Administrative Waste in the U.S. Health Care System in 2003: The Cost to the Nation, the States, and the District of Columbia, with State-Specific Estimates of Potential Savings
Bibliographic record
Abstract
This report provides nationwide and state-specific estimates of U.S. health care administration spending and potential savings in 2003 were the United States to institute a Canadian-style national health insurance system. The United States wastes more on health care bureaucracy than it would cost to provide health care to all its uninsured. Administrative expenses will consume at least dollar 399.4 billion of a total health expenditure of dollar 1,660.5 billion in 2003. Streamlining administrative overhead to Canadian levels would save approximately dollar 286.0 billion in 2003, dollar 6,940 for each of the 41.2 million Americans who were uninsured as of 2001. This is substantially more than would be needed to provide full insurance coverage. The cost of excess health bureaucracy in individual states is equally striking. For example, Massachusetts, with 560,000 uninsured state residents, could save about dollar 8,556 million in 2003 (dollar 16,453 per uninsured resident of that state) if it streamlined administration to Canadian levels. New Mexico, with 373,000 uninsured, could save dollar 1,500 million on health bureaucracy (dollar 4,022 per uninsured resident). Only a single-payer national health insurance system could garner these massive administrative savings, allowing universal coverage without any increase in total health spending. Because incremental reforms necessarily preserve the current fragmented and duplicative payment structure, they cannot achieve significant bureaucratic savings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".