2015 Informs Annual Meeting

SB42

INFORMS Philadelphia – 2015

3 - A Hybrid Social Network-system Dynamics Model of Team Performance Kyle Lewis, Professor, U. California - Santa Barbara, 1318 Phelps Hall, Santa Barbara, CA, 93106, United States of America, klewis@tmp.ucsb.edu, Edward Anderson Social network analysis has explored many aspects of inter-personal interaction, yet it is limited in its power to describe important features of team behavior. We present a hybrid system dynamics-agent based methodology that extends the power of SNA to analyzing emergent team behavior. We present a proof-of- concept hybrid model and use it to simulate the: differential effects of hierarchy; impact of overspecialization; role of generalists; and disruption created by member turnover. 4 - IT Governance Mechanisms and Organizational Performance: Investigating the Moderating Role of Platform Hossein Kalantar, PhD Student, University of Colorado Denver, 1475 Lawrence Street, Denver, CO, 80202, United States of America, Hossein.Kalantar@ucdenver.edu, Jiban Khuntia Information Technology governance plays a key role in creating value through IT within an enterprise. There are studies that show the positive impacts of IT governance on organizational performance. However, there are not many studies that answer “How IT governance improves organizational performance”. In this study, we investigate the moderating role of platforms, on the relationship between IT governance and organizational performance. Context of Health IT organizations was selected to conduct this study. 5 - Organizational Changes to Benefit from Big Data Amit Das, Associate Professor, Qatar University, P.O. Box 2713, Doha, Qatar, amit.das@qu.edu.qa While developments in computing have ushered in the age of Big Data, accounts of Big Data being actually used to improve the management of organizations are still relatively rare. We ascribe this to the incompatibility of traditional management practices with the form of evidence-based decision-making enabled by Big Data. We suggest that the profession of management is not alone in its struggle to incorporate Big Data into its established routines. SB41 41-Room 102A, CC Optimization Methods in Healthcare Scheduling Sponsor: Manufacturing & Service Oper Mgmt/Healthcare Operations Sponsored Session Chair: Retsef Levi, J. Spencer Standish (1945) Professor of Operations Management, Sloan School of Management, MIT, 100 Main Street, BDG E62-562, Cambridge, MA, 02142, United States of America, retsef@mit.edu 1 - Real-time Pooling for Multi-site Imaging Facilities David Shmoys, Cornell University, School of ORIE, Rhodes Hall, Ithaca, NY, 14853, United States of America, david.shmoys@cornell.edu, Chaoxu Tong, Shane Henderson MRI patients waste a great deal of time in the waiting room; this is largely due to the misalignment between scheduled and actual imaging time. By selecting among a pool of nearby MRI facilities, we can redirect patients shortly before their scheduled appointment time. We demonstrate that this improved load- balancing can decrease patient waiting time. Working with New York Presbyterian Hospital, we are implementing a trial for our approach in a complicated real-life environment. 2 - Increasing throughput in a Large Oncology Infusion Unit Ana Cecilia Zenteno Langle, Massachusetts General Hospital, 55 Fruit Street, White 400, Boston, MA, 02114, United States of We describe a data-driven online scheduling algorithm aimed at generating a more predictable and balanced intra-day resource utilization in the Infusion Unit at the Massachusetts General Hospital Cancer Center. The implementation of the algorithm, which is based on integer optimization and simulation methods, has a projected impact of reducing by 30% the average peak utilization and its standard deviation by 35%. The hospital has contracted with an outside vendor to build a customized IT tool. 3 - Simultaneous Scheduling of Nurses in Multiple Hospital Units using Stochastic Integer Programming America, azentenolangle@mgh.harvard.edu, Retsef Levi, Wendi Rieb, Inga Lennes, Mara Bloom, Bethany Daily, Peter Dunn

