50 ISE Magazine | www.iise.org/ISEmagazine

Inside IISE Journals

research

Modeling the surgery scheduler’s 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 efﬁcient 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 efﬁciently 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 trafﬁc 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 trafﬁc 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 trafﬁc network patterns impact

trafﬁc safety? Understanding the impact of trafﬁc 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-

niﬁcant 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.

research

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 model’s

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

applications.

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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