Changes In Hospital Utilization Three Years Into Maryland’s Global Budget Program For Rural Hospitals
Bibliographic record
Abstract
In a substantial shift in payment policy, the State of Maryland implemented a global budget program for acute care hospitals in 2010. Goals of the program include controlling hospital use and spending. Eight rural hospitals entered the program in 2010, while urban and suburban hospitals joined in 2014. Prior analyses, which focused on urban and suburban hospitals, did not find consistent evidence that Maryland's program had contributed to changes in hospital use after two years. However, these studies were limited by short follow-up periods, may have failed to isolate impacts of Maryland's payment change from other state trends, and had limited generalizability to rural settings. To understand the effects of Maryland's global budget program on rural hospitals, we compared changes in hospital use among Medicare beneficiaries served by affected rural hospitals versus an in-state control population from before to after 2010. By 2013-three years after the rural program began-there were no differential changes in acute hospital use or price-standardized hospital spending among beneficiaries served by the affected hospitals, versus the within-state control group. Our results suggest that among Medicare beneficiaries, global budgets in rural Maryland hospitals did not reduce hospital use or price-standardized spending as policy makers had anticipated.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".