Diversity in health datasets is a necessary, quantifiable property of inclusivity across key domains (e.g., demographic, socioeconomic, health, and environmental) that directly shapes how well research generalizes and how fair its impacts are. In its absence, interventions risk encoding bias and exacerbating health disparities. In this work, we outline how dataset diversity can be measured and how these measures can be surfaced in the EHDS via metadata. We argue that simple annotations are insufficient: structured interaction with the data owner is required to assess the utility of health datasets for specific research purposes.

Identifying relevant, measurable health indicators is essential for targeting interventions and monitoring outcomes. Urban aquatic ecosystems (UAEs) provide ecosystem services, e.g. water-quality regulation, flood and temperature buffering, and contact with nature that are known to support physical and mental health. However, most articles focusing on UAEs examine environmental risks instead of human health outcomes. The aim of this scoping review was therefore to identify human health and well-being indicators associated with UAEs as reported in the literature and to map them to the WHO Global Reference List of Core Health Indicators. A scoping review was conducted, including complementary desk research, following the Joanna Briggs Institute nine-phase method, from defining the objective to summarising the results. A literature search in three databases returned N = 5,099 records. Screening was done independently by the authors in two iterations; title/abstract and then full text using predefined inclusion and exclusion criteria. Thirty-two articles were selected. Three health indicator domains within the health status category, one health status indicator group, and two risk related indicators associated with UAEs were identified. Within WHO’s health status indicator category, health indicator domains mental health, physical health, well-being, as well as the indicator group cause-specific mortality were found. Within WHO’s risk factor indicator category, the health indicators “age-standardised prevalence of insufficiently physically active persons aged 18+ years” and “age-standardised prevalence of overweight and obesity in persons aged 18 + years” were identified. Mental and physical health and well-being were mainly assessed through self-report, whereas disease-specific indicators (e.g. prevalence, mortality) were predominantly based on registry or clinical data. This scoping review indicates that the available literature generally reports beneficial associations between UAEs and the identified health indicators, indicator group and domains; for example, improved mental and physical health, greater well-being, increased physical activity, and reduced mortality. The mapping points to several WHO-aligned indicators that merit further investigation, including those capturing potential negative impacts, such as water-related morbidity and mortality. To better assess hazards and causal pathways, future research should not only link environmental and health metrics, but do so using standardised, comparable indicators across studies and settings.

As the population of childhood cancer survivors (CCS) continues to grow, personalized long-term follow-up (LTFU) care has become essential for ensuring optimal quality of life. The Survivorship Passport (SurPass) was developed to support efficient delivery of high-quality LTFU care, with the provision of treatment summaries and personalized follow-up recommendations. Updates to SurPass, from version v1.1 to v2.0, were made possible through two complementary European-funded projects: PanCareFollowUp and PanCareSurPass. Within PanCareFollowUp, SurPass was updated with new variables and algorithms based on International Guideline Harmonization Group/PanCare guidelines (v1.2) and tested in one clinic. During PanCareSurPass, the platform was updated to v2.0, featuring certification as Medical Device (MD) and interoperability with Electronic Health Information Systems, and deployed across clinical sites in six European countries. The current SurPass v2.0 includes 242 variables and 47 algorithms for generation of the Standardized Care Plan. It is certified as MD and supports both manual and semi-automatic data entry through the adoption of Health Level Seven International Fast Healthcare Interoperability Resources (HL7-FHIR). The preliminary experience with SurPass delivered to 207 CCS in Italy within the PanCareFollowUp is reported. Interoperability via HL7-FHIR and the MD certification of the SurPass allow its use in clinical practice, and a reduction in time needed to generate the document providing homogeneous and personalized LTFU care for European CCS. Cost of interoperability through HL7-FHIR is offset by savings due to time reduction for TS generation. Experience in one clinic document the satisfaction of survivors who shared the document with their family doctors. The Survivorship Passport (SurPass) provides childhood cancer survivors with a standardized, personalized summary of their cancer treatment and individualized follow-up recommendations based on international guidelines. Its implementation across multiple European countries demonstrates the feasibility of delivering consistent survivorship information that can support long-term follow-up, facilitate communication between survivors and healthcare providers, and promote informed engagement in survivorship care.

