Using the hierarchy of intervention effectiveness to improve the quality of recommendations developed during critical patient safety incident reviews
Bibliographic record
Abstract
Our Canadian multi-site academic health sciences centre uses a standardized process to review critical patient safety incidents and develop recommendations to prevent incident reoccurrence. We recognized an opportunity to enhance recommendation development by integrating the Hierarchy of Intervention Effectiveness (HIE), a human factors framework, into the incident review process. This project aimed to increase the proportion of system-focused recommendations from critical incident reviews from 16 to 30% over 16 months. A multi-intervention strategy included (1) standardizing the incident analysis review template; (2) earmarking time for recommendation development during reviews; (3) providing participants with just-in-time education and tools; and (4) initiating HIE-based recommendation classification during incident reviews. Statistical process control p-Chart analysis showed an increase in system-focused recommendations from 16 to 30% over 16 months. The HIE promotes system-level change to prevent critical incidents, which other organizations may benefit from incorporating in their patient safety reviews.
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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 | 0.282 | 0.527 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".