Association of a Heart Failure Management Incentive in Primary Care With Clinical Outcomes: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: We aim to examine the association between primary care physicians' billing of Q050A, a pay-for-performance heart failure (HF) management incentive fee code, and the composite outcome of mortality, hospitalization, and emergency department visits. METHODS AND RESULTS: This population-based cohort study linked administrative health databases in Ontario, Canada, for patients with HF aged >66 years between January 1, 2008, and March 31, 2020. Cases were patients with HF who had a Q050A fee code billed. Cases and controls were matched 1:1 on age, sex, patient status on being rostered to a primary care physician, cardiologist, or internist visit in the 6 months before study enrollment, Johns Hopkins Adjusted Clinical Group resource use bands, days between HF diagnosis and study enrollment (±2 years), and the logit of the propensity score. A Cox proportional hazards model assessed the association of Q050A with the outcome. A total of 59 664 cases had a Q050A billed, whereas 244 883 patients did not. Before matching, patients who had a Q050A billed were more likely to be men (52% versus 49%), were rostered to a primary care physician (100% versus 96%), had a higher Charlson Comorbidity Index, and had higher health care costs. The mean follow-up was 481 days for cases and 530 days for controls. The composite outcome (hazard ratio, 1.11 [95% CI, 1.09-1.12]) was significantly higher for cases than controls. CONCLUSIONS: The Q050A incentive improved financial compensation for primary care physicians managing patients with HF but was not associated with improvements in the outcome. Research on promoting evidence-based HF management is warranted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".