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Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

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The EU project Real4Reg: unlocking real-world data with AI.

Jonas Peltner¹, Cornelia Becker², Julia Wicherski²

¹German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.

Health Research Policy and Systems

|February 27, 2025

Summary

The Real4Reg project enhances regulatory decision-making by developing AI-driven solutions for analyzing real-world data (RWD). This initiative aims to improve drug safety and effectiveness evaluations throughout the product lifecycle.

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Area of Science:

Regulatory Science
Health Technology Assessment
Data Science

Background:

Real-world data (RWD) use is established in post-authorization regulatory processes but faces challenges in pre-authorization stages.
Limited availability, heterogeneity, and analytical complexities of RWD hinder its regulatory application.
Emerging AI in healthcare research offers opportunities but requires knowledge on applying it to diverse RWD for regulatory decision-making and health technology assessment (HTA).

Purpose of the Study:

To enable RWD utilization by developing user-friendly analytical solutions for EU regulatory and HTA bodies.
To implement AI algorithms for effective RWD analysis in regulatory decision-making and HTA.
To investigate the value of diverse RWD sources for generating high-quality, population-based information across the product lifecycle.

Main Methods:

The Real4Reg project employs four use cases to demonstrate best practices for RWD analysis.
These use cases cover evaluation, pre-authorization, external validity of observational data, post-authorization safety studies, and comparative effectiveness.
The project introduces its objectives, structure, and role within the European RWD landscape.

Main Results:

Real4Reg identifies and describes benefits and risks of new/optimized RWD analysis methods.
Key aspects evaluated include safety, effectiveness, interoperability, appropriateness, accessibility, value creation, and sustainability.
The project aims to provide evidence supporting better decision-making for medicines.

Conclusions:

The Real4Reg project's findings will support improved decision-making regarding medicines.
Enhanced RWD analysis methods will ultimately benefit patient health.
Real4Reg is registered in the HMA-EMA Catalogues of real-world data sources and studies (EU PAS number EUPAS105544).