IDEAS home Printed from https://ideas.repec.org/a/kap/hcarem/v23y2020i3d10.1007_s10729-020-09511-7.html
   My bibliography  Save this article

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

Author

Listed:
  • Richard M Wood

    (UK National Health Service (BNSSG CCG)
    University of Bath)

  • Christopher J McWilliams

    (University of Bristol)

  • Matthew J Thomas

    (University of Bristol)

  • Christopher P Bourdeaux

    (University of Bristol)

  • Christos Vasilakis

    (University of Bath)

Abstract

Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these ‘capacity-dependent’ deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional ‘capacity-independent’ deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the UK National Health Service.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:hcarem:v:23:y:2020:i:3:d:10.1007_s10729-020-09511-7
    DOI: 10.1007/s10729-020-09511-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10729-020-09511-7
    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/s10729-020-09511-7?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. Kim, Seung-Chul & Horowitz, Ira & Young, Karl K. & Buckley, Thomas A., 1999. "Analysis of capacity management of the intensive care unit in a hospital," European Journal of Operational Research, Elsevier, vol. 115(1), pages 36-46, May.
    2. Eren Demir & Christos Vasilakis & Reda Lebcir & David Southern, 2015. "A simulation-based decision support tool for informing the management of patients with Parkinson’s disease," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7238-7251, December.
    3. Amin Mahmoudian-Dehkordi & Somayeh Sadat, 2017. "Sustaining critical care: using evidence-based simulation to evaluate ICU management policies," Health Care Management Science, Springer, vol. 20(4), pages 532-547, December.
    4. Griffiths, J.D. & Williams, J.E. & Wood, R.M., 2013. "Modelling activities at a neurological rehabilitation unit," European Journal of Operational Research, Elsevier, vol. 226(2), pages 301-312.
    5. Glasgow, Simon M. & Perkins, Zane B. & Tai, Nigel R.M. & Brohi, Karim & Vasilakis, Christos, 2018. "Development of a discrete event simulation model for evaluating strategies of red blood cell provision following mass casualty events," European Journal of Operational Research, Elsevier, vol. 270(1), pages 362-374.
    Full references (including those not matched with items on IDEAS)

    Citations

    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health > Allocation and rationing

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Akira Watanabe & Hiroyuki Matsuda, 2023. "Effectiveness of feedback control and the trade-off between death by COVID-19 and costs of countermeasures," Health Care Management Science, Springer, vol. 26(1), pages 46-61, March.
    6. Berta, Paolo & Bratti, Massimiliano & Fiorio, Carlo V. & Pisoni, Enrico & Verzillo, Stefano, 2021. "Administrative Border Effects in COVID-19 Related Mortality," IZA Discussion Papers 14930, Institute of Labor Economics (IZA).
    7. 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.
    8. Samantha L. Zimmerman & Alexander R. Rutherford & Alexa Waall & Monica Norena & Peter Dodek, 2023. "A queuing model for ventilator capacity management during the COVID-19 pandemic," Health Care Management Science, Springer, vol. 26(2), pages 200-216, June.
    9. 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).
    10. Eugenio F. Sánchez-Úbeda & Pedro Sánchez-Martín & Macarena Torrego-Ellacuría & Ángel Del Rey-Mejías & Manuel F. Morales-Contreras & José-Luis Puerta, 2021. "Flexibility and Bed Margins of the Community of Madrid’s Hospitals during the First Wave of the SARS-CoV-2 Pandemic," IJERPH, MDPI, vol. 18(7), pages 1-22, March.

