Identity Management and Audit Trail Support for Privacy Protection in E-Health Networks
Bibliographic record
Abstract
E-health networks can enable integrated healthcare services and data interoperability in the form of electronic health records accessible via Internet technology. Efficiency and quality of care can be improved for example by: streamlining administrative processes involving prescriptions and insurance payments; providing remote access to specialists through telemedicine; or correlating data from clinics, pharmacies and emergency rooms to detect potential adverse events. However, a major requirement to enable adoption of e-health networks is the ability to address issues around security, privacy and trust in a systematic manner. In particular, privacy legislation, regulatory guidelines, and organizational policies require that a framework for privacy protection must be established. Federated identity management can be used to systematically protect patient and health care provider identities in a single sign on framework that controls access to patient data, but an audit trail and reporting mechanism is needed in order to ensure and validate compliance. In this chapter, the authors use example e-health scenarios to analyze the legal, business and technical issues that need to be addressed.
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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 | 0.000 | 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 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".