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Activity Number: 28 - Computation, Design, and Quality Assurance of Physical Science and Engineering Applications
Type: Contributed
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Quality and Productivity Section
Abstract #317832
Title: Risk Score Monitoring Using Change Point Detection and its Application in Health Management
Author(s): Yuhui Yao* and Subha Chakraborti and Xin Yang and Jason Parton and Dwight Lewis and Matthew Hudnall
Companies: The University of Alabama and The University of Alabama and The University of Alabama and The University of Alabama and The University of Alabama and The University of Alabama
Keywords: SPC; Phase I; Retrospective Monitoring; Change Point Detection; ARMA; Yeo-Johnson Transformation

Risk scores are widely used in health to manage patient care. Many exploratory monitoring schemes are available in healthcare, but few adopt techniques in Statistical Process Control (SPC). To expand the utility of risk score data for insurers, health systems, and health agencies, we propose a new change point detection method to more feasibly monitor patients’ health outcomes. The current data-driven method is centered on ARMA models with the Yeo-Johnson transformation, which better adjusts for situations when the sample size is small and the effect of estimators are severe. The performance of the proposed method is investigated in terms of in-control robustness and out-of-control signal detection capability. A thorough illustration is provided using actual risk score data from a healthcare database. A summary of our findings and some recommendations are provided. An R package is made available for implementing the proposed methodology on demand.

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

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