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A Model of ICU Bumping

Author

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  • Gregory Dobson

    (Simon School of Business, University of Rochester, Rochester, New York 14627)

  • Hsiao-Hui Lee

    (School of Business, University of Connecticut, Storrs, Connecticut 06269)

  • Edieal Pinker

    (Simon School of Business, University of Rochester, Rochester, New York 14627)

Abstract

Many intensive care units (ICUs) face overcrowding. One response to this overcrowding is to bump ICU patients to other departments of the hospital to make room for new patient arrivals. Such bumping clearly has the potential to reduce quality of care. In this paper we develop a stochastic model of a single ICU with patient bumping. The purpose of this model is to enable planners to predict performance, in terms of bumping, under differing arrival patterns and capacity. We develop a Markov chain model and a new aggregation-disaggregation algorithm for this problem that enables us to keep track of the time in system for each patient despite the high dimensionality of the problem. Our approach allows for more accurate modeling of the system than previous work that assumed an exponential distribution for length of stay (LOS). We also demonstrate the superior computational efficiency of our approach over the Gauss-Seidel iterative method for solving the Markov chain. Finally, we use the model to explore how different surgery schedules influence bumping rates.

Suggested Citation

  • Gregory Dobson & Hsiao-Hui Lee & Edieal Pinker, 2010. "A Model of ICU Bumping," Operations Research, INFORMS, vol. 58(6), pages 1564-1576, December.
    • Handle: RePEc:inm:oropre:v:58:y:2010:i:6:p:1564-1576
    DOI: 10.1287/opre.1100.0861
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    References listed on IDEAS

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    Cited by:

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    4. Jie Bai & Andreas Fügener & Jochen Gönsch & Jens O. Brunner & Manfred Blobner, 2021. "Managing admission and discharge processes in intensive care units," Health Care Management Science, Springer, vol. 24(4), pages 666-685, December.
    5. Josephine Varney & Nigel Bean & Mark Mackay, 2019. "The self-regulating nature of occupancy in ICUs: stochastic homoeostasis," Health Care Management Science, Springer, vol. 22(4), pages 615-634, December.
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    8. David D. Cho & Kurt M. Bretthauer & Jan Schoenfelder, 2023. "Patient-to-nurse ratios: Balancing quality, nurse turnover, and cost," Health Care Management Science, Springer, vol. 26(4), pages 807-826, December.
    9. Fermín Mallor & Cristina Azcárate & Julio Barado, 2015. "Optimal control of ICU patient discharge: from theory to implementation," Health Care Management Science, Springer, vol. 18(3), pages 234-250, September.
    10. Kanix Wang & Walid Hussain & John R. Birge & Michael D. Schreiber & Daniel Adelman, 2022. "A High-Fidelity Model to Predict Length of Stay in the Neonatal Intensive Care Unit," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 183-195, January.
    11. Jie Bai & Andreas Fügener & Jan Schoenfelder & Jens O. Brunner, 2018. "Operations research in intensive care unit management: a literature review," Health Care Management Science, Springer, vol. 21(1), pages 1-24, March.
    12. Daniel Gartner & Rainer Kolisch & Daniel B. Neill & Rema Padman, 2015. "Machine Learning Approaches for Early DRG Classification and Resource Allocation," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 718-734, November.
    13. Azcarate, Cristina & Esparza, Laida & Mallor, Fermin, 2020. "The problem of the last bed: Contextualization and a new simulation framework for analyzing physician decisions," Omega, Elsevier, vol. 96(C).
    14. Carri W. Chan & Vivek F. Farias & Gabriel J. Escobar, 2017. "The Impact of Delays on Service Times in the Intensive Care Unit," Management Science, INFORMS, vol. 63(7), pages 2049-2072, July.
    15. Silviya Valeva & Guodong Pang & Andrew J. Schaefer & Gilles Clermont, 2023. "Acuity-Based Allocation of ICU-Downstream Beds with Flexible Staffing," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 403-422, March.

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