Testing and Iterative Improvement of the CEN ISO/TS 82304-2 Health App Quality Assessment: Pilot Interrater Reliability Study
Notice bibliographique
Résumé
BACKGROUND: With the increasing use of health apps and ongoing concerns regarding their safety, effectiveness, and data privacy, numerous health app quality assessment frameworks have emerged. However, assessment initiatives experience difficulties scaling, and there is currently no comprehensive, consistent, internationally recognized assessment framework. Therefore, health apps often need to undergo several quality evaluations to enter different markets, leading to duplication of work. The CEN ISO/TS 82304‑2 health app assessment seeks to address this issue, aiming to provide an internationally accepted quality evaluation through a network of assessment organizations located in different countries. OBJECTIVE: This study aimed to develop and evolve the draft CEN ISO/TS 82304-2 assessment handbook and developer guidance by testing them across organizations in several countries. METHODS: Assessment organizations from 5 countries were engaged to evaluate 24 health apps using the evolving CEN ISO/TS 82304-2 assessment across 3 evaluation rounds. The information submitted by a given health app developer was evaluated by 2 assessment organizations, and interrater reliability was examined. In addition, app developers and assessors were asked to report how much time they spent on information collation or evaluation and to rate the clarity of the developer guidance or assessor handbook, respectively. The collected data were used to iteratively improve the handbook and guidance between rounds. RESULTS: The interrater reliability between assessment organizations improved from round 1 to round 2 and stayed relatively stable between rounds 2 and 3, with 80% (55/69) of assessment questions demonstrating moderate or better (Gwet AC1>0.41) agreement in round 3. The median time required by developers to prepare the assessment information was 8 hours and 59 minutes (IQR 5.7-27.1 hours) in round 3, whereas assessors reported a median evaluation time of 8 hours and 46 minutes (IQR 7.1-11.0 hours). The draft guidance and handbook were generally perceived as clear, with a median round-3 clarity rating of 1.73 (IQR 1.64-1.90) for developers and 1.78 (IQR 1.71-1.89) for assessors (0="very unclear", 1="somewhat unclear", and 2="completely clear"). CONCLUSIONS: To our knowledge, this is the first study to examine the consistency of health app evaluations across organizations located in different countries. Given that the CEN ISO/TS 82304-2 guidance and handbook are still under development, the interrater reliability findings observed at this early stage are promising, and this study provided valuable information for further refinement of the assessment. This study marks an important first step toward establishing the CEN ISO/TS 82304-2 assessment as a consistent, cross-national health app evaluation. It is envisioned that the assessment will ultimately help avoid duplication of work, prevent inequities by facilitating access to smaller markets for developers, and build trust among users, thereby increasing the adoption of high-quality 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 | 0,224 | 0,249 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».