Incentive-Based Primary Care: Cost and Utilization Analysis
Bibliographic record
Abstract
CONTEXT: In its fee-for-service funding model for primary care, British Columbia, Canada, introduced incentive payments to general practitioners as pay for performance for providing enhanced, guidelines-based care to patients with chronic conditions. Evaluation of the program was conducted at the health care system level. OBJECTIVE: To examine the impact of the incentive payments on annual health care costs and hospital utilization patterns in British Columbia. DESIGN: The study used Ministry of Health administrative data for Fiscal Year 2010-2011 for patients with diabetes, congestive heart failure, chronic obstructive pulmonary disease, and/or hypertension. In each disease group, cost and utilization were compared across patients who did, and did not, receive incentive-based care. MAIN OUTCOME MEASURES: Health care costs (eg, primary care, hospital) and utilization measures (eg, hospital days, readmissions). RESULTS: After controlling for patients' age, sex, service needs level, and continuity of care (defined as attachment to a general practice), the incentives reduced the net annual health care costs, in Canadian dollars, for patients with hypertension (by approximately Can$308 per patient), chronic obstructive pulmonary disease (by Can$496), and congestive heart failure (by Can$96), but not diabetes (incentives cost about Can$148 more per patient). The incentives were also associated with fewer hospital days, fewer admissions and readmissions, and shorter lengths of hospital stays for all 4 groups. CONCLUSION: Although the available literature on pay for performance shows mixed results, we showed that the funding model used in British Columbia using incentive payments for primary care might reduce health care costs and hospital utilization.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".