Medicare Shared Savings Program May Reduce Hospitals’ Propensity To Purchase New CT Machines Interview with:
Hui Zhang, Ph.D., MBA

Virginia Polytechnic Institute and State University
Blacksburg What is the background for this study? What are the main findings?

Response: To promote healthcare coordination and contain the rising costs in the US healthcare system, a variety of payment innovations has been developed and field-tested in both public and private sector. Among them, the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs), administered by the Centers for Medicare and Medicaid Services (CMS) has received considerable attention.

Our study took a mathematical modeling approach and comprehensively captured and analyzed the effect of this new payment systems on healthcare stakeholder decisions and system-wide outcomes. Our results provided decision-making insights for payers 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. What should readers take away from your report?

Response: Our study showed that MSSP incentives can effectively decrease the CT over-testing that stems for the fee-for-service (FFS) system, and can reduce hospitals’ propensity to purchase new CT imaging systems. We also found that the participation of ACO members, such as hospitals, primary care physicians, and radiologists, occurs at different cost benchmarks, and that the incentive re-distribution mechanism among ACO members varies based on the cost benchmarks set between payers and ACOs. Our studies also demonstrated the importance of a model-driven and data-driven evaluation of innovative payment programs such as MSSP, specifically when setting policy details. What recommendations do you have for future research as a result of this study?

Response: Our study took a theoretical mathematical modeling approach in combination with an illustrative numerical example. An important future research direction would be the calibration of the theoretical model with real-world data by empirically assessing the model parameters. Some of the model assumptions, such as the decision-making attributes of healthcare stakeholders, payment structures, payer structures, etc., can also be relaxed to better reflect the complexity of the healthcare system in the real world. Thank you for your contribution to the community.


Zhang, H., Wernz, C. & Hughes, D.R. Health Care Manag Sci (2016). doi:10.1007/s10729-016-9377-z

Note: Content is Not intended as medical advice. Please consult your health care provider regarding your specific medical condition and questions.

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