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Record W2133732074 · doi:10.5600/mmrr.001.04.a01

Indirect Medical Education and Disproportionate Share Adjustments to Medicare Inpatient Payment Rates

2011· article· en· W2133732074 on OpenAlexaboutno aff
Nguyen Nguyen, Steven H. Sheingold

Bibliographic record

VenueMedicare & Medicaid Research Review · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
FundersU.S. Department of Health and Human Services
KeywordsQuarter (Canadian coin)PaymentDemographic economicsProspective payment systemActuarial scienceEmpirical researchBusinessMedicineEconomicsFinanceStatistics

Abstract

fetched live from OpenAlex

The indirect medical education (IME) and disproportionate share hospital (DSH) adjustments to Medicare's prospective payment rates for inpatient services are generally intended to compensate hospitals for patient care costs related to teaching activities and care of low income populations. These adjustments were originally established based on the statistical relationships between IME and DSH and hospital costs. Due to a variety of policy considerations, the legislated levels of these adjustments may have deviated over time from these "empirically justified levels," or simply, "empirical levels." In this paper, we estimate the empirical levels of IME and DSH using 2006 hospital data and 2009 Medicare final payment rules. Our analyses suggest that the empirical level for IME would be much smaller than under current law-about one-third to one-half. Our analyses also support the DSH adjustment prescribed by the Affordable Care Act of 2010 (ACA)--about one-quarter of the pre-ACA level. For IME, the estimates imply an increase in costs of 1.88% for each 10% increase in teaching intensity. For DSH, the estimates imply that costs would rise by 0.52% for each 10% increase in the low-income patient share for large urban hospitals.

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)0.010
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.218
GPT teacher head0.420
Teacher spread0.202 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2011
Admission routes1
Has abstractyes

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