Date of Award
8-2016
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Legacy Department
Industrial Engineering
Committee Member
Dr. Kevin Taaffe, Committee Chair
Committee Member
Dr. David Neyens
Committee Member
Dr. Sandra Eksioglu
Committee Member
Dr. Lawrence Fredendall
Abstract
Staff scheduling in healthcare organizations is very challenging compared to manufacturing settings. Hospitals typically operate 24 hours a day, 7 days a week, and are faced with high fluctuations in demand. Surgical patient volume directly impacts workload, and it can be difficult to manage workload fluctuations when planning staff schedules weeks in advance. We have used time series analysis methods to predict daily surgical case volume and, subsequently, assign nurses to shifts while ensuring that the case volume receives sufficient coverage. We developed a seasonal Autoregressive Integrated Moving Average (ARIMA) model to forecast daily patient volumes at least a month in advance. This information was used to create a scenario-based demand to solve our staff scheduling problem. We evaluated our model using data from a Level 1 Trauma Center in the Southeast U.S. With several years of daily surgical case volumes as input, we employ a seasonal ARIMA (SARIMA) model to generate short-term forecasts of future surgical case volumes. The SARIMA model gives an absolute mean percentage error (MAPE) of less than 8% for up to four weeks prior to day of surgery (and only 9% when considering a 3-month forecasting interval). The forecasts outperform the basic hospital prediction by 46%. In particular, the results suggest that the proposed SARIMA model can be useful for estimating case volumes 2-4 weeks prior to the day of surgery, when managers are needing to set a reliable schedule for their staff. The nurse scheduling problem (NSP) in this research is focused on finding the best assignment of nurses to working shifts. We deal with a fixed workforce size with mixed contract types, full-time and part-time. We consider both a risk-neutral as well as a risk-averse approach to find a feasible nurse assignment that minimizes expected labor costs, the costs of highly overstaffed or understaffed situations, or both. An innovative nurse scheduling formulation using conditional value-at-risk (CVaR) is developed to deal with risky staffing situations. The liabilities of overstaffing and understaffing are many. Overstaffing increases payrolls and results in excessive idle times, while understaffing may negatively impact patient safety and health outcomes and may result in loss of revenue. Our approach allows the scheduler to pick whether they want to schedule according to a risk-neutral or a risk-averse policy.
Recommended Citation
Zinouri, Nazanin, "Improving Healthcare Resource Management through Demand Prediction and Staff Scheduling" (2016). All Dissertations. 1682.
https://open.clemson.edu/all_dissertations/1682