Applications of Blockchain Technology for Secure Transaction of Electronic Health Records
Bibliographic record
Abstract
The healthcare industry is not immune to the trend of adopting new IT systems, and this includes hospitals and doctors’ offices. To store a patient’s medical history (including diagnoses and treatments), medical history (including diagnostic pictures), and financial data (including demographics and insurance details), a computerised database known as an “Electronic Health Record” (EHR) is used. Because medical records are typically private and must be shielded from prying eyes, ensuring their secure transfer is a top priority for healthcare systems. Electronic health records (EHRs) are used in the proposed study to ensure the security of all sent medical data, including the Chest X-ray MedPix images. This article describes a blockchain-based solution that patients can use to keep all their data in one secure location. This system was built with the Ganache tool on the Ethereum network, making use of Solidity and web3.js as well as other languages, tools, and approaches. In this study, a systematic method is described wherein blockchain smart contracts are used to record patient information and execute tasks in a distributed environment. After a smart contract has been deployed, all transactional communication occurs within its encrypted confines, protecting both the parties and their personal information. As an added bonus, the validated and broadcasted changes intended by the transaction can be checked. To ensure that government agencies have quick access to protected records, there is a cryptocurrency wallet (MetaMask) that stores the information in an encrypted, decentralised manner. Both patients and physicians can use the wallet to log in. In addition, the system will manage and protect all of the doctor and patient’s information. Things like the following are in scope for the proposed system: Users may now access the same information at the same time because to blockchain’s distributed ledger capabilities, which boosts productivity, establishes trust, and lowers barriers to entry. Setting individual permissions for users allows for safe data storage. In addition, the suggested method enables the safe transmission of patient medical records.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".