Highly Reliable Procedural Teams: The Journey to Spread the Universal Protocol in Diagnostic Imaging
Bibliographic record
Abstract
CONTEXT: The Joint Commission’s Universal Protocol has been widely implemented in surgical settings since publication in 2003. The elements improved patient safety in operating rooms, and the same rigor is being applied to procedures occurring in other health care arenas, in particular, diagnostic imaging. OBJECTIVE: In 2011, Kaiser Permanente West Los Angeles’s Diagnostic Imaging Department desired to adapt previous work on Universal Protocol implementation to improve patient safety in interventional radiology and mammography procedures. DESIGN: The teams underwent human factors training and then adapted key interventions used in surgical suites to their workflows. Time-out posters, use of whiteboards, "glitch books," and regular audits provided structure to overcome the risks that human factors present. MAIN OUTCOME MEASURES: Staff and physician perceptions of the teamwork and safety climates in their modalities were measured using the Safety Attitudes Questionnaire at baseline and at 18 months after training. Unusual Occurrence Reports were also reviewed to identify events and near misses that could be prevented. Implementation of key process changes were identified as process measures. RESULTS: Perception of the safety climate improved 25% in interventional radiology and 4.5% in mammography. Perception of the teamwork climate decreased 5.4% in interventional radiology and 16.6% in mammography. Unusual occurrences were underreported at baseline, and there is ongoing reluctance to document near misses. CONCLUSION: This work provides important considerations of the impact of departmental cultures for the implementation of the Universal Protocol in procedural areas. It also reveals unexpected challenges, and requires long-term effort and focus.
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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 | 0.341 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.010 | 0.029 |
| Research integrity | 0.010 | 0.032 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".