Improving large-scale estimation and inference for profiling health care providers.

ABSTRACT: Provider profiling has been recognized as a useful tool in monitoring health care quality, facilitating inter-provider care coordination, and improving medical cost-effectiveness. Existing methods often use generalized linear models with fixed provider effects, especially when profiling dialysis facilities. As the number of providers under evaluation escalates, the computational burden becomes formidable even for specially designed workstations. To address this challenge, we introduce a serial blockwise inversion Newton algorithm exploiting the block structure of the information matrix. A shared-memory divide-and-conquer algorithm is proposed to further boost computational efficiency. In addition to the computational challenge, the current literature lacks an appropriate inferential approa

SUBMITTER: Wu W 

PROVIDER: S-EPMC9314652 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature