The Anatomy of Physician Payments: Contracting Subject to Complexity
Bibliographic record
Abstract
Why do private insurers closely link their physician payment rates to the Medicare fee schedule despite its well-known limitations?We ask to what extent this relationship reflects the use of Medicare's relative price menu as a benchmark, in order to reduce transaction costs in a complex pricing environment.We analyze 91 million claims from a large private insurer, which represent $7.8 billion in spending over four years.We estimate that 75 percent of services, accounting for 55 percent of spending, are benchmarked to Medicare's relative prices.The Medicare-benchmarked share is higher for services provided by small physician groups.It is lower for capital-intensive treatment categories, for which Medicare's average-cost reimbursements deviate most from marginal cost.When the insurer deviates from Medicare's relative prices, it adjusts towards the marginal costs of treatment.Our results suggest that providers and private insurers coordinate around Medicare's menu of relative payments for simplicity, but innovate when the value of doing so is likely highest.
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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 | 0.012 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".