This article provides details on the modeling and validation of a discrete-event simulation study carried out at the emergency department (ED) of a large regional hospital in Belgium. The ED has 21 beds, and a volume of about 30,000 patients per year of which approximately 33% need to be admitted to the hospital. Like many other hospital EDs all over the world (Pines et al., 2011b), the ED we consider in this case study is struggling with a phenomenon called (over)crowding, especially in the late afternoon. Following Moskop et al. (2009), we will consistently use the term "crowding" in this article. While there is no single agreed-upon definition of crowding in the literature, it can be understood in general as "the situation where the demand for emergency services exceeds the ability of an ED to provide quality care within appropriate time frames" (Higginson, 2012). The crowding problem is aggravated by the inability of the ED to transfer patients that need to be admitted to the inpatient wards, due to lack of available inpatient beds. This is referred to as "access block" or "patient boarding" (Crawford et al., 2013; Gilligan et al., 2008; Moskop et al., 2009); by occupying valuable ED space, time, and resources, boarding patients have a negative impact on the length-of-stay (LOS) of patients that still require treatment. The model developed in this article reflects patient boarding using time-dependent boarding times and boarding probabilities, which may vary across patient types and are estimated from real-life data. While, in reality, the boarding behavior is determined by the time-dependent status of beds at the inpatient units, this approach avoids a detailed modeling of these units. Although some articles have applied queueing theory (Bekker & Koeleman, 2011; Bretthauer et al., 2011; Cochran & Roche, 2008; Gallivan & Utley, 2011; Koizumi et al., 2005; Lin et al., 2014; Shi, 2013; Thompson et al., 2009) to settings in which both the ED and the inpatient unit are being considered, it has been recognized in the literature that simulation is often the preferred tool to study ED operations (Saghafian et al., 2014). The blocking or boarding phenomenon in health care has been studied using simulation from the perspective of the inpatient wards (for instance; Bagust et al. 1999; Bountourelis et al. 2011; El-Darzi et al. 1998; Mustafee et al. 2012) or with a focus on the ED (for instance; Bair et al. 2010; Crawford et al. 2014; Khare et al. 2009; Kolb et al. 2007, 2008; Medeiros et al. 2008; Pines et al. 2011a). None of these studies, however, model the ED in much detail. As will be shown, the general dynamic behavior in the ED is triggered by typical patterns and protocols that have been recognized in the literature, and can be acceptably modeled using ED patient record data (thus avoiding detailed data on the inpatient units). Section 2 provides an overview of the available data, inputs, and assumptions used in the simulation model, while Section 3 discusses model validation. Section 4 summarizes the main findings. The model was built using the Arena R simulation software (V.14) by Rockwell Automation
