50 ISE Magazine | www.iise.org/ISEmagazine
Inside IISE Journals
Modeling the surgery schedulers dilemma:
Will the patient require surgery
and how long will it take?
Patients who undergo surgery often have a clinical appointment
with the surgeon to identify whether the procedure is appropri-
ate for them. If the surgeon and the patient mutually decide to
proceed with surgery during the clinical appointment, the sur-
gery is performed on a later date by the same surgeon.
To accommodate patients’ scheduling preferences and to mit-
igate against long delays between the decision and the surgery
itself, the scheduler is in a dilemma. One does not know ahead
of time if patients will need surgery after their clinical appoint-
ments. If the surgery does occur, there is still significant uncer-
tainty about its duration. Hence, how can the scheduler at that
point of time provide the patient both a clinical and a surgical
appointment while ensuring patient safety and not wasting the
surgeons’ time?
In their work, “A Distributionally Robust Optimization Ap-
proach for Coordinating Clinical and Surgical Appointments,
Ankit Bansal, assistant professor at the University of West Vir-
ginia, Bjorn Berg, assistant professor at the University of Min-
nesota, and Yu-Li Huang, assistant professor at the Mayo Clinic,
propose an optimization-based framework to help the scheduler
resolve this dilemma. They model the uncertainty faced by the
scheduler using distributionally robust optimization (DRO) and
present a mixed-integer linear program to efficiently coordinate
the clinical and surgical schedules of the surgeons while ensur-
ing patient safety. While the methods used to solve DRO prob-
lems are often complex, the analysis in their article also provides
an intuitive interpretation of DRO-based scheduling policies.
The authors demonstrated
the effectiveness of their ap-
proach through a case study
of the transcatheter aortic
valve replacement procedure
at the Mayo Clinic in Roch-
ester, Minnesota. In com-
parison to the current practice
and four heuristic scheduling
policies, they show that the
DRO-based scheduling poli-
cy resulted in lower total surgeon idle-time and overtime per
day, and efcient utilization of clinic capacity of the surgeons.
Their results helped improve the coordination of both types of
appointments for the procedure. In addition, the authors note
the applicability of their methods to other healthcare delivery
settings where patients may require multiple stages of care.
CONTACT: Ankit Bansal; anki.bansal@mail.wvu.edu; (304) 293-9436; 333 En-
gineering Science Building, Department of Industrial and Management Systems
Engineering, West Virginia University, P.O. Box 6107, Morgantown, WV 26506
This month we highlight two articles in IISE Transactions. The first looks into a healthcare scheduling problem.
The authors proposed a distributionally robust optimization approach to efciently coordinate the clinical and
surgical schedules of the surgeons while ensuring patient safety. They demonstrated the effectiveness of the
approach though a collaboration with Mayo Clinic on the study of transcatheter aortic valve replacement procedure.
The second article asks the question: Do traffic network patterns impact trafc safety and what is the practical
implication? The authors found that taxi zones positioned in the shortest paths of the mobility network tend to
have a lower number of accidents, whereas taxi zones having a large number of connections were linked with a
higher number of vehicle crash incidences. Their model and findings can potentially help cities and ride-sharing
companies to devise appropriate policies or incentives for enhancing traffic safety. These articles will appear in the
December 2021 issue of IISE Transactions (Volume 53, No. 12).
Ankit Bansal Bjorn Berg
Yu-Li Huang
November 2021 | ISE Magazine 51
Network centralities are linked
to trafc safety in smart cities
Large investments have been made by cities such as Singapore,
New York City and London toward smart city initiatives in the
areas of traffic safety enhancement and higher mobility. The in-
vestments in smart city initiatives are expected to reach $158
billion globally in 2022. Most of these investments are focused
on providing higher traffic safety and higher mobility services.
So what are the factors that impact traffic safety in a city? Is
it congestion on the roads? Do trafc network patterns impact
traffic safety? Understanding the impact of trafc network pat-
terns on vehicle crashes can serve to enhance road safety in a
smart city.
Not all regions are at the same risk for a vehicle crash. It is
therefore important to gain insights to which regions of the traf-
fic network face vehicle crashes, particularly from the point of
view of a city’s policymakers. Are regions with higher values of
network centralities (indicators of node importance) in traffic
networks at a greater risk of vehicle crashes? Are the effects of
such indicators on vehicle crashes robust with time?
