Abstract 210: Implementation of Appropriate Use Criteria for Cardiology Tests and Procedures: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: The American College of Cardiology appropriate use criteria (AUC) provide clinicians with evidence-informed recommendations for cardiac care. Adopting AUC into clinical workflows may present challenges, and there may be specific implementation strategies that are effective in promoting effective use of AUC. We sought to assess the effect of implementing AUC in clinical practice. Methods: We conducted a meta-analysis of studies found through a systematic search of the MEDLINE, Web of Science, Cochrane, or CINAHL databases. Peer-reviewed manuscripts published after 2005 that reported on the implementation of AUC for a cardiovascular test or procedure were included. The analysis protocol was submitted a priori to the PROSPERO international prospective register of systematic reviews. We used a structured data extraction spreadsheet for elements such as study design, implementation strategy, and primary outcome. Results: We included 18 studies, the majority used pre/post cohort designs; few (n=3) were randomized trials. Most studies used multiple strategies (n=12, 66.7%). Education was the most common individual intervention strategy (n=13, 72.2%), followed by audit & feedback (n=8, 44.4%) and computerized physician order entry (CPOE) (n=6, 33.3%). No studies reported on formal use of stakeholder engagement or “nudges”. In meta-analysis, AUC implementation was associated with a reduction in inappropriate/rarely appropriate care (odds ratio 0.62, 95 % confidence interval 0.49-0.78). Funnel plot suggests the possibility of publication bias. Conclusions: We found most published efforts to implement AUC succeeded at reducing inappropriate/rarely appropriate care. Studies rarely explored how or why the implementation strategy was effective. Because interventions were infrequently tested in isolation, it is difficult to make observations about their effectiveness as stand-alone strategies.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| 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 | 0.001 | 0.001 |
| 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".