Accelerating the National Implementation of Electronic Health Records in Canada
Bibliographic record
Abstract
Trends such as the aging population, long wait times, rising costs, and labour shortages in health professions are notable challenges facing the sustainability of Medicare in Canada. Healthcare reform, especially in primary care, will ensure efficiency and equitable access to healthcare in. Information and communication technologies (ICTs) such as electronic health records (EHRs) will play a pivotal role in reforming and sustaining Medicare. EHRs make healthcare safer, cost efficient and more integrated, and are necessary for the wider application of ICTs in the health sector. EHRs enhance decision-making capabilities for both providers and patients, especially in managing chronic diseases. Notwithstanding the numerous advantages of EHRs, Canada is slow to adopt a nation-wide EHR system. This paper analyzed existing data to establish the factors that may help to accelerate the national implementation of electronic health records in Canada. It defined EHRs, discussed their advantages and disadvantages, and barriers to its full application. Also, it explored key strategies for accelerating EHR initiatives in Canada, and suggested action plans and time frames for doing so.
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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".