Turn Governed Clinical Data Into Research Value
DATA PARTNERSHIPS
Partner With Ario Health to Advance Life Sciences Research.
A More Complete Picture of Real-World Care.
INTEGRATED REAL-WORLD DATA
Healthcare data captures different pieces of the patient experience. Clinical records document diagnoses, procedures, and treatment decisions. Claims reveal utilization and care across settings. Laboratory and diagnostic information add critical clinical detail.
ArioGPT brings complementary real-world data together to support a richer understanding of patient populations, treatment patterns, and outcomes.
Different Data. One Connected View.
DATA HARMONIZATION
Real-world healthcare data is fragmented across systems, formats, and clinical terminologies.
ArioGPT's data foundation is designed to help reconcile these differences, organize complementary information, and support more meaningful analysis across healthcare datasets.
The result is a more consistent foundation for investigating patient populations, treatment pathways, and clinical questions.
Clinical Meaning Changes Everything
THE CLINICAL ONTOLOGY FOUNDATION
Healthcare information is more than a collection of codes, records, and observations. Understanding it requires knowledge of the clinical concepts and relationships that connect diseases, treatments, procedures, and patient experiences.
ArioGPT's Clinical Ontology Engine provides a semantic foundation for interpreting healthcare data in context, connecting medical terminology and related concepts across complex information sources.
By bridging fragmented data and clinical meaning, the ontology helps power more relevant exploration and analysis throughout ArioGPT.
Built on the Language of Healthcare.
CLINICAL REFERENCE DATASETS
Clinical data becomes more valuable when medical terminology, drug information, diagnoses, procedures, laboratory observations, and regulatory context can be interpreted consistently.
ArioGPT's ontology foundation draws on established healthcare terminology and reference resources to connect related clinical concepts and support more meaningful investigation.
Reference Datasets
Healthcare data becomes more meaningful when it can be connected to standardized clinical concepts. ArioGPT draws on clinical terminology, coding systems, and reference datasets to support consistent interpretation across diverse sources.
ICD-10
Diagnosis Classification
Classification of diseases and health conditions.
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How it contributes
Supports standardized diagnosis categories, disease-based cohort definitions, and longitudinal analysis of documented conditions.
SNOMED CT
Clinical Terminology
Standardized clinical concepts and relationships.
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How it contributes
Provides a structured framework for interpreting clinical findings, disorders, procedures, and related concepts across different records.
RxNorm
Medication Terminology
Normalized medication names and relationships.
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How it contributes
Helps align medication references, ingredients, strengths, and clinical drug concepts to support consistent medication analysis.
LOINC
Laboratory & Observations
Identifiers for laboratory tests and clinical observations.
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How it contributes
Supports consistent identification and comparison of laboratory measurements and clinical observations across participating data systems.
NDC
Drug Product Identification
Identifiers for drug products and packaging.
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How it contributes
Adds product-specific drug identification to support interpretation of medication records and utilization patterns.
HCPCS
Services & Procedures
Codes for healthcare services, products, and procedures.
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How it contributes
Supports interpretation of healthcare services, medical products, and selected procedures in applicable claims and utilization data.
FDA SPL
Drug Label Information
Structured regulatory drug labeling information.
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How it contributes
Provides structured reference information about labeled indications, warnings, and other product-specific drug information.
More than codes. Connected clinical meaning. Reference datasets help establish the semantic foundation for interpreting complex healthcare data through ArioGPT.
From Connected Data to
Strategic Intelligence.
THE ARIOGPT INTELLIGENCE FOUNDATION
ArioGPT combines four interconnected layers to make complex healthcare evidence more useful for life sciences decision-making.
CONNECTED DATA TO ACTIONABLE INTELLIGENCE
What Better-Connected Intelligence Enables
Deeper Patient Journey Insights
Understand how patients move through diagnosis,
treatment, and care.
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Earlier Signal Detection
Identify emerging patterns, unexpected trends,
and potential evidence gaps.
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Clearer Unmet Needs
Identify patient populations where care gaps
and treatment limitations persist.
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Stronger Evidence Generation
Develop richer real-world evidence and
insights into clinical outcomes.
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Better Opportunity Prioritization
Evaluate therapeutic opportunities,
patient segments, and strategic potential.
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Continuous Strategic Intelligence
Keep decision-making aligned with
evolving real-world evidence.
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Better-connected data.
More meaningful intelligence.
From understanding the complete patient journey
to identifying the next strategic opportunity.
When fragmented healthcare data becomes connected, clinically meaningful intelligence, life sciences teams can ask more powerful questions, discover deeper insights, and make more informed decisions.
HOW IT WORKS
Ask → Discover → Quantity → Prioritize → Act
Move from questions to strategy in minutes, not months.
