2015 Informs Annual Meeting

MC74

INFORMS Philadelphia – 2015

MC75 75-Room 204B, CC Innovations in Healthcare Products and Services Cluster: New Product Development Invited Session Chair: Nitin Joglekar, Boston University, Questrom School of Business, Boston, MA, United States of America, joglekar@bu.edu 1 - Healthtech Platforms: Barriers to Innovation Edward Anderson, Professor, McCombs School of Business, The University of Texas at Austin, 1 University Station B6500, Austin, TX, 78712-1277, United States of America, Edward.Anderson@mccombs.utexas.edu, Shi Ying Lim The state of mobile and digital health is far behind that of other platform industries, such as travel, retail, and even banking. Using qualitative analysis, we present some of the more important barriers to healthtech startup success (and, but extension, health tech in general) and outline some initial suggestions to create an ecosystem to counter them. 2 - Platform Innovations in Healthcare Delivery Geoffrey Parker, Professor, Tulane University, 7 McAlister Drive, New Orleans, LA, 70118, United States of America, ggparker@tulane.edu Network platform systems have reshaped the computer and telecommunications industries and are now transforming other industries such as transportation, lodging, and contract labor. The shift to platforms is slower in highly regulated industries, but changes are coming quickly. We survey likely mechanisms and entry points for a platform shift in healthcare. 3 - Patient, Heal Thyself! A Learning Algorithm to Predict How Telemedicine Affects Patient Activation Kellas Cameron, PhD Student, Boston University, Questrom School of Business, Boston, MA, 02215, United States of America, kellas@bu.edu, Carrie Queenan, Nitin Joglekar The Patient Activation Measure (PAM) assesses an individual’s knowledge and confidence for managing one’s health. This paper proposes a learning algorithm to predict a patient’s PAM with data from a controlled telemedicine study, accounting for social and technology effects. The algorithm allows for the analysis of Type I and II errors and learning versus testing tradeoffs. Implications of this study create opportunities for operational improvements to reduce patient readmission rates. MC76 76-Room 204C, CC Accounting for Input Uncertainty in Stochastic Simulations Chair: Canan Gunes Corlu, Assistant Professor, Boston University, 808 Commonwealth Avenue, Boston, MA, 02215, United States of America, canan@bu.edu 1 - A Sequential Experiment Design for Input Uncertainty Quantification in Stochastic Simulation Xie Wei, Assistant Professor, Rensselaer Polytechnic Institute, 400 McChesney Ave. Ext. 5-9, Troy, NY, United States of America, xiew3@rpi.edu When we use simulations to estimate the performance of a stochastic system, simulations are often driven by input distributions that are estimated from real- world data. Non-parametric bootstrap could be used to quantify both input model and parameter uncertainty. A sequential experiment design is proposed to efficiently propagate the input uncertainty to output mean and deliver a percentile confidence interval to quantify the impact of input uncertainty on the system performance estimate. 2 - Input Uncertainty in Stochastic Simulations: Dependent Input Variables of Mixed Types Alp Akcay, Eindhoven University of Technology, Department of Industrial Engineering, Netherlands, A.E.Akcay@tue.nl, Bahar Biller We consider stochastic simulations with correlated input random variables having NORmal-To-Anything (NORTA) distributions. We assume that the marginal distribution functions and the NORTA base correlation matrix are unknown. Given that the dependent input variables can take discrete and continuous values, we develop a Bayesian procedure that decouples the input model estimation into two stages. We investigate the role of the corresponding input uncertainty in simulation output data analysis. Sponsor: Simulation Sponsored Session

3 - Quantitative and Qualitative Evaluation of Printed Electronics Based on Microscopic Images Hongyue Sun, Virginia Tech., Grado Department of Industrial and, Systems Engineering, Blacksburg, VA, 24061, United States of America, hongyue@vt.edu, Yifu Li, Chuck Zhang, Ran Jin, Kan Wang Aerosol jet printing is an additive manufacturing technology to fabricate printed electronics. Although various types of machine vision sensors are used to take images for qualitative evaluation, no methods have been reported to use image features to quantitatively characterize the quality of electronics. This work use a quantitative method to model the correlation of image features and quality variables. A case study to fabricate silver conducting wires is used to evaluate the performance. 4 - On the Asymptotics of Pairwise Modeling for Multivariate Gaussian Process Yongxiang Li, Research Assistant, City University of Hong Kong, Multivariate Gaussian process is a popular method for emulating computer models with multiple outputs. But its complexity poses significant challenges to parameter estimation due to high dimensionality and huge computational burden. A pairwise modeling approach is proposed to solve the issue. The asymptotic normality for parameter estimation is studied. Simulation studies are conducted and the pairwise method is applied to model the low-E glass data for such purposes as quality control. Modern Monitoring Applications Sponsor: Quality, Statistics and Reliability Sponsored Session Chair: Irad Ben-Gal, Professor, Tel Aviv University, Tel Aviv, Israel, bengal@tauex.tau.ac.il 1 - An Application of Sensor Selection Based on Information Theoretic Measurements for Change Detection Marcelo Bacher, PhD Candidate, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel, mgbacher@post.tau.ac.il, Irad Ben-Gal Feature selection based on Information Theoretic measurements has been used with great success in Machine Learning applications in special for classification tasks. Nevertheless, less effort has been applied to process monitoring. In this work we propose a framework that aims at finding the most significant subset of features for change detection and bounded false alarm rate when monitoring a process. 2 - Correlated Gamma-based Hidden Markov Model for Asthma Control Status Diagnosis Junbo Son, PhD Candidate, University of Wisconsin-Madison, 1513 University Avenue, Madison, WI, 53706, United States of America, json5@wisc.edu, Patricia Brennan, Shiyu Zhou To effectively manage the asthma as a chronic disease, a statistical model based on the everyday patient monitoring is crucial. Taking advantages from the remote patient monitoring system, we propose a data-driven diagnostic tool for assessing underlying asthma condition of a patient based on hidden Markov model (HMM). The proposed correlated gamma-based HMM can visualize the asthma progression to aid therapeutic decision making. Its promising features are shown in both simulation and case study 3 - Project Management Monitoring Irad Ben-Gal, Professor, Tel Aviv University, Tel Aviv, Israel, bengal@tauex.tau.ac.il We consider the monitoring of large projects (software/hardware) and propose an analytical approach for identifying the optimal project monitoring points by using concepts from the Information Theory. The methodology used is based on simulation-optimization scheme - selecting the monitoring points that provide the highest potential information gain on the project duration. (joint work with Shiva Kashi-Cohen and Shay Rozanes) 4 - Leveraging Analytics to Support Health-monitoring and Management of Infrastructure Facilities Pablo Durango-Cohen, Associate Professor, Northwestern University, 2145 Sheridan Road, A332, Evanston, IL, 60208, United States of America, pdc@northwestern.edu, Yikai Chen Motivated by recent technological advances, we describe the development and validation of a statistical framework to support health-monitoring and management of transportation infrastructure. The framework consists of formulation of structural time-series models to explain, predict, and control for common-cause variation, and use of multivariate control charts to detect special- cause variation. We present several examples from an in-service bridge to validate the framework. Department of SEEM, 83 Tat Chee Avenue, Kowloon, Hong Kong - PRC, novern.li@gmail.com, Qiang Zhou MC74 74-Room 204A, CC

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