Patient safety and health information technology conceptual framework
Bibliographic record
Abstract
Health information technology (HIT) refers to the deployment of electronic systems health care professionals and patients use to store, share, and analyze health information to improve patient safety and outcomes. Some of the best practices to heighten HIT use include procuring and analyzing data, prioritizing interoperability, building dynamic content, accounting for evolving patient expectations, recognizing the human element, and respect for the patient as the health consumer. Providers should target patients with the appropriate HIT information that is tailored to their needs and circumstances. Thus, careful evaluation is required to ensure it meets the needs of the patients. In this paper, we describe the current state of electronic health records use in Canada along with a patient safety and technology conceptual framework. We use this framework and metal hypersensitivity, a medical device-related adverse event, to highlight how health information technology can be leveraged to create a learning health system and enhance patient safety.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".