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Reducing COPD readmissions through predictive modeling and incentive-based interventions

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiang Zhong & Sujee Lee & Cong Zhao & Hyo Kyung Lee & Philip A. Bain & Tammy Kundinger & Craig Sommers & Christine Baker & Jingshan Li, 2019. "Reducing COPD readmissions through predictive modeling and incentive-based interventions," Health Care Management Science, Springer, vol. 22(1), pages 121-139, March.
    • Handle: RePEc:kap:hcarem:v:22:y:2019:i:1:d:10.1007_s10729-017-9426-2
    DOI: 10.1007/s10729-017-9426-2
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    References listed on IDEAS

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    1. Sheldon H. Jacobson & Shane N. Hall & James R. Swisher, 2006. "Discrete-Event Simulation of Health Care Systems," International Series in Operations Research & Management Science, in: Randolph W. Hall (ed.), Patient Flow: Reducing Delay in Healthcare Delivery, chapter 0, pages 211-252, Springer.
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    Cited by:

    1. Kılıç, Hakan & Güneş, Evrim Didem, 2024. "Patient adherence in healthcare operations: A narrative review," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).

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