Privacy and Trust in Healthcare IoT Data Sharing: A Snapshot of the Users’ Perspectives
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".