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Identifying Good Nursing Levels: A Queuing Approach

Author

Listed:
  • Natalia Yankovic

    () (Columbia Business School, New York, New York 10025)

  • Linda V. Green

    () (Columbia Business School, New York, New York 10025)

Abstract

Nursing care is arguably the single biggest factor in both the cost of hospital care and patient satisfaction. Inadequate inpatient nursing levels have also been cited as a significant factor in medical errors and emergency room overcrowding. Yet, there is widespread dissatisfaction with the current methods of determining nurse staffing levels, including the most common one of using minimum nurse-to-patient ratios. In this paper, we represent the nursing system as a variable finite-source queuing model. We develop a reliable, tractable, easily parameterized two-dimensional model to approximate the actual interdependent dynamics of bed occupancy levels and demands for nursing. We use this model to show how unit size, nursing intensity, occupancy levels, and unit length-of-stay affect the impact of nursing levels on performance and thus how inflexible nurse-to-patient ratios can lead to either understaffing or overstaffing. The model is also useful for estimating the impact of nurse staffing levels on emergency department overcrowding.

Suggested Citation

  • Natalia Yankovic & Linda V. Green, 2011. "Identifying Good Nursing Levels: A Queuing Approach," Operations Research, INFORMS, vol. 59(4), pages 942-955, August.
  • Handle: RePEc:inm:oropre:v:59:y:2011:i:4:p:942-955
    DOI: 10.1287/opre.1110.0943
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    File URL: http://dx.doi.org/10.1287/opre.1110.0943
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    References listed on IDEAS

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    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. Holmes E. Miller & William P. Pierskalla & Gustave J. Rath, 1976. "Nurse Scheduling Using Mathematical Programming," Operations Research, INFORMS, vol. 24(5), pages 857-870, October.
    3. Jaumard, Brigitte & Semet, Frederic & Vovor, Tsevi, 1998. "A generalized linear programming model for nurse scheduling," European Journal of Operational Research, Elsevier, vol. 107(1), pages 1-18, May.
    4. Linda V. Green & Peter J. Kolesar, 2004. "ANNIVERSARY ARTICLE: Improving Emergency Responsiveness with Management Science," Management Science, INFORMS, vol. 50(8), pages 1001-1014, August.
    5. John C. Hershey & Elliott N. Weiss & Morris A. Cohen, 1981. "A Stochastic Service Network Model with Application to Hospital Facilities," Operations Research, INFORMS, vol. 29(1), pages 1-22, February.
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    Citations

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    Cited by:

    1. Carri W. Chan & Vivek F. Farias & Nicholas Bambos & Gabriel J. Escobar, 2012. "Optimizing Intensive Care Unit Discharge Decisions with Patient Readmissions," Operations Research, INFORMS, vol. 60(6), pages 1323-1341, December.
    2. Hideaki Takagi & Yuta Kanai & Kazuo Misue, 2017. "Queueing network model for obstetric patient flow in a hospital," Health Care Management Science, Springer, vol. 20(3), pages 433-451, September.
    3. René Bekker & Dennis Moeke & Bas Schmidt, 2019. "Keeping pace with the ebbs and flows in daily nursing home operations," Health Care Management Science, Springer, vol. 22(2), pages 350-363, June.
    4. Linda V. Green & Sergei Savin & Nicos Savva, 2013. "“Nursevendor Problem”: Personnel Staffing in the Presence of Endogenous Absenteeism," Management Science, INFORMS, vol. 59(10), pages 2237-2256, October.
    5. Carmen, Raïsa & Van Nieuwenhuyse, Inneke & Van Houdt, Benny, 2018. "Inpatient boarding in emergency departments: Impact on patient delays and system capacity," European Journal of Operational Research, Elsevier, vol. 271(3), pages 953-967.
    6. Wu, Xiaodan & Li, Juan & Chu, Chao-Hsien, 2019. "Modeling multi-stage healthcare systems with service interactions under blocking for bed allocation," European Journal of Operational Research, Elsevier, vol. 278(3), pages 927-941.
    7. Ingolfsson, Armann & Almehdawe, Eman & Pedram, Ali & Tran, Monica, 2020. "Comparison of fluid approximations for service systems with state-dependent service rates and return probabilities," European Journal of Operational Research, Elsevier, vol. 283(2), pages 562-575.
    8. Pengyi Shi & Mabel C. Chou & J. G. Dai & Ding Ding & Joe Sim, 2016. "Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time," Management Science, INFORMS, vol. 62(1), pages 1-28, January.
    9. 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.
    10. Carri W. Chan & Galit Yom-Tov & Gabriel Escobar, 2014. "When to Use Speedup: An Examination of Service Systems with Returns," Operations Research, INFORMS, vol. 62(2), pages 462-482, April.
    11. Kauhanen, Antti & Kulvik, Martti & Kulvik, Silja & Maijanen, Sirpa & Martikainen, Olli & Ranta, Paula, 2013. "Resource allocation in health care processes: A case study," ETLA Working Papers 10, The Research Institute of the Finnish Economy.

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