2015 Informs Annual Meeting

WC35

INFORMS Philadelphia – 2015

WC33 33-Room 410, Marriott Health Care Operations Management Sponsor: Health Applications Sponsored Session

2 - Visualizing Differences in Patient Use of an EHR Patient Portal Informed by Clickstream Data Sharon Johnson, Associate Professor, Worcester Polytechnic Institute, Foisie School of Business, 100 Institute Road, Worcester, MA, 01609, United States of America, sharon@wpi.edu, Farhan Mushtaq, Bengisu Tulu, Diane Strong, John Trudel, Lawrence Garber In this paper, we explore patient usage behavior of a patient portal by analyzing patterns of use in clickstream data combined with data on demographics and health system utilization. Directed and undirected mining techniques were used to explore the data and to visualize specific patterns. This type of analysis can be used to improve processes for engaging patients through a patient portal, as well as to enhance the portal interface to support different user needs. 3 - Visualization of Care Delivery to Asthma Patients in Pediatric Emergency Departments Mustafa Ozkaynak, Assistant Professor, University of Colorado, 13120 E 19th Ave, Aurora, CO, 80045, United States of America, mustafa.ozkaynak@ucdenver.edu, Marion Sills We used Eventflow (an interactive visualization tool) to examine the temporal relationship between care delivery activities for asthma patients in an academic pediatric emergency department and its four satellite clinics. Time-stamped event logs from Electronic Health Records were processed and workflow patterns in each of the five settings for different acuity level and arrival mode were highlighted. Findings can inform systematic organizational interventions that will improve quality of care. 4 - Healthcare Process Discovery and Visualization United States of America, basole@gatech.edu, Mayank Gupta, Mark Braunstein, Polo Chau, Hyunwoo Park, Robert Pienta, Brian Kahng, Vikas Kumar, Nicoleta Serban, Michael Thompson Healthcare processes are complex activities that span organizational, spatial, and temporal boundaries. Systemic insights are consequently difficult to achieve. Our research develops a data-driven methodology, fusing systems modeling, data mining, and visualization, to identify, describe, and visualize healthcare processes. We illustrate our methodology with a case study in pediatric healthcare. Rahul Basole, Associate Professor, Georgia Institute of Technology, 85 Fifth Street NW, Atlanta, GA, 30332, Andrei Villarroel, Professor, Swiss Entrepreneurship Institute, 4 Chemin du Musee, Fribourg, Switzerland, andreiv@mit.edu Our research spans 54 countries, 44 industries, unveiling the value of a new generation of online education organizations from HEI professors, industry practitioners, and current students. Amongst respondents with first-hand experience with MOOC education, they find it superior to traditional education in 14 out of 15 dimensions long-believed better served by the traditional campus- based education model. 2 - Orientation Determination of Hotel Buildings using 3-d GIS for Maximum Rental Revenue Young-ji Byon, Assistant Professor, Khalifa University, Al Saada St. and Muroor Rd., Abu Dhabi, 127788, United Arab Emirates, youngji.byon@kustar.ac.ae, Joonsang Baek, Chung-suk Cho Hotel room rates are strongly related to the quality of scenery from the rooms. In Dubai and Abu Dhabi, great number of new hotel constructions are being planned. By utilizing the 3-D elevation model in GIS, it is possible to quantifiably determine the optimal direction of the hotel buildings before the constructions begin, that would maximize the scenic views of the hotels and hence also the room rental revenue. 3 - Healthcare Analytics Data and Insights on Patient Satisfaction Satish Nargundkar, Associate Professor, Georgia State University, 35 Broad St., Suite 827, Atlanta, GA, 30302, United States of America, snargundkar@gmail.com, Subhashish Samaddar The lack of patient satisfaction data across the hospital system limits the ability of researchers to investigate the customer service elements of the patient experience. In this paper we collect and organize hospital-level patient satisfaction data over a six year period from multiple websites, and make it available to the research community. We identify research opportunities in healthcare quality analytics. We analyze the data to offer some insights on patient satisfaction across the U.S. WC35 35-Room 412, Marriott Global Issues I Contributed Session Chair: Satish Nargundkar, Associate Professor, Georgia State University, 35 Broad St., Suite 827, Atlanta, GA, 30302, United States of America, snargundkar@gmail.com 1 - Creative Destruction through Online Education: What Industry and Students Can Teach Academics

Chair: Amin Khademi, Assistant Professor, Clemson University, 130-D Freeman Hall, Clemson University, Clemson, SC, 29634, United States of America, khademi@clemson.edu 1 - Improving Outpatient Scheduling through Patient Complexity and Integer Programming Eva Lee, Georgia Institute of Technology, eva.lee@gatech.edu, Prashant Tailor, ptailor3@gatech.edu This work is joint with Emory Brain Center. We develop a clinical tool that classifies and schedules patients based off a patient complexity metric. The goal is to maximize the number of patients seen and increase providers and patient satisfaction. A classification model is first used to predict patient complexity, severity and type of follow-up appointment. This information is then used within the MIP scheduling model. 2 - Classifying Heterogeneous Tumor Subtypes via Matrix Factorization and Mixed-integer Programming Andrew Trapp, Assistant Professor, Worcester Polytechnic Institute, 100 Institute Rd., Worcester, MA, 01609, United States of America, atrapp@wpi.edu, Patrick Flaherty We consider tumor subtype classification via regularized mixed-membership matrix factorization, where one factor matrix has a limited number of non-zero entries, and the other has simplex constraints. This provides a mixed-membership representation for each column of the original matrix with sparse mixing components. We transform the original and NP-hard biconvex optimization problem into a mixed-integer linear program, and discuss exact and approximate solution approaches. 3 - Prediction of Operating Room End Time using Regression Modeling Robert Allen, Clemson University, 130 Freeman Hall, Clemson, SC, United States of America, rallen3@g.clemson.edu, Kevin Taaffe The decision to let staff go home or bring more staff in depends on the ability of the nurse manager to predict when certain rooms will be finished for the day. We explore several different methods of enhancing the nurse’s predictive capability by using hospital process flow data to build several models aimed at predicting the OR end time. We compare varying predictive models such as a simple offset and regression modeling to better predict the room end offsets as they occur during the day. Joint Session HAS/Analytics: Unleashing the Potential of Big Data using Visualization in Health Care Delivery Sponsor: Health Applications Sponsored Session Chair: Mustafa Ozkaynak, Assistant Professor, University of Colorado, 13120 E 19th Ave, Aurora, CO, 80045, United States of America, mustafa.ozkaynak@ucdenver.edu 1 - Understanding Adherence and Prescription Patterns using Large Scale Claims Data Margret Bjarnadottir, Assistant Professor of Management Science and Statistics, Robert H. Smith School of Business, University of Maryland, 4324 Van Munching Hall, College Park, MD, 20742, United States of America, margret@rhsmith.umd.edu, Sana Malik, Catherine Plaisant, Tanisha Gooden, Eberechukwu Onukwugha Traditionally, studies have measured medication adherence using summary statistics. However, advanced computing capabilities and novel visual analytics tools now allow us to move beyond the traditional reporting of “average adherence” to analyze longitudinal adherence patterns. Utilizing EventFlow, a novel discrete event sequence visualization software, we investigates patterns of prescription fills and illustrate the use of visual analytics tools in summarizing large scale claims data. WC34 34-Room 411, Marriott

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