2016 INFORMS Annual Meeting Program

WD21

INFORMS Nashville – 2016

WD21 107A-MCC Predictive Modeling for Healthcare Applications Sponsored: Health Applications Sponsored Session Chair: Ozgur M Araz, University of Nebraska-Lincoln, CBA 260 1240 R Street, P.O. Box 880491, Lincoln, NE, 68588-0491, United States, oaraz2@unl.edu 1 - Predicting Hospital Admissions From Emergency Department Ozgur M Araz, University of Nebraska-Lincoln, oaraz2@unl.edu Emergency departments (EDs) are critical for healthcare services delivery and coordination between EDs and inpatient units are essential for higher quality of service in hospitals. In this study, we are investigating the predictive factors of hospital admissions from the emergency department (ED) in order to inform resource capacity planning for the ED boarding process. We used ED visits data which include demographic and administrative variables from a major hospital in Omaha Metro area and performed analyses using several predictive models, e.g., logistic regression and artificial neural network, to predict admissions from the ED. The predictive accuracy of these models are discussed. 2 - Seasonal Forecasting For Infectious Disease From Multiple Data Sources Zeynep Ertem, University of Texas at Austin, zeynepsertem@gmail.com, Lauren Meyers, Kai Liu, Ravi Srinivasan Epidemics of contagious diseases may cause widespread loss in terms of mortality, morbidity, and economic burden. There have been several studies about forecasting focusing on one data source. However, there can be other more data sources that are correlated with an important data source. I will show a problem formulation of forecasting a target data source related to an epidemic using multiple other data sources. I will also present preliminary results for forecasting when no data is available for the target data source in the current season. Furthermore, I will relax this assumption and show results for forecasting when partial data is available for the target data source in the current season. 3 - Psa Screening For Prostate Cancer: A Dynamic Feedback Model To Understand Long Term Trends In Population Screening Ozge Karanfil, Massachusetts Institute of Technology, Sloan School of Management, Cambridge, MA, United States, karanfil@mit.edu, Hazhir Rahmandad, Jack Homer, John D Sterman Practice guidelines for routine screening have changed significantly over time and often not followed, with significant over-screening for some tests and under- screening for others. In this study we develop a behaviorally realistic simulation model to explore reasons of this phenomenon. The model is firmly grounded in empirical evidence through collection of quantitative and qualitative data. Our formal theory includes a decision theoretic core around costs and benefits, cognitive and social feedbacks. The model can be used as a guide to understand future effects of policy scenarios and be tested in a systematic fashion to find ways to overcome the policy resistance seen in population screening. 4 - A Queueing Model For Nurse Staffing In Critical Care Outreach Team And Intensive Care Unit Ali Haji Vahabzadeh, The University of Auckland Business School, Auckland, New Zealand, a.vahabzadeh@auckland.ac.nz, Valery Pavlov We propose a queueing model of CCOT to examine the effects of this team on the ICU performance and patient outcomes. To gain more insights into the effectiveness of the role of critical care nurses on the ICU utilisation rate and patient outcomes, we analyse different nurse allocation policies between ICU and CCOT. To validate the proposed queueing model, a discrete-event simulation and accordingly an optimisation study have been performed. Finally, the study provides recommendations to hospitals on the functionality of the CCOT and the nurse staffing policy. WD22 107B-MCC Modeling Organ Allocation System Sponsored: Health Applications Sponsored Session Chair: Naoru Koizumi, George Mason University, 3351 N. Fairfax Drive, MS#3B1, Arlington, VA, 22201, United States, nkoizumi@gmu.edu 1 - Mathematical Optimization And Simulation Analyses For Optimal Liver Allocation Boundaries Monica Gentili, University of Louisville, Louisville, KY, 40205, United States, monica.gentili@louisville.edu, Naoru Koizumi, Rajesh Ganesan, Chun-Hung Chen

