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Record W4318474475 · doi:10.1177/27551938231152750

Hospital Expenditures Under Global Budgeting and Single-Payer Financing: An Economic Analysis, 2021–2030

2023· article· en· W4318474475 on OpenAlexaboutno aff
Adam Gaffney, David U. Himmelstein, Steffie Woolhandler, James G. Kahn

Bibliographic record

VenueInternational Journal of Social Determinants of Health and Health Services · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsnot available
Fundersnot available
KeywordsLeverage (statistics)Administration (probate law)FinancePaymentCapital expenditureBusinessHealth careEconomicsActuarial scienceEconomic growth

Abstract

fetched live from OpenAlex

U.S. hospitals provide large amounts of low-value care and devote inordinate resources to administration, while some hospitals leverage market power to realize large profits. Meanwhile, many rural and safety net hospitals are financially distressed. The coexistence of waste and want suggests that U.S. hospital financing is neither efficient nor equitable. We model the economic consequences of adopting the mode of hospital payment used in Canada and the U.S. Veterans Health Administration and proposed in the leading congressional single-payer Medicare-for-All bill: global budgeting. Our models assume increased utilization due to expanded and upgraded coverage; gradual reductions in administrative costs from simplified payment; and the elimination of hospital profits, with hospital capital expenditures funded by explicit grants rather than from profits or borrowing. We estimate that non-federal hospital operating budgets will total $17.2 trillion between 2021 and 2030 under current law versus $14.7 trillion under single-payer with global budgeting. This difference reflects $520 billion in foregone profits and $1,984 billion in reduced expenditures on hospital administration; expenditures on clinical operating budgets, however, would be higher than under current law, funded out of profits.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.300
GPT teacher head0.545
Teacher spread0.245 · 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 designObservational
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

Citations8
Published2023
Admission routes1
Has abstractyes

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