Care coordination for healthcare referrals under a shared‐savings program
Bibliographic record
Abstract
Accountable care organizations (ACOs) are responsible for the quality and cost of care of specified patient populations, including the cost of referrals. Motivated by this environment, we study care coordination for healthcare referrals. We consider an ACO that refers an uncertain number of patients from its attributed population to a preferred external provider for specialized health services. ACOs are typically paid under the Medicare Shared Savings Program (MSSP). Under the MSSP, the payer sets a spending benchmark for the beneficiary population during a fixed time period and shares any gains (losses) relative to it with the ACO. During the billing period, all services delivered to the attributed population by the ACO and external providers continue to be reimbursed under fee‐for‐service. Gains (losses) are determined at the end of the period by comparing the actual spending, which includes all care expenses (regular visits, referrals, and failed treatments) incurred by the payer in the period to the predefined benchmark. In this environment, the ACO and external providers—the latter not compensated under the MSSP—lack incentives to invest enough in care coordination initiatives. We study financial incentive mechanisms between the ACO and its preferred external provider to achieve integrated care coordination in referral markets under the MSSP. We show that traditional fee‐for‐service and capitation agreements do not provide sufficient incentives for care coordination in referral markets. However, a risk‐ and cost‐sharing mechanism can induce integrated care coordination efforts while satisfying the ACO and provider's participation constraints. We characterize a family of such mechanisms and numerically study the variability of the ACO and the external provider's profit. We demonstrate that this type of agreement can be used not only to induce integrated care coordination but can also result in a Pareto improvement in profit variability. We also illustrate the impact of the different MSSP risk tracks parameters on the performance of this care coordination mechanism, including their effect on the quality of care and the payer's mean spending.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".