Is Canada ready for patient accessible electronic health records? A national scan
Bibliographic record
Abstract
BACKGROUND: Access to personal health information through the electronic health record (EHR) is an innovative means to enable people to be active participants in their own health care. Currently this is not an available option for consumers of health. The absence of a key technology, the EHR, is a significant obstacle to providing patient accessible electronic records. To assess the readiness for the implementation and adoption of EHRs in Canada, a national scan was conducted to determine organizational readiness and willingness for patient accessible electronic records. METHODS: A survey was conducted of Chief Executive Officers (CEOs) of Canadian public and acute care hospitals. RESULTS: Two hundred thirteen emails were sent to CEOs of Canadian general and acute care hospitals, with a 39% response rate. Over half (54.2%) of hospitals had some sort of EHR, but few had a record that was predominately electronic. Financial resources were identified as the most important barrier to providing patients access to their EHR and there was a divergence in perceptions from healthcare providers and what they thought patients would want in terms of access to the EHR, with providers being less willing to provide access and patients desire for greater access to the full record. CONCLUSION: As the use of EHRs becomes more commonplace, organizations should explore the possibility of responding to patient needs for clinical information by providing access to their EHR. The best way to achieve this is still being debated.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".