Informs Annual Meeting 2017

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INFORMS Houston – 2017

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mechanisms that prevent fraud. We propose a systems approach for Medicaid fraud inspection and prevention that maximizes the risk of committing fraud for providers serving Medicaid population. 2 - The Lifecycle of Maturity for Organizations to Fight Fraud Arnie Greenland, Robert H. Smith School of Business, 4342 Van Munching Hall, College Park, MD, 20742, United States, agreenland@rhsmith.umd.edu Organizations that are faced with the daunting task of detecting, reducing and eventually eliminating fraud differ greatly in their capabilities to succeed in that endeavor. In this paper, the author will propose a natural lifecycle of development that organizations traverse in developing the data and capabilities to improve their ability to effectively fight fraud; and he will offer some examples, appropriately masked. While the paper will focus primarily on the data and organizational requirements, the author will also discuss the key analytical tools, capabilities and skill sets required as part of the organizational development process. 3 - What Indicators Matter for Fraud Identification in Healthcare and to what Extend do they Matter Didem Egemen, PhD Candidate, Integrity Management Services Inc., Alexandria, VA, United States, didem@gwu.edu, Paulo Macedo, Sewit Araia Detecting healthcare fraud is not a straightforward process since it requires outlier detection methods for large number of indicator. In this study, we recommend combining different methods, which are Mahalanobis distance, Singular Value Decomposition, and One Class Classification. Our goal is to obtain outliers within healthcare providers by taking into account the different information provided by these three methods and try to reduce the number of false positives. For this purpose, the observations flagged differently by these methods are compared by using Multivariate Lorenz curves and the equivalence of these Lorenz curves are tested by using the Bootstrapping method. 322B Smart and Connected Health Sponsored: Health Applications Sponsored Session Chair: Muge Capan, Christiana Care Health System, Wilmington, DE, 19801-3082, United States, mcapan@ncsu.edu 1 - Data-Driven Approach to Early Warning System Implementation Stephen Hoover, MS, Christiana Care Health System, Value Institute, Newark, DE, United States, Stephen.Hoover@ChristianaCare.org Early warning systems are designed to alert healthcare providers to changes in patient status. Despite good intentions, poor implementations can lead to an increase in alert burden and alarm fatigue. We will discuss the prospective analysis of the impact of early warning systems’ on alerts prior to implementation from both an analytical modeling and information technology perspective. Post- implementation results will also be presented. 2 - Signaling Sepsis and Forecasting the Unexpected Transfer to Upgraded Resources in Sepsis Muge Capan, Associate Director of Health Systems Optimization, Newark, DE, Muge.Capan@ChristianaCare.org, Kristen Miller Signaling Sepsis, funded by the National Library of Medicine, creates an evidence- based framework to optimize sepsis clinical decision support. One of its quality improvement projects, FUTURE (Forecasting the Unexpected Transfer to Upgraded Resources in sepsis), uses gamification to elicit predicted patient outcomes from healthcare teams, thus enabling patient-level feedback, raising sepsis awareness, and identifying clinicians who best predict patient decompensation. 3 - Computer-aided Multimodal Health Monitoring System with Wearable Microfluidic Sweat Sensors Daehan Won, Binghamton University, Vestal, NY, United States, dhwon@binghamton.edu Quantitative, simple, and low-cost analysis of biofluids is essential to prevent and diagnosis variety of health conditions along with the understanding metabolisms of human physiology. Therefore, we introduce a new health-related information monitoring framework; various chemicals in human sweat will be measured and analyzed by a newly designed wearable sensors and the mobile application respectively. Our unified framework will hold great potentials for future applications. SA08

320C Design and Evaluation of Differentiated Care Interventions Sponsored: Health Applications Sponsored Session Chair: Diana Maria Negoescu, University of Minnesota, Minneapolis, MN, 55455, United States, negoescu@umn.edu 1 - Machine Learning Discovery of Longitudinal Patterns of Depression and Suicidal Ideation Jue Gong, University of Washington, Industrial and Systems Engineering, MEB B14 BOX 352650, Seattle, WA, 98195, United States, gongjue@uw.edu, Gregory E. Simon, Shan Liu Depression is often accompanied by thoughts of self-harm which are a strong predictor of subsequent suicide attempt and suicide death. We used artificial neural networks to extract latent structures from the trajectory data of depressive symptoms in an on-going treatment population from electronic health record. We discovered correlations between patients’ depression symptoms (PHQ-8) and suicidal ideation (the 9th question of PHQ) through cross-correlation analysis. This work paves the way toward the development of personalized depression monitoring and suicide prevention strategies. 2 - Optimal Learning of the Tumor Response Parameters Distribution in Spatiotemporally Integrated Radiotherapy Ali Ajdari, University of Washington, Seattle, WA, United States, ajdari@uw.edu, Archis Ghate One of the key assumptions in the linear-quadratic formulation of the radiotherapy problem is that the distribution of uncertainty in tumor-response parameters is known to the treatment planner. Recent advances in imaging techniques however, have brought forth the possibility of relaxing this assumption and using tumor-images acquired over the treatment course to learn patient-specific parameter distributions. Therefore, the goal in this study is to learn the tumor’s response while optimally dosing a patient over the treatment course. 3 - Response-adaptive Design of Dose-finding Trials In Clinical trials human participants are assigned to one or more interventions to evaluate the effects of those interventions on health-related outcomes. In a standard dose-finding trial, patients are randomly assigned to predetermined doses such that the number of patients assigned to each dose is roughly equal. Such a design may be inefficient. A better strategy is adaptive design in which modifications are made to dose assignments, while the study is in progress. We consider a response-adaptive dose-finding clinical trial where the decision maker is interested in identifying the optimal dose, and allocation of future patients to doses is based on knowledge gained from previous outcomes 4 - Personalized Prediction of Glaucoma Progression under Different Target Eye Pressure Levels Amir Nasrollahzadeh, Clemson University, Clemson, SC, United States, snasrol@g.clemson.edu, Amin Khademi

Pooyan Kazemian, Harvard University, 50 Staniford Street, 9th Floor, Room 930B, Boston, MA, 02114, United States, pooyan.kazemian@mgh.harvard.edu, Mariel Sofia Lavieri, Mark P. Van Oyen, Joshua Stein

Using perimetric and tonometric data from two randomized clinical trials, we developed and validated Kalman filter models for fast-, slow-, and non- progressing patients with glaucoma. Our models identify patient type and generate personalized and dynamically-updated forecasts of glaucoma progression under different target eye pressure levels. This approach can help eye doctors determine an appropriate treatment regimen and tailor care to individual patients.

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322A Medicaid Fraud Detection and Prevention Sponsored: Health Applications Sponsored Session

Chair: Elham Torabi, James Madison University, torabiex@jmu.edu 1 - Medicaid Fraud Detection and Prevention: A Systems Approach Elham Torabi, Assistant Professor, James Madison University, 1933 Buttonwood Ct., Harrisonburg, VA, 22802, United States, torabiex@jmu.edu, Verbus Counts Medicaid fraud and abuse result in significant waste of resources. Any dollar saved in the Medicaid program can be used in improving access for those who need care. Auditing Medicaid claims and investigating fraudulent activities is time and money consuming. Therefore, what is crucially needed more than ever is

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