Trends in the Purchase of Surgical Care in the Community by the Veterans Health Administration
Bibliographic record
Abstract
BACKGROUND: The 2014 implementation of the Veterans Choice Program increased opportunities for Veterans to receive care in the community. Although surgical care is a Veterans Health Administration (VHA) priority, little is known about the types of surgeries provided in the VHA versus those referred to community care (CC), and whether Veterans are increasing their use of surgical care through CC with these additional opportunities. OBJECTIVES: To examine national trends across VHA facilities in the frequencies and types of surgeries provided in the VHA and through CC, and explore the association between facilities' purchase of care with rurality and surgical complexity designation. RESEARCH DESIGN: Retrospective study using Veterans Administration (VA) outpatient and CC data from the VA's Corporate Data Warehouse (October 1, 2013-September 30, 2016). MEASURES: Veterans' demographics, outpatient surgeries, facility rurality, and surgical complexity. RESULTS: Our sample included 525,283 outpatient surgeries; 79% occurred in the VHA over the study timeframe. The proportion of CC surgeries increased from 16% in October 2013 to 29% in December 2014, and then subsequently declined, leveling off at 21% in June 2016 (trend, P<0.05). These trends varied by surgery type. Increases in CC surgeries were evident for 4 surgery types: cardiovascular, digestive, eye and ocular, and male genital surgeries (all trends, P<0.05). Rural and low-complexity facilities were more likely to purchase surgical CC than their urban and high-complexity counterparts (P<0.0001). CONCLUSIONS: Although the VHA remains the primary provider of surgical care for Veterans, Veterans Choice Program implementation increased Veterans' use of CC relative to the VHA for certain types of surgeries, potentially bringing challenges to the VHA in delivering and coordinating surgical care across settings.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".