Electronic Medical Records and the Challenge to Privacy: How the United States and Canada are Responding
Bibliographic record
Abstract
The rapid and continual advances in electronic record keeping create new and challenging privacy concerns in a variety of contexts. This Note examines the particular privacy issues countries face as their health care systems move towards more centralized, electronic medical record keeping. Electronic medical records provide for the increased collection, availability, aggregation, and dissemination of medical data which can facilitate more effective care, but can also leave people vulnerable to having private information misused. This Note focuses on the current privacy law framework in the United States and Canada. It will then explore whether the legal standards in these countries have or are adapting to provide adequate safeguards against the new privacy concerns resulting from the increasing use of electronic medical records.
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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 | 0.024 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.041 | 0.031 |
| Scholarly communication | 0.034 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.016 | 0.012 |
| 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".