We will present theoretical and computational results on simultaneous scheduling of nurses in multiple hospital units using a two-stage stochastic mixed integer programming model. The model allows a nurse pool as well as sharing of nurses from a more specialized unit to a lesser one. We show that the integrality of the second stage can be convexified in our model, which allows for the solution of larger scale models. 4 - Logic-Based Benders’ Decomposition Approaches with Application to Operating Room Scheduling Vahid Roshanaei, PhD Candidate, University of Toronto, 5 King’s College Road, Toronto, ON, Canada, vroshana@mie.utoronto.ca, Dionne Aleman, David Urbach We develop three logic-based Benders’ decomposition (LBBD) approaches and a cut propagation mechanism to solve location-allocation integer programs (IPs). Each LBBD is implemented in four different ways, yielding 24 distinct LBBD variants. We illustrate the LBBDs’ performance on the distributed operating room scheduling problem, where patients and operating rooms are scheduled across hospitals. Our LBBDs are 10-100x faster than IP+Gurobi and are more successful at finding optimal solutions. SB42 42-Room 102B, CC Healthcare Operations Modeling and Optimization Sponsor: Manufacturing & Service Oper Mgmt/Healthcare Operations Sponsored Session Chair: David Kaufman, University of Michigan, 1205 Beal Ave., 1710 IOE Building, Ann Arbor, MI, United States of America, davidlk@umich.edu 1 - Allocating Operating Room Time for Elective Surgery Steven Shechter, Associate Professor, Sauder School of Business, University of British Columbia, University of British Columbia, Vancouver, BC, Canada, steven.shechter@sauder.ubc.ca, Mahesh Nagarajan, Stephanie Carew We examine how to allocate operating room hours to different surgical specialties at the British Columbia Children’s Hospital. This is a longer-run planning decision which has major effects on the wait time experience of the patient population. To evaluate policies, we construct and validate a simulation model of patient arrival and appointment processes. We then apply optimization and dynamic programming techniques to recommend improved allocation policies. 2 - Panel Size, Office Visits and Care Coordination Events in Primary Care Hari Balasubramanian, University of Massachusetts Amherst, 160 Governors Drive, Amhert, MA, 01002, United States of America, hbalasubraman@ecs.umass.edu, Michael Rossi Using the Medical Expenditure Panel Survey (MEPS, AHRQ), we present a method to estimate office visit and care coordination workload generated by patients in a primary care panel. The method uses individual patient histories for a one year period. 3 - Allocating Scarce Resources in a Patient Centered Medical Home (pcmh) Jingxing Wang, University of Michigan, 1205 Beal Ave., Ann Arbor, MI, 48109, United States of America, jeffwjx@umich.edu, Romesh Saigal We consider a two stage stochastic allocation problem to assign the number of hours of Primary Care Physician to teams in a PCMH. In the first stage, a preliminary assignment is made. In the second stage, the demand is observed and the preliminary assignment adjusted to meet it exactly. We use real options theory and present three ways to achieve a fair and consistent mechanism to price the disruption caused by adjustment. The assignments are made such that the price of disruption is the same. 4 - An Outpatient Planning Optimization Model for Integrated Care and Access Management David Kaufman, University of Michigan, 1205 Beal Ave., 1710 IOE Building, Ann Arbor, MI, United States of America, davidlk@umich.edu, Jivan Deglise-hawkinson, Todd Huschka, Mark Van Oyen, Jonathan Helm We present a data-driven methodology for outpatient scheduling. Our work is the result of a practice-based collaboration with a major medical destination center. Our capacity planning model seeks to meet visit targets on the time delay from the appointment request to the appointment occurrence by patient type while managing the patient mix, which is steered by goals such as increasing the volume of new patient visits. The focus of the talk is on model validation and insights.

Sanjay Mehrotra, Northwestern University, Industrial Engineering and Management, 2145 Sheridan Road, Evanston, IL, 60208, United States of America, mehrotra@northwestern.edu, Kibaek Kim

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