Journal Article

DZNE-2025-00395

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png The EU project Real4Reg: unlocking real-world data with AI.

Peltner, J. (First author)DZNE* ; Becker, C. ; Wicherski, J. ; Wortberg, S. ; Aborageh, M. ; Costa, I. ; Ehrenstein, V. ; Fernandes, J. ; Heß, S. ; Horváth-Puhó, E. ; Korcinska Handest, M. R. ; Lentzen, M. ; Maguire, P. ; Meedom, N. H. ; Moore, R. ; Moore, V. ; Nagy, D. ; McNamara, H. ; Paakinaho, A. ; Pfeifer, K. ; Pylkkänen, L. ; Rajamaki, B. ; Reviers, E. ; Röthlein, C. ; Russek, M.Extern* ; Silva, C. ; De Valck, D. ; Vo, T. ; Bräuner, E. ; Fröhlich, H. ; Furtado, C. ; Hartikainen, S. ; Kallio, A. ; Tolppanen, A.-M. ; Haenisch, B. (Last author)DZNE*

2025
BioMed Central London

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Please use a persistent id in citations: doi:10.1186/s12961-025-01287-y

Abstract: The use of real-world data is established in post-authorization regulatory processes such as pharmacovigilance of drugs and medical devices, but is still frequently challenged in the pre-authorization phase of medicinal products. In addition, the use of real-world data, even in post-authorization steps, is constrained by the availability and heterogeneity of real-world data and by challenges in analysing data from different settings and sources. Moreover, there are emerging opportunities in the use of artificial intelligence in healthcare research, but also a lack of knowledge on its appropriate application to heterogeneous real-world data sources to increase evidentiary value in the regulatory decision-making and health technology assessment context.The Real4Reg project aims to enable the use of real-world data by developing user-friendly solutions for the data analytical needs of health regulatory and health technology assessment bodies across the European Union. These include artificial intelligence algorithms for the effective analysis of real-world data in regulatory decision-making and health technology assessment. The project aims to investigate the value of real-world data from different sources to generate high-quality, accessible, population-based information relevant along the product life cycle. A total of four use cases are used to provide good practice examples for analyses of real-world data for the evaluation and pre-authorization stage, the improvement of methods for external validity in observational data, for post-authorization safety studies and comparative effectiveness using real-world data. This position paper introduces the objectives and structure of the Real4Reg project and discusses its important role in the context of existing European projects focussing on real-world data.Real4Reg focusses on the identification and description of benefits and risks of new and optimized methods in real-world data analysis including aspects of safety, effectiveness, interoperability, appropriateness, accessibility, comparative value creation and sustainability. The project's results will support better decision-making about medicines and benefit patients' health. Trial registration Real4Reg is registered in the HMA-EMA Catalogues of real-world data sources and studies (EU PAS number EUPAS105544).

Keyword(s): Artificial Intelligence (MeSH) ; European Union (MeSH) ; Humans (MeSH) ; Technology Assessment, Biomedical (MeSH) ; Pharmacovigilance (MeSH) ; Decision Making (MeSH) ; Algorithms (MeSH)

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Contributing Institute(s):

1. Pharmacoepidemiology (AG Hänisch)

Research Program(s):

1. 354 - Disease Prevention and Healthy Aging (POF4-354) (POF4-354)

Appears in the scientific report 2025

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