Informs Annual Meeting 2017

MC09

INFORMS Houston – 2017

4 - Hospitalist Service Mix and Patient Length of Stay Masoud Kamalahmadi, Indiana University, 1309 E. 10th Street Room HH 4100, Bloomington, IN, 47405, United States, maskamal@iu.edu, Jonathan Helm, Alex Mills, Kurt M. Bretthauer Hospitalists are physicians that specialize in caring for hospital inpatients, replacing a primary care physician who may only make rounds once per day and thereby reducing delays. Given a limited number of hospitalists in a hospital, we seek to determine their optimal service mix (workload and patient types).

2 - Stochastic Optimization Models for the Active Surveillance of the Prostate Cancer Zheng Zhang, University of Michigan, 2361 Lancashire Drive, Apt 1B, Ann Arbor, MI, 48105, United States, zzhang0409@gmail.com, Brian T.Denton, Christine Barnett, Todd Morgan Active surveillance (AS) has become a popular management option among favorable-risk prostate cancer patients to avoid unnecessary surgeries or radiations. We describe a stochastic programming model for optimization of AS strategies that allow for personalized patient feathers, unobservable health states, and stochastic cancer progression. The results show our model solutions outperformed the benchmark strategies in terms of maximizing the quality-adjust life years. 3 - Optimal Decision Making in Risk Reduction for Women at High Risk of Breast Cancer Mehmet Ali Ergun, University of Wisconsin-Madison, Madison, WI, United States, mergun@wisc.edu, Murtuza Rampurwala, Oguzhan Alagoz Breast cancer (BC) is the most common non-skin cancer and a leading cause of death among U.S. female population. Studies provide estimates on the individual risk of a woman as well as finding interventions to reduce it for the high-risk patients. On the other hand, the guidelines tying individual risk assessments to risk reduction interventions are not as rigorously studied and not as detailed. To fill this gap, we have developed finite-horizon Markov Decision Process (MDP) model that finds optimal preventative actions given the individual risk when the objective is to maximize quality-adjusted life years of the patients. 330A Multi-faceted Supply Chains Sponsored: Manufacturing & Service Oper Mgmt Sponsored Session Chair: Rodney P. Parker, Indiana University, Bloomington, IN, 47405, United States, rodp@indiana.edu 1 - Negative Shocks and Innovation Incentives: Evidence from Medical Device Recalls Medical device firms seek to operate at the frontiers of innovation. When functioning properly, innovative medical devices can prolong and improve lives; when malfunctioning, the same devices may harm patients and lead to product recalls. Consequently, medical device firms’ innovation efforts may be influenced by innovation failures. Using 13 years of FDA product recall and new product submission data, we explore the impact of recalls on subsequent innovation. We utilize a recurrent-event hazard model to examine how recalls of varying severity influence the hazard of different types of new product submissions by firms. The results identify a relationship between recalls and future innovation. 2 - Social Responsibility Auditing of Supply Chain Networks Han Zhang, Indiana University, Bloomington, IN, 47404, United States, hz8@indiana.edu, Goker Aydin, Rodney P.Parker We study a buyer’s problem of auditing its supply network for social responsibility concerns, when the suppliers’ identities and relations further upstream are initially invisible, and only discernible via costly auditing. The buyer may suffer economic damages if an upstream supplier, even if unknown to the buyer, is exposed as unethical. We characterize the equilibrium in the production stage, and study the buyer’s auditing strategies before the production stage. 3 - Supply Chain Strategy: Beyond Fisher’s Framework Tava Olsen, University of Auckland, ISOM, Business School, University of Auckland, Auckland, 1142, New Zealand, t.olsen@auckland.ac.nz, Quan Spring Zhou Devising an effective supply chain strategy is essential for exporters. Fisher’s framework provides exporters some guidance on this and has also been extended in a number of ways. In this talk we first discuss what is known (mathematically, empirically, and qualitatively) about applying Fisher’s framework in practice. Then we discuss our own empirical analysis based on New Zealand data, where we investigate the practice of using efficient, responsive, and, potentially, ambidextrous supply chain strategies and their associated impacts. MC09 George Ball, Indiana University, Kelley School of Business, 1309 E. 10th St, Bloomington, IN, 47405, United States, gpball@indiana.edu, Jeffrey Macher, Ariel Stern

MC07

322A Real-time Models for Epidemiological Risk Assessment Sponsored: Health Applications Sponsored Session Chair: Lauren Gardner, University of New South Wales, l.gardner@unsw.edu.au 1 - Influenza Phylogeography for Public Health Surveillance Matthew Scotch, Arizona State University, Tempe, AZ, United States, Matthew.Scotch@asu.edu Next-generated sequencing has resulted in a tremendous growth in the amount of virus sequences such as influenza. Phylogeography, which estimates the evolutionary diffusion of viruses, is potentially a valuable technique for utilizing this influx of sequence data. Unfortunately, most health agencies lack the bioinformatics expertise to implement these techniques. Here, we will highlight ZooPhy, a bioinformatics resource for virus phylogeography, via case studies of seasonal and avian influenza. We will also address potential issues with providing a “black box” model for informing policy decisions at the agency level as part of critical analyses of the system. 2 - Modeling HIV Transmissions Across At-risk Populations Lu Zhong, City University of Hong Kong, Kowloon, Hong Kong, luzhong2-c@my.cityu.edu.hk, Qingpeng Zhang We proposed a 2-layer social network framework to model the HIV transmissions across two at-risk populations, and evaluated the effectiveness of a set of structural intervention strategies. Experiments demonstrated the important role played by bridge individuals across layers. Isolating such nodes could segregate at- risk populations for a better prevention outcome. 3 - A Generalized Inverse Infection Model for Inferring the Spread of the 2015-16 Zika Outbreak Lauren Gardner, University of New South Wales, Sydney, Australia, l.gardner@unsw.edu.au, Andras Bota Most network-based infection spreading and diffusion models require a real value or (transmission) probability on the edges of the network as an input, which is often unknown in real-life applications. This work presents a general framework to estimate the value of these probabilities on a network exposed to an infection process, where spatiotemporal information on the outbreak pattern is known. The model is applied to the 2015-16 Zika outbreak in the Americas. This general model works with a range of infection models, and is able to handle an arbitrary number of observations on such processes. 322B Stochastic Models in Healthcare Sponsored: Health Applications Sponsored Session Chair: Oguzhan Alagoz, University of Wisconsin-Madison, Madison, WI, 53706, United States, alagoz@engr.wisc.edu Co-Chair: Ali Hajjar, University of Wisconsin-Madison, Madison, WI, 53706, United States, hjaar@wisc.edu 1 - An Explicit Stochastic Model of ICU Length of Stay Mehmet Yasin Ulukus, University of Pittsburgh, 3700 O’Hara Street, 1048 Benedum Hall, Pittsburgh, PA, 15261, United States, myu1@pitt.edu, Andrew J.Schaefer, Guodong Pang, Gilles Clermont There have been numerous attempts to predict ICU LOS. Statistical models, are commonly used to make predictions of the LOS but almost none consider time progression of the health status of patients. In this study, we present an explicit stochastic ICU LOS model by (1) considering the health progression of the patients via a novel score process, (2) modeling the transfer decision via a threshold policy, and (3) considering the downstream bed availability and transfer delay dynamics. We characterize LOS for different stochastic score processes, and test the estimation power with empirical data. MC08

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