| dc.description.abstract | Although healthcare operations management has been an active and popular research
direction over the past few years, there is a lack of formal quantitative models to analyze
the ambulance o oad delay problem. O oad delays occur when an ambulance arriving at
a hospital Emergency Department (ED) is forced to remain in front of the ED until a bed
is available for the patient. Thus, the ambulance and the paramedic team are responsible
to care for the patient until a bed becomes available inside the ED. But it is not as simple
as waiting for a bed, as EDs also admit patients based on acuity levels. While the main
cause of this problem is the lack of capacity to treat patients inside the EDs, Emergency
Medical Services (EMS) coverage and availability are signi cantly a ected. In this thesis,
we develop three network queueing models to analyze the o oad delay problem. In order
to capture the main cause of those delays, we construct queueing network models that
include both EMS and EDs. In addition, we consider patients arriving to the EDs by
themselves (walk-in patients) since they consume ED capacity as well.
In the rst model, ED capacity is modeled as the combination of bed, nurse, and
doctor. If a patient with higher acuity level arrives to the ED, the current patient's
service is interrupted. Thus, the service discipline at the EDs is preemptive resume. We
also assume that the time the ambulance needs to reach the patient, upload him into the
ambulance, and transfer him to the ED (transit time) is negligible. We develop e cient
algorithms to construct the model Markov chain and solve for its steady state probability
distribution using Matrix Analytic Methods. Moreover, we derive di erent performance
measures to evaluate the system performance under di erent settings in terms of the
number of beds at each ED, Length Of Stay (LOS) of patients at an ED, and the number
of ambulances available to serve a region. Although capacity limitations and increasing
demand are the main drivers for this problem, our computational analysis show that
ambulance dispatching decisions have a substantial impact on the total o oad delays
incurred.
In the second model, the number of beds at each ED is used to model the service
capacity. As a result of this modeling approach, the service discipline of patients is
assumed to be nonpreemptive priority. We also assume that transit times of ambulances
are negligible. To analyze the queueing network, we develop a novel algorithm to construct
the system Markov chain by de ning a layer for each ED in a region. We combine the
Markov chain layers based on the fact that regional EDs are only connected by the number
of available ambulances to serve the region. Using Matrix Analytic Methods, we nd the
limiting probabilities and use the results to derive di erent system performance measures.
Since each ED's patients are included in the model simultaneously, we solve only for small
instances with our current computational resources.
In the third model, we decompose the regional network into multiple single EDs. We
also assume that patients arriving by ambulances have higher nonpreemptive priority
discipline over walk-in patients. Unlike the rst two models, we assume that transit
times of ambulances are exponentially distributed. To analyze the decomposed queueing
network performance, we construct a Markov chain and solve for its limiting probabilities
using Matrix Analytic Methods. While the main objective for the rst two models is
performance evaluation, in this model we optimize the steady state dispatching decisions
for ambulance patients. To achieve this goal, we develop an approximation scheme for the
expected o oad delays and expected waiting times of patients. Computational analysis
conducted suggest that larger EDs should be loaded more heavily in order to keep the
total o oad delays at minimal levels. | en |
