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Flexible nurse staffing based on hourly bed census predictions

Author

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  • Kortbeek, N.
  • Braaksma, A.
  • Burger, C.A.J.
  • Bakker, P.J.M.
  • Boucherie, R.J.

Abstract

Workloads in nursing wards depend highly on patient arrivals and lengths of stay, both of which are inherently variable. Predicting these workloads and staffing nurses accordingly are essential for guaranteeing quality of care in a cost-effective manner. This paper introduces a stochastic method that uses hourly census predictions to derive efficient nurse staffing policies. The generic analytic approach minimizes staffing levels while satisfying so-called nurse-to-patient ratios. In particular, we explore the potential of flexible staffing policies that allow hospitals to dynamically respond to their fluctuating patient population by employing float nurses. The method is applied to a case study of the surgical inpatient clinic of the Academic Medical Center Amsterdam (AMC). This case study demonstrates the method׳s potential to evaluate the complex interaction between staffing requirements and several interrelated planning issues such as case mix, care unit partitioning and size, as well as surgical block planning. Inspired by the quantitative results, the AMC concluded that implementing this flexible nurse staffing methodology will be incorporated in the redesign of the inpatient care operations in the upcoming years.

Suggested Citation

  • Kortbeek, N. & Braaksma, A. & Burger, C.A.J. & Bakker, P.J.M. & Boucherie, R.J., 2015. "Flexible nurse staffing based on hourly bed census predictions," International Journal of Production Economics, Elsevier, vol. 161(C), pages 167-180.
    • Handle: RePEc:eee:proeco:v:161:y:2015:i:c:p:167-180
    DOI: 10.1016/j.ijpe.2014.12.007
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    References listed on IDEAS

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    2. Fügener, Andreas & Pahr, Alexander & Brunner, Jens O., 2018. "Mid-term nurse rostering considering cross-training effects," International Journal of Production Economics, Elsevier, vol. 196(C), pages 176-187.
    3. Di Martinelly, Christine & Meskens, Nadine, 2017. "A bi-objective integrated approach to building surgical teams and nurse schedule rosters to maximise surgical team affinities and minimise nurses' idle time," International Journal of Production Economics, Elsevier, vol. 191(C), pages 323-334.
    4. Ana Batista & Jorge Vera & David Pozo, 2020. "Multi-objective admission planning problem: a two-stage stochastic approach," Health Care Management Science, Springer, vol. 23(1), pages 51-65, March.
    5. Bekker, René & uit het Broek, Michiel & Koole, Ger, 2023. "Modeling COVID-19 hospital admissions and occupancy in the Netherlands," European Journal of Operational Research, Elsevier, vol. 304(1), pages 207-218.
    6. Carina Fagefors & Björn Lantz, 2021. "Application of Portfolio Theory to Healthcare Capacity Management," IJERPH, MDPI, vol. 18(2), pages 1-9, January.
    7. Carina Fagefors & Björn Lantz & Peter Rosén, 2020. "Creating Short-Term Volume Flexibility in Healthcare Capacity Management," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    8. Debora Sarno & Maria Elena Nenni, 2016. "Daily nurse requirements planning based on simulation of patient flows," Flexible Services and Manufacturing Journal, Springer, vol. 28(3), pages 526-549, September.

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