SEMR: A Framework for Sharing Electronic Medical Records Using Emerging Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electronic Medical Records (EMRs) play a crucial role in healthcare systems, providing secure, comprehensive medical information and reducing errors. However, they are often fragmented and stored in separate databases owned by different institutions (e.g., hospitals, labs, clinics), posing challenges for healthcare professionals in sharing, preserving, and monitoring patients’ EMRs. To address the aforementioned challenges, this paper proposes a framework integrating many new emerging technologies and underscores their pivotal role in revolutionizing EMR systems. In particular, Hyperledger Indy empowers patients with complete authority over their EMRs, while Hyperledger Fabric manages authentication, authorization, and traceability. The InterPlanetary File System (IPFS) is used for secure sharing of EMRs. The Internet of Medical Things (IoMT) achieves real-time monitoring of patients’ health characteristics. Finally, WebAuthn provides strong protection for used cryptographic keys and cryptographic operations.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it