Assessing Information Confidentiality in Telemedicine Platforms Using Public Web Data: A Case Study of Persian Televisit Websites.
Bibliographic record
Abstract
Background: The shift from traditional office visits to internet-based care has accelerated during the COVID-19 pandemic, increasing reliance on telemedicine platforms. This growth has led to more health-related electronic data and heightened risks of unauthorized access, making it essential to prioritize information confidentiality issues. This study aims to reveal the disclosure of confidential information on a Persian televisit website. Methods: In this observational case study, we gathered public health-related electronic data from a Persian televisit website in 2022. SAS software was used to harvest messy data about patients and doctors and create a structured dataset. Meanwhile, Microsoft Excel and RStudio software programs were used for data visualization. A hashing algorithm was applied to encode personal information, preventing the identification of individuals. Results: Our study showed how public web data about 263 patients and 194 doctors, harvested from the target website, can be used to reveal patients' private information. Using such data, we explored the patient-doctor interaction patterns. With access to the patients' identifiable information, we recognized the identity of the clients who received internet-based care services and their possible diseases. Conclusion: This analysis revealed that exposure of patients' confidential information could compromise their identities and underlying medical conditions. This highlights the need for a national framework to ensure the security of health-related electronic data. Health authorities should enforce comprehensive laws, while the owners of televisit websites should implement privacy by design principles into the development of such platforms to prevent the disclosure of sensitive information.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".