    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. Kozlowski, Dawid & Worthington, Dave, 2015. "Use of queue modelling in the analysis of elective patient treatment governed by a maximum waiting time policy," European Journal of Operational Research, Elsevier, vol. 244(1), pages 331-338.
    2. Rachuba, Sebastian & Imhoff, Lisa & Werners, Brigitte, 2022. "Tactical blueprints for surgical weeks – An integrated approach for operating rooms and intensive care units," European Journal of Operational Research, Elsevier, vol. 298(1), pages 243-260.
    3. Yuta Kanai & Hideaki Takagi, 2021. "Markov chain analysis for the neonatal inpatient flow in a hospital," Health Care Management Science, Springer, vol. 24(1), pages 92-116, March.
    4. John Bowers, 2013. "Balancing operating theatre and bed capacity in a cardiothoracic centre," Health Care Management Science, Springer, vol. 16(3), pages 236-244, September.
    5. 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.
    6. Pengfei Guo & Christopher S. Tang & Yulan Wang & Ming Zhao, 2019. "The Impact of Reimbursement Policy on Social Welfare, Revisit Rate, and Waiting Time in a Public Healthcare System: Fee-for-Service Versus Bundled Payment," Service Science, INFORMS, vol. 21(1), pages 154-170, January.
    7. Yariv Marmor & Thomas Rohleder & David Cook & Todd Huschka & Jeffrey Thompson, 2013. "Recovery bed planning in cardiovascular surgery: a simulation case study," Health Care Management Science, Springer, vol. 16(4), pages 314-327, December.
    8. 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.
    9. Eren Demir & David Southern, 2017. "Enabling better management of patients: discrete event simulation combined with the STAR approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(5), pages 577-590, May.
    10. Sam Afrane & Alex Appah, 2014. "Queuing theory and the management of Waiting-time in Hospitals: The case of Anglo Gold Ashanti Hospital in Ghana," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 4(2), pages 34-44, February.
    11. William S. Lovejoy & Ying Li, 2002. "Hospital Operating Room Capacity Expansion," Management Science, INFORMS, vol. 48(11), pages 1369-1387, November.
    12. J D Griffiths & N Price-Lloyd & M Smithies & J E Williams, 2005. "Modelling the requirement for supplementary nurses in an intensive care unit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(2), pages 126-133, February.
    13. Gogi, Anastasia & Tako, Antuela A. & Robinson, Stewart, 2016. "An experimental investigation into the role of simulation models in generating insights," European Journal of Operational Research, Elsevier, vol. 249(3), pages 931-944.
    14. Willoughby, Keith A. & Chan, Benjamin T.B. & Marques, Shauna, 2016. "Using simulation to test ideas for improving speech language pathology services," European Journal of Operational Research, Elsevier, vol. 252(2), pages 657-664.
    15. De Vuyst, Stijn & Bruneel, Herwig & Fiems, Dieter, 2014. "Computationally efficient evaluation of appointment schedules in health care," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1142-1154.
    16. Gregory Dobson & Hsiao-Hui Lee & Edieal Pinker, 2010. "A Model of ICU Bumping," Operations Research, INFORMS, vol. 58(6), pages 1564-1576, December.
    17. J K Cochran & K Roche, 2008. "A queuing-based decision support methodology to estimate hospital inpatient bed demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(11), pages 1471-1482, November.
    18. Yin-Chi Chan & Eric W. M. Wong & Gavin Joynt & Paul Lai & Moshe Zukerman, 2018. "Overflow models for the admission of intensive care patients," Health Care Management Science, Springer, vol. 21(4), pages 554-572, December.
    19. Shima Azizi & Cem Deniz Caglar Bozkir & Andrew C. Trapp & O. Erhun Kundakcioglu & Ali Kaan Kurbanzade, 2021. "Aid Allocation for Camp‐Based and Urban Refugees with Uncertain Demand and Replenishments," Production and Operations Management, Production and Operations Management Society, vol. 30(12), pages 4455-4474, December.
    20. Bokhorst, Jos A.C. & van der Vaart, Taco, 2018. "Acute medical unit design – The impact of rearranged patient flows," Socio-Economic Planning Sciences, Elsevier, vol. 62(C), pages 75-83.

    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:23:y:2020:i:3:d:10.1007_s10729-020-09511-7. 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.