emergency medicine community is a system in which ED beds and care teams are segregated and patients are "streamed" based on … streaming is most effective in ED's with (1) a high percentage of admitted patients, (2) longer care times for admitted patients … than discharged patients, (3) a high day-to-day variation in the percentage of admitted patients, (4) long patient boarding …

Hospital Emergency Departments (ED's) typically use triage systems that classify and prioritize patients almost … treatment time between simple and complex patients, and a relatively equal number of simple and complex patients benefit most … high as 25%. (2) Streaming patients based on complexity information and prioritizing them based on urgency is better than …

) researchers. Having timevarying arrivals and heterogeneous patients that need to be treated in consecutive processing steps by … (Jayaprakash et al., 2009) and may cause a myriad of operational problems like patients being treated in hallways, excessive length … of stay (LOS) and wait times, patients leaving without treatment or even medical errors and increased mortality …

for patients admitted from the emergence department (ED) to inpatient wards, bed occupancy rate of the inpatient wards …, and overflow proportions (proportions of patients that are admitted to a non-primary ward). The document also reports …

A major reason behind crowding in emergency departments (ED) is non-urgent patients' visits to ED. In this paper we … investigate how non-urgent ED visits are influenced by patients' imperfect perception of their urgency and their self … where heterogeneous patients with imperfect perceptions choose between an expensive/congested ED and less expensive general …

Boarding patients and the extra workload they introduce are a major concern in emergency departments. Not in the least … because they confront the physicians with a challenging task: prioritizing between boarding patients and patients currently … policies for the physicians when needy boarding patients are added to the analysis. Using discrete-event simulation, three …

primary study period representing peak arrivals, 3.9% (n = 4,038) of patients LWBS. Using the derived model, the predicted … patient arrivals, treatment time, and ED boarding on the rate of patients who LWBS at one institution. More studies are needed …

In the face of high staffing costs, uncertain patient arrivals, and patients unsatisfied with long wait times, staffing … or in teams, and with patients of varying acuity levels. We show how our model helps to balance staffing costs and …