Several studies have shown a strong association between nurse staffing and patient outcomes. When a nursing unit is chronically short-staffed, nurses must maintain an intense pace to ensure that patients receive timely care. Over time this can result in nurse burnout, as well as dissatisfied patients and even medical errors. Improved accuracy in the allocation of nursing staff can mitigate these operational risks and improve patient outcomes. Nursing care is identified as the single biggest factor in both the cost of hospital care and patient satisfaction. 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. Nurse shortage implications go beyond healthcare quality, extending to health economics as well. In addition, implementation of mandatory nurse-to-patient ratios in some states creates a risk of under- or over-estimating required nurse resources. With this motivation, this dissertation aims to develop methodologies that generate feasible six-week nurse schedules and efficiently assign nurses from various profiles to these schedules while controlling staffing costs and understaffing ratios in the medical unit. First, we develop and test various medium-term staff allocation approaches using mixed-integer optimization and compare their performance with respect to a hypothetical full information scenario. Second, using stochastic integer programming approach, we develop a short-term staffing level adjustment model under a sizable list of patient admission scenarios. We begin by providing an overview of the organization of the dissertation. (Abstract shortened by ProQuest.)
|Advisor:||Ross, Anthony D.|
|Commitee:||Kuzu, Kaan, Peoples, James H., Smunt, Timothy L.|
|School:||The University of Wisconsin - Milwaukee|
|School Location:||United States -- Wisconsin|
|Source:||DAI-A 79/02(E), Dissertation Abstracts International|
|Subjects:||Business administration, Management, Operations research|
|Keywords:||Healthcare operations, Nurse scheduling, Nurse staffing, Personnel scheduling, Stochastic optimization, Stochastic programming|
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