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, Jingshan Li

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)121-139
Number of pages19
JournalHealth Care Management Science
Volume22
Issue number1
DOIs
Publication statusPublished - 2019 Mar 15
Externally publishedYes

Keywords

  • Chronic obstructive pulmonary disease (COPD)
  • Incentive
  • Intervention
  • Predictive modeling
  • Readmission

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Professions(all)

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