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

Area of Science:

• Healthcare Management
• Nursing Workforce Optimization
• Predictive Analytics in Healthcare

Background:

• Nurse absences increase costs through agency use or overtime.
• A properly sized nursing resource team (NRT) can mitigate these costs.
• Optimizing NRT size is crucial for minimizing overall expenditure.

Purpose of the Study:

• To determine the optimal size and skill composition of a nursing resource team (NRT).
• To minimize the costs associated with filling registered nurse (RN) absences.

Main Methods:

• Utilized registered nurse (RN) absence data from an academic teaching hospital (2014-2018).
• Developed a generalized additive model (GAM) to forecast weekly absence frequencies for ten types.
• Employed an optimization model incorporating forecasts to define the cost-minimizing NRT composition.

Main Results:

• Forecasts for ten absence types showed median weekly frequencies between 12 and 65.5.
• Generalized additive models (GAMs) achieved root mean squared errors from 4.55 to 9.07 on test data.
• The optimized NRT design projected an annual cost saving of $277,683 (7% reduction).

Conclusions:

• Accurate forecasting of registered nurse (RN) absences is achievable.
• Using forecasting and optimization models to size NRTs substantially reduces the cost of covering absences.
• This adaptable methodology enables hospitals to optimize nurse staffing levels.