The Effect of an Incentive Billing Code on Heart Failure Management in Primary Care: A Population-Based Study
Bibliographic record
Abstract
Background To support family physicians (FPs) in managing patients with heart failure (HF), the Ministry of Health in Ontario, Canada, implemented the Q050 billing code in 2008, a pay-for-performance (P4P) incentive for guideline-based HF care. We studied whether the incentive was associated with any change in the prescriptions of HF medications. Methods We identified all patients with HF in Ontario of age≥66, who were managed by FPs claiming the Q050 incentive between 2008 and 2021. We determined the proportion of patients who were prescribed renin-angiotensin system inhibitors (RASi), beta-blockers (BB), mineralocorticoid receptor antagonists (MRA), and diuretics three months before and after the Q050 billing code was claimed for these patients. Where applicable, we classified the agents by whether they are guideline-directed as recommended by the Canadian Cardiovascular Society (CCS). Results We included 39,425 HF patients in the study. The median age was 80 (IQR 73-85) years; 49% were female. Compared to pre-Q050, there was an increase in RASi prescriptions from 45.2% to 45.8%, BB from 51.9% to 54.4%, MRA from 9.2% to 11.7%, and diuretics from 63.2% to 65.7% after the incentive (p<0.05). There was a decrease in those not on any HF medications from 27.5% to 24.9% (p<0.001). Those with newly diagnosed HF and prompt follow-up with FPs experienced the largest but clinically modest increase in HF medications. Conclusions The Q050 incentive led to a minimal increase in the prescription of HF medications; there is underutilization of disease-modifying agents.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".