Development and evaluation of a quality improvement framework for healthcare
Bibliographic record
Abstract
OBJECTIVE: Develop and evaluate a framework for quality improvement which will provide a common approach, terminology and greater likelihood of success in achieving demonstrable and sustained improvement. DESIGN: Descriptive with mixed methods evaluation. SETTING: Tertiary care academic hospital in Ottawa, Canada. PARTICIPANTS: Students enrolled in the Quality and Patient Safety Leadership Executive Program at the local university. METHODS: The quality improvement (QI) framework was developed though comparison and coding of key strengths across four commonly applied quality improvement frameworks. Effectiveness of the framework was evaluated through a satisfaction survey of students who were taught the framework, and independent assessment of student improvement initiatives that followed the QI Framework versus initiatives from a prior year who did not. MAIN OUTCOME MEASURES: Acceptability and effectiveness of the QI Framework. RESULTS: All participants found the QI Framework to be useful and easy to follow. Independent evaluation of improvement initiatives following the QI Framework, as compared to those that did not, scored higher (95% CI: 3.0 ± 0.49) than the projects completed without the QI Framework (95% CI: 2.2 ± 0.30), P value < 0.01. Scores were higher for cohort 2017/18 in all criteria except scaling and spreading, in which there was no change (2015/16 95% CI: 1.2 ± 0.24, 2017/18 95% CI: 1.2 ± 0.38). CONCLUSIONS: The method we have developed is acceptable and helpful to users, and overall application resulted in higher quality initiatives. We believe this method, which we have branded as The Ottawa Hospital Innovation Framework, can be beneficial in healthcare settings for a variety of change initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".