Medicare Advantage Plans With High Numbers Of Veterans: Enrollment, Utilization, And Potential Wasteful Spending
Bibliographic record
Abstract
Medicare Advantage (MA) plans are increasingly enrolling veterans. Because MA plans receive full capitated payments regardless of whether or not veterans use Medicare services, the federal government can incur substantial duplicative, wasteful spending if veterans in MA plans predominantly seek care through the Veterans Health Administration (VHA) system. The recent growth of MA plans that disproportionately enroll veterans could further exacerbate such wasteful spending. Using national data, we found that veterans increasingly enrolled in MA between 2016 and 2022, including in a growing number of MA plans in which 20 percent or more of the enrollees were veterans. Notably, about one in five VHA enrollees in these high-veteran MA plans did not incur any Medicare services paid by MA within a given year-a rate 2.5 times that of VHA enrollees in other MA plans and 5.7 times that of the general MA population. Meanwhile, VHA enrollees in high-veteran MA plans were significantly more likely to receive VHA-funded care. In 2020, the Centers for Medicare and Medicaid Services paid more than $1.32 billion to MA plans for VHA enrollees who did not use any Medicare services, with 19.1 percent going to high-veteran MA 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".