Choosing Outcomes-Based Reimbursement Policies: Should We Worry About Collusion?
Saša Zorc et al.
What the paper says
Outcomes-based reimbursement rewards health providers with better health outcomes with higher payments. Such reimbursement policies require several design choices, including the type of contract (e.g., capitation or fee-for-service), measure (e.g., population- or provider-level outcomes), and whether to contract with individual providers or larger groups. We explore which outcomes-based reimbursement policies may be vulnerable to potentially illegal collusion, and whether collusion issues can be averted through incentive design. We present a game-theoretic model a chronic care pathway in a two-tier healthcare system. We identify differences in the impact of collusion on health, costs, and system efficiency under different reimbursement policies. Theoretical and numerical results (calibrated to data from two pathways for diabetes) show that whether an outcomes-adjusted reimbursement system is vulnerable to collusion depends critically on one trait: whether the income of physicians who receive referrals scales with volume. In systems where it does, as with fee-for-service models in the United States, there exist financial incentives to collude, underlining the importance of addressing collusion through laws. Systems that lack this trait (e.g., the UK NHS) are more resistant to collusion. Exploring theoretically optimal contracts, we find evidence of strong performance of outcomes-adjusted capitation contracts with individual providers using population-level data. This paper was accepted by Terry Taylor, operations management. Funding: S. E. Chick acknowledges research support through the Novartis Chair for Healthcare Management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02283 .
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.