Nursing resource team capacity planning using forecasting and optimization methods: a case study

AIM: To estimate the cost-minimizing size and skill mix of a nursing resource team (NRT). BACKGROUND: Nurse absences can be filled by an NRT at lower hourly cost than staffing agencies or nurses working overtime, but an NRT must be appropriately sized to minimize total cost. METHODS: Using all registered nurse (RN) absences at an academic teaching hospital from 1 October 2014 to 31 March 2018, we developed a generalized additive model (GAM) to forecast the weekly frequency of each of ten types of absence over 52 weeks. We used the forecasts in an optimization model to determine the cost-minimizing NRT composition. RESULTS: The median weekly frequencies for the ten absence types ranged between 12 and 65.5. The root mean squared errors of the GAMs ranged between 4.55 and 9.07 on test data. The NRT dimensioned by the optimization model yields an estimated annual cost reduction of $277,683 (Canadian dollars) (7%). CONCLUSIONS: The frequency of RN absences in a hospital can be forecasted with high accuracy, and the use of forecasting and optimization to dimension an NRT can substantially reduce the cost of filling RN absences. IMPLICATIONS FOR NURSING MANAGEMENT: This methodology can be adapted by any hospital to optimize nurse staffing.