Pilot Study: Introducing a Quality Assurance Process for a Team-Centered Approach Involving Nonphysician Providers in Radiology
Bibliographic record
Abstract
PURPOSE: The objective of this study was to introduce 3 new quality initiatives in radiology that engage various members of the department including radiologists, residents, technologists, and booking clerks. These pilot programs provide work-related learning opportunities in semiautomated, easy-to-use, email-based, standardized forms that are used to identify cases where imaging could have been performed in a more optimal way (either due to technical reasons or a different protocol could have been chosen). In the case of the Kudos quality initiative program (QUIP), this is used to provide positive feedback to an individual in the department for a job well done. METHODS: Since inception in January 2012 to September 2013, we reviewed Technical QUIPs, protocols under questions (PUQ), and Kudos QUIPs. These were collated through receipt of standardized emails for each category. RESULTS: A total of 62 Kudos QUIPs, 8 Technical QUIPs, and 58 PUQs were received in the abdominal and pelvic imaging division since inception. CONCLUSIONS: Though still a relatively new pilot programs, PUQs and Technical QUIPs have afforded technologists and booking clerks opportunities to become engaged in improving patient care as well as learning from their own performance gaps. Future standardization of received data for each modality still needs to be established as well as an action plan to implement long-term changes.
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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 | 0.045 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".