Health Care Administration in the United States and Canada: Micromanagement, Macro Costs
Bibliographic record
Abstract
A decade ago, U.S. health administration costs greatly exceeded Canada's. Have the computerization of billing and the adoption of a more business-like approach to care cut administrative costs? For the United States and Canada, the authors calculated the 1999 administrative costs of health insurers, employers' health benefit programs, hospitals, practitioners' offices, nursing homes, and home care agencies; they analyzed published data, surveys of physicians, employment data, and detailed cost reports filed by hospitals, nursing homes, and home care agencies; they used census surveys to explore time trends in administrative employment in health care settings. Health administration costs totaled at least dollar 294.3 billion, dollar 1,059 per capita, in the United States vs. dollar 9.4 billion, dollar 307 per capita, in Canada. After exclusions, health administration accounted for 31.0 percent of U.S. health expenditures vs. 16.7 percent of Canadian. Canada's national health insurance program had an overhead of 1.3 percent, but overhead among Canada's private insurers was higher than in the U.S.: 13.2 vs. 11.7 percent. Providers' administrative costs were far lower in Canada. Between 1969 and 1999 administrative workers' share of the U.S. health labor force grew from 18.2 to 27.3 percent; in Canada it grew from 16.0 percent in 1971 to 19.1 percent in 1996. Reducing U.S. administrative costs to Canadian levels would save at least dollar 209 billion annually, enough to fund universal coverage.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".