The European Health Data Space (EHDS) Regulation enables the secure sharing of health data, unlocking a wide range of valuable applications from continuous health monitoring, to digital twins, and precision health. To ensure that this sharing is both effective and efficient, the EHDS must be deployed on secure and trustworthy architectures. However, this presents a substantial challenge for two main reasons: (i) health data may originate from or be sent to untrusted devices (such as end-user smartphones), and (ii) while the EHDS establishes the legal framework for security, it does not provide concrete technical specifications to enforce it. Building on the technical work of the xShare project and its "Yellow Button" mechanism, we propose a data sharing framework that translates European data protection obligations (e.g., GDPR) into actionable technical controls. These controls encompass encryption, pseudonymization, secure coding practices, and auditability. The result is a operational model for implementing trust for the EHDS.

Homeless individuals face major barriers in accessing regular healthcare, leading to the development of street medicine as a distinct humanitarian field. To enhance continuity and quality of care, consistent documentation is crucial. However, electronic health records are rarely used in European street medicine. An interdisciplinary European workshop identified four key research fields for medical informatics in street medicine: (1) Research ethics: The creation of ethical guidelines that promote co-creation and active communication with patients. (2) System development: Designing a secure, anonymous and European Health Data Space-compliant personal Electronic Health and Social Record (pEHSR), establishing standardised health and social care datasets, and implementing dynamic consent management methods. (3) Education and training: Developing targeted programmes to improve digital and health literacy among street medicine clients and training professionals in the effective use of the pEHSR system. (4) Management and evaluation: Creating management structures and evaluation frameworks suited to the unique challenges of street medicine.This manifesto calls for internationally coordinated scientific efforts to build effective, integrated digital health solutions for vulnerable populations. It encourages researchers and practitioners from medical informatics and related fields to engage with these priorities and support innovation that advances equitable healthcare access.

Advances in paediatric oncology have led to a growing population of childhood cancer survivors at risk of long-term and late effects requiring lifelong follow-up. However, follow-up data collection remains heterogeneous, limiting interoperability and reuse across care settings and research infrastructures. Within the PanCareSurPass project, we aimed to standardize the follow-up questionnaire used in the Survivorship Passport (SurPass) and implement it as a computable artefact based on HL7 FHIR (Fast Healthcare Interoperability Resources). Starting from CTCAE (Common Terminology Criteria for Adverse Events), we developed a survivorship-oriented extension to support longitudinal documentation of late effects during routine clinical follow-up, including in adulthood. The questionnaire was modelled using FHIR Questionnaire and QuestionnaireResponse resources and integrated into the PanCareSurPass FHIR Implementation Guide to support both primary clinical use and secondary use of data in alignment with the European Health Data Space. This work provides a reusable foundation for interoperable late-effects data capture, supporting continuity of care and enabling observational studies and registries across Europe.

Rare cancers hinder data sharing and secondary use due to low incidence, heterogeneous pathways, and fragmented sources. The IDEA4RC project addresses these issues by creating a federated ecosystem for harmonized data exchange across European Centers of Excellence. This paper presents the IDEA4RC HL7 FHIR Implementation Guide (IG), which operationalizes the IDEA4RC common data model-organizing 279 clinical variables across 19 entities-into computable specifications for semantic and computational interoperability. The IDEA4RC HL7 FHIR IG combines logical models, constrained resource profiles, terminology bindings, and grouped ConceptMaps that document traceable mappings from model entities (e.g., Cancer Episode) to FHIR profiles. Developed within a multi-stakeholder European consortium, the data model was co-designed with clinical partners and it is the topic of a specific paper; here we report its technical translation into a computable FHIR IG primarily led by technological partners; with clinicians supporting concept interpretation and early implementation feedback. Compared to existing initiatives such as mCODE, our approach strengthens longitudinal and episode-based representation needed for European research contexts. The resulting IDEA4RC HL7 FHIR IG underpins robust rare cancer data sharing and paves the way for broader harmonization across oncology standards, including future convergence with the HL7 Cancer Common Model Project.

