European survey: citizens' attitudes on personalized medicine, genetic testing and health data sharing – design and delivery.

In the transformative landscape of healthcare, personalized medicine emerges as a pivotal shift, harnessing genetic, environmental and lifestyle data to tailor medical treatments for enhanced outcomes and cost efficiency. Central to its success is public engagement and consent to share health data amidst rising data privacy concerns. To investigate European public opinion on this paradigm, we executed a comprehensive cross-sectional survey to capture the general public's views on personalized medicine and data-sharing modalities, including digital tools and electronic records. The survey was distributed in eight major European Union countries and the results aim at guiding future policymaking and trust-building measures for secure health data exchange. This article delineates our methodological approach, whereby survey findings will be expounded in subsequent publications. Executive summary A collaborative cross-sectional online survey was developed by researchers from Università Cattolica del Sacro Cuore and Amsterdam UMC to assess European Union (EU) citizens' attitudes toward personalized medicine and genomic data sharing. Structured into four thematic modules comprising 37 questions in total, the survey comprehensively addressed individuals' knowledge, attitudes towards data sharing, governance issues and perceived personal needs related to personalized medicine. Translations into eight European languages were undertaken to ensure broad accessibility, with distribution managed by a private company, ensuring high-quality research standards and privacy compliance. The survey aims to inform and shape policy on personalized medicine by capturing a snapshot of current EU views, with an outlook on enhancing healthcare through data-driven and patient-tailored approaches. While providing valuable insights for EU policymaking in healthcare, the survey acknowledges limitations such as potential online user bias, language restrictions and self-reported data accuracy. [ABSTRACT FROM AUTHOR]