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Heterogeneous length-of-stay modeling of post-acute care residents in the nursing home with competing discharge dispositions

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Abstract

Post-acute care (PAC) residents in nursing homes (NHs) are recently hospitalized patients with medically complex diagnoses, ranging from severe orthopedic injuries to cardiovascular diseases. A major role of NHs is to maximize restoration of PAC residents during their NH stays with desirable discharge outcomes, such as higher community discharge likelihood and lower re/hospitalization risk. Accurate prediction of the PAC residents’ length-of-stay (LOS) with multiple discharge dispositions (e.g., community discharge and re/hospitalization) will allow NH management groups to stratify NH residents based on their individualized risk in realizing personalized and resident-centered NH care delivery. Due to the highly heterogeneous health conditions of PAC residents and their multiple types of correlated discharge dispositions, developing an accurate prediction model becomes challenging. Existing predictive analytics methods, such as distribution-/regression-based methods and machine learning methods, either fail to incorporate varied individual characteristics comprehensively or ignore multiple discharge dispositions. In this work, a data-driven predictive analytics approach is considered to jointly predict the individualized re/hospitalization risk and community discharge likelihood over time in the presence of varied residents’ characteristics. A sampling algorithm is further developed to generate accurate predictive samples for a heterogeneous population of PAC residents in an NH and facilitate facility-level performance evaluation. A real case study using large-scale NH data is provided to demonstrate the superior prediction performance of the proposed work at individual and facility levels through comprehensive comparison with a large number of existing prediction methods as benchmarks. The developed analytics tools will allow NH management groups to identify the most at-risk residents by providing them with more proactive and focused care to improve resident outcomes.

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Authors and Affiliations

  1. Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL, 33620, USA

    Nazmus Sakib & Mingyang Li

  2. College of Media Engineering, Communication University of Zhejiang, Hangzhou, 310019, China

    Xuxue Sun

  3. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, 47907, USA

    Nan Kong

  4. Greystone Health Network, Tampa, FL, 33610, USA

    Chris Masterson

  5. School of Aging Studies, University of South Florida, Tampa, FL, 33620, USA

    Hongdao Meng & Kelly Smith

Authors
  1. Nazmus Sakib
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  2. Xuxue Sun
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  3. Nan Kong
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  4. Chris Masterson
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  5. Hongdao Meng
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  6. Kelly Smith
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  7. Mingyang Li
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Corresponding author

Correspondence to Mingyang Li.

Additional information

This work was supported in part by National Science Foundation under Grant Nos. 1825761 and 1825725.

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Sakib, N., Sun, X., Kong, N. et al. Heterogeneous length-of-stay modeling of post-acute care residents in the nursing home with competing discharge dispositions. Front. Eng. Manag. 9, 577–591 (2022). https://doi.org/10.1007/s42524-022-0203-7

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  • DOI: https://doi.org/10.1007/s42524-022-0203-7

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