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Background <p>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.</p> Methods <p>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.</p> Discussion <p>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.</p> <p><i>Trial registration</i> Real4Reg is registered in the HMA-EMA Catalogues of real-world data sources and studies (EU PAS number EUPAS105544).</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月27日

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  • Jonas Peltner,
  • Cornelia Becker,
  • Julia Wicherski,
  • Silja Wortberg,
  • Mohamed Aborageh,
  • Inês Costa,
  • Vera Ehrenstein,
  • Joana Fernandes,
  • Steffen Heß,
  • Erzsébet Horváth-Puhó,
  • Monika Roberta Korcinska Handest,
  • Manuel Lentzen,
  • Peggy Maguire,
  • Niels Henrik Meedom,
  • Rebecca Moore,
  • Vanessa Moore,
  • Dávid Nagy,
  • Hillary McNamara,
  • Anne Paakinaho,
  • Kerstin Pfeifer,
  • Liisa Pylkkänen,
  • Blair Rajamaki,
  • Evy Reviers,
  • Christoph Röthlein,
  • Martin Russek,
  • Célia Silva,
  • Dirk De Valck,
  • Thuan Vo,
  • Elvira Bräuner,
  • Holger Fröhlich,
  • Cláudia Furtado,
  • Sirpa Hartikainen,
  • Aleksi Kallio,
  • Anna-Maija Tolppanen,
  • Britta Haenisch

摘要

Background

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.

Methods

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.

Discussion

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).