2015 Informs Annual Meeting

TD36

INFORMS Philadelphia – 2015

TD34 34-Room 411, Marriott Joint Session HAS/MSOM-Healthcare: Operational Issues and Information Sharing in Healthcare Sponsor: Health Applications Sponsored Session Chair: Subodha Kumar, Carol And G. David Van Houten, Jr. ‘71 Professor, Mays Business School, Texas A&M University, Wehner 301F - 4217 TAMU, College Station, TX, 77843, United States of America, skumar@mays.tamu.edu 1 - Sustainability Planning for Healthcare Information Exchanges Tharanga Rajapakshe, Assistant Professor, University of Florida, W. University Ave, Gainesville, FL, 32611, United States of America, tharanga@ufl.edu We develop an analytical framework to study sustainability of Healthcare Information Exchanges and to demonstrate its use when the revenue is generated (i) under one revenue model (like membership fee), and (ii) under a combination of multiple revenue models (like membership fee and rebate structure for the practice from supporting vendors). 2 - The Impacts of Healthcare Information Exchanges: An Empirical Investigation Emre Demirezen, Assistant Professor, Binghamton University SUNY, 4400 Vestal Parkway East, AA-242, Binghamton, NY, 13902, United States of America, edemirezen@binghamton.edu, Subodha Kumar, Ramkumar Janakiraman In the last decade, the U.S. government has been aggressively promoting the use of electronic health records and the establishment of regional healthcare information exchanges (HIEs). HIEs facilitate electronic health information exchange among healthcare providers that is considered to be beneficial for the society. However, the real benefits of HIEs are not well understood. Hence, in this study, we work with an HIE provider based in the state of New York to investigate the benefits of HIEs. 3 - Chance Constrained Operating Room Scheduling with Uncertain and Ambiguous Information Zheng Zhang, University of Michigan, 1205 Beal Ave, Ann Arbor, MI, 48105, United States of America, zzhang0409@gmail.com, Brian Denton, Xiaolan Xie We describe stochastic programming and distributionally robust optimization models that allow for uncertain or ambiguous surgery duration data, respectively. Each of the models considers surgery-to-OR allocation decisions in the context of probabilistic constraints on completion time that vary by OR. We describe column generation approaches that are tailored to these two model formulations. Results are presented to illustrate the potential use of these models in practice. 4 - Bundled Payments for Healthcare Services: A Framework for the Healthcare Provider Selection Problem Seokjun Youn, PhD Student, Research Asistant, Mays Business School, Texas A&M University, 320R Wehner Building,, 4217 Texas A&M University, College Station, TX, 77843, United States of America, syoun@mays.tamu.edu, Chelliah Sriskandarajah, Subodha Kumar Identifying competitive healthcare providers is an important issue for the successful operation of bundled payments. We develop a selection framework via data envelopment analysis and combinatorial auction (CA). Based on efficiency and effectiveness measures, outstanding performers are pre-selected. Finally, CA determines winners. To evaluate the impact of design issues on the CA performance, we combine and utilize several real dataset from the healthcare sector. TD35 35-Room 412, Marriott Disaster and Emergency Management II Contributed Session Chair: Shaligram Pokharel, Professor, Qatar University, Doha, Qatar, shaligram@qu.edu.qa 1 - Solution Methodologies for Debris Removal in Disaster Response Bahar Y. Kara, Associate Professor, Bilkent University, Industrial Engineering, Ankara, Turkey, bkara@bilkent.edu.tr, Oya E. Karasan, Nihal Berktas In this study we provide solution methodologies for debris removal problem in the response phase. Debris removal activities on certain blocked arcs have to be scheduled in order to reach a set of critical nodes such as schools and hospitals. Two mathematical models are developed with different objectives. The models are tested over real data from districts of Istanbul.

2 - Service-based Distribution Network Model for Location of Temporary Relief Facilities Shaligram Pokharel, Professor, Qatar University, Doha, Qatar, shaligram@qu.edu.qa, Rojee Pradhananga, Fatih Mutlu, Jose Holguin-Veras A supply allocation and distribution model is proposed to minimize the total waiting times at the demand points by considering the possibilities of transferring excess resources between the temporary facilities and backordering of demand in different time periods. Model is applied on a test instance to analyze the service and cost trade-offs. (This research is made possible by a NPRP Award NPRP 5-200- 5-027 from the Qatar National Research Fund (a member of the Qatar Foundation). The statements herein are solely the responsibility of the authors.) 3 - The Network Structure: What it Can Tell About Disaster Warning Effectiveness? Xiangyang Guan, University of Washington, 201 More Hall, Box 352700, Seattle, WA, 98195-2700, United States of America, guanxy@uw.edu, Cynthia Chen Knowing how public awareness and action change after disaster warning is critical for effectiveness of warning issuance. Multiple data sources – social media, taxi trips and subway ridership – are leveraged. Temporal evolutions of the structure (motifs) of social media network and subway network are established as measures of public awareness and action. Our result identified a lag of one day in average between warning issuance and public awareness, and between public awareness and action. Chair: Laura Mclay, Associate Professor, University of Wisconsin, 1513 University Ave, ISYE Department, Madison, WI, 53706, United States of America, lmclay@wisc.edu 1 - Predicting the Spatial Distribution of Heart Attack Incidence in Alberta Armann Ingolfsson, University of Alberta, Edmonton, Canada, aingolfs@ualberta.ca, Amir Rastpour, Reidar Hagtvedt We use Poisson regression with a linear-without-intercept link function to predict the incidence of heart attacks by geographic region, as a function of age, gender, education, and income characteristics. We discuss model specification and validation and we present results based on 10 years of empirical data. 2 - Dynamic Ambulance Allocation Utilizing Demand Data Analytics for Pre-hospital EMS Yu-Ching Lee, Assistant Professor, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu, Taiwan - ROC, yclee@ie.nthu.edu.tw, Albert Chen, Yu-shih Chen Pre-hospital Emergency Medical Services (EMS) provide the critical function of on-site medical treatment and stabilization of patients. The quality of EMS affects the survival of patients in emergency situations. A better management of ambulances could potentially improve the effectiveness and efficiency of EMS. We study a real-time decision support system featuring demand prediction, distribution estimation, scenario generation, robust ambulance deployment, and the optimal ambulance dispatching 3 - A Simulation Optimization Method for Emergency Service Location Rozhin Doroudi, PhD Student, Northeastern University, 260 Washington St. Apt. 305, Malden, MA, 02148, United States of America, doroudi.r@husky.neu.edu, Gerald Evans, Gail Depuy A fire department with an available fleet wants to determine the location of its fire stations and how to distribute its fleet among those locations. An iterative approach involving the use of a linear program and a simulation model is proposed for the problem TD36 36-Room 413, Marriott Fire and Emergency Medical Services Sponsor: Public Sector OR Sponsored Session

357