A Distributed Ledger Architecture for Cross-Jurisdictional Healthcare Professional Authentication and Tracking in Universal EHR Systems
Bibliographic record
Abstract
While there are mechanisms for identifying patients and professionals in most jurisdictions, cross-jurisdictional identification of who accessed health information remains a significant challenge. Initiatives like EHDS, TEHDAS, XpanDH and the European Electronic Health Record Exchange Format (EEHRxF), and xShare have been working to enable cross-border health data usage for healthcare, research, and policy purposes, by proposing and establishing legal, operational and technical foundations. However, the absence of global healthcare professional databases and federated identification and authentication mechanisms perpetuates some challenges for cross-border usage at scale. Based on the work of the EU-funded Blockchain.PT R&D initiative, we propose a distributed ledger technology (DLT)-based architecture and protocol to support a universal EHR system, which combines standardized off-chain storage of sensitive health data with an on-chain layer utilising smart contracts for data certification, access management, and logging of consent and data sharing, processing and transfer agreements. The architecture addresses key requirements of the EHR lifecycle and incorporates emerging decentralised digital identity, authentication and verification protocols to establish the trust layer between the several participants of the healthcare value chain, put citizens in control of their data and partially disintermediate health data exchange and verification. Leveraging the emerging DLT infrastructures such as EBSI/Europeum, and on established digital identity regulations and frameworks such as eIDAS and EUDI, we propose to develop on this EHR-focused architecture and protocol and integrate cross-jurisdictional healthcare professional identification and authentication capabilities. The combination of a universal EHR system with a trusted healthcare professional identity and access management layer could work as a cornerstone for realising the EHDS vision.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".