Reclassification to Avoid Consumer Cost-Sharing in Group Health Plans
Bibliographic record
Abstract
We examine how consumers respond to being effectively double insured under two systems: group health (GH) and workers' compensation (WC).Many GH plans have substantial consumer cost-sharing burden, while WC coverage has no cost-sharing for medical services for workrelated injuries.As a result, a consumer facing a large deductible under their group health plan will have a strong financial incentive to make a claim under WC instead.We use a unique data set of claims under both GH and WC to study how "case shifting" to WC responds to GH deductibles for the most common set of injuries that are covered under both types of insurance.We identify the impact of case shifting by using interactions of deductible levels and previous spending.We find that a typical claim is about 1.4 percentage points (5.3%) more likely to be filed as a WC claim when facing an average deductible (about $630) compared to a plan with no deductible, and that total WC costs in the U.S. are more than $1.2 billion higher as a result.At the same time, we find that consumers do not appear to be forward looking, focusing on the "spot price" rather than the full "end of year price" in deciding whether to claim under WC.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".