Abstract 336: Relationship of Provider and Practice Volume to Performance Measure Adherence: Results from the NCDR®
Bibliographic record
Abstract
Background: Previous research demonstrates an association between higher surgical volume and improved outcomes; however, there has been limited literature evaluating the association between volume and quality of care in cardiovascular patients. We examined the relationship between volume and compliance with selected outpatient quality measures for coronary artery disease (CAD). Methods: Using a contemporary cohort from the PINNACLE Registry ® (2009-2012), average monthly provider and practice volumes were calculated for CAD encounters. Performance for four American Heart Association (AHA) CAD measures (antiplatelets, beta blockers, ACE inhibitor/ARBs, lipid therapy) was assessed at the most recent encounter for each patient. The percentage of patients meeting 100% of quality measures was calculated for both providers and practices. We fit 3-level hierarchical logistic regression models to assess the relationship between provider and practice volume and performance on quality measures. Results: Data were available for 577,645 patients, representing 1,134 providers from 78 practices nationwide. Monthly practice volumes (median (IQR)) were 683 (251-1466) for CAD encounters and monthly provider volumes were 67 (33-119). Overall, 48% of patients met all CAD measures. The relationship between provider and practice volumes and adherence to performance measures varied significantly. Practice volume was not associated with adherence to performance measures (p = .49), however higher provider volume was positively associated with adherence to performance measures (p < 01) (See Figure 1 ). Conclusions: In the PINNACLE Registry ® performance on published quality measures for CAD was modest and variable. Higher provider volume was positively associated with adherence to performance measures, but there was no association between practice volume and performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".