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Operational patient-bed assignment problem in large hospital settings including overflow and uncertainty management

Author

Listed:
  • Fabian Schäfer

    (Technical University of Munich)

  • Manuel Walther

    (Catholic University of Eichstätt-Ingolstadt)

  • Alexander Hübner

    (Technical University of Munich)

  • Heinrich Kuhn

    (Catholic University of Eichstätt-Ingolstadt)

Abstract

Managing patient to bed allocations is an everyday task in hospitals which in recent years has moved into focus due to a general rise in occupancy levels and the resulting need to efficiently manage tight hospital bed-capacities. This holds true especially when being faced with high volatility and uncertainty regarding patient arrivals and lengths of stay. In our work with a large German hospital we identified three main stakeholders, namely patients, nurses, and doctors, whose individual objectives and constraints regarding patient-bed allocation (PBA) lead to a potential trade-off situation. We developed a decision support model that tackles the PBA problem considering this trade-off, while also being capable of handling overflow situations. In addition, we anticipate emergency patient arrivals based on historical probability distributions and account for uncertainty regarding patient arrival and discharge dates. We develop a greedy look-ahead heuristic which allows for generating solutions for large real-life operational planning situations involving high ratios of emergency patients. We demonstrate the performance of our heuristic approach by comparison with the results of a near-optimal solution achieved by Gurobi’s MIP solver. Finally, we tested our approach using data sets from the literature as well as actual clinic data from our case study hospital, for which we were able to reduce overflow by over 96% while increasing overall utilization by 5%.

Suggested Citation

  • Fabian Schäfer & Manuel Walther & Alexander Hübner & Heinrich Kuhn, 2019. "Operational patient-bed assignment problem in large hospital settings including overflow and uncertainty management," Flexible Services and Manufacturing Journal, Springer, vol. 31(4), pages 1012-1041, December.
  • Handle: RePEc:spr:flsman:v:31:y:2019:i:4:d:10.1007_s10696-018-9331-0
    DOI: 10.1007/s10696-018-9331-0
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    References listed on IDEAS

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    1. Wim Vancroonenburg & Patrick Causmaecker & Greet Vanden Berghe, 2016. "A study of decision support models for online patient-to-room assignment planning," Annals of Operations Research, Springer, vol. 239(1), pages 253-271, April.
    2. Daniel Gartner, 2014. "Scheduling the Hospital-Wide Flow of Elective Patients," Lecture Notes in Economics and Mathematical Systems, in: Optimizing Hospital-wide Patient Scheduling, edition 127, chapter 0, pages 33-54, Springer.
    3. Fügener, Andreas & Hans, Erwin W. & Kolisch, Rainer & Kortbeek, Nikky & Vanberkel, Peter T., 2014. "Master surgery scheduling with consideration of multiple downstream units," European Journal of Operational Research, Elsevier, vol. 239(1), pages 227-236.
    4. Gartner, Daniel & Kolisch, Rainer, 2014. "Scheduling the hospital-wide flow of elective patients," European Journal of Operational Research, Elsevier, vol. 233(3), pages 689-699.
    5. Belien, Jeroen & Demeulemeester, Erik, 2007. "Building cyclic master surgery schedules with leveled resulting bed occupancy," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1185-1204, January.
    6. Alexander Hübner & Heinrich Kuhn & Manuel Walther, 2018. "Combining clinical departments and wards in maximum-care hospitals," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(3), pages 679-709, July.
    7. Peter J. H. Hulshof & Martijn R. K. Mes & Richard J. Boucherie & Erwin W. Hans, 2016. "Patient admission planning using Approximate Dynamic Programming," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 30-61, June.
    8. Range, Troels Martin & Lusby, Richard Martin & Larsen, Jesper, 2014. "A column generation approach for solving the patient admission scheduling problem," European Journal of Operational Research, Elsevier, vol. 235(1), pages 252-264.
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    Cited by:

    1. Aleida Braaksma & Martin S. Copenhaver & Ana C. Zenteno & Elizabeth Ugarph & Retsef Levi & Bethany J. Daily & Benjamin Orcutt & Kathryn M. Turcotte & Peter F. Dunn, 2023. "Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients," Health Care Management Science, Springer, vol. 26(3), pages 501-515, September.
    2. Fabian Schäfer & Manuel Walther & Dominik G. Grimm & Alexander Hübner, 2023. "Combining machine learning and optimization for the operational patient-bed assignment problem," Health Care Management Science, Springer, vol. 26(4), pages 785-806, December.

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