Health Care Provider Clustering Using Fusion Penalty in Quasi-Likelihood.

ABSTRACT: There has been growing research interest in developing methodology to evaluate the health care providers' performance with respect to a patient outcome. Random and fixed effects models are traditionally used for such a purpose. We propose a new method, using a fusion penalty to cluster health care providers based on quasi-likelihood. Without any priori knowledge of grouping information, our method provides a desirable data-driven approach for automatically clustering health care providers into different groups based on their performance. Further, the quasi-likelihood is more flexible and robust than the regular likelihood in that no distributional assumption is needed. An efficient alternating direction method of multipliers algorithm is developed to implement the proposed method. We show

SUBMITTER: Liu L 

PROVIDER: S-EPMC11568107 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature