In partnership with the Department of Health Management and Policy (HMP) at the University of Michigan School of Public Health, the Department of Urology at the University of Michigan Medical School invites applications for a one-year full-time post-doctoral fellow to join a federally-funded study, investigating the effects of a recent overhaul to the Medicare Shared Savings Program (MSSP) on healthcare quality and spending among older Americans. The fellow will be working with Drs. John Hollingsworth (Urology) and Andrew Ryan (HMP).
Specifically, the National Institute on Aging-funded study examines the launch of Pathways to Success in 2019, which forced accountable care organizations (ACOs) participating in the MSSP to more rapidly take on downside risk. This study aims to understand organizational decision-making around continued MSSP participation, as well as the effects of program exit on healthcare delivery. To support the work, we are in need of a post-doctoral fellow to help lead analyses, as well as participate in manuscript preparation. We are looking to hire for the upcoming academic year, beginning in September 2021. There is the potential for the fellow to transition to a full-time research faculty position after the fellowship.
You will be expected to collaborate with the research team, which includes three Masters-level biostatisticians, to develop analytic methods and lead analyses on ACO decisions around MSSP participation, the effect of MSSP exit on clinical quality and healthcare costs, and unintended consequences for older Americans from continued program participation. You will also be expected to prepare manuscripts for peer-reviewed journals and present findings at scientific meetings.
- A doctoral degree in Health Economics, Health Policy, Biostatistics, or a related field by appointment start date.
- Expertise in econometrics and methods for making causal inference from observational data
- Proficiency in SAS or Stata
- Background in value-based payment reforms
- Prior experience working with medical claims data
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