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Budgeting Costs of Nursing in a Hospital

Author

Listed:
  • Edward P. C. Kao

    (Quantitative Management Science Department, University of Houston, Houston, Texas 77004)

  • Maurice Queyranne

    (Faculty of Commerce, University of British Columbia, Vancouver, British Columbia, Canada V6T 1W5)

Abstract

This paper examines issues in building decision support models for budgeting nursing workforce requirements in a hospital. We determine regular-time, overtime, and agency workforce levels for various skill classes in a budget cycle. We introduce a family of eight models ranging from a single-period, aggregate and deterministic model to a multiperiod, disaggregate and probabilistic model. In a single-period model, we ignore the time-varying nature of demand for nursing hours. Aggregation is done over the nurse skill class mix. For probabilistic models, we consider demand uncertainty. Using empirical data, we evaluate the effects of level of sophistication in model building and in information requirements on their relative performances. The results suggest that ignoring the time-varying nature of demand does not induce gross errors in budget estimates. However, ignoring demand uncertainty produces underestimates (about five to six percent) of budget needs---a consequence of a Madansky (Madansky, A. 1960. Inequalities for stochastic linear programming problem. Management Sci. 6 197--204.) inequality. It also induces added costs to the system due to implementing nonoptimal regular-time workforce levels. Finally, we find that a simple formula using a single-period demand estimate gives excellent approximations to the budget estimates obtainable from the more precise models.

Suggested Citation

  • Edward P. C. Kao & Maurice Queyranne, 1985. "Budgeting Costs of Nursing in a Hospital," Management Science, INFORMS, vol. 31(5), pages 608-621, May.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:5:p:608-621
    DOI: 10.1287/mnsc.31.5.608
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    Citations

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    Cited by:

    1. Siyun Yu & Vidyadhar G. Kulkarni & Vinayak Deshpande, 2020. "Appointment Scheduling for a Health Care Facility with Series Patients," Production and Operations Management, Production and Operations Management Society, vol. 29(2), pages 388-409, February.
    2. Gergely Mincsovics & Nico Dellaert, 2010. "Stochastic dynamic nursing service budgeting," Annals of Operations Research, Springer, vol. 178(1), pages 5-21, July.
    3. Cipriano Santos & Tere Gonzalez & Haitao Li & Kay-Yut Chen & Dirk Beyer & Sundaresh Biligi & Qi Feng & Ravindra Kumar & Shelen Jain & Ranga Ramanujam & Alex Zhang, 2013. "HP Enterprise Services Uses Optimization for Resource Planning," Interfaces, INFORMS, vol. 43(2), pages 152-169, April.
    4. P R Harper & N H Powell & J E Williams, 2010. "Modelling the size and skill-mix of hospital nursing teams," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(5), pages 768-779, May.
    5. Ali Kokangul & Serap Akcan & Mufide Narli, 2017. "Optimizing nurse capacity in a teaching hospital neonatal intensive care unit," Health Care Management Science, Springer, vol. 20(2), pages 276-285, June.
    6. Jonathan Bard & David Morton & Yong Wang, 2007. "Workforce planning at USPS mail processing and distribution centers using stochastic optimization," Annals of Operations Research, Springer, vol. 155(1), pages 51-78, November.
    7. Venkataraman, R. & Brusco, M. J., 1996. "An integrated analysis of nurse staffing and scheduling policies," Omega, Elsevier, vol. 24(1), pages 57-71, February.
    8. Wang, Wen-Ya & Gupta, Diwakar & Potthoff, Sandra, 2009. "On evaluating the impact of flexibility enhancing strategies on the performance of nurse schedules," Health Policy, Elsevier, vol. 93(2-3), pages 188-200, December.
    9. Kayse Lee Maass & Boying Liu & Mark S. Daskin & Mary Duck & Zhehui Wang & Rama Mwenesi & Hannah Schapiro, 2017. "Incorporating nurse absenteeism into staffing with demand uncertainty," Health Care Management Science, Springer, vol. 20(1), pages 141-155, March.
    10. Kibaek Kim & Sanjay Mehrotra, 2015. "A Two-Stage Stochastic Integer Programming Approach to Integrated Staffing and Scheduling with Application to Nurse Management," Operations Research, INFORMS, vol. 63(6), pages 1431-1451, December.
    11. repec:dau:papers:123456789/4010 is not listed on IDEAS
    12. Malaki, Saha & Izady, Navid & de Menezes, Lilian M., 2023. "A framework for optimal recruitment of temporary and permanent healthcare workers in highly uncertain environments," European Journal of Operational Research, Elsevier, vol. 308(2), pages 768-781.
    13. Easton, F. F. & Rossin, D. F., 1997. "Overtime schedules for full-time service workers," Omega, Elsevier, vol. 25(3), pages 285-299, June.
    14. G M Campbell, 2011. "A two-stage stochastic program for scheduling and allocating cross-trained workers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1038-1047, June.
    15. Sergey V. Ivanov & Andrey I. Kibzun & Nenad Mladenović & Dragan Urošević, 2019. "Variable neighborhood search for stochastic linear programming problem with quantile criterion," Journal of Global Optimization, Springer, vol. 74(3), pages 549-564, July.
    16. Liping Zhou & Na Geng & Zhibin Jiang & Shan Jiang, 2022. "Integrated Multiresource Capacity Planning and Multitype Patient Scheduling," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 129-149, January.
    17. Douglas S. Altner & Anthony C. Rojas & Leslie D. Servi, 2018. "A two-stage stochastic program for multi-shift, multi-analyst, workforce optimization with multiple on-call options," Journal of Scheduling, Springer, vol. 21(5), pages 517-531, October.
    18. Dellaert, Nico & Jeunet, Jully & Mincsovics, Gergely, 2011. "Budget allocation for permanent and contingent capacity under stochastic demand," International Journal of Production Economics, Elsevier, vol. 131(1), pages 128-138, May.

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