User journeys in cross-European secondary use of health data: insights ahead of the European Health Data Space
Bibliographic record
Abstract
The European Health Data Space (EHDS) regulation aims to facilitate cross-border sharing of health data across Europe. However, practical challenges related to data access, interoperability, quality, and interpretive competence remain, particularly when working with health systems across countries. This study aimed to evaluate and report the user journey of researchers accessing and utilizing health data across four European countries for secondary research purposes prior to implementation of EHDS. We conducted a narrative reflection of individual and collective experiences on key aspects of the user journey-discovery, access, use, and finalization. Data were gathered from various structured and unstructured sources, including an online log, prospective questionnaires, regular meetings, and interviews. Researchers faced challenges at different steps of the user journey, which included lack of data quality in national metadata catalogues (discovery stage). Differences in national regulations led to inconsistent timelines for gaining access to data (access stage), with approval times ranging from a few months to over a year. At the use stage, researchers experienced challenges in harmonizing health data due to variations in coding practices and data quality. Issues related to computational capacity caused further delays. Substantial challenges must be addressed for EHDS to succeed. Establishing knowledge hubs, fostering collaborations, and streamlining access processes are essential. Close collaboration with experts will likely be essential for an effective user journey. This analysis underscores the importance of collaboration, analytical reproducibility, and clear documentation to ensure the success and timely delivery of cross-border projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".