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Project deliverable . 2025
License: CC BY
Data sources: Datacite
ZENODO
Project deliverable . 2025
License: CC BY
Data sources: Datacite
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HealthData@EU Pilot - Deliverable 9.1 Use cases report

Authors: Health Data Hub;

HealthData@EU Pilot - Deliverable 9.1 Use cases report

Abstract

This deliverable provides an overview of the experiences, challenges, and findings of the five use cases developed within the HealthData@EU Pilot. These use cases were designed to explore the feasibility of reusing health data across multiple European countries for secondary purposes. The report presents a general overview of the use cases and a detailed analysis of each one. Overview of the Use Cases Predicting nationwide cardio-metabolic disease trajectories using machine learning Lead: HDH / University of Helsinki Participating Nodes: University of Helsinki (FI), AHeaD (FR), Copenhagen University (DK), Norwegian Institute of Public Health (NO) Brief Overview: This use case applied machine learning to predict a 5-year cardiovascular risk. It encountered challenges related to the interpretation of data minimization in France and Norway. Test use, hospitalisation, and vaccination adherence in vulnerable sub-populations Lead: Sciensano Participating Nodes: Sciensano (BE), Central Denmark Region (DK), Croatian Institute of Public Health (HR), Finnish Institute for Health and Welfare (FI) Brief Overview: This use case analyzed COVID-19 testing, hospitalization, and vaccination rates in vulnerable groups. Issues were encountered in accessing socio-economic data. Identifying the risks of coagulation disorders in patients with COVID-19 Lead: EMA Participating Nodes: Croatian Institute of Public Health (HR), Danish Health Data Authority (DK), Finnish Institute for Health and Welfare (FI), Health Data Hub (FR), DARWIN EU Network Brief Overview: This use case investigated the venous/arterial thromboembolic risks in COVID-19 patients, using the OMOP CDM for analysis. Antimicrobial resistance (AMR) surveillance Lead: ECDC Participating Nodes: Sciensano (BE), Croatian Institute of Public Health (HR), Finnish Institute for Health & Welfare (FI) Brief Overview: This use case tested the EHDS architecture for communicable disease surveillance, focusing on data concordance, legal barriers, and federated data processing. Identifying genomic signatures of colorectal cancer Lead: ELIXIR / BSC Participating Nodes: BBMRI-ERIC, Sciensano (BE), Danish Health Data Authority (DK), Norwegian Institute of Public Health (NO), National Institute of Oncology (HU) Brief Overview: This use case used genomic and clinical data for colorectal cancer analysis but faced significant delays in access, eventually leading to transfer to the GDI project. Key Challenges and Findings Data Access & governance Data minimization interpretation: This posed a challenge in France and Norway, restricting access to some datasets. Socio-economic data restrictions: The Sciensano use case faced significant challenges in accessing this data. Genomic data complexity: The ELIXIR use case encountered major delays and feasibility issues in accessing genomic data. Technical & interoperability challenges Heterogeneous data formats: Different nodes used varying data formats (e.g., OMOP CDM vs native formats). Federated data processing limitations: The ECDC use case highlighted scalability concerns related to federated data processing. Legal & ethical considerations Varying national regulations: Different regulations impacted data access and timelines. Data privacy and security concerns: Cross-country challenges emerged in ensuring compliance with data privacy and security standards.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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