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Scheduling the hospital-wide flow of elective patients

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  • Gartner, Daniel
  • Kolisch, Rainer

Abstract

In this paper, we address the problem of planning the patient flow in hospitals subject to scarce medical resources with the objective of maximizing the contribution margin. We assume that we can classify a large enough percentage of elective patients according to their diagnosis-related group (DRG) and clinical pathway. The clinical pathway defines the procedures (such as different types of diagnostic activities and surgery) as well as the sequence in which they have to be applied to the patient. The decision is then on which day each procedure of each patient’s clinical pathway should be done, taking into account the sequence of procedures as well as scarce clinical resources, such that the contribution margin of all patients is maximized. We develop two mixed-integer programs (MIP) for this problem which are embedded in a static and a rolling horizon planning approach. Computational results on real-world data show that employing the MIPs leads to a significant improvement of the contribution margin compared to the contribution margin obtained by employing the planning approach currently practiced. Furthermore, we show that the time between admission and surgery is significantly reduced by applying our models.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:233:y:2014:i:3:p:689-699
    DOI: 10.1016/j.ejor.2013.08.026
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    Cited by:

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    2. Yasar A. Ozcan & Elena Tànfani & Angela Testi, 2017. "Improving the performance of surgery-based clinical pathways: a simulation-optimization approach," Health Care Management Science, Springer, vol. 20(1), pages 1-15, March.
    3. Marynissen, Joren & Demeulemeester, Erik, 2019. "Literature review on multi-appointment scheduling problems in hospitals," European Journal of Operational Research, Elsevier, vol. 272(2), pages 407-419.
    4. Karsten Schwarz & Michael Römer & Taïeb Mellouli, 2019. "A data-driven hierarchical MILP approach for scheduling clinical pathways: a real-world case study from a German university hospital," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 597-636, December.
    5. Burdett, Robert L. & Kozan, Erhan, 2018. "An integrated approach for scheduling health care activities in a hospital," European Journal of Operational Research, Elsevier, vol. 264(2), pages 756-773.
    6. Range, Troels Martin & Kozlowski, Dawid & Petersen, Niels Chr., 2016. "Dynamic job assignment: A column generation approach with an application to surgery allocation," Discussion Papers on Economics 4/2016, University of Southern Denmark, Department of Economics.
    7. 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.
    8. José Carlos Ferrão & Mónica Duarte Oliveira & Daniel Gartner & Filipe Janela & Henrique M. G. Martins, 2021. "Leveraging electronic health record data to inform hospital resource management," Health Care Management Science, Springer, vol. 24(4), pages 716-741, December.
    9. Nguyen, Hong Ngoc & O’Donnell, Christopher, 2023. "Estimating the cost efficiency of public service providers in the presence of demand uncertainty," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1334-1348.
    10. Shuwan Zhu & Wenjuan Fan & Shanlin Yang & Jun Pei & Panos M. Pardalos, 2019. "Operating room planning and surgical case scheduling: a review of literature," Journal of Combinatorial Optimization, Springer, vol. 37(3), pages 757-805, April.
    11. 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.
    12. Ines Verena Arnolds & Daniel Gartner, 2018. "Improving hospital layout planning through clinical pathway mining," Annals of Operations Research, Springer, vol. 263(1), pages 453-477, April.
    13. Huaxin Qiu & Dujuan Wang & Yanzhang Wang & Yunqiang Yin, 2019. "MRI appointment scheduling with uncertain examination time," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 62-82, January.
    14. Vandenberghe, Mathieu & De Vuyst, Stijn & Aghezzaf, El-Houssaine & Bruneel, Herwig, 2019. "Surgery sequencing to minimize the expected maximum waiting time of emergent patients," European Journal of Operational Research, Elsevier, vol. 275(3), pages 971-982.
    15. Namakshenas, Mohammad & Mazdeh, Mohammad Mahdavi & Braaksma, Aleida & Heydari, Mehdi, 2023. "Appointment scheduling for medical diagnostic centers considering time-sensitive pharmaceuticals: A dynamic robust optimization approach," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1018-1031.
    16. M. A. Miranda & S. Salvatierra & I. Rodríguez & M. J. Álvarez & V. Rodríguez, 2020. "Characterization of the flow of patients in a hospital from complex networks," Health Care Management Science, Springer, vol. 23(1), pages 66-79, March.
    17. Range, Troels Martin & Kozlowski, Dawid & Petersen, Niels Chr., 2019. "Dynamic job assignment: A column generation approach with an application to surgery allocation," European Journal of Operational Research, Elsevier, vol. 272(1), pages 78-93.
    18. 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.
    19. 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.
    20. Koppka, Lisa & Wiesche, Lara & Schacht, Matthias & Werners, Brigitte, 2018. "Optimal distribution of operating hours over operating rooms using probabilities," European Journal of Operational Research, Elsevier, vol. 267(3), pages 1156-1171.

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