In their work, “Do the Mobility Patterns for Urban Taxicabs
Impact Road Safety?” Satyam Mukherjee, associate professor at
Shiv Nadar University, Greater Noida (previously employed in
Indian Institute of Management Udaipur), and Tarun Jain, as-
sociate professor in Indian Institute of Management Bangalore,
took full advantage of large datasets of taxi trips and motor ve-
hicle collisions in New York City, which are available in the site
of NYC OpenData, and estimated the impact of various struc-
tural aspects of traffic networks on vehicle crashes. The authors
created a traffic network of origin-destination pairs (OD pairs)
of vehicular traffic in the form of pick-up and drop-off pairs.
Using certain econometric methods, the authors demonstrat-
ed that taxi zones positioned in the shortest paths of the mobility
network (i.e., high betweenness centrality) tend to have a lower
number of accidents. Furthermore, their work also showed that
the taxi zones having large number of connections (i.e., high
degree centrality) were linked with a higher number of vehicle
crash incidences.
Their empirical approach revealed some crucial insights
for smart city policymakers and operations managers of ride-
sharing companies on how to leverage the information on the
mobility patterns of the high accident risk regions to ameliorate
traffic safety.
CONTACT: Satyam Mukherjee; satyam.mukherjee@snu.edu.in; Shiv Nadar
University, Greater Noida; Tarun Jain; tarun.jain@iimb.ac.in; Indian Institute of
Management Bangalore
Human factors considerations
in ambulance-based communications
for treating stroke victims
Emergency departments and first responders are taking ad-
vantage of our increased access to wireless technology to
improve patient care en route to the hospital. Stroke care in
particular benefits from the more effective communication
between the ambulance and the on-site healthcare profes-
sionals it provides.
Past research investigating the effectiveness of telemed-
icine in ambulance stroke care has found significant im-
provement in patient hospital stay, treatment rates, reduced
door-to-needle time and survival. Yet few studies have fo-
cused on the telemedicine technology itself or the work
environment where these technologies are implemented.
Hunter Rogers, Ph.D., a former doctoral student at
Clemson University currently employed at the Wright Pat-
terson Air Force Research Lab, and Kapil Chalil Madathil,
Ph.D., the Tiencken Endowed Associate Professor at the
university, in collaboration with colleagues Anjali Joseph,
Ph.D., and Nathan McNeese, Ph.D., at Clemson, Chris-
tine Holmstedt, M.D., and James McElligott, M.D., at the
Medical University of South Carolina and Richard Holden,
Ph.D., from the Indiana University School of Public Health,
examined the ambulance-based telemedicine program and
the work system as a whole using a comprehensive set of
This month we highlight two articles from IISE
Transactions on Healthcare Systems Engineering
(Volume 11, No. 3). The first addresses the need
for effective communications and on-site health
professionals in caring for stroke victims. The
team interacted with the telemedicine interface and
the caregivers in various roles in the system and
conducted a heuristic evaluation of the telemedicine
interface to determine usability issues and potential
for user errors. In the second article, a team of
researchers developed a statistics-based optimization
model to optimize personal adaptive treatment
strategies for chronic pain management. The research
will influence how interdisciplinary pain management
is implemented.
Satyam Mukherjee Tarun Jain
52 ISE Magazine | www.iise.org/ISEmagazine
task and usability analy-
ses. The findings from this
work, “Task, Usability and
Error Analyses of Ambu-
lance-based Telemedicine
for Stroke Care,” were pub-
lished in a special issue of
IISE Transactions on Health-
care Systems Engineering fo-
cusing on Smart Healthcare
Services and Technology-
Driven Healthcare Systems.
The team interacted with the telemedicine interface and
the caregivers in various roles in the system, including neu-
rologists, paramedics and nurses, conducting a task analysis
to determine the flow in the care process using telemedi-
cine. Based on this analysis, the team subsequently con-
ducted a heuristic evaluation of the telemedicine interface
and used the systematic human error reduction and predic-
tion approach to determine usability issues and potential
for user errors. These analyses determined that there are
design issues in the interface and the telemedicine system as
a whole potentially contributing to an adverse event.