The European Health Data Space (EHDS) is driving Europe toward a harmonized framework for the exchange and secondary use of health data. Within this framework, the European project xShare explores the potential of the European Electronic Health Record Exchange Format (EEHRxF) to support public health dashboards that draw data directly from primary systems. This paper presents three integrated pilot initiatives-conducted by Charité (Germany), Monasterio (Italy), and Sciensano (Belgium)-that have been developed in the context of the European Project xShare. These pilots illustrate how health data from categories prioritized under the EHDS Regulation can be reused to monitor key public health indicators, and how the adoption of a single standard format could enable near-real-time visualization of such data. The pilots address topics such as healthcare-associated infections, influence-like illnesses, antimicrobial resistance, and the impact of circadian rhythm disruption in critical care. Together, they illustrate the opportunities and challenges of implementing interoperable dashboards for evidence-based public health across Europe. Preliminary work highlights both the promise of EEHRxF for reducing administrative burden and improving timely data access, as well as the ongoing legal, ethical, and technical barriers that must be overcome.

Diversity in health datasets refers to the inclusion of individuals with different characteristics, including sociodemographic and clinical, lifestyle and biological factors, like genetics. Diverse health data sets are crucial for ensuring health interventions benefit everyone and promote health equity. We explore how diversity of datasets can be measured and how this information can be made available in the EHDS through metadata. We argue that simple annotation does not suffice, and structured interaction with the data owner is needed to appropriately assess utility of multimodal datasets for research purposes.

The effective and meaningful exchange of data is pivotal for patient care, informed decision-making, and advancements in research and technology. This opinion piece explores the critical role of semantic interoperability (SI) in ensuring meaningful health data sharing across diverse systems. Emphasizing the imperative of synchronizing the use of data standards, we address the challenges posed by disparate data formats and underscore the impact on patient outcomes. International, harmonized standards are presented as a cornerstone for achieving SI, while the drawbacks of proprietary standards are examined. Case studies, including the complementary use of International Organization for Standardization (ISO), Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR), and Clinical Data Interchange Standards Consortium (CDISC) standards, offer practical insights. We offer here five simple principles [reuse existing standards where possible, avoid mapping, implement standards at the start of a project, participate in standards development activities with standards development organizations (SDOs), and work toward harmonization of standards across SDOs] for achieving semantic meaning in support of Trustworthy, Reusable, Understandable data Elements (TRUE) research data for healthcare. We hope to provide a view to a future where standards are in sync and the proposed five principles are deployed globally to ensure the conduct of trustworthy research for the sake of improving health outcomes for all.

The scope of this work is to describe the overall process of assessing the compliance of the main Digital Objects produced in the OneAquaHealth project with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) via a custom FAIR Data Maturity Model. The model was designed and developed according to the project features, and in according with the One Digital Health Framework. Its goal is also to provide a tool characterized by a solid educational ground, so as to set the foundation of a timely FAIRification process for the next project steps.

BackgroundThe European Health Data Space Regulation (EHDS), published in the Official Journal since 5 March 2025, requires Electronic Health Record (EHR) systems across the European Union (EU) to adopt harmonized interoperability and logging components using the European EHR Exchange Format (EEHRxF).What is at stakeThe EHDS has come into force and the deadlines for implementation acts are approaching, Clarity on scope, obligations, and implementation options is needed to meet these deadlines.Policy optionsThis article examines ways to accelerate EEHRxF adoption, including hybrid implementation models, and coordinated national and EU-level support.RecommendationsUtilize the European EHRxF Standards and Policy Hub, align national and regional strategies, support SMEs and providers with guidance and funding, and promote digital health literacy to ensure effective EEHRxF implementation.

