Safety Requirements for Health Electronic File; Comparison between Selected Countries
Bibliographic record
Abstract
Introduction: With increasing production of health information, information technologies have been used for better management and usage of such data. This enormous increase in gathering and storing of information and widespread accessibility also concerns individuals regarding privacy and security of information. This research is concerned with this issue due to decisions on establishing individual health electronic files in Iran. Methods: During this descriptive-comparative study, security requirements of electronic health files in Iran, England and Canada were reviewed and compared. Checklist was used for data collection. Data was collected from journal papers, and books accessed through libraries and other credible online sources between 1995-2006. Results: Security requirements regarding health electronic file such as information security systems, safety of communication and operations management, access control were established in those countries except for Iran. There is no safety and security requirements in this regard in Iran. Conclusion: Security and safety of health electronic file is one of the basic requirements, which lacks in Iran. Due to recent interests in establishing health electronic file in Iran by Ministry of Health and Medical Education, it is necessary that such requirements been established by responsible bodies. Keywords: Confidentiality; Electronics, Medical; Electronics; Medical Records; Medical Records System, Computerized
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".