Developing and implementing a new health information technology innovation to improve patient safety in the Canadian context
Bibliographic record
Abstract
Adverse Drug Events (ADEs) are unintended and harmful events related to medication use. Many ADEs recur because patients are unintentionally re-exposed to medications that previously caused harm. To help address this, we designed ActionADE, an interoperable Health Information Technology (HIT) that allows clinicians to communicate ADEs across health sectors. We completed ethnographic workplace observations and a systematic review to inform design. After piloting, we integrated ActionADE with the provincial medication dispensing database to alert pharmacists when patients seek to fill a prescription for the same or a same-class drug as one that previously caused harm. Co-design, application of clinically meaningful field labels and data standards, and integration with other health information systems were critical to ActionADE's functionality and use. However, health system decision-makers need to proactively plan for how to spread and scale pilot project in the HIT ecosystem to ensure public benefit from successful innovation.
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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 | 0.038 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".