Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other literature type . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Project deliverable . 2026
License: CC BY
Data sources: Datacite
ZENODO
Project deliverable . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

Towards Fitness-for-purpose assessment in HealthData@EU

Authors: Sáez, Carlos; Doñate-Martínez, Ascensión; Sánchez-García, Ángel; Proietti Mercuri, Claudio; Estupiñán Romero, Francisco; Bernal-Delgado, Enrique;

Towards Fitness-for-purpose assessment in HealthData@EU

Abstract

Proposal Design of a Fitness‑for‑Purpose (F4P) Mechanism for HealthData@EU Executive Summary This report proposes an initial design for a Fitness‑for‑Purpose (F4P) mechanism within HealthData@EU, aimed at complementing the existing QUANTUM Data Quality, Utility, and Maturity Label. The F4P mechanism introduces a structured way to capture and share Data Users’ assessments of how well datasets meet specific secondary‑use purposes under the European Health Data Space (EHDS). These assessments are intended to enhance transparency, usability, and trust by making real‑world dataset performance visible through Health Data Access Bodies (HDAB) catalogues. At its core, the mechanism enables purpose‑specific scoring of datasets, aligned with EHDS Article 53(1) use cases, including scientific research (e.g., AI training/testing), public health surveillance, policymaking and Health Technology Assessment (HTA), statistics, and education. Evaluations are anchored in QUANTUM’s established data quality dimensions, such as completeness, consistency, and accuracy; while also incorporating qualitative feedback through user comments and optional supporting materials (e.g., reports, code, persistent identifiers). The design defines a post‑use reporting model, requiring feedback submission within EHDS Article 61(4) timelines, while allowing optional early feedback to Data Holders (DHs). Governance is ensured through HDAB‑led validation and moderation, combined with DH rights to review and respond before publication. The solution emphasises open‑source, reusable tooling to support consistent implementation across Member States. To maximise usability, F4P results are integrated into HDAB portals with intuitive visualisation features, such as filtering by purpose or score thresholds, and dynamic displays including sunburst charts and trend analyses. The mechanism also supports semantic interoperability, using RDF and the Data Quality Vocabulary (DQV) to link feedback with datasets, QUANTUM labels, and access permits in a machine‑readable format. The proposed model is grounded in stakeholder input, including 14 expert interviews, a 20‑participant workshop, and validation through an external forum of up to 18 experts. However, it intentionally focuses on operational and structural aspects, leaving the detailed scoring methodology out of its scope. This methodology will be developed through a community‑driven, data‑type‑agnostic process, aligned with QUANTUM principles. The report concludes with 11 recommendations and highlights key open issues, including the degree of obligatoriness, the granularity of purpose definitions, and the resources required for validation. These will need to be addressed during future implementation and alignment with HealthData@EU, ensuring the mechanism evolves into a robust and scalable component of the EHDS ecosystem.

Keywords

quality label, fitness-for-purpose, secondary use, health data, data quality, EHDS

  • BIP!
    Impact byBIP!
    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).
    0
    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.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
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