2016 INFORMS Annual Meeting Program

WA22

INFORMS Nashville – 2016

2 - Dialysis Facility Network Design Michael Klein, McGill University, 1001 Sherbrooke Street West, Montreal, QC, H3A 1G5, Canada, michael.klein2@mail.mcgill.ca, Vedat Verter, Brian Moses We study the problem of improving access for patients in a rural area by designing a network of dialysis facilities. We incorporate the possibility of home dialysis for which the patients need to travel to a city centre for training. WA22 107B-MCC Healthcare Analytics Sponsored: Health Applications Sponsored Session Chair: Zahra Gharibi, Southern Methodist University, 3145 Dyer Street, Suite 372, Dallas, TX, 75205, zgharibi@mail.smu.edu 1 - Optimal Care Pathways For Lower Back Pain Patients Danny R Hughes, Harvey L. Neiman Health Policy Institute, Aggressive treatment of lower back pain is frequently cited as low value care that contributes to rising health care costs with limited effects on patient outcomes. In order to identify cost-effective early treatment policies for these patients, we model the physician’s sequential decision problem as a finite-horizon Markov decision process where the boundary condition is defined as the patient completing the episode of care. We compare the results from historical claims data with optimal decisions solved via stochastic dynamic programming to determine desirable initial treatment strategies. 2 - Effect Of Report Cards On Kidney Transplantation Related Decision Making zahra Gharibi, Dept. of Engineering Management, Information, Report card programs collect and publicize information on patient outcomes as a means of improving quality. However, it is unclear whether behavioral responses to such programs improve patient outcomes. We study the report cards as an incentive mechanism to induce socially-optimal medical decisions in the context of kidney transplantation. Using a game theoretic framework, we investigate how performance reporting and flagging for low performance influence acceptance/rejection decisions for offered kidneys and patient selection by transplant centers. We also study the implications of new allocation system for such decisions. 108-MCC Operations Research for Public Health: Data-Driven and Dynamic Decion Making Approaches Sponsored: Health Applications Sponsored Session Chair: Soroush Saghafian, Harvard Univeristy, 79 John F. Kennedy Street, Mailbox 37, Cambridge, MA, 02138, United States, soroush_saghafian@hks.harvard.edu 1 - Impact Of Breast Density And Supplemental Screening Methods On Breast Cancer Screening Policies Mucahit Cevik, University of Wisconsin - Madison, Madison, WI, United States, cevik2@wisc.edu, Burhaneddin Sandikci Mammography screening is the golden standard for breast cancer screening, but it is less accurate for women with dense breasts. Supplemental screening methods are recently introduced to improve detection accuracy. We study the impact of supplemental tests through incorporating breast density information in a partially observable Markov decision process model. 2 - Impact Of Ambiguity on Medications Management Strategies: An Application To NODAT Alireza Boloori, Arizona State University, Tempe, AZ, United States, alireza.boloori@asu.edu, Soroush Saghafian, Harini A. Chakkera, Curtiss B. Cook Patients after organ transplantations receive high amounts of immunosuppressive drugs (e.g., tacrolimus) to reduce the risk of organ rejection. However, this practice has been shown to increase the risk of New-Onset Diabetes After Transplantation (NODAT). We propose an ambiguous POMDP framework to and Systems Bobby B. Lyle School of Engineering, SMU, zgharibi@smu.edu, Mehmet U.S. Ayvaci, Michael Hahsler WA23 Reston, VA, United States, dhughes@neimanhpi.org Danny R Hughes, George Mason University, Fairfax, VA, United States, dhughes@neimanhpi.org, Jeremy Eckhause, Katharina Ley Best

generate effective medication management strategies for tacrolimus and insulin. Our approach increases the patient’s quality of life while reducing the effect of transition probability estimation errors. We also provide several managerial and medical implications for policy makers and physicians. 3 - Robust Dynamic Programming For Medical Decision Making Lauren N. Steimle, University of Michigan, Ann Arbor, MI, United States, steimle@umich.edu, Brian T Denton Markov Decision Processes (MDPs) are useful for studying the management of chronic diseases, which is characterized by a series of treatment decisions under uncertainty about the future progression of the disease. Dynamic programming algorithms can be used to determine the optimal treatment policies for these diseases, but these policies may not be robust to perturbations of the model parameters. We discuss robust dynamic programming algorithms that provide protection against variation in the estimates of MDP model parameters. We present our results in the context of treatment of cardiovascular disease. 4 - Optimal Intervention Strategies For Hypertensive Disorders Of Pregnancy Aysegul Demirtas, Arizona State University, Tempe, AZ, United States, Aysegul.Demirtas@asu.edu, Esma S Gel, Soroush Saghafian, Dean Coonrod Hypertensive disorders of pregnancy (HDP) constitute one of the leading causes of maternal and neonatal mortality and morbidity. We consider the decision problem of timing and mode of child delivery for women with HDP. We formulate a discrete-time Markov decision process (MDP) model that minimizes the risks of maternal and neonatal adverse outcomes, and assess its outcomes with clinical data by performing probabilistic sensitivity analysis. We also build a robust MDP model in which the transition probabilities are contained in a controllable uncertainty set. Our robust MDP approach considers the sensitivity of estimated transition probabilities while avoiding over-conservative policies. 109-MCC Scheduling and Capacity Management in Healthcare Sponsored: Health Applications Sponsored Session Chair: Maya Bam, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI, 48109, United States, mbam@umich.edu 1 - Scheduling Operating Rooms With Elective And Emergency Surgeries Kyung Sung Jung, University of Florida, 364 Stuzin Hall, PO Box 117169, Gainesville, FL, 32611, United States, ksjung@ufl.edu, Michael L Pinedo, Chelliah Sriskandarajah, Vikram Tiwari Hospital accounted for 30% of total health expenditures. Operating rooms (ORs) are typically a bottleneck during the entire processes. We solve multiple-OR scheduling problems with elective and emergency patients. First, we provide general guidelines for these scheduling problems, and then develop several scheduling and rescheduling methods for these patients. 2 - Two-stage Robust Optimization Of Multi-stage Care Planning: Formulation And Computational Challenges Saba Neyshabouri, George Mason University, Fairfax, VA, 22030, United States, sneyshab@gmu.edu, Bjorn Berg We study the problem of scheduling surgeries in a block-booking setting in which both the surgery duration and length-of-stay (LOS) in the surgical intensive care unit are subject to uncertainty. We utilize the theory of robust optimization and propose a novel formulation that captures the complexities in modeling uncertainty in LOS, which is modeled as a discrete random variable. We propose an exact solution approach and perform computational experiments to analyze the quality of the solutions obtained. Computational challenges and future directions are discussed. 3 - Capacity And Flow Management In Emergency Departments With A Fast Track Elham Torabi, University of Cincinnati, 2925 Campus Green Dr., Cincinnati, OH, 45221, United States, torabiem@mail.uc.edu Elham Torabi, University of Cincinnati Medical Center, Cincinnati, OH, United States, torabiem@mail.uc.edu, Craig Froehle, Craig Froehle, Craig Froehle, Christopher Miller In EDs with a fast track, sub-optimal allocation of patients to capacity results in under-utilization of the fast track while main ED area is congested. Using data- mining we identify sub-groups of moderate-acuity patients who can be treated in the fast track instead of the main ED. We use simulation analysis to find routing policies that better allocate the identified patients to the two capacity segments. The proposed routing policy results in less patient waiting and more parity in utilization of the capacity segments. WA24

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