Multi-objective Optimization for Determining Nursing Staff Demand in Nursing Home

Abstract

Due to the need for improving the utilization of existing nursing staff and better sharing their workload, nursing staff demand modelling (NSDM) plays a critical role. Nevertheless, NSDM relies on human experience and often leads to ineffective planning. In previous studies, various critical factors affecting NSDM were commonly ignored. In this study, the formulation of a multi-objective optimization model for the planning of optimal size and mix of nursing teams in nursing homes is described. Critical factors, such as heterogeneous workforce and resident case mix, are considered. A multi-objective evolutionary algorithm, namely Non-dominated Sorting Genetic Algorithm II is applied to solve the proposed model. A case study of a subvented nursing home in Hong Kong was conducted to illustrate the effectiveness of the proposed model and algorithm. Results showed that the total overtime work (in hours) were minimized while the nurse-to-resident ratio were significantly higher within the given budgetary boundary. The model permits for the study of changing overtime work and nursing staff demand as a consequence of changes in resident case mix and resident service requirement. Further, results suggest that it is cost beneficial to introduce temporary staff in the workforce and to increase the degree of skill mix for NSDM.