IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v24y2021i2d10.1007_s10729-021-09548-2.html
   My bibliography  Save this article

Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation

Author

Listed:
  • G.J. Melman

    (University of Cambridge
    Cambridge University Hospitals NHS Foundation Trust
    Eindhoven University of Technology)

  • A.K. Parlikad

    (University of Cambridge)

  • E.A.B. Cameron

    (Cambridge University Hospitals NHS Foundation Trust)

Abstract

COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

Suggested Citation

  • G.J. Melman & A.K. Parlikad & E.A.B. Cameron, 2021. "Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation," Health Care Management Science, Springer, vol. 24(2), pages 356-374, June.
    • Handle: RePEc:kap:hcarem:v:24:y:2021:i:2:d:10.1007_s10729-021-09548-2
    DOI: 10.1007/s10729-021-09548-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-021-09548-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10729-021-09548-2?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. Blake, John T. & Carter, Michael W., 2002. "A goal programming approach to strategic resource allocation in acute care hospitals," European Journal of Operational Research, Elsevier, vol. 140(3), pages 541-561, August.
    2. Richard M Wood & Christopher J McWilliams & Matthew J Thomas & Christopher P Bourdeaux & Christos Vasilakis, 2020. "COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care," Health Care Management Science, Springer, vol. 23(3), pages 315-324, September.
    3. Joshua M. Epstein, 2009. "Modelling to contain pandemics," Nature, Nature, vol. 460(7256), pages 687-687, August.
    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. Weiwei Zhang & Shiyong Liu & Nathaniel Osgood & Hongli Zhu & Ying Qian & Peng Jia, 2023. "Using simulation modelling and systems science to help contain COVID‐19: A systematic review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 40(1), pages 207-234, January.
    2. Tine Buyl & Thomas Gehrig & Jonas Schreyögg & Andreas Wieland, 2022. "Resilience: A Critical Appraisal of the State of Research for Business and Society," Schmalenbach Journal of Business Research, Springer, vol. 74(4), pages 453-463, December.
    3. Jesús Isaac Vázquez-Serrano & Rodrigo E. Peimbert-García & Leopoldo Eduardo Cárdenas-Barrón, 2021. "Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review," IJERPH, MDPI, vol. 18(22), pages 1-20, November.
    4. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    5. Stephan Zellmer & Ella Bachmann & Anna Muzalyova & Alanna Ebigbo & Maria Kahn & Claudia Traidl-Hoffmann & Roland Frankenberger & Fabian M. Eckstein & Thomas Ziebart & Axel Meisgeier & Helmut Messmann , 2021. "One Year of the COVID-19 Pandemic in Dental Medical Facilities in Germany: A Questionnaire-Based Analysis," IJERPH, MDPI, vol. 19(1), pages 1-11, December.
    6. Alec Morton & Ebru Bish & Itamar Megiddo & Weifen Zhuang & Roberto Aringhieri & Sally Brailsford & Sarang Deo & Na Geng & Julie Higle & David Hutton & Mart Janssen & Edward H Kaplan & Jianbin Li & Món, 2021. "Introduction to the special issue: Management Science in the Fight Against Covid-19," Health Care Management Science, Springer, vol. 24(2), pages 251-252, June.
    7. Beate Jahn & Sarah Friedrich & Joachim Behnke & Joachim Engel & Ursula Garczarek & Ralf Münnich & Markus Pauly & Adalbert Wilhelm & Olaf Wolkenhauer & Markus Zwick & Uwe Siebert & Tim Friede, 2022. "On the role of data, statistics and decisions in a pandemic," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 349-382, September.