The product information of a medicinal product includes the summary of product characteristics (SmPC), package label, and patient information leaflet (PIL), previously available only in paper or pdf format. The European Medicines Agency (EMA) in 2020 launched the electronic product information (ePI) project to make PILs available in HL7 FHIR®. The Vulcan/ Gravitate-Health ePI project created and tested a global HL7 FHIR Implementation Guide (IG). In parallel, Gravitate-Health conceptualized G-lens®, using information in the Electronic Health Record (EHR) and the International Patient Summary (IPS) to tailor leaflets to individual needs. This paper explores the potential of Large Language Models (LLMs) to generate concise personalized summaries of PILs, guided by data from IPS. The aim is to enhance patient understanding of and adherence to medication by providing digestible personalized summaries of PILs in simplified language to foster shared decision making and enhance patient communication with their health team. We experimented with three different open-source LLMs, employing prompts with and without personalization. Results indicate that LLMs can generate coherent summaries. However, accuracy and personalization need to improve, perhaps by engaging tailored PILs in patient-physician interaction and shared decision-making. Promising future research directions in personalization of PILs include prompt engineering, fine-tuning, and evaluation models for testing at scale.

Background: Stream buffer and riparian zones surrounding urban water bodies have profound impacts on the health and well-being of human communities who live near streams. Among the aims of the OneAquaHealth project is to set up a framework to detect and monitor human health outcomes linked to ecosystem changes in urban streams in five European cities (Coimbra, Benevento, Toulouse, Ghent, Oslo, Heraklio). The objective of this particular protocol is to examine how human health indicators can be identified, measured and sampled to explore the relationship between human health and urban freshwater ecosystems. Methods: Activities in OneAquaHealth are implemented under the OneDigitalHealth (ODH) framework. Operationalizing health indicators for well-being, mental and physical health, physical activity, restorative experience, annual mortality within this framework involves a comprehensive process of integrating diverse health and ecosystem data to create actionable tools for improving public health and environmental sustainability. This includes quantifying the interconnections between urban aquatic ecosystems and human health using citizen science approaches, classic domain-expert, statistical and AI-driven approaches (e.g. large language models) and designing an alignment model that account for disparities regarding health-related data availability and quality. Mechanisms will be established for collecting and integrating data, considering the heterogeneous data sources; e.g. literature reviews, health registries, community surveys, citizen science and large language models for screening literature and extracting indicators. Health indicators will be identified based on relevance and feasibility, considering that their availability and quality can change over time and between sites. Indicator selection will be relevant to levels of granularity, meaning the degree of details or specificity, the degree of precision depending on how the values were collected, the time distance between the data collection and the effective use and the correlation (known or expected) with aquatic ecosystem health indicators (e.g biodiversity, pollutant levels, water pH). A foundation for standardizing and operationalizing the indicators will be created, including controlled vocabularies and ontologies (e.g. the Medical Informatics Digital Health Multilingual Ontologies), a framework alignment for categorizing human health and ecosystem health indicators into the ODH dimensions, an indicator scoring system to evaluate integration withing the ODH, fitting and enhancing via operationalization a specific list of Digital Determinants of Health using the digital clusters suggested by WHO Data and Digital Health, data quality assurance under the FAIR principles, performance reviews by evaluating the reliability and impact of metrics and data collection methods, stakeholder feedback and iterative improvements to update models, indicators, and analytics based on emerging data and technologies. Leveraging advanced computational methods will be performed to integrate the collected data and analyze data related to indicators. Predictive modelling, geospatial and trends analyses, and causal relationships between ecosystem changes and health outcomes to inform evidence-based decision-making will be developed. This will support (near) real-time monitoring of indicators, enabling dynamic updates and allowing decision-making in due time. Conclusion/Anticipated Impact: The operationalization of the health indicators from the various sources and applied strategies will lead to transforming insights (indicators and their related analysis) into practical interventions, such as policy recommendations (advice on urban planning, water management, public health initiatives), health interventions (targeted campaigns to address health risks, e.g. water safety education, disease prevention measures), preparedness planning (strategies for managing natural disasters, e.g. floods, droughts), multidisciplinary collaboration (with public health experts, environmental scientists, and policymakers for comprehensive solutions) and community participation (empowerment through education and involvement in data collection and interpretation).