This study combines mathematical programming models and Discrete Event Simulation to advance existing research on organ allocation system and geographic equity and efficiency in liver transplantation system. The main objectives of the study are: (i) to identify key factors determining geographic disparity in kidney transplantation; (ii) to identify optimal locations for both existing and new liver transplant centers (iii) to identify new OPO boundaries and (iv) to test whether the mathematically produced liver allocation system can perform better than the actual system. We will show the results of our combined approach when applied to liver transplantation in USA. 2 - Small Representations Of Big Kidney Exchange Graphs John Dickerson, Carnegie Mellon University, Pittsburgh, PA, United States, dickerson@cs.cmu.edu, John Dickerson, University of Maryland, College Park, MD, United States, dickerson@cs.cmu.edu, Aleksandr Mark Kazachkov, Ariel Procaccia, Tuomas W Sandholm Kidney exchanges are organized markets where patients swap willing but incompatible donors. We observe that if the kidney exchange compatibility graph can be encoded by a constant number of patient and donor attributes, fundamental problems in kidney exchange are solvable in polynomial time. We give conditions for losslessly shrinking the representation of an arbitrary compatibility graph. Then, using data from the UNOS nationwide kidney exchange, we show how many attributes are needed to encode real compatibility graphs. The experiments show that, indeed, small numbers of attributes suffice. This has application to optimal pre-transplant immunosuppression policies. 3 - Offer Batching For Organ Placement Tinglong Dai, Assistant Professor, Johns Hopkins University, 100 International Drive, Baltimore, MD, 21202, United States, dai@jhu.edu, Sommer Gentry, Sommer Gentry, Sridhar R Tayur, David Axelrod, Dorry Segev, Dorry Segev In this study, we consider an organ procurement organization’s problem of determining the optimal batch size of simultaneous offers made to transplantation centers. We model the strategic interaction among transplant centers both within and across batches, leading to structural properties and computational insights. 4 - Redistricting Liver Allocation: Challenges And Extensions Sommer Gentry, United States Naval Academy, gentry@usna.edu Sommer Gentry, Johns Hopkins University, Baltimore, MD, United States, gentry@usna.edu, Josh Pyke, Eric K Chow, Dorry Segev Livers for transplant in the U.S. are distributed within eleven regions, and are much more available in some geographic areas, leading to dramatic disparities in transplant rates. We have used redistricting to design novel sharing districts which significantly reduce these geographic disparities. The improved districts might be implemented soon, if concerns about increased transport time for organs and about the variability of organ supply and demand can be addressed. We will explore the efficient frontier of the policy space, trading off organ transport for disparity reduction. We will also discuss a robust formulation of the redistricting problem. 108-MCC Socially-responsible Healthcare Operations Sponsored: Health Applications Sponsored Session Chair: Priyank Arora, Georgia Institute of Technology, 800 West Peachtree, NW, Atlanta, GA, 30308, United States, priyank.arora@scheller.gatech.edu 1 - Ambulance Routing In Resource Constrained Settings Milind G. Sohoni, Indian School of Business, milind_sohoni@isb.edu, Lavanya Marla, Achal Bassamboo, Chandrasekhar Manchiraju Using real-world data we look at optimal dispatch policies and compare those with current best practices. We develop insights and guidelines for practicing managers. 2 - Healthcare Payment Model Impact On Hospital Readmissions We examine the transition from Fee-for-Service (FFS) to pay-for-performance (P4P) reimbursement plans, such as bundled payments and the Hospital Readmission Reduction Program (HRRP). We use a game theory approach to understand how healthcare providers interact to improve their individual contribution margins. Results show that P4P plans do motivate extra readmission reduction effort, but that misalignments can occur between the player’s efforts and the minimum total system cost effort. We find that the smaller post-discharge player can be over-motivated to reduce readmissions and that HRRP is not necessary with well-designed bundled payment plans. WD23 Jon M Stauffer, Texas A&M University, College Station, TX, United States, jstauffer@mays.tamu.edu, Jonathan Helm, Kurt M Bretthauer

462