Background—Research published in Circulation has shown that cardiac mortality is highest
during December and January. We investigated whether some of this spike could be …

The ability to predict patient visits to emergency departments (ED) is crucial for designing
strategies aimed at avoiding overcrowding. A good working knowledge of the mathematical …

Background: Shifts in the supply of and demand for emergency department (ED) resources
make the efficient allocation of ED resources increasingly important. Forecasting is a vital …

Objective To develop and validate models to predict emergency department (ED)
presentations and hospital admissions for time and day of the year. Methods Initial model …

STUDY OBJECTIVE: The goals of this investigation were to study the temporal relationships
between the demands for key resources in the emergency department (ED) and the …

The pandemic outbreak of coronavirus disease 2019 (COVID-19) affects health care
systems globally and leads to other challenges besides infection and its direct medical …

Abstract Emergency Departments (EDs) are the most overcrowded places in public
hospitals. Machine learning can support decisions on effective ED resource management by …

This is a sequel to Whitt and Zhang (2017), in which we developed an aggregate stochastic
model of an emergency department (ED) based on the publicly available data from the large …

Objective: To develop a prediction equation for the number of patients seeking urgent care.
Methods: In the first phase, daily patient volume from February 1998 to January 1999 was …

We studied the association between calendar and weather variables and daily unscheduled
patient volume in a walk-in clinic and emergency department. Calendar variables (season …