The ethical and legal complications surrounding the implementation of a pan-Canadian electronic health record (EHR) system
Bibliographic record
Abstract
Canada lags behind other countries in the development of electronic health records. If Canada develops a pan-Canadian electronic health record (EHR) system, the quality of patient care can improve. A review of the literature lists potential benefits of EHRs such as improvements in medical research, a reduction in emergency room and diagnostic test wait times. Such a system will make medical records readily available to health care providers which will help them make informed critical decisions. Regardless of the benefits of such a system, there are legal and ethical implications hindering its development and implementation. The federal and provincial governments are at odds as to who is in charge of health care. Canadians need to be consulted on its implementation, and their concerns regarding privacy legislation addressed. Canada Health Infoway has undergone initiatives to create an interoperable EHR system in Canada with audit trails, smart card technology, etc. The benefits of such a system are seen in an analysis of Alberta that has created its own provincial EHR system. Case studies of both Alberta and the United Kingdom's EHR systems should be used as a foundation to begin developing Canada's national system. If Canada addresses the concerns surrounding the implementation of a national EHR system through policies with sanctions to deal with the ethical implications of such a system (informed consent, unlawful access, etc), then studies have shown that Canadians will support a pan-Canadian EHR system initiative. Before addressing ethical dilemmas, the governments must assume responsibility of who will develop and maintain this system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.083 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.042 | 0.042 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".