Case Article—Developing Data-Driven 24/7 Nurse Staffing and Shift Scheduling Plans

This case study offers a comprehensive hands-on approach to addressing hospital workforce planning challenges by integrating demand forecasting errors into mathematical programming. Structured around two interconnected projects—refined over a decade of experience—students develop data-driven 24/7 nurse staffing and shift scheduling plans that ensure continuous coverage while balancing cost, service quality, operational constraints, and nurse satisfaction. By employing time-series models to predict hourly patient volumes and integrating these forecasts into integer programming models, the case demonstrates how to construct 24/7 coverage matrices and incorporate auxiliary variables that allow for controlled deviations in staffing levels and service delays due to forecasting errors. Emphasizing multiobjective optimization and Pareto frontier analysis, the case effectively evaluates tradeoffs between overstaffing and understaffing. Designed for both undergraduate and graduate courses in healthcare management science or operations research, this case bridges theoretical concepts with real-world applications, thereby enhancing educators’ ability to deliver effective decision-making training in healthcare operations. Funding: This work was supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant CMMI-0928936]. Supplemental Material: The Teaching Note and supplemental material are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .