2015 Informs Annual Meeting

TA35

INFORMS Philadelphia – 2015

TA34 34-Room 411, Marriott Operations in Emergency Medicine Sponsor: Health Applications Sponsored Session

4 - On a Class of Reflected AR(1) Processes Josh Reed, Associate Professor, NYU, 44 W. 4th St., New York, NY, 10012, United States of America, jreed@stern.nyu.edu, Michel Mandjes, Onno Boxma We study the the recursion Z(n+1) = max(aZ(n) + X(n),0) when X(n) is i.i.d. with distribution the same as the difference of a positive random variable and an independent, exponential random variable. We find the transform of Z(n) and, when |a|<1, we perform a stationary analysis. In heavy-traffic, we show that the process converges to a reflected Ornstein-Uhlenbeck process and the steady-state distribution converges to the distribution of a normal random variable conditioned to be positive. TA33 33-Room 410, Marriott Medical Decision Making in Chronic Disease Screening and Treatment Sponsor: Health Applications Sponsored Session Chair: John Silberholz, PhD Student, MIT, 77 Mass Ave, Bldg E40-130, Cambridge, MA, 02139, United States of America, josilber@mit.edu 1 - An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer Dimitris Bertsimas, Professor, MIT, 77 Massachusetts Ave., We present a data-driven approach for designing new chemotherapy regimens for advanced gastric and breast cancer. Our approach combines (i) construction of a large-scale database of clinical trial results, (ii) statistical modeling to predict outcomes of new drug combinations, and (iii) optimization models to select novel treatments that strike a balance between maximizing patient outcomes (exploitation) and learning new things about treatments that may be useful in the future (exploration). 2 - On Estimating Optimization Model Parameters in Health and Medicine Thomas Trikalinos, Associate Professor, Brown University, thomas_trikalinos@brown.edu Combining information from independent sources (meta-analysis) can increase the likelihood of optimal actions in operational problems. Using as example the optimization of breast cancer screening strategies, I will discuss methods for and implications of synthesizing model parameter estimates from independent studies, while accounting for biases (systematic errors) and nontransferability (differences between the setting specified by the optimization and the settings of the data sources). 3 - A Robust Approach to Designing Cancer Screening Strategies John Silberholz, PhD Student, MIT, 77 Mass Ave, Bldg E40-130, Cambridge, MA, 02139, United States of America, josilber@mit.edu, Dimitris Bertsimas, Thomas Trikalinos Many models have been proposed to evaluate screening strategies for detecting cancer. Though each model for some cancer could be used to identify effective screening strategies, models’ assumptions and structures can vary dramatically, leading to differing conclusions about the most effective strategy. Using robust and stochastic optimization, we identify screening strategies that are effective across multiple models, which could increase confidence in the quality of the identified strategies. 4 - Prioritizing Hepatitis C Treatment in United States Prisons Can Zhang, Georgia Institute of Technology, 499 Northside Cir NW, Apt. 315, Atlanta, GA, 30309, United States of America, czhang2012@gatech.edu, Anthony Bonifonte, Turgay Ayer, Jagpreet Chhatwal, Anne Spaulding Correctional populations, which represent about 30% of the national Hepatitis C virus (HCV) prevalence, offer a great opportunity to control the HCV epidemic. New HCV treatments are very effective but also outrageously expensive. Therefore, prisons are pressed to prioritize treatment decisions for HCV-infected inmates. We propose a mathematical modeling framework for HCV treatment prioritization decisions in prisons and present extensive numerical results based on large datasets from US prisons. Cambridge, MA, 02139, United States of America, dbertsim@mit.edu, Allison O’hair, Stephen Relyea, John Silberholz

Chair: Yu Wang, PhD Student, Indiana University, yw39@indiana.edu Co-Chair: Alex Mills, Assistant Professor, Indiana University, 1309 E. 10th Street, Bloomington, IN, 47405, United States of America, millsaf@indiana.edu 1 - Coordinated Response of Health Care Networks in Mass Casualty Incidents Mercedeh Tariverdi, PhD Student, University of Maryland, mercedeh@umd.edu, Elise Miller-Hooks, Thomas Kirsch, Scott Levin A hybrid analytical-simulation and system-based approach is presented for assessing the benefits of coordinated response of a health care network in a mass casualty incident. The method accounts for incident-related operational disruptions along with other sources of transient system behavior. Critical resource management is included. 2 - An Empirical Study of Patient Discharge Decisions in Emergency Departments Eric Park, Postdoctoral Associate, The University of British Columbia, 2053 Main Mall, Vancouver, BC, V6T1Z2, Canada, eric.park@sauder.ubc.ca, Yichuan Ding, Mahesh Nagarajan We analyze the physician’s patient discharge decision in EDs. We study how inpatient wards play a role as additional resources to the ED in the discharge process. We study over 530,000 patient discharges in five Canadian EDs. 3 - Allocation Models for Cooperation between Ambulance Services Lavanya Marla, Assistant Professor, University of Illinois at Urbana-Champaign, 104 S. Mathews Avenue, 216E, Urbana, IL, 61801, United States of America, lavanyam@illinois.edu We consider a setting where multiple ambulance service providers cooperate to serve a population. Such settings have been observed in the case of large casualties; and in emerging economies where 911-type services compete with existing ad-hoc services. We first demonstrate the opportunity costs due to lack of cooperation. Then we present a game-theoretic framework to model the allocation of ambulances from competing service providers. We conclude with results from a real-world case study. 4 - Surge: Smoothing Usage of Resources is Good for Emergencies Yu Wang, PhD Student, Indiana University, yw39@indiana.edu, Alex Mills, Jonathan Helm Major hospitals often experience demand surges close to or above their capacity. We study the interplay between reactive and proactive surge strategies and their impacts on the hospital’s immediate response and recovery. We find that immediate recourse actions at best sacrifice long-term recovery for short-term capacity improvement, while proactive workload smoothing provides a Pareto- improving response in both short- and long-term operational performance. Panel Discussion: Infusing Learning from Hospitality and Service Design to Healthcare: A Panel Discussion Cluster: Hospitality, Tourism, and Healthcare Invited Session Chair: Rohit Verma, Professor, Cornell University, School of Hotel Administration, 338 Statler Hall, Ithaca, NY, 14853-6902, United States of America, rohit.verma@cornell.edu 1 - Infusing Learning from Hospitality and Service Design to Healthcare: A Panel Discussion Moderator: Rohit Verma, Professor, Cornell University, School of Hotel Administration, 338 Statler Hall, Ithaca, NY, 14853-6902, United States of America, rohit.verma@cornell.edu, Panelists: Craig Froehle, Nitin Joglekar While fundamentally different from each other, the Healthcare and Hospitality industries also share many common characteristics, challenges and constraints. The purpose of this session is to discuss if and how lessons learnt from hospitality can be infused to design better services within the context of healthcare, wellness and senior living. TA35 35-Room 412, Marriott

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