Compatibility of the CEN-ISO/TS 82304-2 Health App Assessment Framework With Catalan and Italian Health Authorities’ Needs: Qualitative Interview Study
Bibliographic record
Abstract
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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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 | 0.039 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".