Online Program

Data Science Techniques for Aiding the Estimation of Models Allowing Heterogeneous effects of Accountable Care Organizations on patients’ hospital admissions (307801)

Keywords: ACO, DID analysis, Mixed model, Gaussian mixture model.

First introduced in early 2000s, the Accountable Care Organization (ACO) has become one of the most important coordinated care technologies in the United States since it is designed to lower health care costs while improving quality of care. In this research, we use the Medicare fee-for-service claims data from 2009-2014 to estimate the heterogeneous effects of Medicare ACO programs on hospital admissions across various hospital referral regions (HRRs) and practice groups. To conduct our analysis, a model for a difference-in-difference (DID) study is embellished in multiple ways to account for intricacies and complexity with the data. Of particular note, we propose a Gaussian mixture model to account for the inability to observe the practice group affiliation of physicians if the organization they worked for did not become an ACO, which is needed to ensure appropriate partitioning of variation across the different units. The results suggest that the ACO programs reduced the rate of readmission to hospital and the effect of becoming an ACO varied considerably across medical groups.