The Alignment and Blending of Payment Incentives within Physician Organizations
Bibliographic record
Abstract
OBJECTIVE: To analyze the blend of retrospective (fee-for-service, productivity-based salary) and prospective (capitation, nonproductivity-based salary) methods for compensating individual physicians within medical groups and independent practice associations (IPAs) and the influence of managed care on the compensation blend used by these physician organizations. DATA SOURCES: Of the 1,587 medical groups and IPAs with 20 or more physicians in the United States, 1,104 responded to a one-hour telephone survey, with 627 providing detailed information on physician payment methods. STUDY DESIGN: We calculated the distribution of compensation methods for primary care and specialty physicians, separately, in both medical groups and IPAs. Multivariate regression methods were used to analyze the influence of market and organizational factors on the payment method developed by physician organizations for individual physicians. PRINCIPAL FINDINGS: Within physician organizations, approximately one-quarter of physicians are paid on a purely retrospective (fee-for-service) basis, approximately one-quarter are paid on a purely prospective (capitation, nonproductivity-based salary) basis, and approximately one-half on blends of retrospective and prospective methods. Medical groups and IPAs in heavily penetrated managed care markets are significantly less likely to pay their individual physicians based on fee-for-service than are organizations in less heavily penetrated markets. CONCLUSIONS: Physician organizations rely on a wide range of prospective, retrospective, and blended payment methods and seek to align the incentives faced by individual physicians with the market incentives faced by the physician organization.
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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 | 0.014 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".