Reducing Inappropriate Imaging Orders For Lower Back Pain Using MRI And CT Checklists: A Quality Improvement Study In Saskatchewan, Canada
Bibliographic record
Abstract
Purpose: The objectives of this quality improvement study were: a) to develop Checklists for healthcare professionals to improve appropriateness of lumbar spine imaging orders and referrals in concordance with Choosing Wisely recommendations and guidelines; and b) to trial the Checklists, assessing their impact on reducing inappropriate imaging orders in Saskatchewan, Canada. Methods: A Clinical Development Team developed and adopted evidence-based lumbar spine magnetic resonance imaging (MRI) and computed tomography (CT) Checklists (quality improvement interventions) into the radiology requisition for both lumbar spine MRI and CT in Saskatchewan. Using a pre-post study design, data were obtained from the Radiology Information System (RIS). Control charts compared monthly number of imaging requests pre- and post-Checklists from June 2014 to August 2017. Results: Results showed a 23% reduction in the monthly average number of MRI requisitions one year after implementation of the lumbar spine MRI Checklist. On average, monthly volumes of lumbar spine CT requests decreased by 27% after implementation of the lumbar spine CT Checklist. Conclusions: Implementation of the two Checklists with evidence-based clinical indications and guidelines to order imaging may reduce volume of inappropriate urgent to elective MRI and CT requisitions for adult outpatients. Our results may help the design of other local and national quality improvement studies (e.g., appropriate ordering of knee MRI imaging), by replicating the integration of a Checklist into the ordering process to mitigate inappropriate imaging requests.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".