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

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  • 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
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    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.
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    Cited by:

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    6. 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.
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    10. 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.

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