Making Case for Using RAFT in Healthcare Through Hyperledger Fabric
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
Blockchain technology is enabled by consensus algorithms to manage the relationships among several economic or business operators without human intervention. With the help of consensus algorithms, distributed systems can reliably reach agreement even if part of the system is faulty. Blockchain yields many benefits, among others, traceability, transparency, and security. We consider using the RAFT consensus algorithm to achieve robust and scalable decentralized applications, with focus on healthcare. We propose a stylized healthcare network, enabled by RAFT and built upon Hyperledger Fabric to showcase the use of RAFT in healthcare blockchain. However, RAFT is by no means limited to healthcare record systems, and can be applied to any other record system and value chain. Our paper offers several insights to those working in value chains and information management-related fields. In addition, we end our study with some future research avenues that may inspire managers and scholars to build or refine new decentralized systems in healthcare and other related fields.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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