2016 INFORMS Annual Meeting Program

434 / 561

INFORMS Nashville – 2016
434
4 - Optimizing Admission And Discharge Decisions In Icu With
Flexible Bed Allocation
Xuanjing Li, Tsinghua University, Room 519A,Shunde
Building,Tsinghua Univ, Beijing, Beijing, 100084, China,
, Dacheng Liu, Xiaolei Xie, Ye Wang
This paper studies the admission and premature discharge policy in the intensive
care unit (ICU) at Peking University Third Hospital. Patients are classified into two
categories based on their survival benefit and discharge cost. A Markov Decision
Process (MDP) model is established to strike balance between those two factors.
Structural properties are obtained and a new admission policy is proposed.
WC22
107B-MCC
Appointment Scheduling Models and Analytics
Sponsored: Health Applications
Sponsored Session
Chair: Nan Liu, Columbia University, 722 W. 168th. St, New York, NY,
10032, United States,
Co-Chair: Zhankun Sun, Eyes High postdoctoral scholar, University of
Calgary, 2500 University Dr. NW, Calgary, AB, T2N 1N4, Canada,
1 - Improving Patient Satisfaction: Customizing Patient Appointment
Yutian Li, University of Miami, 421 Jenkins Building, 5250
University Drive, Coral Gables, FL, 33124, United States,
Joseph Johnson, Yu Tang
In this paper, we develop a Bayesian logit model which improves no-show
prediction accuracy over the widely-used simple logit model. The accuracy gain
arises from the individual patient-level coefficients provided by the Bayesian
approach. Comparison of model fit on 12-months of appointment data shows the
Bayesian model outperforms the simple logit model. In simulation studies, our
results show that applying Bayesian model’s prediction to scheduling algorithm
can reduce patient’s waiting time and physician’s idle time and increase clinic’s
profit.
2 - Managing Appointment-based Services In The Presence Of
Walk-in Customers
Shan Wang, Shanghai Jiao Tong University, 1954 Huashan Road,
Shanghai, 200030, China, , Nan Liu,
Guohua Wan
Walk-in customers are accepted in many service industries and especially in
healthcare. Motivated by practice in and data collected from two large community
health care networks in New York City, we study how to coordinate scheduled
patients in a clinic session in anticipation for random walk-ins. We use a Poisson
regression framework to analyze the temporal pattern of walk-in patients based
on 3-year data, and propose data-driven optimization models to identify the
optimal appointment schedule. Our models can incorporate other practical
aspects of appointment scheduling such as patient no-shows, patient preferences
and restricted walk-in windows.
3 - Physician Scheduling To Improve Patient Flow Through
Emergency Rooms
Farzad Zaerpour, PhD Candidate, Haskayne School of Business,
University of Calgary, Calgary, AB, T3A 2E1, Canada,
, Zhankun Sun,
Marco Bijvank
Emergency department (ED) crowding has become a serious concern worldwide.
Hours of waiting is the main consequence of crowding in emergency
departments. In this study, we develop a mixed-integer stochastic program for
scheduling physicians to improve patient flow through an emergency department.
The operational performance of an emergency department is vulnerable to
mismatch between demand and supply. Therefore, the proposed model takes into
account the stochastic natures of both demand and supply. We use physician
productivity to evaluate the performance of each physician in the emergency
department.
4 - When Waiting To See A Doctor Is Less Irritating:
Understanding Patient Preferences And Choice Behavior In
Appointment Scheduling
Nan Liu, Columbia University, 722 W. 168th St., Room 476,
New York, NY, 10032, United States, ,
Stacey Finkelstein, Margaret Kruk, David Rosenthal
This talk examines patient preferences and choice behavior in scheduling medical
appointments. We conduct four discrete choice experiments on two distinct
populations and identify several operational attributes that affect patient choice.
We observe an interesting gender effect with respect to how patients tradeoff
speed (delay to care) vs. quality (doctor of choice), and demonstrate that risk-
attitudes mediate the impact of gender. As many operational strategies aim to
improve patient experience by making tradeoffs between speed and quality, we
make suggestions for when managers should intervene and how such
interventions might look based on the patient mix and current delay level.
WC23
108-MCC
Optimization in Radiation Therapy Treatment
Planning
Sponsored: Health Applications
Sponsored Session
Chair: Victor Wu, University of Michigan, 1205 Beal Avenue,
Ann Arbor, MI, 48109, United States,
1 - Deriving Imrt Treatment Plans From Dvh Curves
Aaron Babier, University of Toronto, Toronto, ON, Canada,
, Justin James Boutilier,
Andrea McNiven, Michael Sharpe, Timothy Chan
Plan quality is often assessed using dose volume histograms (DVHs), which are a
high level representation of a dose distribution. Clinical quality DVHs can be
accurately predicted, however their corresponding treatment plans are more
challenging to determine. We present an inverse optimization model that can
produce treatment plans from DVH curves, with minimum treatment complexity.
The model is applied to several clinical head and neck treatment plans, and the
outcomes are compared to their corresponding clinical plans.
2 - Evaluation Of Multi-source Treatments For Prostate
Brachytherapy Optimized Using An Interior Point Constraint
Generation Algorithm
Dionne Aleman, University of Toronto, Toronto, ON, Canada,
, Rachel Mok Tsze Chung, William Song
A novel approach to treat prostate cancer using multi-source high-dose-rate
brachytherapy is investigated. The effectiveness of different combinations of the
radionuclides 192Ir, 60Co, and 169Yb is analyzed. We use an inverse planning
interior point algorithm to generate treatment plans for every possible
combination of the three sources, and then compare treatment quality to the
192Ir plan. Overall, for the same target coverage, double- and triple-source plans
provided better organ-at-risk sparing than the 192Ir plan.
3 - Threshold-driven Optimization For Reference-based
Auto-planning
Troy Long, University of Texas Southwestern, Dallas, TX, United
States, , Steve Jiang, Mingli Chen,
Weiguo Lu
We study the procedure of reference-based auto-planning for treatment plan
optimization. We develop a threshold-driven optimization methodology for
automatically generating an intensity-modulated radiation therapy treatment
plan that is motivated by a reference dose-volume histogram. The commonly
used voxel-based quadratic penalty objective functions have three components:
an overdose weight, and underdose weight, and some target dose threshold. The
proposed methodology directly relates reference information to threshold values,
which influence the optimization in an effective, intuitive way.
4 - Optimal Fractionation With Two Modalities
Sevnaz Nourollahi, University of Washington Seattle,
We introduce an optimal fractionation problem with two modalities. This involves
finding the number of treatment sessions and the dose per session administered
via each modality. The goal is to maximize the biological effect of such bimodal
treatment on the tumor while keeping the toxic effects on nearby normal tissue
within tolerable limits. We formulate this problem as a nonconvex quadratically
constrained quadratic program. We show that the KKT conditions for this
problem reduce to solving a quartic equation. We are thus able to provide an
analytical solution to the KKT system. We study properties of the resulting
solutions via numerical experiments.
WC22