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Claims data-driven modeling of hospital time-to-readmission risk with latent heterogeneity

Author

Listed:
  • Suiyao Chen

    (University of South Florida)

  • Nan Kong

    (Purdue University)

  • Xuxue Sun

    (University of South Florida)

  • Hongdao Meng

    (University of South Florida)

  • Mingyang Li

    (University of South Florida)

Abstract

Hospital readmission risk modeling is of great interest to both hospital administrators and health care policy makers, for reducing preventable readmission and advancing care service quality. To accommodate the needs of both stakeholders, a readmission risk model is preferable if it (i) exhibits superior prediction performance; (ii) identifies risk factors to help target the most at-risk individuals; and (iii) constructs composite metrics to evaluate multiple hospitals, hospital networks, and geographic regions. Existing work mainly addressed the first two features and it is challenging to address the third one because available medical data are fragmented across hospitals. To simultaneously address all three features, this paper proposes readmission risk models with incorporation of latent heterogeneity, and takes advantage of administrative claims data, which is less fragmented and involves larger patient cohorts. Different levels of latent heterogeneity are considered to quantify the effects of unobserved factors, provide composite measures for performance evaluation at various aggregate levels, and compensate less informative claims data. To demonstrate the prediction performances of the proposed models, a real case study is considered on a state-wide heart failure patient cohort. A systematic comparison study is then carried out to evaluate the performances of 49 risk models and their variants.

Suggested Citation

  • 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.
  • Handle: RePEc:kap:hcarem:v:22:y:2019:i:1:d:10.1007_s10729-018-9431-0
    DOI: 10.1007/s10729-018-9431-0
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    References listed on IDEAS

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    1. Jonathan E. Helm & Adel Alaeddini & Jon M. Stauffer & Kurt M. Bretthauer & Ted A. Skolarus, 2016. "Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization," Production and Operations Management, Production and Operations Management Society, vol. 25(2), pages 233-257, February.
    2. 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.
    3. Mingyang Li & Qingpei Hu & Jian Liu, 2014. "Proportional hazard modeling for hierarchical systems with multi-level information aggregation," IISE Transactions, Taylor & Francis Journals, vol. 46(2), pages 149-163.
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    6. Hao Helen Zhang & Wenbin Lu, 2007. "Adaptive Lasso for Cox's proportional hazards model," Biometrika, Biometrika Trust, vol. 94(3), pages 691-703.
    7. repec:awi:wpaper:0417 is not listed on IDEAS
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