Foundations for Meaningful Consent in Canada’s Digital Health Ecosystem: Retrospective Study (Preprint)
Notice bibliographique
Résumé
BACKGROUND Canadians are increasingly gaining web-based access to digital health services, and they expect to access their data from these services through a central patient access channel. Implementing data sharing between these services will require patient trust that is fostered through meaningful consent and consent management. Understanding user consent requirements and information needs is necessary for developing a trustworthy and transparent consent management system. OBJECTIVE The objective of this study is to explore consent management preferences and information needs to support meaningful consent. METHODS A secondary analysis of a national survey was conducted using a retrospective descriptive study design. The 2019 cross-sectional survey used a series of vignettes and consent scenarios to explore Canadians’ privacy perspectives and preferences regarding consent management. Nonparametric tests and logistic regression analyses were conducted to identify the differences and associations between various factors. RESULTS Of the 1017 total responses, 716 (70.4%) participants self-identified as potential users. Of the potential users, almost all (672/716, 93.8%) felt that the ability to control their data was important, whereas some (385/716, 53.8%) believed that an all or none control at the data source level was adequate. Most potential users preferred new data sources to be accessible by health care providers (546/716, 76.3%) and delegated parties (389/716, 54.3%) by default. Prior digital health use was associated with greater odds of granting default access when compared with no prior use, with the greatest odds of granting default access to digital health service providers (odds ratio 2.17, 95% CI 1.36-3.46). From a list of 9 information elements found in consent forms, potential users selected an average of 5.64 (SD 2.68) and 5.54 (SD 2.85) items to feel informed in consenting to data access by care partners and commercial digital health service providers, respectively. There was no significant difference in the number of items selected between the 2 scenarios (P>.05); however, there were significant differences (P<.05) in information types that were selected between the scenarios. CONCLUSIONS A majority of survey participants reported that they would register and use a patient access channel and believed that the ability to control data access was important, especially as it pertains to access by those outside their care. These findings suggest that a broad all or none approach based on data source may be accepted; however, approximately one-fifth of potential users were unable to decide. Although vignettes were used to introduce the questions, this study showed that more context is required for potential users to make informed consent decisions. Understanding their information needs will be critical, as these needs vary with the use case, highlighting the importance of prioritizing and tailoring information to enable meaningful consent.
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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 | 0,007 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,007 |
| Études des sciences et des technologies | 0,017 | 0,005 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».