IDEAS home Printed from https://ideas.repec.org/a/pal/jorsoc/v59y2008i11d10.1057_palgrave.jors.2602499.html
   My bibliography  Save this article

A queuing-based decision support methodology to estimate hospital inpatient bed demand

Author

Listed:
  • J K Cochran

    (Arizona State University)

  • K Roche

    (Arizona State University)

Abstract

Hospital inpatient bed capacity might be better described as evolved than planned. At least two challenges lead to this behaviour: different views of patient demand implied by different data sets in a hospital and limited use of scientific methods for capacity estimation. In this paper, we statistically examine four distinct hospital inpatient data sets for internal consistency and potential usefulness for estimating true patient bed demand. We conclude that posterior financial data, billing data, rather than the census data commonly relied upon, yields true hospital bed demand. Subsequently, a capacity planning tool, based upon queuing theory and financial data only, is developed. The delivery mechanism is an Excel spreadsheet. One adjusts input parameters including patient volume and mix and instantaneously monitors the effect on bed needs across multiple levels of care. A case study from a major hospital in Phoenix, Arizona, USA is used throughout to demonstrate the methodologies.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:11:d:10.1057_palgrave.jors.2602499
    DOI: 10.1057/palgrave.jors.2602499
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/palgrave.jors.2602499
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/palgrave.jors.2602499?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. Lapierre, Sophie D. & Goldsman, David & Cochran, Roger & DuBow, Janice, 1999. "Bed allocation techniques based on census data," Socio-Economic Planning Sciences, Elsevier, vol. 33(1), pages 25-38, March.
    2. 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.
    3. Ridge, J. C. & Jones, S. K. & Nielsen, M. S. & Shahani, A. K., 1998. "Capacity planning for intensive care units," European Journal of Operational Research, Elsevier, vol. 105(2), pages 346-355, March.
    4. P R Harper & A K Shahani, 2002. "Modelling for the planning and management of bed capacities in hospitals," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(1), pages 11-18, January.
    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. Andersen, Anders Reenberg & Nielsen, Bo Friis & Reinhardt, Line Blander, 2017. "Optimization of hospital ward resources with patient relocation using Markov chain modeling," European Journal of Operational Research, Elsevier, vol. 260(3), pages 1152-1163.
    2. Josephine Varney & Nigel Bean & Mark Mackay, 2019. "The self-regulating nature of occupancy in ICUs: stochastic homoeostasis," Health Care Management Science, Springer, vol. 22(4), pages 615-634, December.
    3. Jaime González & Juan-Carlos Ferrer & Alejandro Cataldo & Luis Rojas, 2019. "A proactive transfer policy for critical patient flow management," Health Care Management Science, Springer, vol. 22(2), pages 287-303, June.
    4. Yen-Yi Feng & I-Chin Wu & Tzu-Li Chen, 2017. "Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm," Health Care Management Science, Springer, vol. 20(1), pages 55-75, March.
    5. 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.
    6. 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.
    7. Fermín Mallor & Cristina Azcárate & Julio Barado, 2016. "Control problems and management policies in health systems: application to intensive care units," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 62-89, June.
    8. Fanwen Meng & Jin Qi & Meilin Zhang & James Ang & Singfat Chu & Melvyn Sim, 2015. "A Robust Optimization Model for Managing Elective Admission in a Public Hospital," Operations Research, INFORMS, vol. 63(6), pages 1452-1467, December.

    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. 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.
    2. Josephine Varney & Nigel Bean & Mark Mackay, 2019. "The self-regulating nature of occupancy in ICUs: stochastic homoeostasis," Health Care Management Science, Springer, vol. 22(4), pages 615-634, December.
    3. Vahab Vahdat & Jacqueline Griffin & James E. Stahl, 2018. "Decreasing patient length of stay via new flexible exam room allocation policies in ambulatory care clinics," Health Care Management Science, Springer, vol. 21(4), pages 492-516, December.
    4. Kim, Seung-Chul & Horowitz, Ira, 2002. "Scheduling hospital services: the efficacy of elective-surgery quotas," Omega, Elsevier, vol. 30(5), pages 335-346, October.
    5. Dominic J. Breuer & Shashank Kapadia & Nadia Lahrichi & James C. Benneyan, 2022. "Joint robust optimization of bed capacity, nurse staffing, and care access under uncertainty," Annals of Operations Research, Springer, vol. 312(2), pages 673-689, May.
    6. P R Harper & S Phillips & J E Gallagher, 2005. "Geographical simulation modelling for the regional planning of oral and maxillofacial surgery across London," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(2), pages 134-143, February.
    7. 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.
    8. 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.
    9. 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.
    10. Yue Zhang & Martin L. Puterman & Matthew Nelson & Derek Atkins, 2012. "A Simulation Optimization Approach to Long-Term Care Capacity Planning," Operations Research, INFORMS, vol. 60(2), pages 249-261, April.
    11. repec:dgr:rugsom:02a63 is not listed on IDEAS
    12. Goutam Dutta & Ajay Naik & Dipa Gosai & Priyanko Ghosh, 2021. "A mathematical model for predicting length of postoperative intensive care requirement following cardiac surgery in an Indian hospital," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 330-350, June.
    13. Broyles, James R. & Cochran, Jeffery K. & Montgomery, Douglas C., 2010. "A statistical Markov chain approximation of transient hospital inpatient inventory," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1645-1657, December.
    14. Valdmanis, Vivian & Bernet, Patrick & Moises, James, 2010. "Hospital capacity, capability, and emergency preparedness," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1628-1634, December.
    15. Fermín Mallor & Cristina Azcárate, 2014. "Combining optimization with simulation to obtain credible models for intensive care units," Annals of Operations Research, Springer, vol. 221(1), pages 255-271, October.
    16. Md Asaduzzaman & Thierry Chaussalet & Nicola Robertson, 2010. "A loss network model with overflow for capacity planning of a neonatal unit," Annals of Operations Research, Springer, vol. 178(1), pages 67-76, July.
    17. Jing Li & Ming Dong & Wenhui Zhao, 2015. "Admissions optimisation and premature discharge decisions in intensive care units," International Journal of Production Research, Taylor & Francis Journals, vol. 53(24), pages 7329-7342, December.
    18. Vincent Knight & Izabela Komenda & Jeff Griffiths, 2017. "Measuring the price of anarchy in critical care unit interactions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 630-642, June.
    19. 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.
    20. 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.
    21. Berta, P. & Lovaglio, P.G. & Paruolo, P. & Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand," Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, c/o Department of Economics, University of York.

    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:pal:jorsoc:v:59:y:2008:i:11:d:10.1057_palgrave.jors.2602499. 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.palgrave-journals.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.