Optimizing real-world data

Identifying real-world data to transform into transparent, reliable, and replicable real-world evidence (RWE).

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Identify Fit-For-Purpose Data

One of the most common critiques in the regulatory approval process is data that isn’t fit for RWE generation. Too often, relevant variables and endpoints are not captured consistently, or, the data isn’t representative of the target population. Reliable RWE hinges on the selection of fit-for-purpose data and scientifically rigorous analysis, particularly for regulatory-grade studies.

Selecting that fit-for-purpose data, out of the massive amount of healthcare data generated each year, is a tremendous challenge. Fortunately, it’s one that Aetion’s data strategist are passionate about resolving.

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Ensure Suitable Data Investment

Aetion’s data experts understand the landscape and nuances of real-world data, and communicate directly with vendors of real-world data sources, allowing them to make custom recommendations to prioritize data purchasing decisions and increase the likelihood of a client’s success. Aetion’s data services include:

Ensure Suitable Data Investment


Enterprise data strategy

Assess, prioritize, acquire data for an entire asset portfolio; measure the ROI on data.


Data landscape evaluation

Understand the landscape of available data sources for a particular therapeutic area or geographic region, and understand how they compare.


Fit-for-purpose assessment

Using The Structured Process to Identify Fit-for-Purpose Data, (SPIFD) identify which data source(s) are fit-for-purpose for the research question.


Feasibility assessment

Understand which licensed data source is best to use for a specific study.


Synthetic data generation

Synthetic data generation. Aetion® Generate and our Privacy & Synthetic Data Services, use artificial intelligence (AI) for synthetic health data generation, ensuring privacy and integrity of the original data source.


Customizable and reusable measures

Clinical definitions mapping back to the source data can take months to code. Aetion’s data strategists make it easy by providing a library of customizable and reusable algorithms built with the most current clinical definitions.


Data Preparation and Hosting

Preparation of real-world data for analysis can be complex and unwieldy, especially when leveraging traditional programming tools for large and complex data structures. When our healthcare data scientists integrate real-world data into AEP, that work is done for you and our world-class expertise is encoded directly into each data source that is made available for analysis on our platform.


Data Transformation

Longitudinal healthcare data can be difficult to prepare for analysis. Data ingested onto AEP are validated and optimized for longitudinality, which is foundational for causal inference. All data transformations are fully and automatically documented.


Data Stacking

Creating a super dataset but stacking datasets when sample sizes are small. This allows for disparate data sources to be combined for robust comparative analytics.