MrC: A medical-record chain system based on blockchain
Bibliographic record
Abstract
Who is the owner of your health documents? Although the answer to this question may seem straightforward and intuitive, today, we are far from the situation where you are the one who has the key to this critical information. Hospitals, health maintenance organizations (HMO), private doctors, and different medical institutions produce large quantities of medical information about each of us, and there is a need to maintain this information privately , synchronously and accessible to each one of us. In this paper, we propose the medical record chain ( MrC ) system, with its Blockchain architecture, as a tool to achieve this goal. The MrC system would be accessible to the patients so their medical data would be organized and available for viewing, regardless of where the information was produced. Furthermore, each patient can give a recognized medical service provider temporary permission to view and update his medical records. The architectural design of the MrC system ensures that no centralized authority controls the medical records. To enable the assimilation of the system in different institutions, a bridge to the system is proposed so that no change is required in the existing information systems of the medical body but a convenient and simple interface. Moreover, medical institutions would be incentivized to employ the system by allowing access to extensive medical datasets that have undergone de-identification. Implementing the MrC system would return ownership of the data to where it belongs- the patient. It would improve patients' health by allowing multiple medical institutions to access accurate information quickly. Finally, a by-product of the MrC system is improving public health by making comprehensive de-identified datasets available for medical research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".