Decision-Ready Evidence, Any Data Model, No Compromises

Written by Aetion | Mar 7, 2025

Real-world data (RWD) exists in multiple formats—claims, electronic health records, registries, and proprietary models—each structured to capture different aspects of patient care and outcomes. Standardization enhances interoperability, allowing data from diverse sources to be analyzed at scale, but no single model fully preserves the clinical specificity needed for decision-grade evidence. Achieving both interoperability and scientific rigor requires an approach that adapts to different data models without compromising transparency, traceability, or methodological integrity.

Built for Any Data Model, Designed for Scientific Rigor

Aetion® provides the infrastructure and expertise to generate decision-grade evidence from any data model. Rather than imposing a singular approach, Aetion ensures that any data model—whether standardized or proprietary—supports rigorous, transparent, and fit-for-purpose evidence generation. Standardization enhances efficiency where applicable, but clinical specificity and methodological integrity always take priority.

Aetion Evidence Platform® (AEP) is built for this complexity. To date, Aetion has ingested more than 140 unique datasets across multiple data types: EHR, U.S. claims, oncology-specific datasets (COTA NSCLC, COTA Breast, etc.), registries, labs, and clinical trial data. These data can be ingested in their native format or in a common data model such as OMOP to ensure that organizations can generate high-confidence evidence from any source.

Real-World Data Sources Supported by Aetion

Category	Description
Healthcare Data Sources	Directly sourced real-world data (RWD) that provides clinical, claims, and outcomes insights.
EHR-based data	Captures rich clinical details directly from patient encounters, preserving clinical specificity for robust evidence generation.
Claims-based data	Provides structured, large-scale insights into treatment patterns, healthcare utilization, and payer interactions.
Registry Data	Aggregates patient outcomes and clinical insights across specific populations, diseases, or treatments.
Oncology-Specific Datasets	Captures detailed cancer care data, including biomarker testing, disease staging, and treatment outcomes.
Laboratory Data	Provides access to diagnostic test results, enhancing clinical specificity in real-world studies.
Clinical Trial Data	Integrates structured trial datasets to enable hybrid analyses combining clinical and real-world evidence.
Common Data Models (CDMs)	Enable interoperability while maintaining scientific rigor, allowing data harmonization across multiple sources.
OMOP	Facilitates cross-database research with standardized structures while ensuring regulatory traceability.
Aetion Data Model (ADM)	Supports interoperability while maintaining the analytical integrity of both EHR and claims datasets, allowing organizations to optimize study designs without rigid data model constraints.
Industry Standards & Networks	Established data exchange models and research networks that enable large-scale collaboration.
FHIR	Facilitates seamless data exchange and integration across healthcare systems, supporting regulatory and clinical research.
PCORnet	Enables patient-centered outcomes research by linking diverse datasets for population-level insights.
Sentinel	Designed to support active safety surveillance and post-market drug monitoring for regulatory agencies.
I2B2	Provides a scalable, federated approach to analyzing healthcare data across multiple institutions.
Proprietary Frameworks	Supports custom data structures while ensuring full transparency and scientific validity.
Other Industry Standards	Covers additional emerging standards and proprietary data models tailored to specific research and regulatory needs.

Turning Any Dataset into Longitudinal, Decision-Grade Evidence

Data model selection should support—not restrict—evidence generation. Every transformation within Aetion’s platform follows best-in-class epidemiologic and analytic methods to maintain scientific validity. This approach ensures:

Preservation of clinical specificity: Data fidelity is safeguarded to ensure findings accurately reflect real-world treatment patterns.
Full transparency and traceability: Every transformation is auditable, meeting regulatory and payer expectations for defensibility.
Methodological rigor in every analysis: Advanced epidemiologic frameworks ensure findings are reliable, reproducible, and fit for decision-making.

While common data models (CDMs) facilitate interoperability and large-scale research, they are not the only path to generating decision-grade RWE. Aetion’s platform works within OMOP, ADM or any common or proprietary model, enabling organizations to maximize their data’s potential while applying rigorous analytical frameworks.

Standardization Where It Matters, Flexibility Where It Counts

Data standardization enhances consistency across datasets and should serve to enhance study design. Some organizations adopt OMOP to harmonize multi-database research, while others maintain proprietary models to preserve critical clinical attributes. Aetion ensures both approaches deliver scientifically valid, high-confidence results, balancing interoperability with the need for clinical specificity and regulatory alignment. Indeed, many of our customers maintain two views of a dataset: a CDM view to maximize the benefits of CDMs, and a native view when the original data content is required.

Driven by interoperability requirements within the European Union, OMOP adoption continues to accelerate globally. As this standard gains traction, organizations in the U.S. must be equipped to work within OMOP while maintaining flexibility for other data structures. Aetion enables this balance—leveraging OMOP where needed while preserving scientific rigor and clinical specificity.

For organizations using OMOP: The model’s benefits are optimized while Aetion’s safeguards are layered in to ensure regulatory traceability, methodological rigor, and clinical fidelity.
For organizations with proprietary structures: Aetion ensures flexibility without compromising scientific integrity, enabling decision-grade evidence generation across any data structure. Proprietary data can be converted to a CDM like OMOP or ADM, used in its original format, or both.

Enabling High-Confidence RWE Across Any Data Model

Aetion Evidence Platform® (AEP) ensures that study design and execution uphold the highest standards of scientific rigor rather than being limited by data model constraints. Organizations retain control over how their data is structured while gaining the confidence that their results meet the highest regulatory, payer, and commercial standards. Designed for adaptability, Aetion seamlessly integrates across diverse data landscapes to support decision-grade evidence generation by:

Supporting multi-database research: Ensuring global evidence generation is methodologically sound, reproducible, and scalable for long-term insights.
Providing full regulatory traceability: Supporting compliance with FDA, EMA, and HTA expectations through transparent methodologies and auditable workflows.
Enabling fit-for-purpose study designs: Aligning analytic approaches with regulatory and payer needs rather than enforcing rigid data model constraints, ensuring that studies meet both scientific and operational goals.

Aetion empowers life sciences companies, payers, and regulators to generate transparent, traceable, and decision-grade evidence—no matter the data model.

Standardized when needed, adaptable where necessary, and decision-grade always.

Ready to transform real-world data into trusted evidence? Let’s get to work.

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