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The Optimal Workforce Staffing Solutions With Random Patient Demand in Healthcare Settings
Abstract
Staffing planning is paramount for cost-efficient workforce management. An accurate assessment of the required staffing level for the specific time period is an integral part of the hospital budgeting and planning process. Daily fluctuations of patient census create staffing planning challenges to many organizations. There is a growing trend for hospitals to use data analytics for determining the optimal staffing solutions. The dynamic nature of the staffing process creates two types of issues: (1) overstaffing vs. the planned budgeted level, which hurts operations margins; or (2) understaffing, which requires costly overtime and/or premium pay that also hurts margins and causes substandard quality of care. The goal of this chapter is providing an overview and examples of application of the methodology called the “newsvendor” framework. This methodology helps developing the optimal nursing and other skill mix staffing solutions that minimize the total cost of over- and understaffing occurrences within the specified time period for the units with random patient census fluctuations.
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