Balancing Health Information Exchange and Privacy Governance from a Patient-Centred Connected Health and Telehealth Perspective
Bibliographic record
Abstract
OBJECTIVES: Connected healthcare is an essential part of patient-centred care delivery. Technology such as telehealth is a critical part of connected healthcare. However, exchanging health information brings the risk of privacy issues. To better manage privacy risks we first need to understand the different patterns of patient-centred care in order to tailor solutions to address privacy risks. METHODS: Drawing upon published literature, we develop a business model to enable patient-centred care via telehealth. The model identifies three patient-centred connected health patterns. We then use the patterns to analyse potential privacy risks and possible solutions from different types of telehealth delivery. RESULTS: Connected healthcare raises the risk of unwarranted access to health data and related invasion of privacy. However, the risk and extent of privacy issues differ according to the pattern of patient-centred care delivery and the type of particular challenge as they enable the highest degree of connectivity and thus the greatest potential for privacy breaches. CONCLUSION: Privacy issues are a major concern in telehealth systems and patients, providers, and administrators need to be aware of these privacy issues and have guidance on how to manage them. This paper integrates patient-centred connected health care, telehealth, and privacy risks to provide an understanding of how risks vary across different patterns of patient-centred connected health and different types of telehealth delivery.
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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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".