IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v281y2020i3p517-531.html
   My bibliography  Save this article

Latent topic ensemble learning for hospital readmission cost optimization

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221719304102
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2019.05.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Menglei Ji & Mohammad Mosaffa & Amir Ardestani-Jaafari & Jinlin Li & Chun Peng, 2024. "Integration of text-mining and telemedicine appointment optimization," Annals of Operations Research, Springer, vol. 341(1), pages 621-645, October.
    2. 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.
    3. 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.
    4. 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.
    5. Borchert, Philipp & Coussement, Kristof & De Weerdt, Jochen & De Caigny, Arno, 2024. "Industry-sensitive language modeling for business," European Journal of Operational Research, Elsevier, vol. 315(2), pages 691-702.
    6. Singha, Sumanta & Arha, Himanshu & Kar, Arpan Kumar, 2023. "Healthcare analytics: A techno-functional perspective," Technological Forecasting and Social Change, Elsevier, vol. 197(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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).
    4. 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.
    5. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:281:y:2020:i:3:p:517-531. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.