Reducing COPD readmissions through predictive modeling and incentive-based interventions

Xiang Zhong (), Sujee Lee (), Cong Zhao (), Hyo Kyung Lee (), Philip A. Bain (), Tammy Kundinger (), Craig Sommers (), Christine Baker () and Jingshan Li ()
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Xiang Zhong: University of Florida
Sujee Lee: University of Wisconsin
Cong Zhao: University of Wisconsin
Hyo Kyung Lee: University of Wisconsin
Philip A. Bain: Dean Health System
Tammy Kundinger: St. Mary’s Hospital
Craig Sommers: St. Mary’s Hospital
Christine Baker: St. Mary’s Hospital
Jingshan Li: University of Wisconsin

Health Care Management Science, 2019, vol. 22, issue 1, No 8, 139 pages

Abstract: Abstract This paper introduces a case study at a community hospital to develop a predictive model to quantify readmission risks for patients with chronic obstructive pulmonary disease (COPD), and use it to support decision making for appropriate incentive-based interventions. Data collected from the community hospital’s database are analyzed to identify risk factors and a logistic regression model is developed to predict the readmission risk within 30 days post-discharge of an individual COPD patient. By targeting on the high-risk patients, we investigate the implementability of the incentive policy which encourages patients to take interventions and helps them to overcome the compliance barrier. Specifically, the conditions and scenarios are identified for either achieving the desired readmission rate while minimizing the total cost, or reaching the lowest readmission rate under incentive budget constraint. Currently, such models are under consideration for a pilot study at the community hospital.

Keywords: Chronic obstructive pulmonary disease (COPD); Readmission; Predictive modeling; Intervention; Incentive (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10729-017-9426-2

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