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who should be examined immediately or within 48
hours; the classification P3 denotes emergency
outpatients who should be examined in 2 to 10 days;
and the classification of P4 denotes general
outpatients. The waiting time for MRI examinations
should not exceed 28 days. However, the average
MRI waiting time for adult outpatients is 59 days
(Health Quality Ontario, 2018). The waiting time in
Manitoba, Canada, is approximately 68 workdays
(Province of Manitoba, 2018). Taiwan currently has
no similar statistics for the waiting time for MRI
examination. However, the waiting time for MRI
examination is still recognized as long.
In the health care management field, to understand
the effects of new policies and new technology
introductions, organizations may need to analyze
relevant economic roles (e.g., competitors and
suppliers), responses from users, and environmental
impact. However, exploring the effectiveness of a
new strategy by traditional techniques has its
limitations. Most of the traditional techniques can
only analyze data theoretically or qualitatively. Even
some new strategies require actual or trial investment
in human and material resources so that managers can
understand the impact and determine whether a
strategy deserves continued investment. To avoid the
losses caused by failure after investment, many
simulation analyses have been applied to ED research
since 1999 (Hurwitz et al., 2014, Saoud et al., 2016).
Additionally, a small number of studies have been
applied to rehabilitation (New et al., 2015),
orthopedics (Rohleder et al., 2011), surgery (Sobolev
et al., 2011), hospitalization (Hahn-Goldberg et al.,
2014), ophthalmology and radiology (Lindsköld et
al., 2012, Viana, 2014). All these studies show that a
good simulation model is adaptable (Paranjape, 2009)
and can be adapted to practice changes as an aid to
the evaluation decision before the new strategy is
adopted.
A patient’s waiting time for an examination in a
radiology department includes the duration from
when a radiology request is made in the clinic, the
radiology department receives the request, the
radiology department vets the request, the radiology
department schedules the examination, to when the
patient attends, waits and completes the examination
(Olisemeke et al., 2014). Retrospective to 1987, a
study simulated the daily non-admission patients
through the radiology department of a large acute care
hospital. This study showed that the addition of one
more radiologist would lead to a reduction in the
length of stay of non-admission patients (Klafehn,
1987).
Later studies focused on radiology department
services, such as mammography (Coelli et al., 2007),
sonography (Johnston et al., 2009), computed
tomography (CT) (Ramakrishnan et al., 2004, van
Lent et al., 2012), and X-ray (Oh et al., 2011,
Lindsköld et al., 2012),. The examination process has
been simulated to explore the relevant key
performance indicators (KPIs) before and after the
improvement plan. These studies assessed the number
of patients examined within one hour, the time of
completing image reports, the time of the patient
waits for the examination, the length of time the
patient stays in the department, and the utilization rate
of the radiologist as effective indicators as to assist in
the formulation and implementation of preplanning
decisions (Ramakrishnan et al., 2004, Coelli et Al.,
2007, Johnston et al., 2009, Oh et al., 2011). The MRI
examination workflow and KPIs are different in
different organizations and units. Additionally, an
MRI scanner cannot provide all examination services.
It depends on if the needed coil types are adopted by
the scanner or not. However, a review of past studies
in radiology, mostly using discrete event simulation
as the main analytical method, the characteristic of
patient, staff, and scanner were lack to control and
define in a simulated workflow. The KPIs of the
individual patient, scanner and the department could
not be accurately estimated.
Hence, the purpose of the study was to propose a
new strategy to maximize patients throughput so that
the waiting time of a patient to undergo a magnetic
MRI examination can be shortened as well as the
utilization rates of MRI scanners can be increased.
The specific aims in this study are (a) to develop a
discrete event (DES) and agent-based simulations
(ABS) model to simulate the MRI examination
workflow at an MRI department in a medical center
of Taiwan, and (b) to identify which time frames of
day have fewer patients examined, to experiment with
the proposed strategy by hiring radiographers in those
time periods in the simulation model, and then to
explore the changes in KPIs, including the daily MRI
scanner utilization rate, monthly gross income, and
waiting time to undergo an MRI examination.
2 RADIOLOGY DEPARTMENT
SETTING
2.1 MRI Examination Services
The research setting was the MRI department, under
the radiology department, of Taipei Veterans General
Hospital (TVGH), which is a 2,735-bed medical
center in northern Taiwan. This department provides
MRI examination services for ED patients, inpatients,
and outpatients. The MRI examination service runs
SIMULTECH 2019 - 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications
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