Privacy and Trust in Healthcare IoT Data Sharing: A Snapshot of the Users’ Perspectives (Preprint)
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Notice bibliographique
Résumé
<sec> <title>BACKGROUND</title> Healthcare services in Canada are slowly shifting from in-hospital care to patient-centred, home-care services. Collecting and sharing personal data from individuals via Internet of Things (IoT) devices has become a critical part of this change, which can lead to better decision-making and better support for patients from healthcare providers. However, some challenges come from using technology, including concerns around trust in organizations holding individuals' data and privacy and security related to data sharing that needs to be considered as part of this new model of care. </sec> <sec> <title>OBJECTIVE</title> This study investigates users' trust in sharing their data collected using healthcare IoT devices via different organizations. </sec> <sec> <title>METHODS</title> This research project leveraged a literature review and online questionnaires to understand how general users of IoT for Health perceive and trust different types of organizations (large companies, government, healthcare providers, and insurance companies). A total of 400 participants were recruited using Mechanical Turk for the online questionnaire, using a between- subjects design. Each participant was presented with a scenario related to using various IoT technologies, information about data sharing, and a list of privacy concerns associated with specific organizations that handle health-related data. Based on this scenario, participants were asked to answer 16 trust-related questions. Results were analyzed using Analysis of Variance (ANOVA), followed by posthoc comparisons using the pairwise t-test with the Bonferroni correction. </sec> <sec> <title>RESULTS</title> The study showed no significant differences regarding privacy concerns (LConcern) in Canada, the United States (USA), and Europe (F (2, 389) = 0.736, P = .480). Overall levels of trust (Ltrust) in the USA varied significantly between large companies, government, healthcare providers, and insurance companies (F (3, 388) = 10.107, P < .05). The same results were observed in Canada, with a significant difference between the four types of organizations (F (3, 125) = 6.882, P < .05), USA (F (3, 128) = 4.488, P =.05), and in Europe, as well (F (3, 127) = 4.451, P < 0.05). </sec> <sec> <title>CONCLUSIONS</title> The results suggest differences in users' perceptions of trust associated with the types of organizations. Additionally, levels of concern regarding privacy and data ownership varied among users. The findings identified differences in the perception of trust between the different regions of the participants. </sec>
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle