2015 Informs Annual Meeting

TB34

INFORMS Philadelphia – 2015

2 - Contact Center Qualifying Transfer Rate Modeling and Analysis Jie Yu, Operations Research Scientist, University of Phoenix, 3137 E Elwood St, Phoenix, AZ, 85034, United States of America, jie.yu@phoenix.edu, Roger Gung, Lin Wang Impact analysis on transferring marketing inquiries to qualifying leads for potential enrollments and the performance of contact center agents are crucial to contact center and enrollment operations. An impact analysis was conducted on transfer rate with drivers including speed to lead, lead source, time of request as well as program level. A mixed effect logistic regression model was built to rank agents’ performance in terms of expected transfer rate with given marketing inquiries. The model was also employed to evaluate the impact of reducing contact center and enrollment operating hours. 3 - Enrollment Service Contact Strategy Optimization Pan Hu, Operations Research Scientist, University of Phoenix, 3137 E Elwood St, Phoenix, AZ, 85034, United States of America, pan.hu@phoenix.edu, Yun Ouyang, Jie Yu, Lin Wang, Roger Gung This project is to study how contact behaviors of enrollment representatives influence enrollment progression of higher education pursuers. To better serve the needs of potential students, it is critical to communicate effectively by bringing up right topics in the best timing. We examined a list of conversation topics suggested in the internal guideline of University of Phoenix for enrollment representatives, and identified the best contact strategy using statistical models. TB33 33-Room 410, Marriott Joint Session HAS/MSOM-Healthcare: Modeling Applications for Emergency Departments Sponsor: Health Applications Sponsored Session Chair: Sean Barnes, University of Maryland, 4352 Van Munching Hall, University of Maryland, College Park, MD, 20742, United States of America, sbarnes@rhsmith.umd.edu 1 - Review of Queueing Theory Applied to Emergency Departments with Comparable Simulation Studies Queueing Theory (QT) is an important tool for Emergency Department (ED) design and management. By reviewing all papers with ED QT analysis or applications since 1972, this survey examines the contributions of QT to modeling EDs and identify its benefits and limitations when compared to discrete-event simulation (DES) under similar ED operational settings. Our results indicate that the combination of queueing and DES methods can be a powerful approach to better ED modelling. 2 - Using Simulation to Assess the Impact of an Observation Unit in a Pediatric Emergency Department Mark Grum, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI, 48109, United States of America, mgrum@umich.edu, Gabriel Zayas-Caban, Michelle Macy, Allison Cator, Amy Cohn Observation units (OUs) provide an alternative disposition decision for ED patients who may benefit from further observation, such as those are not ill enough to be admitted, but not well enough to be discharged. Patients can be placed in an OU for monitoring, diagnostic evaluation, and/or treatment prior to disposition. In this talk, we discuss our approaches (e.g. simulation) for assessing the impact of an OU in the Pediatric ED at the University of Michigan. 3 - Operational Causes of Patients Leaving Before Treatment is Completed in Emergency Departments David Anderson, Assistant Professor, Baruch, davidryberganderson@gmail.com, Bruce Golden, Edward Wasil, Laura Pimentel, Jon Mark Hirshon Patients leaving before treatment (LBTC) is completed is an indicator of poor Emergency Department performance. Contrary to previous research, volume is not the main driver of patients leaving before treatment is complete. First provider time and lengths of treatment are much more strongly associated with LBTC rate. We show that operational factors such as treatment time and staffing decisions play a role in waiting time and, thus, in determining the LBTC rate. Summer (Xia) Hu, PhD Student, University of Maryland, Department of Mathematics, College Park, United States of America, xhu64@umd.edu, Sean Barnes, Bruce Golden

4 - Strategies for Ebola Containment: A Biological-behavioral- operational Modeling Decision Framework Eva Lee, Georgia Tech, Atlanta, GA, eva.lee@gatech.edu This work is joint with CDC. We present a computational decision modeling framework that integrates an agent-based biological disease spread model, a dynamic network-based social-behavior model that captures human behavior and interaction, and a stochastic queueing model that describes treatment characteristics, day-to-day hospital and homecare processes, and resource usage. An optimization engine determines the minimum resource needed to contain the Ebola epidemic in W. Africa. TB34 34-Room 411, Marriott Data-driven Modeling and Analysis of Health Care Systems Sponsor: Health Applications Sponsored Session Chair: Anil Aswani, UC Berkeley, 4141 Etcheverry Hall, Berkeley, CA, 94720-1777, United States of America, aaswani@berkeley.edu 1 - Constructing Behavioral Models for Personalized Weight Loss Interventions using Integer Programming Yonatan Mintz, Graduate Student, UC Berkeley, 1822 Francisco St., Apt. 10, Berkeley, CA, 94703, United States of America, ymintz@berkeley.edu, Philip Kaminsky, Yoshimi Fukuoka, Anil Aswani, Elena Flowers In this paper we describe two (a machine learning and a utility maximization) models for weight loss using clinical trial data. We believe these quantitative models of behavior change can be used to provide personalized interventions, improve adherence and lower costs of current weight loss programs. Given the high prevalence of obesity, these results provide significant insight into more effective approaches to implement weight loss programs. 2 - Modeling Treatment Adherence Behavior in the Treatment of Obstructive Sleep Apnea Yuncheol Kang, Pennsylvania State University, 236 Leonhard Building, State College, 16801, United States of America, kang.yuncheol@gmail.com, Paul Griffin, Vittal Prabhu, Amy Sawyer We target patients who suffered from Obstructive Sleep Apnea (OSA) and their treatment behaviors when using Continuous Positive Airway Pressure (CPAP) devices. We model underlying dynamics and patterns of patient treatment behavior using Markov models as a basis for designing effective and economical intervention. Also we suggest a guideline for designing a cost-effective Auyon Siddiq, UC Berkeley, 4141 Etcheverry Hall, University of California, Berkeley, Berkeley, 94720, United States of America, auyon.siddiq@berkeley.edu, Zuo-jun Max Shen, Anil Aswani We present an approach for inverse parametric optimization with noisy solution data for convex forward problems. The proposed method yields well-behaved estimates that attain risk consistency or parameter estimation consistency under reasonable conditions. While the formulation is non-convex in general, we provide an approximation algorithm that yields consistent estimates for a class of quadratic programs. Numerical results show competitive performance with state- of-the-art techniques. 4 - Quantifying the Resilience of Hospital Unit Management under High Workloads Mo Zhou, PhD Student, UC-Berkeley, 4470 Etcheverry Hall, Berkeley, CA, 94709, United States of America, mzhou@berkeley.edu, Anne Miller, Anil Aswani, Jason Slagle, Daniel France Hospital unit shifts with high admissions/discharges (ADTs) and low nurse-to- patient ratios (NPRs) increase mortality. Nurse managers promote unit resilience, and we quantify this using time series and network analysis of hourly phone calls, ADTs, and NPRs over 2 years from an Intensive Care Unit. Statistical variable selection assessed variable dependency, and time-series estimation demonstrated the validity of phone calls as a resilience measure. Future studies will elucidate adaptive limits. intervention to economically treat the patients. 3 - Inverse Optimization with Noisy Data

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