Medicare Costs and Surgeon Supply in Hospital Service Areas
Bibliographic record
Abstract
In Brief Objective: To quantify the correlates of variations of Medicare per beneficiary costs at the hospital service area level and determine whether physician supply and the specialty of physicians has a significant relationship with cost variation. Background: The American Medical Association Masterfile data on physician and surgeon location, characteristics and specialty; Census derived sociodemographic data from 2006 ZIP code level Claritas PopFacts database; and Medicare per beneficiary costs from the Dartmouth Atlas of Health Care project. Methods: A correlational analysis using bivariate plots and fixed effects linear regression models controlling for hospital service area sociodemographics and the number and characteristics of the physician supply. Data were aggregated to the Dartmouth hospital service area level from ZIP code level files. Results: We found that costs are strongly related to the sociodemographic character of the hospital service areas and the overall supply of physicians but a mixed correlation to the specialist supply depending on the interaction of the proportion of the physician supply who are international medical graduates. The ratio of general surgeons and surgical subspecialists to population are associated with lower costs in the models, again with difference depending on the influence of international medical graduates. There is a strong association between higher costs and the local proportion of physician supply made up of graduates of non-US or Canadian medical schools and female graduates. Conclusions: These results suggest that strategies to reduce overall costs by changing physician supply must consider more than just overall numbers. The supply of physicians and surgeons has been identified as one potential driver of costs. This study examines the relationship of the structure of physician and surgeon supply to Medicare reimbursements. Other local factors are associated with higher and lower costs as well as characteristics of the practitioner supply.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".