Modeling and designing health care payment innovations for medical imaging.

Payment innovations that better align incentives in health care are a promising approach to reduce health care costs and improve quality of care. Designing effective payment systems, however, is challenging due to the complexity of the health care system with its many stakeholders and their often conflicting objectives. There is a lack of mathematical models that can comprehensively capture and efficiently analyze the complex, multi-level interactions and thereby predict the effect of new payment systems on stakeholder decisions and system-wide outcomes. To address the need for multi-level health care models, we apply multiscale decision theory (MSDT) and build upon its recent advances. In this paper, we specifically study the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs) and determine how this incentive program affects computed tomography (CT) use, and how it could be redesigned to minimize unnecessary CT scans. The model captures the multi-level interactions, decisions and outcomes for the key stakeholders, i.e., the payer, ACO, hospital, primary care physicians, radiologists and patients. Their interdependent decisions are analyzed game theoretically, and equilibrium solutions - which represent stakeholders' normative decision responses - are derived. Our results provide decision-making insights for the payer on how to improve MSSP, for ACOs on how to distribute MSSP incentives among their members, and for hospitals on whether to invest in new CT imaging systems.