    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. Vivek Shastry & D Cale Reeves & Nicholas Willems & Varun Rai, 2022. "Policy and behavioral response to shock events: An agent-based model of the effectiveness and equity of policy design features," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-21, January.
    2. Linying Yang & Teng Zhang & Peter Glynn & David Scheinker, 2021. "The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE)," Health Care Management Science, Springer, vol. 24(2), pages 375-401, June.
    3. Pablo Santibáñez & Mehmet Begen & Derek Atkins, 2007. "Surgical block scheduling in a system of hospitals: an application to resource and wait list management in a British Columbia health authority," Health Care Management Science, Springer, vol. 10(3), pages 269-282, September.
    4. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    5. van Oostrum, J.M. & Parlevliet, T. & Wagelmans, A.P.M. & Kazemier, G., 2008. "A method for clustering surgical cases to allow master surgical scheduling," Econometric Institute Research Papers EI 2008-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Elaine O Nsoesie & Richard J Beckman & Madhav V Marathe, 2012. "Sensitivity Analysis of an Individual-Based Model for Simulation of Influenza Epidemics," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-16, October.
    7. Richard M. Wood & Adrian C. Pratt & Charlie Kenward & Christopher J. McWilliams & Ross D. Booton & Matthew J. Thomas & Christopher P. Bourdeaux & Christos Vasilakis, 2021. "The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study," Medical Decision Making, , vol. 41(4), pages 393-407, May.
    8. Aisha Tayyab & Saif Ullah & Mohammed Fazle Baki, 2023. "An Outer Approximation Method for Scheduling Elective Surgeries with Sequence Dependent Setup Times to Multiple Operating Rooms," Mathematics, MDPI, vol. 11(11), pages 1-15, May.
    9. Michael Samudra & Carla Van Riet & Erik Demeulemeester & Brecht Cardoen & Nancy Vansteenkiste & Frank E. Rademakers, 2016. "Scheduling operating rooms: achievements, challenges and pitfalls," Journal of Scheduling, Springer, vol. 19(5), pages 493-525, October.
    10. Wang, Derek D., 2019. "Performance-based resource allocation for higher education institutions in China," Socio-Economic Planning Sciences, Elsevier, vol. 65(C), pages 66-75.
    11. Zhang, Ganggang & Wu, Jie & Zhu, Qingyuan, 2020. "Performance evaluation and enrollment quota allocation for higher education institutions in China," Evaluation and Program Planning, Elsevier, vol. 81(C).
    12. X Li & P Beullens & D Jones & M Tamiz, 2009. "An integrated queuing and multi-objective bed allocation model with application to a hospital in China," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 330-338, March.
    13. Costase Ndayishimiye & Christoph Sowada & Patrycja Dyjach & Agnieszka Stasiak & John Middleton & Henrique Lopes & Katarzyna Dubas-Jakóbczyk, 2022. "Associations between the COVID-19 Pandemic and Hospital Infrastructure Adaptation and Planning—A Scoping Review," IJERPH, MDPI, vol. 19(13), pages 1-22, July.
    14. Kumar, Ashwani & Costa, Alysson M. & Fackrell, Mark & Taylor, Peter G., 2018. "A sequential stochastic mixed integer programming model for tactical master surgery scheduling," European Journal of Operational Research, Elsevier, vol. 270(2), pages 734-746.
    15. Belií«n, Jeroen & Demeulemeester, Erik, 2008. "A branch-and-price approach for integrating nurse and surgery scheduling," European Journal of Operational Research, Elsevier, vol. 189(3), pages 652-668, September.
    16. Burdett, Robert L & Corry, Paul & Yarlagadda, Prasad & Cook, David & Birgan, Sean & McPhail, Steven M, 2023. "A mathematical framework for regional hospital case mix planning and capacity appraisal," Operations Research Perspectives, Elsevier, vol. 10(C).
    17. Cardoen, Brecht & Demeulemeester, Erik & Beliën, Jeroen, 2010. "Operating room planning and scheduling: A literature review," European Journal of Operational Research, Elsevier, vol. 201(3), pages 921-932, March.
    18. Datta, Amitava & Winkelstein, Peter & Sen, Surajit, 2022. "An agent-based model of spread of a pandemic with validation using COVID-19 data from New York State," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    19. Matthias Klumpp & Dominic Loske & Silvio Bicciato, 2022. "COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(8), pages 1263-1285, November.
    20. Paola Cappanera & Filippo Visintin & Carlo Banditori, 2018. "Addressing conflicting stakeholders’ priorities in surgical scheduling by goal programming," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 252-271, June.

    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:kap:hcarem:v:24:y:2021:i:2:d:10.1007_s10729-021-09548-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.