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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

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    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. Reza Maleki & Mohammadreza Taghizadeh-Yazdi & Rohollah Ghasemi & Samar Rivandi, 2024. "A Hybrid Mathematical-Simulation Approach to Hospital Beds Capacity Optimization for COVID-19 Pandemic Conditions," SN Operations Research Forum, Springer, vol. 5(4), pages 1-33, December.
    7. 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.
    8. 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.

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