A Framework for Enhancing Transparency and Security in Telehealth Record Access Using Dual-One-Time Passwords
Bibliographic record
Abstract
The rapid adoption of telehealth systems has significantly improved healthcare accessibility; however, it has also introduced critical challenges related to data security, privacy, and transparency in medical record sharing.Existing telehealth record (THR) management solutions primarily focus on user authentication and often fail to provide real-time visibility and consent to patients and treating physicians regarding record access by third-party platforms.To address these limitations, this paper proposes a dual-consent, one-time password (OTP)-based telehealth record access framework that ensures secure, transparent, and ethical data sharing.The proposed framework requires simultaneous authorization from both the patient and the treating physician before granting access to telehealth records.A lightweight system architecture incorporating random OTP generation, role-based access control, and time-bound validation is designed and implemented as a web-based application.The framework is experimentally evaluated under controlled network conditions using performance metrics such as OTP delivery time, API response time, telehealth record load time, and authentication success rate.Experimental results demonstrate that the proposed approach achieves an average OTP delivery time of 25 ms, API response time of 35 ms, telehealth record load time of 18 ms, and an authentication success rate of 94%, indicating its suitability for real-time telehealth environments.Compared to blockchain-based, biometric, and traditional multi-factor authentication mechanisms (MFA), the proposed framework offers lower deployment complexity, enhanced transparency, and explicit patient-physician consent.The study contributes a practical and deployable solution for secure telehealth record management and establishes a foundation for future research in patient-centric healthcare data governance.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".