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Record W2795542725 · doi:10.1377/hlthaff.2018.0112

Changes In Hospital Utilization Three Years Into Maryland’s Global Budget Program For Rural Hospitals

2018· article· en· W2795542725 on OpenAlexaff
Eric T. Roberts, Laura A. Hatfield, J. Michael McWilliams, Michael E. Chernew, Nicolae Done, Sule Gerovich, Lauren Gilstrap, Ateev Mehrotra

Bibliographic record

VenueHealth Affairs · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsHealth Care Foundation
FundersNational Institute on Aging
KeywordsAcute careMedicineProgram evaluationPaymentRural areaBusinessPopulationHealth careEnvironmental healthEconomic growthFinanceEconomicsPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.324
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations48
Published2018
Admission routes1
Has abstractyes

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