Protocol for a stepped wedge cluster randomized quality improvement project to evaluate the impact of medical safety huddles on patient safety
Bibliographic record
Abstract
Introduction: Physician engagement is crucial for furthering patient safety and quality improvement within healthcare organizations. Medical Safety Huddles, which are physician-specific huddles, is a novel way to engage physicians with patient safety and may reduce adverse events experienced by patients. We plan to conduct a multi-center quality improvement (QI) initiative to implement and evaluate Medical Safety Huddles. The primary objective is to determine the impact of the huddles on adverse events experienced by patients. Secondary objectives include assessing the impact of the huddles on patient safety culture and physician engagement, and a process evaluation to assess the fidelity of implementation. Methods: This stepped wedge cluster randomized study will be conducted at four academic inpatient hospitals over 19 months. Each site will adapt Medical Safety Huddles to its own practice context to best engage physicians. We will review randomly selected patient charts for adverse events. Generalized linear mixed effects regression will be used to estimate the overall intervention effect on adverse events. Process measures such as physician attendance rates and number of safety issues raised per huddle will be tracked to monitor implementation adherence. Conclusion: Medical Safety Huddles may help healthcare organizations and medical leaders to better engage physicians with patient safety. The project results will assess the fidelity of implementation and determine the impact of Medical Safety Huddles on patient safety.
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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 | 0.077 | 0.071 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.100 | 0.018 |
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".