Internally, staffing offices are chaotic, budgets are exceeded, staffing is frequently misaligned, and dissatisfaction with work schedules abounds. Externally, a growing number of state legislatures are mandating nurse staffing levels. The growing misalignment between supply and demand suggests that the current methods used to project labor demand, and to budget, staff, and schedule nurses have become both increasingly more complex and less effective. The multifaceted staffing problems we now face cannot be solved using historic averages or simple algebraic formulas. Because there are multiple dimensions to each staffing subprocess that interacts with or is somehow impacted by other subprocesses, mathematical models are needed along with the power of computer science to simulate solutions for complex staffing problems.
A growing body of evidence in a wide variety of diverse industries suggests that systematic and scientifically based process improvement that leverages mathematical optimization can produce a myriad of innovative solutions to intractable business problems. Logistics science offers new insight into planning and deploying clinical human capital. Optimal outcomes are possible if there is recognition of the complexity and interconnectedness of the systems and processes involved in workforce planning and deployment. The process of optimizing human capital in a complex environment involves 3 distinct steps: (1) analysis or demand planning, (2) optimizing resources, and (3) executing the optimized model. This final session will outline how optimization modeling works for nurse staffing in order to improve the quality of nursing worklife.