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Integral resource capacity planning for inpatient care services based on bed census predictions by hour

Author

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  • Nikky Kortbeek

    (1] Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands[2] Academic Medical Center, Amsterdam, The Netherlands[3] Academic Medical Center, Amsterdam, The Netherlands)

  • Aleida Braaksma

    (1] Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands[2] Academic Medical Center, Amsterdam, The Netherlands[3] Academic Medical Center, Amsterdam, The Netherlands)

  • Ferry HF Smeenk

    (1] Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands[2] Academic Medical Center, Amsterdam, The Netherlands)

  • Piet JM Bakker

    (Academic Medical Center, Amsterdam, The Netherlands)

  • Richard J Boucherie

    (1] Center for Healthcare Operations Improvement and Research (CHOIR), University of Twente, Enschede, The Netherlands[2] Academic Medical Center, Amsterdam, The Netherlands)

Abstract

The design and operations of inpatient care facilities are typically largely historically shaped. A better match with the changing environment is often possible, and even inevitable due to the pressure on hospital budgets. Effectively organizing inpatient care requires simultaneous consideration of several interrelated planning issues. Also, coordination with upstream departments like the operating theatre and the emergency department is much-needed. We present a generic analytical approach to predict bed census on nursing wards by hour, as a function of the Master Surgical Schedule and arrival patterns of emergency patients. Along these predictions, insight is gained on the impact of strategic (ie, case mix, care unit size, care unit partitioning), tactical (ie, allocation of operating room time, misplacement rules), and operational decisions (ie, time of admission/discharge). The method is used in the Academic Medical Center Amsterdam as a decision-support tool in a complete redesign of the inpatient care operations.

Suggested Citation

  • Nikky Kortbeek & Aleida Braaksma & Ferry HF Smeenk & Piet JM Bakker & Richard J Boucherie, 2015. "Integral resource capacity planning for inpatient care services based on bed census predictions by hour," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(7), pages 1061-1076, July.
  • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:7:p:1061-1076
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    Cited by:

    1. Veneklaas, W. & Leeftink, A.G. & van Boekel, P.H.C.M. & Hans, E.W., 2021. "On the design, implementation, and feasibility of hospital admission services: The admission lounge case," Omega, Elsevier, vol. 100(C).
    2. 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).
    3. Samuel Davis & Nasser Fard, 2020. "Theoretical bounds and approximation of the probability mass function of future hospital bed demand," Health Care Management Science, Springer, vol. 23(1), pages 20-33, March.

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