Natalie Schibell, MPH and Jaquelyn Bilog
A Bottleneck in RWD Research: Translating Clinical Intent Into Actionable Definitions
Operational definitions are foundational to real-world data (RWD) research, yet building them remains one of the most time-consuming and resource-intensive steps. Researchers must translate clinical intent into reproducible, code-based definitions that function across datasets, whether defining eligibility criteria or specifying endpoints.
In large RWE Centers of Excellence, Aetion® Discover is emerging as the platform where epidemiologists validate operational definitions—establishing a scientific framework that maintains methodological rigor while enabling researchers to work confidently within pre-validated parameters. This approach not only preserves scientific integrity but also creates a scalable pathway for accelerating evidence generation across diverse datasets and study designs, a process that is inherently iterative and challenging to standardize across teams, geographies, and data sources.
Discover addresses this complexity by enabling users to define populations and outcomes using the Aetion Measure Library (AML)—a curated set of over 1,600 pre-built, scientifically validated measures. To further support flexibility and enterprise consistency, Discover now also allows researchers to import custom measures built in Aetion® Substantiate for direct use in Aetion® Discover workflows.
Why Consistency of Operational Definitions Matters: Scalability, Transparency, and Reproducibility
Defining real-world outcomes is more complex than it appears. For instance, identifying asthma-related emergency department visits may require a precise blend of diagnosis codes, revenue codes, provider types, and service locations. Even slight variations can significantly impact cohort composition, data comparability, and study reproducibility.
Establishing standardized, versioned definitions aligns internal stakeholders and builds scalable frameworks that can be reused across studies, accelerating setup, ensuring scientific credibility, and maintaining regulatory transparency.
Aetion Measure Library: Built-In Scientific Rigor
Discover includes access to the Aetion Measure Library (AML)—a collection of standardized, study-ready definitions covering commonly used populations, interventions, and outcomes. This reduces time spent on manual coding and promotes consistency from the outset.
Current AML coverage includes:
- 780+ diagnosis-based measures: Standardized definitions for clinical conditions (e.g., asthma exacerbation, myocardial infarction) based on ICD coding systems
- 480+ therapy-based measures: Drug exposure definitions (e.g., GLP-1 receptor agonists, chemotherapy) using CPT/HCPCs, WHO ATC, or other treatment coding standards
- 380+ procedure-based measures: Operationalized interventions (e.g., colonoscopy, PCI) using CPT, HCPCS, or ICD-10 codes
All measures in the AML are:
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Developed and validated by Aetion scientists
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Designed to operate across datasets mapped to the Aetion Data Model (ADM)
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Structured for transparency, reuse, and cross-study documentation
These measures accelerate early-stage analysis while ensuring consistency across studies, users, and data sources.
Image 1: AML measures in Discover
New Feature: Import Measures from Substantiate to Discover
While the Aetion Measure Library (AML) supports a broad range of research questions and therapeutic areas, certain studies require more tailored definitions, particularly when organizations follow internal standards that differ from the library. To address this need, Aetion now supports importing custom measures created in Substantiate into Discover—a capability available to clients who license both products.
Substantiate allows users to define custom, code-based measures using the same tools, interface, and logic applied across Aetion Evidence Platform® for full cohort and outcome design. These measures can now be imported directly into Discover, ensuring workflow consistency without duplicating logic or introducing variability.
With this enhancement, users can:
- Import custom measures from Substantiate into Discover, maintaining alignment across exploratory and inferential analyses
- Apply imported measures in Discover to define inclusion/exclusion criteria or outcome definitions, applying them with the same precision as standard AML measures.
For example, if your team prefers to define COVID-19 vaccination exposure using procedure codes—excluding the therapy codes included in AML’s standard measure—you can now build that definition in Substantiate and use it directly in Discover. This enables alignment with internal definitions while preserving transparency and scientific rigor.
Image 2: Measures imported from Substantiate to Discover
Defining Consistency: How Discover Aligns Operational Standards
From Exploration to Execution: Enabling Aligned, Reproducible Analysis
Integrating Substantiate Measures advances Discover’s role as a unified environment for consistent, study-ready definitions. By enabling teams to bring in custom measures—without duplicating logic or disrupting workflows—Discover now supports faster analysis, better alignment with internal standards, and greater confidence in how populations and outcomes are defined across studies. It’s a practical step toward scalable, transparent real-world evidence generation.
Contact us to schedule a working session and learn how your team can define and deploy consistent, validated measures across Aetion Evidence Platform®.