Redesign of a University Hospital Preanesthesia Evaluation Clinic Using a Queuing Theory Approach

Abstract

Queuing theory provides robust methods to evaluate alternative designs for the organization of PACs. In this article, we show that queuing modeling is an adequate approach for redesigning processes in PACs.

Integrated decision making in healthcare : an operations research and management science perspective

“…Performance is typically measured by total visit time, waiting time, and queue length. Methods: computer simulation [97,187,264,348,454], queueing theory [264,535].…”

“…However, implementation of walk-in/advanced access can provide significant benefits to patient access time, doctor idle time and doctor overtime, when the probability of patients not showing up is relatively large [390,419]. A proper balance between traditional appointment planning and walk-in/advanced access further decreases access times and increases utilization [417,535]. The specification of such a balanced design will be discussed below.…”

“…Methods: computer simulation [12,178,333,390,417,496], heuristics [333], Markov processes [390], queueing theory [419,535].…”

“…References differ in the extent in which various aspects are incorporated in the applied models. Frequently modeled aspects that influence the performance of an appointment schedule are patient punctuality [178,323,518], patients not showing up ('no-shows') [178,179,255,283], walk-in patients or urgent patients [12,178,417,535], doctor lateness at the start of a consultation session [178,179,332,421], doctor interruptions (e.g., by comfort breaks or administration) [179,323], and the variance of consultation duration [254]. These factors can be taken into account when modeling the following key decisions that together design an appointment schedule.…”

“…Often, unscheduled patients arrive in varying volumes during the day and during the week. When an appropriate number of slots is reserved for unscheduled patients, and appointments are scheduled at moments that the expected unscheduled demand is low, patient waiting times decrease and resource utilization increases [380,417,535]. In the online operational level of this section, we discuss referring unscheduled patients to a future appointment slot when the facility is overcrowded.…”