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Cluster :  Doing Good with Good OR

Session Information  : Sunday Nov 01, 13:30 - 15:00

Title:  Doing Good with Good OR II
Co-Chair: Lisa Maillart,lisa.maillart@engr.pitt.edu
Chair: Itai Ashlagi,MIT, 100 Main st, Cambridge Ma 02139, United States of America, iashlagi@mit.edu

Abstract Details

Title: Finding Patterns with a Rotten Core: Data Mining for Crime Series Detection
 Presenting Author: Tong Wang,Graduate Student, MIT, 70 Pacific Street, apt 242A, Cambridge MA 02139, United States of America, tongwang@mit.edu
 
Abstract: We worked with the Cambridge Police Department to build a model that can automatically detect crime series, which analysts spend hours per day doing it manually. NYPD is currently working with our code, aiming to incorporate it into a custom software package they are developing which can assist in their daily job. This project has received widespread media attention.
  
Title: Infusion Center Process Improvement and Patient Wait Time Reduction
 Presenting Author: Mengnan Shen,Georgia Tech, Atlanta GA, United States of America, motion0720@gatech.edu
 Co-Author: Sung Keun Baek,Georgia Tech,
 Xiaoyang Li,Georgia Tech,
 Allen Liu,Georgia Tech,
 James Micali,Georgia Tech,
 Jisu Park,Georgia Tech,
 Yunjie Sun,Georgia Tech,
 Emilie Wurmser,Georgia Tech,
 
Abstract: Winship experienced long wait times and low patient satisfaction. Combining data analytics, stakeholder interviews, queuing network principles, and detailed simulation analysis, we improved flow, communication, and visibility throughout the process. Winship implemented our suggestions, resulting in a 28% reduction in patient wait times from check-in to chair, a 8.5% increase in patient satisfaction, and a 6 patients/day increase in throughput.
  
Title: Using Operations Research to Improve the Health of Patients with Type 2 Diabetes
 Presenting Author: Yuanhui Zhang,NC State, United States of America, yuanhui.zhang@gmail.com
 
Abstract: We developed OR models for policy evaluation and robust optimization of clinical regimens for glycemic control for patients with type 2 diabetes. We used the models to address controversial questions including: whether protocols based on new medications are more effective than standard regimens. A publication from this work received substantial press and may help inform treatment recommendations in the future.