Impact of announced wait time information on emergency department overcrowding mitigation: a simulation study.

Abstract

Despite widespread implementation of predicted patient wait time information systems in hospital emergency departments (EDs), the relationship between quality of announced wait time information and ED overcrowding mitigation remains unclear. This study investigates how prediction accuracy, update frequency, and patient adoption rates affect ED overcrowding level. A data-calibrated simulation model was developed using patient visit records from three metropolitan EDs in Hong Kong. We systematically varied patient adoption rates and evaluated seven wait time prediction methods across four update frequencies. Key performance metrics included the mean and standard deviation of patient wait times and percentage of patients left without being seen (LWBS rate). Accurate prediction methods combined with frequent updates significantly reduced the mean and standard deviation of patient wait times and LWBS rate as patient adoption rate increased. Conversely, inaccurate prediction methods exhibited a U-shaped performance curve. Specifically, when the patient adoption rate was sufficiently high, these methods significantly increased the mean and standard deviation of wait times and LWBS rate, compared to the case with no predicted wait time. Implementing information systems to display predicted patient wait times requires carefully balancing prediction accuracy, update frequency, and patient adoption. Accurate and timely updates can help redistribute patient load across hospital networks and improve efficiency, while poor accuracy or infrequent updates risk worsening ED congestion, especially when patient adoption rate is high. Our study calls for immediate attention from ED managers to carefully evaluate the impact of announced wait time system before wide implementation.