Effectiveness of Quality Improvement Interventions at Reducing Inappropriate Cardiac Imaging
Bibliographic record
Abstract
BACKGROUND: Between 5% and 25% of cardiac imaging tests are performed for inappropriate indications. Studies have examined the impact of appropriate use criteria-based quality improvement initiatives on inappropriate testing, but they have not been systematically evaluated. METHODS AND RESULTS: We performed a systematic review of studies evaluating quality improvement initiatives aimed at reducing inappropriate cardiac imaging. The primary outcome was the proportion of inappropriate tests based on appropriate use criteria. Studies were analyzed using a random effects meta-analysis model, and heterogeneity was examined using subgroup analyses. We identified 6 observational studies and 1 randomized control trial. Most interventions (n=6) had a formal education component, and 5 included a mechanism for physician audit and feedback. Although these interventions were associated with lower odds of inappropriate testing (odds ratio, 0.44 [95% confidence interval, 0.32-0.61]; P<0.001), significant heterogeneity was observed (I(2)=70%), which was best explained by the utilization of physician audit and feedback. Interventions that employed physician audit and feedback were associated with significantly lower odds of inappropriate testing (odds ratio, 0.36 [95% confidence interval, 0.31-0.41]; P<0.001; I(2)=0%), whereas those that did not had no effect (odds ratio, 0.89 [95% confidence interval, 0.61-1.29]; P=0.51; I(2)=0%; P value for difference <0.001). All studies had potential sources of bias that could have affected the observed estimates. CONCLUSIONS: Interventions using physician audit and feedback are associated with lower odds of inappropriate cardiac testing. Further research is needed to evaluate a greater diversity of intervention types, with improved study designs.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| 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".