A Bayesian Network Approach for Modeling Resident Physician Vigilance

Abstract

Vigilance, the ability to sustain attention, is critical in healthcare, yet resident physicians face significant sleep deprivation, increasing their risk of vigilance decrement and medical errors. This study aimed to develop a predictive model of vigilance in this population using contextual factors, physiological measures, and eye-tracking data. Fifteen resident physicians participated in psychomotor vigilance tests (PVT) under sleep-deprived and non-sleep-deprived conditions, and completed questionnaires assessing sleep, anxiety, and workload. Bayesian Networks (BN) were employed to model vigilance, featuring layers for contextual factors (sleep, anxiety), performance (PVT reaction time), and observable features (eye movement, physiological responses). The three-layered BN integrating both contextual and multi-sensor (eye-tracking and physiological) data demonstrated the best prediction accuracy, compared to BNs with fewer layers and/or only one sensor type. This demonstrates that combining continuous physiological and eye-tracking data with contextual information enhances the prediction of vigilance decrement in resident physicians. This study contributes to the development of predictive tools for mitigating vigilance decrement and the future design of intervention strategies in demanding clinical settings.