Abstract for: Modeling the Impact of Staff Turnover on Productivity of the Screening System in Critical Care Settings

Staff turnover poses a significant challenge to the productivity of screening systems in critical care settings, affecting the timely identification and management of patients. This study develops a system dynamics simulation model to examine the impacts of turnover on a screening system performance, measured by approaching and screening rates. Through group model building sessions, we mapped the system structure, identified key feedback loops, and incorporated stakeholder perspectives to enhance model validity. The model was then calibrated using data obtained from a screening system in a Children Hospital. Then, it was used to test multiple interventions, including turnover-induced hiring, goal-oriented hiring, endogenous goal-oriented hiring, hiring staff who can gain experience faster, combined hiring strategies, retention policies, and work engagement initiatives. The findings reveal that while turnover-induced hiring alone leads to performance declines due to an increased proportion of inexperienced staff, strategies such as goal-oriented hiring, recruiting staff who can gain experience faster, retention policies, and enhancing work engagement effectively mitigate turnover-related disruptions. These insights provide evidence-based recommendations for healthcare administrators to enhance staffing strategies, ensuring sustainable workforce management and improved screening system efficiency in critical care settings. ChatGPT was used to improve language and enhance readability of the text.