Claims-shifting: The problem of parallel reimbursement regimes
Bibliographic record
Abstract
Parallel reimbursement regimes, under which providers have some discretion over which payer gets billed for patient treatment, are a common feature of health care markets. In the U.S., the largest such system is under Workers' Compensation (WC), where the treatment workers with injuries that are not definitively tied to a work accident may be billed either under group health insurance plans or under WC. We document that there is significant reclassification of injuries from group health plans into WC, or "claims shifting", when the financial incentives to do so are strongest. In particular, we find that injuries to workers enrolled in capitated group health plans (such as HMOs) see a higher incidence of their claims for soft-tissue injuries (which are hard to classify specifically as work related) under WC than under group health, relative to those in non-capitated plans. Such a pattern is not evident for workers with traumatic injuries. Moreover, we find that such reclassification is more common in states with higher WC fees, once again for soft tissue but not traumatic injuries. Our results imply that a significant shift towards capitated reimbursement, or reimbursement reductions, under GH could lead to a large rise in the cost of WC plans.
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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 | 0.032 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 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".