Compatibility of the CEN-ISO/TS 82304-2 Health App Assessment Framework With Catalan and Italian Health Authorities’ Needs: Qualitative Interview Study
Notice bibliographique
Résumé
BACKGROUND: Health authorities of European Union (EU) member states are increasingly working to integrate quality health apps into their health care systems. Given the current lack of unified EU assessment criteria, the European Commission initiated Technical Specification (TS) CEN-ISO 82304-2:2021-Health and wellness apps-Quality and reliability (hereinafter the "TS") to address the scattered EU landscape of assessment frameworks (AFs) for health apps. The adoption of an AF, such as the TS, falls within member state competence and is considered an uncertainty-reduction process. Evaluations by peers as well as ensuring the compatibility of the TS with the needs of health authorities can reduce uncertainty and mediate harmonization. OBJECTIVE: This study aims to examine the compatibility of the TS with the needs of Catalan and Italian health authorities. METHODS: Semistructured interviews were conducted with key informants from a regional (Catalonia in Spain) and national (Italy) health authority, and a thematic analysis was carried out. Main themes were established deductively, following the aspects defined by the value proposition canvas: (1) health authorities' needs ("gains," "pains," and "jobs") and (2) the TS "products and services" and their distinct characteristics ("gain creators" and "pain relievers"). Subthemes were generated inductively. The compatibility of the needs with the TS was theoretically determined by the researchers. The results were visualized using the value proposition canvas. Two participant validation steps confirmed that the most relevant aspects of the predefined themes had been captured. RESULTS: Despite the diversity of the 2 health authorities, subthemes were common and categorized into 9 gains, 9 pains, and 11 jobs. Key findings include the health authorities' perceived value of, and need for, integrating quality health apps and using an AF (gains), along with the related policy, implementation, and operational activities (jobs). The lack of enabling EU legislation and standardization, resulting in a need for the multiple authorities involved to consent, made achieving an AF challenging (pains). Nine products and services related to the TS and 17 distinct characteristics (eg, its multistakeholder evidence base) were found to be compatible with 3 gains (eg, stimulating the prescription and use of apps), 7 pains (eg, legislation and harmonization issues), and 6 jobs (eg, assessing apps). Indirect effects, 3 anticipated future services, and 1 anticipated gain creator and pain reliever increase this compatibility. CONCLUSIONS: Our results suggest that the health authorities share common fundamental needs, and that the TS is compatible with these needs. The identified needs and compatibility can potentially reduce peer authorities' uncertainties in adopting an AF in general and the TS in particular. More research is recommended to confirm and translate our results in other contexts and further fine-tune compatibility to achieve wide adoption of the TS and accelerate the uptake of health apps.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,039 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,010 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».