HealthBlock: A Framework for a Collaborative Sharing of Electronic Health Records Based on Blockchain
Bibliographic record
Abstract
Electronic health records (EHRs) play an important role in our life. However, most of the time, they are scattered and saved on different databases belonging to distinct institutions (hospitals, laboratories, clinics, etc.) geographically distributed across one or many countries. Due to this decentralization and the heterogeneity of the different involved systems, medical staff are facing difficulties in correctly collaborating by sharing, protecting, and tracking their patient’s electronic health-record history to provide them with the best care. Additionally, patients have no control over their private EHRs. Blockchain has many promising future uses for the healthcare domain because it provides a better solution for sharing data while preserving the integrity, the interoperability, the availability of the classical client–server architectures used to manage EHRS. This paper proposes a framework called HealthBlock for collaboratively sharing EHRs and their privacy preservation. Different technologies have been combined to achieve this goal. The InterPlanetary File System (IPFS) technology stores and shares patients’ EHRs in distributed off-chain storage and ensures the record’s immutability; Hyperledger Indy gives patients full control over their EHRs, and Hyperledger Fabric stores the patient-access control policy and delegations.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".