Who Pays When VA Users Are Hospitalized in the Private Sector?
Bibliographic record
Abstract
BACKGROUND: Older veterans enrolled in VA healthcare receive much of their medical care in the private sector, through Medicare. Less is known about younger VA enrollees' use of the private sector, or its funding. We compare payers for younger and older enrollees' private sector use in 3 hospitalization datasets. RESEARCH DESIGN: From 1998 to 2000, using private sector discharge data for VA enrollees in New York State, we categorized hospitalizations according to payer (self/family, private insurance, Medicare, Medicaid, other sources). We compared this payer distribution to population-weighted national Medical Expenditure Panel Survey (MEPS) data from 1996-2003 for veterans in VA healthcare. We also compared Medicare utilization in either dataset to hospitalizations for New York veterans from 1998-2000 in the VA-Medicare dataset. Analyses separated patients younger than age 65 from those age 65 or older. RESULTS: VA enrollees under age 65 obtain roughly half their hospitalizations in the private sector; older enrollees use the private sector at least twice as often as the VA. Datasets generally agree on payer distributions. Although older enrollees rely heavily on Medicare, they also use commercial insurance and self/family payments substantially. Half of younger enrollees' non-VA hospitalizations are paid by private insurance, but Medicare, Medicaid, and self/family each pay for one-quarter to one-third of admissions. CONCLUSIONS: VA enrollees use the private sector for most of their inpatient care, which is funded by multiple sources. Developing a national UB-92/VA dataset would be critical to understanding veterans' use of the private sector for specific diagnoses and procedures, particularly for the fast growing population of younger veterans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".