WP1 - Integration of Big Data and Digital Solutions in Healthcare
In Task 1.1 we evaluated the use of machine learning in public health, highlighting the growing interest and the need for standardized methodologies to optimize benefits for health systems. In Task 1.2 we assessed the feasibility of integrating omics and epidemiological data, focusing on achieving both technical and semantic interoperability while addressing critical data privacy concerns. In Task 1.3 we developed an ELSI (Ethical, Legal, and Social Issues) assessment framework to guide the integration of big data, with a particular focus on implementing electronic informed consent (eIC) forms to enable personalized interactions. In Task 1.4 we created policy recommendations for the effective use of health data by engaging clinicians, researchers, and policymakers.
WP2 - Innovative Citizen Engagement Models
In Task 2.1 we mapped active citizen engagement programs in precision health across the EU through a scoping review covering the period from January 2015 to July 2020. In Task 2.2 we assessed the feasibility and effectiveness of these engagement models by establishing Key Performance Indicators (KPIs). A literature review revealed a gap in precision health-specific KPIs, which led us to adjust the methodology for objective evaluations, carried out through focus group discussions with ExACT representatives. In Task 2.3 we developed a citizen engagement model aimed at improving communication between health professionals and citizens, ensuring that health policies reflect the needs and values of well-informed citizens.
WP3 - Education and Leadership for Healthcare Professionals in Precision Health
We analyzed the competencies required for healthcare professionals, identifying significant gaps in leadership skills. In Tasks 3.1 and 3.2 we examined the barriers and facilitators associated with the implementation of precision health, emphasizing the need for a systematic review of qualitative evidence. In Task 3.3 we focused on enhancing educational training for non-genetics specialists (such as general practitioners and nurses), identifying 24 studies on various educational methods but noting a lack of evaluation of long-term behavioral changes and practical applications.
WP4 - Adoption of Health Technology Assessment (HTA) for Omics Technologies
In Task 4.1 and 4.2 we conducted a systematic review of 22 frameworks for genetic technologies, identifying the ACCE framework as the most influential and we conducted a survey involving 41 health HTA agencies from 29 countries, focusing on their experiences in evaluating omics technologies.
WP5 - Ethical-Legal and Policy Issues in Precision Health
In Task 5.1 we performed a comparative analysis of guidelines related to NGS data sharing in Europe, the US, and Canada. In Task 5.2 we examined the use of electronic informed consent in biomedical research for NGS and genomic data. In Task 5.3 we conducted a survey to gather European citizens' attitudes toward precision health, personalized medicine, genetic testing, and mHealth technology, collecting responses from over 6,500 participants across eight languages.
In summary, the ExACT project has produced research across various work packages, addressing key areas in precision health, public health, and the integration of big data and digital solutions. These include evaluations of machine learning in public health, the integration of omics and epidemiological data, and the analysis of a comprehensive ethical framework for big data in healthcare. Further efforts have focused on innovative citizen engagement models, education and leadership development for healthcare professionals, adoption of health technology assessment for omics technologies, and ethical and legal considerations in precision health. We have developed a number of publications that are available on the ExACT project website
http://www.exactproject.net/site/(öffnet in neuem Fenster) along with a video explaining our research outcomes in a more accessible format:
https://youtu.be/B7IFlJ9Nskk?si=37c4bm0ORCRKThDYWP1(öffnet in neuem Fenster)
