Hospital Expenditures Under Global Budgeting and Single-Payer Financing: An Economic Analysis, 2021–2030
Bibliographic record
Abstract
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.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".