Medicare Payments for Common Inpatient Procedures: Implications for Episode‐Based Payment Bundling
Bibliographic record
Abstract
BACKGROUND: Aiming to align provider incentives toward improving quality and efficiency, the Center for Medicare and Medicaid Services is considering broader bundling of hospital and physician payments around episodes of inpatient surgery. Decisions about bundled payments would benefit from better information about how payments are currently distributed among providers of different perioperative services and how payments vary across hospitals. STUDY DESIGN: Using the national Medicare database, we identified patients undergoing one of four inpatient procedures in 2005 (coronary artery bypass [CABG], hip fracture repair, back surgery, and colectomy). For each procedure, price-standardized Medicare payments from the date of admission for the index procedure to 30 days postdischarge were assessed and categorized by payment type (hospital, physician, and postacute care) and subtype. RESULTS: Average total payments for inpatient surgery episodes varied from U.S.$26,515 for back surgery to U.S.$45,358 for CABG. Hospital payments accounted for the largest share of total payments (60-80 percent, depending on procedure), followed by physician payments (13-19 percent) and postacute care (7-27 percent). Overall episode payments for hospitals in the lowest and highest payment quartiles differed by U.S.$16,668 for CABG, U.S.$18,762 for back surgery, U.S.$10,615 for hip fracture repair, and U.S.$12,988 for colectomy. Payments to hospitals accounted for the largest share of variation in payments. Among specific types of payments, those associated with 30-day readmissions and postacute care varied most substantially across hospitals. CONCLUSIONS: Fully bundled payments for inpatient surgical episodes would need to be dispersed among many different types of providers. Hospital payments--both overall and for specific services--vary considerably and might be reduced by incentives for hospitals and physicians to improve quality and efficiency.
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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 | 0.067 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".