A population-based analysis of incentive payments to primary care physicians for the care of patients with complex disease
Bibliographic record
Abstract
BACKGROUND: In 2007, the province of British Columbia implemented incentive payments to primary care physicians for the provision of comprehensive, continuous, guideline-informed care for patients with 2 or more chronic conditions. We examined the impact of this program on primary care access and continuity, rates of hospital admission and costs. METHODS: We analyzed all BC patients who qualified for the incentive based on their diagnostic profile. We tracked primary care contacts and continuity, hospital admissions (total, via the emergency department and for targeted conditions), and cost of physician services, hospital care and pharmaceuticals, for 24 months before and 24 months after the intervention. RESULTS: Of 155 754 eligible patients, 63.7% had at least 1 incentive payment billed. Incentive payments had no impact on primary care contacts (change in contacts per patient per month: 0.016, 95% confidence interval [CI] -0.047 to 0.078) or continuity of care (mean monthly change: 0.012, 95% CI -0.001 to 0.024) and were associated with increased total rates of hospital admission (change in hospital admissions per 1000 patients per month: 1.46, 95% CI 0.04 to 2.89), relative to preintervention trends. Annual costs per patient did not decline (mean change: $455.81, 95% CI -$2.44 to $914.08). INTERPRETATION: British Columbia's $240-million investment in this program improved compensation for physicians doing the important work of caring for complex patients, but did not appear to improve primary care access or continuity, or constrain resource use elsewhere in the health care system. Policymakers should consider other strategies to improve care for this patient population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".