Design and Implementation of a Blockchain-Based E-Health Consent Management Framework
Bibliographic record
Abstract
Transformation of data into knowledge is the hallmark of modern medicine. As an evolving field of medicine, e-Health involves the electronic processing of a patient's personal, medical and other health-related data to improve healthcare delivery. Data captured from clinical interactions between patients and their care providers, as well as health data collected through medical sensors, provide a rich source of data, known as patient medical records (PMRs), which can be processed in various ways to enhance the delivery of healthcare services. However, indiscriminate processing of PMRs could potentially result in the violation of the security or privacy of patients. To ensure that PMRs are not processed in ways that could be harmful to the security or privacy of the patients, modern data protection regulations, such as the European General Data Protection Regulation (GDPR), requires healthcare service provides to obtain the consent of a patient for any processing operation on their PMRs. The mechanism by which a patient exercises their right to control who can process their PMRs, when and for what purpose, is referred to as consent management in e-Health. Existing health information technology systems do not provide adequate support for consent management; there is a lack of transparency and auditability in the existing systems to monitor and ensure that healthcare service providers comply to the relevant data protection regulations in processing PMRs. The emerging blockchain technology offers an opportunity to design an e-Health consent management system that is compliant with modern data protection regulations such as GDPR. In this paper, we present the design and implementation of an e-Health consent management framework, based on the state-of-the-art blockchain technologies, for processing PMRs. Our analysis confirms that our system satisfies the requirements for consent management in e-Health.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".