The Future of E-Health: Blockchain Solutions with Hyperledger Fabric and IPFS
Bibliographic record
Abstract
The electronic representation of a patient's medical information is called an electronic health record, or EHR.Normally, these records are kept on cloud-based or central servers.Blockchain technology is a new technology used to solve security and privacy problems of EHR data in a decentralized way.Hence, they are accessible to authorized health providers for better management of patient care.Building secure e-health systems with public blockchains such as Ethereum faces several problems.The main issue is that these blockchains are permissionless; everybody can join in and obtain access to the data, which becomes a cause of significant privacy concerns.This research proposes a private and decentralized e-healthcare system using Hyperledger Fabric and the Interplanetary File System (IPFS) for securely and effectively storing and retrieving EHRs.Privacy and security are guaranteed with Hyperledger Fabric in ensuring that only authorized parties can access sensitive medical information.The system is further enhanced to include decentralized storage based on IPFS for the storage of medical images and files that cannot be directly stored in the blockchain.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".