Blockchain Empowered Interoperable Framework for Smart Healthcare
Bibliographic record
Abstract
In the past, healthcare industry used paper-based systems to manage and store medical records.However, these systems are vulnerable to data breaches, loss, and errors.To overcome these issues, a research study has been conducted to create a safe and efficient Electronic Data Interchange (EDI) system for healthcare using blockchain technology.The study utilized various tools and methods including Python as the programming language to implement the blockchain environment, the pyQT5 library for graphical user interface (GUI), and the MySQL database management system as a repository for Electronic Health Records (EHR) with DBeaver, a cross-platform tool for data management.The research work involves the development of a blockchain-based smart contract for the storage, exchange, and retrieval of EHR.Additionally, a Python application based on pyQT5 is created to provide users with a friendly GUI.The proposed blockchain-based healthcare system provides a secure and efficient platform for storing and managing EHR as well as enabling secure EDI among healthcare stakeholders like practices, doctors, labs, and pharmacies.Furthermore, the system is scalable and user-friendly, and includes various features like patient visits, history, practices, doctors, and appointment scheduling.Blockchain technology ensures EHR integrity, secure EDI, and confidentiality, while the user-friendly interface enhances the user experience compared to the existing EDI standards like health level 7 (HL7).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".