The Impact of the Electronic Health Record on Patient Safety: An Alberta Perspective
Bibliographic record
Abstract
Alberta is at the leading edge in developing its electronic health record (EHR ), a provincial initiative to provide healthcare providers with immediate access to a patient's medication history and laboratory test results, regardless of where they are in the province, or where the patient's drugs or other treatments were ordered. The Alberta EHR was launched in October 2003. So far 6,000 healthcare providers have voluntarily signed on to use it, and benefits to patient safety have been reported. The EHR is an important part of healthcare renewal that is required to improve patient safety; however, it must not be viewed as a stand alone cure-all solution to Canada's patient safety challenge. The EHR will only reach its full potential if it is part of an integrated approach to health renewal that stresses consistency of healthcare, practice and information standards, and consistency and standardization of healthcare data. Without a sector-wide EHR like Alberta's, the proliferation of computerized electronic medical records (EMRs) in hospitals, clinics and pharmacies might create "islands of information" that are not widely compatible. A national EHR approach must acknowledge the importance of improving broadly accepted practice standards and data consistency in order to reduce the islands of information and protect patients from medical errors as they move between them.
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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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.050 | 0.024 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".