Session Detail Information
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Cluster :  Health Applications

Session Information  : Tuesday Nov 11, 13:30 - 15:00

Title:  Hospital Capacity and Resource Management
Chair: Cecilia Zenteno,Massachusetts Institute of Technology, 77 Massachusetts Avenue, E62-389, Cambrdige Ma 02139, United States of America, ceciliaz@mit.edu

Abstract Details

Title: Stochastic Operating Room Planning with Recovery Flow
 Presenting Author: Maya Bam,University of Michigan, 1205 Beal Avenue, 2828 IOE Building, Ann Arbor MI 48109-2117, United States of America, mbam@umich.edu
 Co-Author: Brian Denton,University of Michigan, 1205 Beal Avenue, 2893 IOE Building, Ann Arbor MI 48109-2117, United States of America, btdenton@umich.edu
 Mark Van Oyen,University of Michigan, 1205 Beal Avenue, 2853 IOE Building, Ann Arbor MI 48109-2117, United States of America, vanoyen@umich.edu
 
Abstract: Surgery scheduling is impacted by operating room availability, surgeons, and downstream resources, like the post-anesthesia care unit. We present a new approach, based on our collaboration with a mid-sized hospital, that combines mixed integer programming with discrete event simulation to create schedules that optimize the tradeoff between operating room overtime and operating room blocking.
  
Title: Staffing Service Systems with Load Dependent Service Rate
 Presenting Author: Galit Yom-Tov,Technion-Israel Institute of Technology, Technion City, Haifa, Israel, gality@tx.technion.ac.il
 Co-Author: Jing Dong,Columbia University, Columbia University, New-York, United States of America, jd2736@columbia.edu
 Pnina Feldman,UC Berkeley, Haas School of Business, 2220 Piedmont Ave, Berkeley CA 94720, United States of America, feldman@haas.berkeley.edu
 
Abstract: Most OM literature assumes that service times are independent of the load of the system. However, in many Healthcare systems the two are correlated. For example, patients’ condition may worsen if treatment is delayed, resulting in longer stays. We examine how load-related slowdown affects the operational performance. We develop and analyze fluid and diffusion approximations of an Erlang-A model with load-dependent service times. We propose methods to stabilize and improve system performance.
  
Title: Efficient Resource Allocation and Cost Accounting in Healthcare Networks
 Presenting Author: Fernanda Bravo,MIT, 100 Main St, E62-459, Cambridge MA 02139, United States of America, fbravo@mit.edu
 Co-Author: Ali Aouad,PhD Candidate, MIT, 99 1/2 Myrtle St Apt 2, Boston MA 02114, United States of America, aaouad@mit.edu
 Marcus Braun,MIT, 77 Massachusetts Avenue, Building E40-315, Cambridge, United States of America, mdbraun@mit.edu
 Retsef Levi,MIT Sloan School of Management, 100 Main Street, E62-562, Cambridge MA 02139, United States of America, retsef@mit.edu
 
Abstract: This paper presents a novel optimization driven approach to address the issue of effective consolidation in healthcare delivery networks. Specifically, we provide a framework that allows us to understand the true cost of service and to support strategic resource allocation and system design decisions in a multi-site network. In addition, we report on the application of our approach to a real healthcare delivery network and describe the projected impact and managerial insights derived from it.
  
Title: Surgical Supply Inventory Management in Large Academic Medical Centers
 Presenting Author: Cecilia Zenteno,Massachusetts Institute of Technology, 77 Massachusetts Avenue, E62-389, Cambrdige Ma 02139, United States of America, ceciliaz@mit.edu
 Co-Author: Bethany Daily,Massachusetts General Hospital, 55 Fruit Street, White 400, Boston MA 02114, United States of America, bdaily@partners.org
 Peter Dunn,Massachusetts General Hospital, 55 Fruit Street, White 400, Boston MA 02114, United States of America, pdunn@partners.org
 Retsef Levi,MIT Sloan School of Management, 100 Main Street, E62-562, Cambridge MA 02139, United States of America, retsef@mit.edu
 Matt Schlanser,Nike, 638 SW 5th Ave, Portland OR 97201, United States of America, mschlanz@gmail.com
 
Abstract: Massachusetts General Hospital performs over 36,000 operations per year. Surgical supplies come either individually packaged and sterilized (soft goods), or procured in pre-assembled custom packs. We study the composition of the surgical packs and how to combine them with soft goods to satisfy the demand driven by the surgeons’ requirements, while optimizing their base stock levels. We prescribe a modular pack structure to reduce inventory levels and associated costs via demand pooling.