Privacy and Trust in Healthcare IoT Data Sharing: A Snapshot of the Users’ Perspectives (Preprint)
Bibliographic record
Abstract
BACKGROUND 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. OBJECTIVE This study investigates users' trust in sharing their data collected using healthcare IoT devices via different organizations. METHODS 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. RESULTS 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). CONCLUSIONS 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".