Toward the Use of Real-World Data for Regulatory and HTA Decision-Making: Experiences and Recommendations from Six EU-Funded Projects.

Authors

Martin Russek, Jonas Peltner, Kerstin Pfeifer, Theodoros N Arvanitis, Dirk De Valck, Sieta T de Vries, Michel Dumontier, Vera Ehrenstein, Omaima El Hakouni, Gregory Epiphaniou, Gokhan Ertaylan, Joana Fernandes, Scott Hansen, Stuart Harrison, Erzsébet Horváth-Puhó, Saif Ul Islam, Pekka Kahri, Julia K Mader, Carsten Maple, Nikolaos Matragkas, Shivani Misra, Peter G M Mol, Anne Paakinaho, Sami Pakarinen, Martina Radanović, Evy Reviers, Kathrine Kold Sørensen, Marco Spruit, Christina Tikka, Anna-Maija Tolppanen, Christian Torp-Pedersen, Mirna Vela, Thuan Vo, Puriya Daniel Würtz Yazdanfard, Britta Haenisch

Published in

Clinical pharmacology and therapeutics. Oct 02, 2026. Epub Oct 02, 2026.

Abstract

Real-world data are increasingly important for regulatory and health technology assessment decision-making, yet their effective use across Europe remains limited by technical, methodological, regulatory, and societal challenges. Drawing on the experiences of six Horizon Europe projects in the MetReal cluster, this review summarizes common barriers and emerging solutions for the use of real-world data in regulatory decision-making. Key challenges include complex and lengthy data access procedures, fragmented governance frameworks, variable data quality and completeness, limited interoperability, methodological difficulties in analyzing heterogeneous datasets, and infrastructure constraints within secure data environments. The article highlights opportunities to address these barriers through improved metadata, harmonization, federated analytics, synthetic data sandboxes, sustainable funding, and stronger collaboration between researchers, regulators, health technology assessment bodies, data holders, and citizens. These recommendations aim to strengthen Europe's health data ecosystem and support more robust, efficient, and trusted evidence generation for public health and patient benefit.

PMID:
42823907
Bibliographic data and abstract were imported from PubMed on 02 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.