title: Incorporating nurse absenteeism into staffing with demand uncertainty.
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.
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA. leekayse@umich.edu.
authors.forename: Kayse Lee
authors.initials: KL
authors.lastname: Maass
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Boying
authors.initials: B
authors.lastname: Liu
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Mark S
authors.initials: MS
authors.lastname: Daskin
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.|Program and Operations Analysis, University of Michigan Health System, Ann Arbor, MI, USA.
authors.forename: Mary
authors.initials: M
authors.lastname: Duck
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Zhehui
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.|Program and Operations Analysis, University of Michigan Health System, Ann Arbor, MI, USA.
authors.forename: Rama
authors.initials: R
authors.lastname: Mwenesi
authors.affiliation: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Hannah
authors.initials: H
authors.lastname: Schapiro
mesh_terms: D000041:Absenteeism; D000465:Algorithms; D006301:Health Services Needs and Demand / Q000458:organization & administration / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D009741:Nursing Staff, Hospital / Q000458:organization & administration* / Q000706:statistics & numerical data; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration* / Q000706:statistics & numerical data; D013269:Stochastic Processes; D035501:Uncertainty
keywords: Absenteeism; Genetic Algorithm; Nurse staffing; Stochastic