Improving departmental Quality Improvement Plans through standardisation, structured peer-to-peer feedback and building improvement capacity and culture
Bibliographic record
Abstract
INTRODUCTION: Quality Improvement Plans (QIPs) can improve healthcare quality by raising awareness and providing a focus for improvement efforts. The physician-led quality committee at our institution set out to improve the previously heterogenous quality and content of clinical department QIPs and increase alignment between clinical department and hospital quality improvement (QI) priorities. We describe these initiatives and assess their impact on the quality of departmental QIPs. METHODS: The Physician Quality Committee at our academic tertiary care hospital implemented a series of interventions, including a peer-to-peer feedback mechanism, longitudinal education and coaching, standardised QI project templates and efforts to facilitate culture change. The QIPs from 13 clinical departments were reviewed for the years before (2018-2019) and after the interventions (2022-2023) and scored according to a structured rubric, created by consensus among physician quality leads. Data are reported as means and medians (IQR). A Wilcoxon signed-rank test was used to evaluate for statistical significance. A Likert-scale survey was used to assess physician QI leads' perception of the impact of the initiatives. RESULTS: The mean score on the structured rubric was 4.4/12 for the QIPs from 2018 to 2019 and 8.0/12 for the QIPs from 2022 to 2023 (Z=3.06, p=0.0005). The median score (25th, 75th percentile) in 2018-2019 was 4.5 (3.5, 5.13), which increased to 8.5 (7.0, 9.0) in 2022-2023. The survey response for physician QI leads was 10/13 (76.9%). The most positive response was the QI lead's knowledge and understanding of how to structure a QI project (mean score of 4.4/5); the least positive response was related to departmental focus and clarity regarding QI priorities (mean score of 3.9/5). CONCLUSIONS: Multifaceted physician-led interventions resulted in improvements in the quality and content of clinical department QIPs, improved physician knowledge of QI methodology, enhanced focus and clarity around departmental QI priorities, and improved awareness of hospital-wide improvement efforts.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.031 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".