Incorporating nurse absenteeism into staffing with demand uncertainty

Kayse Lee Maass (), Boying Liu (), Mark S. Daskin (), Mary Duck (), Zhehui Wang (), Rama Mwenesi () and Hannah Schapiro ()
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Kayse Lee Maass: University of Michigan
Boying Liu: University of Michigan
Mark S. Daskin: University of Michigan
Mary Duck: University of Michigan
Zhehui Wang: University of Michigan
Rama Mwenesi: University of Michigan
Hannah Schapiro: University of Michigan

Health Care Management Science, 2017, vol. 20, issue 1, No 10, 155 pages

Abstract: Abstract Increased nurse-to-patient ratios are associated negatively with increased costs and positively with improved patient care and reduced nurse burnout rates. Thus, it is critical from a cost, patient safety, and nurse satisfaction perspective that nurses be utilized efficiently and effectively. To address this, we propose a stochastic programming formulation for nurse staffing that accounts for variability in the patient census and nurse absenteeism, day-to-day correlations among the patient census levels, and costs associated with three different classes of nursing personnel: unit, pool, and temporary nurses. The decisions to be made include: how many unit nurses to employ, how large a pool of cross-trained nurses to maintain, how to allocate the pool nurses on a daily basis, and how many temporary nurses to utilize daily. A genetic algorithm is developed to solve the resulting model. Preliminary results using data from a large university hospital suggest that the proposed model can save a four-unit pool hundreds of thousands of dollars annually as opposed to the crude heuristics the hospital currently employs.

Keywords: Nurse staffing; Stochastic; Genetic Algorithm; Absenteeism (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10729-015-9345-z

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