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A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD

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  • Issac Shams
  • Saeede Ajorlou
  • Kai Yang

Abstract

Hospital readmission has become a critical metric of quality and cost of healthcare. Medicare anticipates that nearly $17 billion is paid out on the 20 % of patients who are readmitted within 30 days of discharge. Although several interventions such as transition care management have been practiced in recent years, the effectiveness and sustainability depends on how well they can identify patients at high risk of rehospitalization. Based on the literature, most current risk prediction models fail to reach an acceptable accuracy level; none of them considers patient’s history of readmission and impacts of patient attribute changes over time; and they often do not discriminate between planned and unnecessary readmissions. Tackling such drawbacks, we develop a new readmission metric based on administrative data that can identify potentially avoidable readmissions from all other types of readmission. We further propose a tree-based classification method to estimate the predicted probability of readmission that can directly incorporate patient’s history of readmission and risk factors changes over time. The proposed methods are validated with 2011–12 Veterans Health Administration data from inpatients hospitalized for heart failure, acute myocardial infarction, pneumonia, or chronic obstructive pulmonary disease in the State of Michigan. Results shows improved discrimination power compared to the literature (c-statistics >80 %) and good calibration. Copyright Springer Science+Business Media New York 2015

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  • Issac Shams & Saeede Ajorlou & Kai Yang, 2015. "A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD," Health Care Management Science, Springer, vol. 18(1), pages 19-34, March.
  • Handle: RePEc:kap:hcarem:v:18:y:2015:i:1:p:19-34
    DOI: 10.1007/s10729-014-9278-y
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    References listed on IDEAS

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    1. Mark Fackrell, 2009. "Modelling healthcare systems with phase-type distributions," Health Care Management Science, Springer, vol. 12(1), pages 11-26, March.
    2. Mollie Shulan & Kelly Gao & Crystal Moore, 2013. "Predicting 30-day all-cause hospital readmissions," Health Care Management Science, Springer, vol. 16(2), pages 167-175, June.
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    4. Westert, Gert P. & Lagoe, Ronald J. & Keskimaki, Ilmo & Leyland, Alastair & Murphy, Mark, 2002. "An international study of hospital readmissions and related utilization in Europe and the USA," Health Policy, Elsevier, vol. 61(3), pages 269-278, September.
    5. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
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    Cited by:

    1. Zhao, Heng & Liu, Zixian & Li, Mei & Liang, Lijun, 2022. "Optimal monitoring policies for chronic diseases under healthcare warranty," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    2. Mahsa Ashouri & Kate Cai & Furen Lin & Galit Shmueli, 2018. "Assessing the Value of an Information System for Developing Predictive Analytics: The Case of Forecasting School-Level Demand in Taiwan," Service Science, INFORMS, vol. 10(1), pages 58-75, March.
    3. Suiyao Chen & Nan Kong & Xuxue Sun & Hongdao Meng & Mingyang Li, 2019. "Claims data-driven modeling of hospital time-to-readmission risk with latent heterogeneity," Health Care Management Science, Springer, vol. 22(1), pages 156-179, March.
    4. David M. Vanlandingham & Wesley Hampton & Kimberly M. Thompson & Kamran Badizadegan, 2020. "Modeling Pathology Workload and Complexity to Manage Risks and Improve Patient Quality and Safety," Risk Analysis, John Wiley & Sons, vol. 40(2), pages 421-434, February.
    5. Francesca Ieva & Anna Maria Paganoni & Teresa Pietrabissa, 2017. "Dynamic clustering of hazard functions: an application to disease progression in chronic heart failure," Health Care Management Science, Springer, vol. 20(3), pages 353-364, September.
    6. Baechle, Christopher & Huang, C. Derrick & Agarwal, Ankur & Behara, Ravi S. & Goo, Jahyun, 2020. "Latent topic ensemble learning for hospital readmission cost optimization," European Journal of Operational Research, Elsevier, vol. 281(3), pages 517-531.

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