Self-Sovereign Patient as a Cornerstone of Healthcare 4.0
Bibliographic record
Abstract
In Healthcare 4.0, we are witnessing a fundamental shift from provider-centric systems to patient-centric models, where individuals, empowered by technologies such as blockchain, the Internet of Medical Things, and artificial intelligence (AI), assume the role of the Self-Sovereign Patient, exercising control over their health data and care journey. These technologies enable new forms of data ownership, interoperability, and personalized care, building on the structured reliability of legacy systems. However, significant challenges remain. Tensions between blockchain immutability and regulatory rights such as data erasure, the unresolved question of digital inheritance, and ethical concerns surrounding consent, monetization, and health equity must all be addressed. In addition, institutional barriers such as clinical integration, data governance, and uneven access to digital infrastructure pose risks of deepening existing disparities. AI agents, when responsibly deployed, offer promising pathways to augment care delivery and alleviate workforce burdens. Realizing this vision requires coordinated action across clinical, technical, legal, and ethical domains to design trustworthy, privacy-preserving systems that enhance transparency and accountability.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".