The project xShare supports citizens' empowerment within the EHDS through the use of the "yellow button" which allows individuals to share their own structured data with third parties, also for secondary use purposes. Focusing on the six information domains listed by the EHDS, the project articulates the artefacts for each domain and for identified real-live use cases. To identify current and prospective useful use cases for Public Health (PH), it is important to understand if and how the different European states are getting ready for the implementation of the EHDS and the main existing challenges. To collect this information, we analysed the available literature and launched a survey exploring the current state of consolidation of PH datasets, the standards used, the associated workflows and the possible prospective use cases. We also identified and studied four countries that represent the best "only once" practice examples in Europe.

Background: Stream buffer and riparian zones are critical areas surrounding water bodies with profound impacts on the health and well-being of human communities who live near streams. Among the aims of the OneAquaHealth project is to set up a framework to detect and monitor human health outcomes linked to ecosystem changes in urban streams in five European cities (Coimbra, Benevento, Toulouse, Ghent, Oslo). The objective of this particular protocol is to establish a data collection structure to identify environmental pressure, human exposure, and feedback mechanisms between ecosystem and the health of people living near the urban streams. Methods: Geospatial health mapping, particularly remote sensing and Geographic Information Systems (GIS) will be used to correlate health indicators (well being, mental and physical health, physical activity, restorative experience, annual mortality) with environmental changes through the years. Ecosystem health and services assessment will be further implemented to assess water quality (contaminants, waterborne diseases), species biodiversity (diatoms, vegetation) both inside the water and in the riparian zone around the stream and soil health (contamination, erosion, and agricultural activity). This will be performed using public databases to collect information for the selected stream, remote sensing (aerial photos, satellite imagery) and field surveys (for example geophysical mapping at selected sites). The research will be conducted for a certain period so that data can be collected at various spatial and temporal scales to detect patterns over time and provide reliable predictions using machine learning models. Conclusion/Anticipated Impact: The protocol is flexible for monitoring human health in relation to ecosystem degradation. The integration of health and environmental data and the employment of modern tools like GIS enables this system to detect health risks timely. The emphasis on real-time monitoring and community engagement provides a capable framework to manage the growing health challenges posed by environmental degradation and climate change.

PURPOSE:The pursuit of eHealth interoperability across Europe has seen substantial public investment in EU-funds (over €200 million) and in experts' time over the last two decades. In an era of preparation towards the European Health Data Space (EHDS) recovering this knowledge is essential and this article aims to identify such EU-funded projects and assess the long-term accessibility of their outputs. It derives consequences for impact and knowledge transfer and highlights implications for health policy worldwide and the developing EHDS. METHODS:We conducted a narrative review, informed by methodical searching and reported following PRISMA-ScR guidance, of the outputs of 31 EU-funded digital health interoperability projects spanning two decades (2005-2024). Project websites, access to deliverables and peer-reviewed publications were methodically assessed. The findings were critically analysed regarding current EU policy frameworks and discussed considering global relevance. FINDINGS:Despite the ambitious goals and funding allocated to these projects, a substantial portion of their outputs is no longer accessible, published as academic or even as grey literature, hindering the development of EU interoperable eHealth systems. 8 projects generated no peer-reviewed publications and nearly half lacks functional project websites post-project terminus. Existing EU repository CORDIS appears insufficient or inappropriately used as just half of projects have comprehensive outputs available there. These shortcomings limit cumulative learning, knowledge transfer and the assessment of the sustainability, value and impact of the public funds invested. IMPLICATIONS:Our findings underscore the importance of strong knowledge governance for publicly funded international digital health initiatives. We recommend funders to implement stronger instruments for mandating open-access publications that are peer-reviewed and the systematic archiving of all project deliverables with citable identifiers. Implementing these measures within publicly funded programmes can limit loss of knowledge and foster sustainable open innovation, promoting more effective and efficient public investment backing digital health systems worldwide.

The proliferation of health data available in real-time and the availability of low-cost computation are powering a new research area: Digital Twins. In the context of the European Health Data Space (EHDS), an enabler for personal Digital Twins is the xShare "Yellow" Button that aims to empower individuals to share their data, one time, or for a period with applications of their choice. This paper presents different use cases that are made possible by the personal health twins and reflects on relevant challenges and opportunities, debating whether ethical and privacy concerns can overcome the benefits of personalized precision medicine powered with the dynamic real-time feedback to digital twins.