and direct integrations with Power BI, QuickSights, Tableau, Snowflake, Databricks, and more ^{14, 15, 16}.

Challenge: Traditional analytics rely on batch pipelines that delay critical insights by hours or days, jeopardizing patient care and regulatory responsiveness ^{17, 18}.

Solution: Hyperion™ ingests and processes data in real time and batch, enabling streaming dashboards, automated alerts for care gaps, and up to the minute analytics for prior authorization requests and care coordination ^{19, 20, 21}.

The industry can no longer sustain fragmented ETL ecosystems across the countless data sources available. Hyperion™ represents a paradigm shift: a FHIR-native data intelligence platform that eliminates redundancy, scales effortlessly, and integrates out of the box with existing analytics ecosystems.

By embracing FHIR as a universal data model, Hyperion™ becomes not just a solution for today's problems but a platform for tomorrow’s innovation. It empowers healthcare organizations to modernize their data architecture while maintaining flexibility, regulatory alignment, and clinical responsiveness ^{22, 23, 24}.

Hyperion™ transforms payer data architectures from a collection of disconnected ETL processes into a streamlined engine for healthcare intelligence.

¹ OHDSI ETL using FHIR https://www.ohdsi.org/wp content/uploads/2021/08/44 Development of an ETL Process for Bulk and Incremental Load of German Patient Data into OMOP CDM Using FHIR.pdf

² Surety Systems: FHIR Data Model Guide https://www.suretysystems.com/insights/understanding the fhir data model a comprehensive guide/

³ OHDSI: https://www.ohdsi.org/wp-content/uploads/2021/08/44-Development-of-an-ETL-Process-for-Bulk-and-Incremental-Load-of-German-Patient-Data-into-OMOP-CDM-Using-FHIR.pdf

⁴ AWS HealthLake: https://docs.aws.amazon.com/healthlake/latest/devguide/what-is.html

⁵ Google Open Health Stack https://developers.google.com/open health stack/fhir analytics/data pipes

⁶ NCQA on Digital Data https://www.ncqa.org/resources/preparing data for digital measurement/

⁷ AWS HealthLake FHIR Guide https://aws.amazon.com/healthlake/

⁸ Microsoft FHIR Data Export https://learn.microsoft.com/en us/industry/healthcare/healthcare data solutions/ahds data export overview

⁹ SmileCDR Documentation https://smilecdr.com/docs/fhir_repository/deleting_data.html

¹⁰ Azure FHIR to Delta Lake https://techcommunity.microsoft.com/blog/healthcareandlifesciencesblog/connecting fhir data to azure databricks delta lake in azure health data service/3682104

¹¹ Google Cloud Healthcare API https://cloud.google.com/healthcare api/docs/fhir etag

¹² PMC Case Studies on Batch Limitations https://pmc.ncbi.nlm.nih.gov/articles/PMC10690355/

¹³ HealthIT.gov FHIR Resources https://ecqi.healthit.gov/fhir

¹⁴ HeliosHub iPaaS https://www.virtualhealth.com/news/helioshub launches as first ever fhir ipaas/

¹⁵ Relevant Software FHIR Bundles https://www.intersystems.com/

¹⁶ CData: FHIR Sync to Azure Data Lake https://www.cdata.com/data/integration/fhir to microsoft azure data lake/

¹⁷ HealthIT.gov: https://www.healthit.gov/

¹⁸ Azure FHIR Import https://learn.microsoft.com/en us/azure/healthcare apis/fhir/import data

¹⁹ QRvey Healthcare Analytics https://qrvey.com/fhir healthcare analytics/

²⁰ FHIR + FAIR Principles https://confluence.hl7.org/spaces/SOA/pages/104570026/HL7+FHIR+and+FAIR+principles

²¹ HealthLake Batch vs Real Time https://docs.aws.amazon.com/healthlake/latest/devguide/what is.html

²² HL7 US Core FHIR https://build.fhir.org/ig/HL7/US Core/

²³ FHIR OMOP IG https://build.fhir.org/ig/HL7/fhir omop ig/

²⁴ Google FHIR Data Pipes https://google.github.io/fhir data pipes/installation_pipeline/

²⁵ https://www.pragmaticinstitute.com/resources/articles/data/overcoming-the-80-20-rule-in-data-science/