2016 INFORMS Annual Meeting Program

WB02

INFORMS Nashville – 2016

WB02 101B-MCC Data Mining in Healthcare 2 Sponsored: Data Mining Sponsored Session Chair: zihao Jiao, MD, United States, zihaobit@163.com 1 - Doctors Performance In Emergency Rooms Amir Mousavi, George Washington University, 2700 Wisconsin Ave, Unit 305, Washington, DC, 20007, United States, ahmn00@gmail.com Emergency Rooms are known as a vital ward in a hospital. Improving the efficiency in the ER has been a challenging question for researchers. Throughout the academic literature, people have defined different efficiency indexes in order to tackle this problem. This research aims to apply Data Envelopment Analysis technique in order to identify the performance (i.e. productivity) variation among doctors and use this result as a component for the optimization model in order to improve the efficiency of the system. The final goal will be to show how patient waiting time is affected by doctors’ productivity and how the proposed scheduling model using this information can reduce patient wait times in the ER. 2 - A Deep Feature Selection Approach For Personalized Medicine Personalized Medicine has been defined in different ways in the literature. A good interpretation for Personalized Medicine is “the use of combined knowledge (genetic or otherwise) about a person to predict disease susceptibility, disease prognosis, or treatment response and thereby improve that person’s health”. In this research, we propose a new deep feature selection method based on deep learning. Our method used stacked auto-encoders for feature representation in higher level abstraction. We applied our approach to a specific precision medicine problem. The results show that our feature learning and representation approach leads to better results in comparison with otherwise. WB03 101C-MCC Advances in Emergency Department Operations Management/Research Sponsored: Manufacturing & Service Oper Mgmt, Healthcare Operations Sponsored Session Chair: Soroush Saghafian, Harvard Univeristy, 79 John F. Kennedy Street, Mailbox 37, Cambridge, MA, 02138, United States, Eva Lee, Georgia Tech, eva.lee@isye.gatech.edu, William Wang This is joint with Grady Health System, Children’s Healthcare of Atlanta and Emory University School of Medicine. Most scheduling is done based on availability of physician’s preference time. The patients are then offered the best possible time that may fit his/her doctor’s schedule. This study will identify the needs of patients and develop a predictive model to estimate the individual needs (and thus LOS for the appointment). This information is then incorporated within a scheduling optimization framework for dynamic optimization. This allows for optimizing the scheduled service as well as unexpected emergency service. 2 - The Impact Of Health Information Exchanges On Emergency Department Length Of Stay Jan Vlachy, Georgia Institute of Technology, Atlanta, GA, United States, vlachy@gatech.edu, Turgay Ayer, Mehmet U.S. Ayvaci, Zeynal Karaca Health information exchanges (HIEs) are expected to improve information coordination in emergency departments (EDs), but their impact on ED operations remains poorly understood. We study the effect of HIE on ED length of stay (LOS) based on about 5.8 million ED visits in Massachusetts. We find that HIE a) reduces ED LOS overall by 11.1%, b) is even more effective in teaching hospitals, c) is less effective in crowded EDs, and d) its effectiveness depends on severity and complexity of the patients. Our findings have implications for the nationwide HIE adoption. Milad Zafar Nezhad, PhD Student, Wayne State University, Wayne State Univesity, Detroit, MI, 48202, United States, fq3963@wayne.edu, Kai Yang soroush_saghafian@hks.harvard.edu 1 - Managing Emergency Operations

3 - Assignment Policy To Improve Emergency Department Boarding Time: A Safety And Quality Of Care Perspective Derya Kilinc, Arizona State University, dkilinc@asu.edu, Soroush Saghafian, Stephen J Traub One important reason for ED crowding problem is prolonged boarding time of admit patients. We study effective ways of reducing ED boarding times by focusing on the trade off between keeping patients in ED and assigning them to a secondary inpatient unit. We model the patient flow problem as a parallel queueing system and show that the optimal policy is a state-dependent threshold policy. Since the optimal policy is hard to implement, we use a simple and effective policy which we term penalty adjusted Largest Expected Workload Cost. Using simulation model, we show that implementing the proposed policy can improve patient safety by reducing the boarding times while controlling the overflow of patients. 4 - Robust Data-driven Emergency Department Management Via Percentile Optimization: Multi-class Queueing Systems With Model Ambiguity Austin Bren, Arizona State University, Phoenix, AZ, United States, asbren@asu.edu, Soroush Saghafian To help hospital Emergency Departments address overcrowding issues and increase patient safety, we implement a robust multi-class queueing model to overcome inherent ambiguities arising in parameter specification. Our technique, based on percentile optimization, is uniquely suited to incorporate both learning and the degree of optimism expressed by the manager. We demonstrate the benefits of using our framework for improving current patient flow policies using real-world data collected from a leading U.S. hospital and utilizing highly effective, easy-to-implement management strategies. Chair: Andrew Liu, Purdue University, 315 N. Grant Street, West Lafayette, IN, 47907, United States, andrewliu@purdue.edu 1 - Online Opf With Quasi-Newton Algorithm Yujie Tang, California Institute of Technology, Pasadena, CA, United States, ytang2@caltech.edu, Krishnamurthy Dvijotham, Steven Low Optimal power flow is a central problem in the operation of power systems. So far the majority of the literature deals with offline algorithms for traditional power system applications, but the proliferation of distributed energy resources and smart appliances in power networks motivates real-time, decentralized and scalable algorithms. In this talk we will introduce an online OPF algorithm based on quasi-Newton methods that is real-time and can track the optimal operation when the state of the network is changing. 2 - Power System State Estimation In The Presence Of Bad Data Ramtin Madani, University of California, Berkeley, Berkeley, CA, United States, ramtin.madani@berkeley.edu, Javad Lavaei, Ross Baldick This talk introduces a method for finding the unknown operating point of a power network based on a given set of potentially corrupted measurements including nodal active powers, nodal reactive powers, nodal voltage magnitudes and line flows. We propose a conic optimization problem in order to handle nonconvexity and deal with bad data simultaneously. The proposed convex program is guaranteed to recover the exact vector of complex voltages as long as the number of bad measurements is small. Numerical experiments on a large- scale European system are performed to demonstrate the efficacy of the proposed framework. 3 - Parallelized Interior Point Method For Security Constrained Optimal Power Flow Problem Na Li, Assistant Professor, Harvard University, 33 Oxford St,, MD 147, Cambridge, MA, 02139, United States, nali@seas.harvard.edu, Ariana Minot, Yannick Meier Solving the security constrained optimal power flow problem (SCOPF) is challenging due to the large size of the power system and large number of contingencies. However, in SCOPF, different contingencies are only coupled via the power injection control variables, yielding a sparse system. We design a domain decomposition technique based on this sparsity to parallelize the problem across different contingencies. For each subproblem associated with a contingency, we exploit the network structure through graph coloring techniques to enhance parallelism. In summary, we design an effective method to utilize two layers of parallelism: 1) across contingencies and 2) across buses in the network. WB04 101D-MCC Optimization Methods in Smart Grid Sponsored: Energy, Natural Res & the Environment, Energy I Electricity Sponsored Session

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