Estimating heterogeneous effects of a policy intervention across organizations when organization affiliation is missing for the control group: application to the evaluation of accountable care organizations

摘要

First introduced in early 2000s, the accountable care organization (ACO) is designed to lower health care costs while improving quality of care and has become one of the most important coordinated care technologies in the United States. 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 hospital referral regions and provider groups. To conduct our analysis, a model for a difference-in-difference study is embellished in multiple ways to account for intricacies and complexity with the data not able to be accounted for using existing models. 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, that the ACO program may have reduced heterogeneity in readmission rates, and that the effect of joining an ACO varied considerably across medical groups.