16-01-2014, 12:37 PM
Reducing Wait Times through Operations Research: Optimizing the Use of Surge Capacity
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Abstract
Widespread public demand for improved access, political pressure for shorter wait
times, a stretched workforce, an aging population and overutilized equipment and
facilities challenge healthcare leaders to adopt new management approaches. This
paper highlights the significant benefits that can be achieved by applying operations
research (OR) methods to healthcare management. It shows how queuing theory pro-
vides managers with insights into the causes for excessive wait times and the relation-
ship between wait times and capacity. It provides a case study of the use of several OR
methods, including Markov decision processes, linear programming and simulation,
to optimize the scheduling of patients with multiple priorities. The study shows that
by applying this approach, wait time targets can be attained with the judicious use of
surge capacity in the form of overtime. It concludes with some policy insights.
Why Are There Waits for Access to Healthcare?
Wait times for health services arise because
capacity does not match demand,
capacity or demand is not well managed and
there is significant variability over time in the demand for healthcare services.
By capacity, we mean the maximum rate at which a resource can deliver a service when
operating at peak efficiency (Anupindi et al. 2005). Capacity is controlled through
investment in and scheduling the use of people, physical plant and equipment. Setting
capacity levels entails an unavoidable trade-off between wait times and resource utili-
zation.
When capacity significantly exceeds average demand, queues will be short and
wait times minimal. Unfortunately, because of variability in the demand over time,
resources will be idle a large portion of the time (Figure 1).
When capacity is significantly below average demand, system resources will be
fully utilized, but wait times will be excessive and will grow over time.
Even when capacity equals or slightly exceeds average demand, queuing theory
(Hillier and Lieberman 2001) implies that there will be long waits (in theory, they
will be infinite in the limit). In this case, resources will be utilized most of the time.
The Need for Optimal Scheduling
In 2003, a team of investigators from the University of British Columbia (including
the authors) began a study with the Vancouver Coastal Health Authority aimed at
improving diagnostic imaging processes at several Vancouver hospitals. The team iden-
tified porter services and patient scheduling as promising areas for investigation. As a
result, we reviewed porter services (Odegaard et al. 2007) and sought to develop new
methods to improve patient scheduling (Patrick and Puterman 2007; Patrick et al.
2007). This paper translates the latter research into a decision-making context.
In most healthcare settings, patient scheduling is carried out by schedulers who
must make complex trade-offs in the absence of intelligent software and precise deci-
sion rules to support their decisions.