European researchers have made leading contributions to the large genomic, transcriptomic and clinical datasets from patients with chronic diseases. Advances in information science provide unprecedented opportunities for using these datasets to elucidate the complex biology of these disorders, its influence by environmental triggers, and to personalise their management. Currently, exploitation of these opportunities is limited by a shortage of researchers with the required informatics skills and knowledge of requisite data protection principles. HELICAL addresses this unmet need by developing a trans-sectoral and interdisciplinary training programme that builds on the expertise and existing collaborations of its partners. It provides 15 early stage researchers with training in analysis of large datasets, using autoimmune vasculitis as a paradigm as it is scalable, and as comprehensive biological and clinical datasets are already available. The HELICAL training program focuses on three complementary areas: application of informatics to such datasets to gain new biological insights; translation of these into practical clinical outputs and management of ethical constraints imposed on such studies. The programme will be delivered through a multidisciplinary, trans-sectoral partnership of Academic and Industry researchers with expertise in basic biomedical research, epidemiology, statistics, machine learning, health data governance and ethics. Therefore, HELICAL exploits recent advances in data science to link research datasets with longitudinal healthcare records, based on the robust ethical foundation required for linkage studies using near-patient data, to address key experimental questions. The results will have obvious potential for transforming healthcare in the field of autoimmune disease. The training provided addresses a key skills gap in the European workforce and should make the ESR eminently employable in academic, industrial and clinical sectors.

SYNTHIA is an ambitious collaboration between public and private institutions to facilitate the responsible use of Synthetic Data (SD) in healthcare applications. The project will improve the methodological and technical aspects of SD Generation (SDG) by developing new techniques and advancing established ones for different data modalities, including genomics and imaging, to improve the generation of realistic multimodal and longitudinal data. This project will provide the research community with approaches for transparent benchmarking of alternative SDG methods for specific applications, identify and establish evaluation metrics and methodologies, and contribute to the standardisation of an evaluation assessment framework for SD. Robust evidence of SD applicability in a set of use cases across a broad spectrum of medical conditions will be crucial to demonstrate the potential of SD to accelerate data-driven solutions of equivalent quality to those derived from real patient data. Furthermore, legal and regulatory implications of SD use will be analysed with the aim of delivering an assurance framework to guide secure SD utilization in healthcare. These significant breakthroughs will be implemented through the open SYNTHIA federated platform, facilitating responsible SD use by the health research community. The platform will facilitate users´ long-term access to extensively validated, reusable synthetic datasets, as well as to SDG workflows and SD assessment frameworks. The federated infrastructure will rely on extended open-source frameworks for interoperability with other data-sharing infrastructures in the context of the European Health Data Space. A multidisciplinary collaboration of SDG developers, FAIR data experts, clinical researchers, developers of therapies and data-based tools, legal experts, socio-economic analysts, regulatory, policy advocacy, and communication experts will provide a 360º vision on how to advance healthcare applications through SD use.

xShare envisions everyone sharing their health data in EEHRxF with a click-of-a-button. The xShare button to be featured across health portals and patient apps and allow people to exercise their data portability rights under GDPR. Hence, the European EHRxF will be the driver for research and innovation in EHDS. xShare will establish the European EHRxF Standards and Policy Hub, the “Hub” partnership of six standards developing organizations (CEN/TC251, HL7 Europe, IHE Europe, SNOMED, CDISC, IEEE) market actors (DIGITAL Europe, MedTech-Europe and EUCROF), supported by competence centers, nationals and regional authorities and European SMEs. xShare will develop: 1) Harmonized common specifications, create and maintain xBundles i.e., collection of common data specifications including FHIR implementation guides, tools and data sets, and educational support for key EHRxF health information domains as noted in the EHDS draft regulation Annex 1. 2) A set of common elements across EHRxF health information domains applicable across EHDS-1 (JA-9), public/population health (EHDS-2), and clinical research. 3) Extended harmonized IPS specification to include care plans and making it fit for the purpose of clinical research use cases i.e. clinical trial eligibility, real world data, patient reported outcomes, and returning clinical research data to patients. 4) xShare feature the xShare Button in 8 adoption settings in Hospital network (Italy), National portal (Greece, Ireland, Cyprus), regional network with emphasis in medical tourism and the connection of the public to the private sector (Catalunya and Madeira), entry of digital health applications to the myHealthSpace ecosystem in France. Care plans will be demonstrated in Denmark. xShare investigate the feasibility and value of the EU xShare Industry label as a vehicle towards implementing the draft EHDS regulation. Lastly open calls at the last year of the project aim to onboard with EHRxF almost 100 settings across Europe.

Integrated, high-quality personal health data (PHD) represents a potential wealth of knowledge for healthcare systems, but there is no reliable conduit for this data to become interoperable, AI-ready and reuse-ready at scale across institutions, at national and EU level. AIDAVA will fill this gap by prototyping and testing an AI-powered, virtual assistant maximizing automation of data curation & publishing of unstructured and structured, heterogeneous data. The assistant includes a backend with a library of AI-based data curation tools and a frontend based on human-AI interaction modules that will help users when automation is not possible, while adapting to users? preferences. The interdisciplinary team of the consortium will develop and test two versions of this virtual assistant with hospitals and emerging personal data intermediaries, around breast cancer patient registries and longitudinal health records for cardio-vascular patients, in three languages. The team will work around four technology pillars: 1) automation of quality enhancement and FAIRification of collected health data, in compliance with EU data privacy; 2) knowledge graphs with ontology-based standards as universal representation, to increase interoperability and portability; 3) deep learning for information extraction from narrative content; and 4) AI-generated explanations during the process to increase users? confidence. By increasing automation of data quality enhancement, AIDAVA will decrease the workload of clinical data stewards; by providing high-quality data, AIDAVA will improve the effectiveness of clinical care and support clinical research. In the long-term, AIDAVA has the potential to democratise participation in data curation & publishing by citizens/patients leading to overall savings in health care costs (through disease prevention, early diagnosis, personalized medicine) and supporting delivery of the European Health Data Space.

ConcePTION partners have united around a shared vision that we have a societal obligation to radically and rapidly reduce uncertainty about the safety of medication use in pregnancy and lactation. What do we deliver? ConcePTION aims to create a paradigm shift in how we study medication safety in pregnancy. We will establish (1) a successful, sustainable open-science based EU non-proprietary ecosystem of public and private stakeholders, pregnant women and researchers to generate and disseminate timely and reliable evidence on drugs across maternal, neonatal and long term outcomes of medication exposure in pregnancy and breastfeeding (2) a catalogue with fully characterized data sources for rapid selection of suitable data sources; 3) operational, business, network, information and data governance models, (4) quality assured and tested methodological approaches, analytical tools and data platforms allowing for distributed analyses, (5) procedures and tools for collection of digital data and samples directly from pregnant women, (6) In vitro, in silico and in vivo models for prediction of drug transfer in human milk, (7) a biobank and analytical network for quantification of drug in human milk, (8) best practice documents endorsed by regulators and health authorities and (9) a web-based drug information knowledge bank. How do we deliver? (1) Experienced leaders, able to manage challenging networks and public-public or public-private partnerships. (2) Defragmentation by connecting all key stakeholders and EU networks in this area. (3) Re-use of data, tools and foreground from prior European commission funded projects. (4) Connecting to leaders of similar initiatives in the USA, Canada, Asia and other parts of the world. (5) Systematic consensus & endorsement building. (6) Quality throughout as a precondition to trust the results and information by all users.