Abstract 208: Association of Hospital Prices for Coronary Artery Bypass Graft Surgery with Hospital Quality and Reimbursement
Bibliographic record
Abstract
Background: Although prices for medical services are known to vary markedly between hospitals especially for patients without medical insurance, it remains unknown whether this large variation in price is explained by differences in hospital quality or reimbursement from major insurers. Methods: We obtained an “out-of-pocket” hospital and physician price estimate for a coronary artery bypass graft (CABG) surgery for a hypothetical patient without medical insurance via telephone call from a random sample of U.S. hospitals during January 2014. We examined whether the price provided by a hospital was associated with its 1) structural characteristics obtained from the American Hospital Association data, 2010 2) risk-standardized CABG mortality obtained from the Society of Thoracic Surgeons data, 2013 and 3) “fair price” estimate for CABG base don reimbursement data from major insurers in the zip code of the hospital location (Healthcare Bluebook, www.healthcarebluebook.com). Results: We contacted a total of 111 U.S. hospitals, of which 56 (50.5%) were able to provide a price estimate for CABG. The mean price for CABG obtained from the hospitals was $151,784.64, and ranged from $44,824 - $448,039. Except geographic location (p=0.03), which was weakly associated, hospital prices for a CABG surgery were not associated with teaching status, for-profit/not-for-profit/government classification, urban/rural location, or CABG volume (p >0.10 for all). Likewise, we found no association between a hospital’s quoted price for CABG and its risk-standardized CABG mortality (r= -0.03 p=0.89), or its average reimbursement for CABG surgery from major insurers within the same zip code (r= 0.07, p=0.6). Conclusion: We found a 10-fold variation in the prices obtained from U.S. hospitals for CABG surgery for an uninsured patient. Although there was slight variation in CABG surgery prices based on geographic region, we found no evidence to suggest that hospitals with higher prices have lower mortality or receive higher reimbursement from insured patients.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".