Care‐coordination: Gain‐sharing Agreements in Bundled Payment Models
Bibliographic record
Abstract
We study gain‐sharing agreements in a target price‐minimum quality payment system. Our work is inspired by the Centers for Medicare and Medicaid Services’ (CMS) Comprehensive Care for Joint Replacement (CJR) bundled payment model. In our model, patients receive care from a hospital and a post‐acute care provider. A third‐party payer establishes target levels for total billing by the hospital and provider, and a target on the overall quality of care. The hospital and provider receive fee‐for‐service (FFS) billings during an episode of care, defined as the period that starts with an admission of a patient to the hospital and ends 90 days post‐discharge. The hospital may also receive an incentive payment if total FFS billing by both parties is below the target price and total quality by both parties is above the minimum quality. The goal of the incentive payment is to encourage hospitals to enter into “gain‐sharing” agreements with providers. We model the interactions between the three parties. We show that while using a gain‐sharing agreement might be a “win‐win‐win” scenario for the three parties, good design of the payment scheme by the payer is essential to incentivize a hospital to participate in the bundled payment model (e.g., CJR) and sign a gain‐sharing agreement with the provider. Furthermore, we illustrate that a target price‐minimum quality bundled payment model would be more effective, in care‐coordination, in healthcare settings where the provider is much more effective than the hospital in reducing its billing.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".