EOSC-Life Public database inventorying the national health databases and registries and describing their access procedures for reuse for research purposes
Notice bibliographique
Résumé
The digitisation of healthcare has brought new opportunities to complement and enhance the data traditionally utilized in regulatory decision-making. According to the EMA, real world evidence (RWE) has been defined as the information derived from analysis of routinely collected real world data (RWD) relating to a patient’s health status or the delivery of healthcare from a variety of sources other than traditional clinical trials. Before fostering the enormous potential presented by the use of routinely collected RWD (e.g. electronic health records, medical claims, insurance data etc.) several challenges need to be addressed: operational, technical, methodological and ELSI. Reusing RWD for research purposes in Europe and especially in a crossborder manner is hampered by the fact that health databases and registries are not easily discoverable and, even when they are, understanding what data they contain and their suitability for addressing a specific research question remains not trivial due to the lack of detailed data catalogues with adequate metadata (especially in English). The present report is entitled “D4.5 Public database inventorying the national health databases and registries and describing their access procedures for reuse for research purposes”. As the title indicates, the report delivers an inventory of national health databases and registries covering 15 European countries: Austria, Czech Republic, France, Germany, Hungary, Ireland, Italy, the Netherlands, Norway, Poland, Portugal, Slovakia, Spain, Sweden, Switzerland. For each country the reader can find information on the national healthcare system, a list of health databases and registries, their description (or links to websites where this description can be found) and information on data access for research purposes. Although this deliverable was initially conceived as a “public database” in the form of a website, the EOSC-Life WP4 partners agreed that this would be unnecessary as the European Health Information Portal1 is already playing this role. Instead, this report will become publicly available through Zenodo and disseminated to relevant stakeholders working on similar issues (including the actors behind the Health Information Portal) as a way to “join forces” and complement each other’s work instead of duplicating efforts. In summary, we conclude that the picture across Europe is diverse and at times patchy as the health databases and registries are subject to different governance and sustainability models but also to different local laws and access rules. Interestingly, there is still, on a European level, great debate around the terms “anonymisation”, “pseudonymisation” and “de-identification” and when data can be considered anonymised and as such exempted from the GDPR. Additionally, even when the current barriers of discoverability and accessibility (that are the main focus of this report) are lifted, there remains the major question of whether such data sources are suitable for research, as concerns around their quality, completeness and structure (or lack of) are still to be addressed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».