  Over the past several years, across the globe, there has been an increase in
people seeking care in emergency departments (EDs). ED resources, including
nurse staffing, are strained by such increases in patient volume. Accurate
forecasting of incoming patient volume in emergency departments (ED) is crucial
for efficient utilization and allocation of ED resources. Working with a
suburban ED in the Pacific Northwest, we developed a tool powered by machine
learning models, to forecast ED arrivals and ED patient volume to assist
end-users, such as ED nurses, in resource allocation. In this paper, we discuss
the results from our predictive models, the challenges, and the learnings from
users' experiences with the tool in active clinical deployment in a real world
setting.
