Privacy and Trust in Healthcare IoT Data Sharing: A Snapshot of the Users’ Perspectives
Notice bibliographique
Résumé
Background: Healthcare services in Canada are slowly shifting from in-hospital care to more patient-centred, home-care services. Collecting and sharing personal data from individuals via Internet of Things (IoT) devices is a critical part of this change that potentially leads to better decision-making and better support for patients from healthcare providers. However, there are challenges that come from using technology, including concerns around trust in organizations holding individuals’ data, as well as privacy and security related to data sharing that needs to be considered as part of this new model of care. \nObjective: This study seeks to investigate users' trust in sharing their data collected using healthcare IoT devices via different types of organizations. \nMethods: This research project leveraged a literature review and online questionnaires to understand how general users of IoT for Health 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 answered questions about one type of organization, where a scenario related to the use of different IoT technologies, information about data sharing and a list of privacy concerns were presented. Based on this scenario, participants were asked to answer 16 trust-related questions. Results were analyzed using Analysis of Variance (ANOVA), followed by post-hoc comparisons using the pairwise t-test with the Bonferroni correction. \nResults: The study showed no significant differences in regards to privacy concerns (LConcern) in Canada, 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). \n Conclusion: Initial evidence supports differences in users' perception of trust in healthcare IoT data sharing among the aforementioned types of organizations and levels of concern amongst users regarding privacy and data ownership. Differences in the perception of trust were also identified between the different regions of the participants. Future research using more specific types of organization and larger samples for each age group are needed to fill knowledge gaps. In addition, further research is also needed to understand how external factors can affect user’s levels of trust and acceptance of healthcare IoT with potential consequences for the implementation of new healthcare delivery models.
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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 | 0,012 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,007 | 0,009 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».