Using these findings, the research team developed rec-
ommendations for improving the task flow, information
retrieval, data formatting and communication among the
healthcare team. This study has the potential to have a sig-
nicant impact on stroke care, important because strokes
currently result in one death every four minutes in the
United States.
CONTACT: Hunter Rogers, Research General Engineer; hunter.rog-
ers.1@us.af.mil; (256) 529-5300; Air Force Research Laboratory,
Wright-Patterson Air Force Base, OH 45433; Kapil Chalil Madathil,
Wilfred P. Tiencken Endowed Associate Professor; kmadath@clemson.
edu; (864) 656-0856; College of Engineering, Computing and Applied
Sciences, Departments of Civil and Industrial Engineering, Clemson
University, Clemson, SC 29634
Recommending treatment for patients
to help them manage their chronic pain
Pain is a major health problem for many people, and pain
management is currently innovating because of the opi-
oid crisis in the United States. Everyone experiences pain
at various times and to varying degrees, both short-term
and long-term. Short-term pain that lasts a maximum six
months is also known as acute pain. If acute pain is not ap-
propriately treated, it can persist and become chronic.
In “Multi-Objective Two-Stage Stochastic Program-
ming for Adaptive Interdisciplinary Pain Management
with Piecewise Linear Network Transition Models,” Gazi
Md Daud Iqbal, Ph.D., of Coppin State University, Jay
Rosenberger, Ph.D., Victoria Chen, Ph.D., and Robert
Gatchel, Ph.D., of The University of Texas at Arlington,
and Carl Noe, M.D., of the Eugene McDermott Center for
Pain Management at the University of Texas Southwestern
Medical Center developed a statistics-based optimization
model to optimize personal adaptive treatment strategies
for chronic pain management.
The data set used in this research is from the center. Be-
cause the center uses a prevalent and standardized dataset,
the research will be applicable to approximately 100 pain
centers across the nation and will influence how interdisci-
plinary pain management is implemented. In this research,
the authors used five different commonly used pain out-
comes to identify pain intensity. Pain management experts
submitted surveys to compare different levels of different
pain outcomes. Since the center administers an interdisci-
plinary two-stage pain management program, the authors
used a two-stage stochastic optimization approach to ac-
count for uncertainty in treatment recommendations.
Anjali Joseph Nathan McNeeseHunter Rogers Kapil Chalil Madathil
Dr. James McElligottRichard Holden Dr. Christine Holmstedt
November 2021 | ISE Magazine 53
All the experimental set-
tings are based upon conver-
sations and surveys of phy-
sicians and domain experts.
The optimization model
yields treatment recommen-
dations within 10 minutes.
Because of this reasonable
solution time, physicians
can execute the model after
a patient receives a pretreat-
ment evaluation and will be
able to review the models
treatment recommendations
during the patient’s appoint-
ment. The model will also assist physicians with treatment
recommendations in subsequent midtreatment appointments
as well.
CONTACT: Gazi Md Daud Iqbal; giqbal@coppin.edu; (410) 951-3487;
College of Business, Coppin State University, 2500 W North Avenue,
Baltimore, MD 21216
Yu Ding is the Mike and Sugar Barnes Professor of Industrial
and Systems Engineering at Texas A&M University and Associ-
ate Director for Research Engagement at the Texas A&M Institute
of Data Science. He is editor-in-chief of IISE Transactions and
a fellow of IISE.
Oguzhan Alagoz is a professor in the Department of Industrial and
Systems Engineering at the University of Wisconsin-Madison. He
is editor-in-chief of IISE Transactions on Healthcare Systems
Engineering and a fellow of IISE.
IISE Transactions (link.iise.org/iisetransactions) is IISE’s flagship
research journal and is published monthly. It aims to foster
exchange among researchers and practitioners in the industrial
engineering community by publishing papers that are grounded
in science and mathematics and motivated by engineering
IISE Transactions on Healthcare Systems Engineering (link.iise.org/
iisetransactions_healthcare) is a quarterly, refereed journal that
publishes papers about the application of industrial engineering
tools and techniques to healthcare systems.
To subscribe, call (800) 494-0460.
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