24 International quality & safety best practices to implement IHI’s whole system quality framework
Bibliographic record
Abstract
Background The establishment of robust quality and safety (Q&S) best practices is crucial to ensuring patients receive the best care possible. As such, the University Health Network (UHN) is embarking on a Q&S transformation centered around the Institute for Healthcare Improvement’s (IHI’s) Whole System Quality (WSQ) framework. Objectives The purpose of this project was to go beyond the published literature and glean behind-the-scenes strategies, approaches, advice, and lessons learned on the design and implementation of Q&S best practices from centres with preeminent international reputations in Q&S (see table 1), to inform our own and others’ respective Q&S transformations. Methods Nine semi-structured open-ended interviews were conducted with leadership from centres spanning three continents. Questions centered on building infrastructure around Quality Planning (e.g., how did you develop and carry out your Q&S vision?), Quality Control (e.g., how do you effectively track and report Q&S metrics?), and Quality Improvement (e.g., how do you train and enable your staff to do Quality Improvement work?). Results Inductive thematic analyses revealed common recommendations (see table 2) for Quality Planning (e.g., make Q&S the central focus of entire organization; reimagine Q&S governance structures that focus on quality in addition to safety including the creation a Chief Quality Officer position with a direct reporting line to the hospital President/Chief Executive Officer), Quality Control (e.g., apply advanced analytics that leverage artificial intelligence, and triangulate Q&S metrics with complementary datasets to drive change), and Quality Improvement (e.g., develop and grow Q&S champions at every layer of the organization). Conclusions Our findings provide a blueprint for the successful implementation of the three IHI WSQ pillars. They serve as a conduit and call to action for the effective building of enterprise-wide Quality & Safety infrastructure with the potential for far-reaching downstream impacts on the quality and safety of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.151 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".