System Dynamics Modeling for Nurse Reallocation Strategy to Respond to Emerging Infectious Diseases

This study aims to analyze the feedback structure among the predictors of nurse reallocation reassignment strategies within healthcare facilities during an infectious disease outbreak using system dynamics research methodology. Additionally, a simulation model was established to conduct simulations of scenarios based on compensation level, effectiveness of nurse education, and impact of infectious disease spread, thereby providing foundational data for policy and decision-making regarding nurse reallocation strategies.Based on feedback loops, four models (nursing job system, patients with infection, effectiveness of nurse educational interventions, and nurse compensation level) were developed and validated. Finally, scenario simulations were run with compensation adjustment, effectiveness of nurse education, and impact of infectious disease spread adjusted as policy variables to observe the results and significant dynamic behaviors of the objective variables, namely, Level2RN retention rate, retention intention of nurses, total number of nurses on the medical surgical floor, effectiveness of education per individual, and workload in infection care. The dynamic behaviors were analyzed on the basis of the feedback structures and archetypes.In this study, a suitable predictive model was developed through strategic scenario simulations for nurse reallocation in secondary hospitals in response to emerging infectious disease outbreaks. This model enables the prediction of the dynamic behaviors of nurse reallocation strategies and their key variables over time. Additionally, the study provided foundational data for nurse staffing policies through additional simulations, with adjustments for relevant variables. The conclusion provides some research implications and future research direction.