Balancing Health Information Exchange and Privacy Governance from a Patient-Centred Connected Health and Telehealth Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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 it