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Latent topic ensemble learning for hospital readmission cost optimization

Author

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  • Baechle, Christopher
  • Huang, C. Derrick
  • Agarwal, Ankur
  • Behara, Ravi S.
  • Goo, Jahyun

Abstract

Unplanned hospital readmission is a costly problem in the United States, and in 2013 the U.S. federal government began to reduce payments to hospitals with preventable patient readmissions. Predictive modeling using machine learning and data analytics can be a useful decision support tool to help identify patients most likely to be readmitted. However, current systems have several shortcomings, such as difficulties in utilizing unstructured data and combining data from multiple hospitals. In this paper, we propose Latent Topic Ensemble Learning, which uses an ensemble of topic specific models to leverage data from multiple hospitals, as key data analytic algorithm for predicting hospital readmission. Models are built and evaluated incorporating federal financial penalties and tested using dataset containing data collected from 16 regional hospitals. It is found that LTEL significantly outperforms the best performing baseline method for readmission cost optimization.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:281:y:2020:i:3:p:517-531
    DOI: 10.1016/j.ejor.2019.05.008
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Liu, Yezheng & Qian, Yang & Jiang, Yuanchun & Shang, Jennifer, 2020. "Using favorite data to analyze asymmetric competition: Machine learning models," European Journal of Operational Research, Elsevier, vol. 287(2), pages 600-615.
    2. Yi Feng & Yunqiang Yin & Dujuan Wang & Lalitha Dhamotharan & Joshua Ignatius & Ajay Kumar, 2023. "Diabetic patient review helpfulness: unpacking online drug treatment reviews by text analytics and design science approach," Annals of Operations Research, Springer, vol. 328(1), pages 387-418, September.
    3. Yu, Haiyan & Yang, Ching-Chi & Yu, Ping, 2023. "Constrained optimization for stratified treatment rules in reducing hospital readmission rates of diabetic patients," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1355-1364.
    4. Singha, Sumanta & Arha, Himanshu & Kar, Arpan Kumar, 2023. "Healthcare analytics: A techno-functional perspective," Technological Forecasting and Social Change, Elsevier, vol. 197(C).

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