Charges for Medical Care at Different Hospitals
Bibliographic record
Abstract
BACKGROUND: The United States has a high proportion of people without health insurance (15%) and a low proportion of people without employment (5%), resulting in millions who lack insurance but have some ability to pay. We tested whether hospitals charge similar prices for well-specified elective services to individuals paying out-of-pocket for medical care. METHODS: We surveyed the 2 largest general hospitals from every large city (population >500 000) in the United States and Canada. At each hospital we evaluated 5 diagnostic, 7 therapeutic, and 3 nonclinical services to determine the total charge to patients who pay directly. RESULTS: Overall, 66 hospitals were included (average, 758 beds; not-for-profit, 97% [n = 64]; teaching, 80% [n = 53]). The range in charges was substantial; for example, a screening mammogram was $40 at one hospital in Los Angeles, Calif, and $346 at one hospital in Quebec City. Charges for a screening mammogram were relatively stable between 1996 and 1997 (r=0.79; 95% confidence interval, 0.68-0.87) and unrelated to the hospital's location or charges for other services. The relative amount of variation in charges was similar for high-priced and low-priced services, similar for diagnostic and therapeutic services, and similar for the United States and Canada. CONCLUSIONS: Charges for the same hospital service vary substantially. Greater visibility might reduce some variation by bringing outliers into closer scrutiny. Patients seeking care and paying out-of-pocket could save financially by comparison shopping.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".