Effect of incentive payments on chronic disease management and health services use in British Columbia, Canada: Interrupted time series analysis
Bibliographic record
Abstract
We studied the effects of incentive payments to primary care physicians for the care of patients with diabetes, hypertension, and Chronic Obstructive Pulmonary Disease (COPD) in British Columbia, Canada. We used linked administrative health data to examine monthly primary care visits, continuity of care, laboratory testing, pharmaceutical dispensing, hospitalizations, and total h ealth care spending. We examined periods two years before and two years after each incentive was introduced, and used segmented regression to assess whether there were changes in level or trend of outcome measures across all eligible patients following incentive introduction, relative to pre-intervention periods. We observed no increases in primary care visits or continuity of care after incentives were introduced. Rates of ACR testing and antihypertensive dispensing increased among patients with hypertension, but none of the other modest increases in laboratory testing or prescriptions dispensed reached statistical significance. Rates of hospitalizations for stroke and heart failure among patients with hypertension fell relative to pre-intervention patterns, while hospitalizations for COPD increased. Total hospitalizations and hospitalizations via the emergency department did not change. Health care spending increased for patients with hypertension. This large-scale incentive scheme for primary care physicians showed some positive effects for patients with hypertension, but we observe no similar changes in patient management, reductions in hospitalizations, or changes in spending for patients with diabetes and COPD.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".