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A Stochastic Model for an Optimal Priority Bed Distribution Problem in a Hospital Ward

Author

Listed:
  • Augustine O. Esogbue

    (Georgia Institute of Technology, Atlanta, Georgia)

  • Amar J. Singh

    (Veterans Administration Hospital, Los Angeles, California)

Abstract

Ward beds are a primary resource under the control of hospital management. We develop a method for determining an optimum distribution of beds in a ward by assuming that ward patients can be classified into two categories, that admissions follow Poisson distribution, and that length of stay in the ward follows the negative exponential distribution. After defining a cut-off level as “the number of beds beyond which type 2 (non-serious) patients are not admitted,” we develop a system of differential and difference (birth and death process) equations for the process. An objective function made up of shortage and holding costs is then developed and optimized for various values of cut-off priority level. An application of this model to a university teaching hospital in Cleveland is illustrated. The model is then extended to a situation where overflows are temporarily housed in a buffer accommodation or inappropriate ward.

Suggested Citation

  • Augustine O. Esogbue & Amar J. Singh, 1976. "A Stochastic Model for an Optimal Priority Bed Distribution Problem in a Hospital Ward," Operations Research, INFORMS, vol. 24(5), pages 884-898, October.
    • Handle: RePEc:inm:oropre:v:24:y:1976:i:5:p:884-898
    DOI: 10.1287/opre.24.5.884
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    Cited by:

    1. Iannoni, Ana Paula & Chiyoshi, Fernando & Morabito, Reinaldo, 2015. "A spatially distributed queuing model considering dispatching policies with server reservation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 75(C), pages 49-66.
    2. Van-Anh Truong, 2015. "Optimal Advance Scheduling," Management Science, INFORMS, vol. 61(7), pages 1584-1597, July.
    3. Diwakar Gupta & Lei Wang, 2008. "Revenue Management for a Primary-Care Clinic in the Presence of Patient Choice," Operations Research, INFORMS, vol. 56(3), pages 576-592, June.
    4. (Ai-Chih) Chang, Jasmine & Lu, Haibing & (Junmin) Shi, Jim, 2019. "Stockout risk of production-inventory systems with compound Poisson demands," Omega, Elsevier, vol. 83(C), pages 181-198.
    5. Zhou, Liping & Geng, Na & Jiang, Zhibin & Wang, Xiuxian, 2018. "Multi-objective capacity allocation of hospital wards combining revenue and equity," Omega, Elsevier, vol. 81(C), pages 220-233.
    6. X Li & P Beullens & D Jones & M Tamiz, 2009. "An integrated queuing and multi-objective bed allocation model with application to a hospital in China," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 330-338, March.
    7. 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.
    8. Saied Samiedaluie & Beste Kucukyazici & Vedat Verter & Dan Zhang, 2017. "Managing Patient Admissions in a Neurology Ward," Operations Research, INFORMS, vol. 65(3), pages 635-656, June.
    9. Li, L. X. & Benton, W. C., 1996. "Performance measurement criteria in health care organizations: Review and future research directions," European Journal of Operational Research, Elsevier, vol. 93(3), pages 449-468, September.
    10. 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.

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