Designing a Confidentiality Principles Model of Electronic Health Record for Iran 2007
Bibliographic record
Abstract
Introduction: Today increasing growing of health information is results in applying of new technologies for suite manages and utilization of information technologies such as electronic health record. A growing capacity of information technologies in collection, storage and transmission of information has added a great deal of concerns since electronic records can be accessed by numerous consumers at various locations. Now considering Iran's new approach to make electronic health records, the basic question is "what is the appropriate model for their confidentiality principles? Methods: The present study is a descriptive investigation made in Iran in 2007. confidentiality principles of Electronic Health Record, gathered from comparative study that carried out about Australia, Canada and England countries. Then designed initial model. Final model information gathered by questionnaire and use of Delphi Technique. Rate under 50 percent eliminated from model and 75 percent and up added to model. The items from the pattern confirmed among 50 percent up to 75 percent and suggested items from participants, again put to votes. Results: Offered model for our country emphasize on patient consent. no emphasis on the use and release of health information in marketing and purposes outside the care cycle without the patient's consent or any other people having an intimate relation with the patient, and the unavailability to his health information by the patient in certain cases. But in contrast to the selected countries, there is a reference in the suggested model to fines for disclosure of electronic health records, data ownership, and inclusion of information accessibility laws in all organizations, responsibility for inaccessibility to information, and finally the conditions for movement of data abroad. Conclusion: Comprehensive model of Electronic Health Records Confidentiality principles is Designed for Iran and Recommend approve and appliying of regulations with usage of proposed model for Protection of Electronic Health Information Confidentiality.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".