In this paper, the flow of different classes of patients
into a hospital is modelled and analyzed by using the queueing
network analyzer (QNA) algorithm and discrete event simulation.
Input data for QNA are the rate and variability parameters of the
arrival and service times in addition to the number of servers in each
facility. Patient flows mostly match real flow for a hospital in Egypt.
Based on the analysis of the waiting times, two approaches are
suggested for improving performance: Separating patients into
service groups, and adopting different service policies for sequencing
patients through hospital units. The separation of a specific group of
patients, with higher performance target, to be served separately from
the rest of patients requiring lower performance target, requires the
same capacity while improves performance for the selected group of
patients with higher target. Besides, it is shown that adopting the
shortest processing time and shortest remaining processing time
service policies among other tested policies would results in,
respectively, 11.47% and 13.75% reduction in average waiting time
relative to first come first served policy.