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Activity Number: 428 - Recent Advances in Statistical Methods for Healthcare Provider Profiling
Type: Invited
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 AM
Sponsor: ENAR
Abstract #309588
Title: Profiling Dialysis Facilities for Adverse Recurrent Events
Author(s): Damla Senturk and Danh Nguyen* and Jason Estes
Companies: UCLA and UC Irvine and Google
Keywords: profiling; end stage renal disease; dialysis facility; Poisson regression

Profiling analysis aims to evaluate health care providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. Previous profiling methods have considered binary outcomes, such as 30-day hospital readmission or mortality. For the unique population of dialysis patients, regular blood works are required to evaluate effectiveness of treatment and avoid adverse events, including dialysis inadequacy, imbalance mineral levels, and anemia among others. For example, anemic events (when hemoglobin levels exceeds normative range) are recurrent and common for patients on dialysis. Thus, we propose high-dimensional Poisson and negative binomial regression models for rate/count outcomes and introduce a standardized event ratio (SER) measure to compare the event rate at a specific facility relative to a chosen normative standard, typically defined as an “average” national rate across all facilities. Our proposed estimation and inference procedures overcome the challenge of high-dimensional parameters for thousands of dialysis facilities. Also, we investigate how overdispersion affects inference in the context of profiling analysis.

Authors who are presenting talks have a * after their name.

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