Exploring Data Analytics to Identify Time-Dependent Factors of Emergency Department Crowding

During the period of the COVID-19 pandemic, there is a notable change in the congestion levels of emergency departments (ED). This phenomenon offers an opportunity to study the influence factors of ED crowding. In this work, we crawl real-time information from the ED of major hospitals in Taiwan and conduct data analytics to obtain a comprehensive view of the situation during the COVID-19 pandemic. Note that the data we used contain nonprivate information, avoiding the issue of confidentiality of data. Our goal is to provide valuable information on the appropriate timing of nonemergency patients' visits to the ED and to help nonemergency patients make informed decisions about when to visit the ED, ultimately improving their experience and the overall quality of medical care. The findings of this work have potential applications in developing intelligent systems or mobile applications that could offer valuable insights into optimizing nonemergency patient visits, thereby relieving the ED crowding problem.