Patient Access to Electronic Health Record: A Comparative Study on Laws, Policies and Procedures in Selected Countries
Bibliographic record
Abstract
INTRODUCTION: The e-health system must have the capability of patient access to electronic health record. The advantage of access to their record lets them have better understanding of their condition and treatment. It can also raise the reliability of consistency and correctness of data in health care system. Finally it will improve the maintenance quality of medical records and guarantee better results of medication. This study aimed to carry out a comparative study concerning laws, policies and procedures upon patients' access right to EHR in selected countries and to suggest appropriate solutions for Iran. METHOD: This was a comparative descriptive study. The study population was the laws, policies and procedures of patients' access right to EHR belong to countries like Canada, Australia, New Zealand and Iran. Data were collected by taking notes on index cards. In this study in order to collect data, at first, the researcher studied the websites related to Health Ministry of the countries and existing laws and policies through related links in the websites. In next step, the health information management association websites were studied and the related data were collected. The gathered data were analyzed through content analysis. RESULTS: The findings of research showed that in every four countries there are generally some laws, policies and procedures. Although Canada and New Zealand concerning the number of laws and policies related to the subject subsequently are ranked after Australia, they are ranked prior to Australia regarding benefiting the laws and specified policies. CONCLUSION: Given the necessity of EHR implementing and codifying the planning of SEPAS in Iran, as there is no specified laws or procedures regarding patients' access right to EHR, the obligation of paying attention to assigning a law or at least obvious policies and procedures and providing the details is absolutely apparent.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".