de Lange

Gertjan de Lange


Elevate Decision Making with Analytics and Optimization in Day-to-Day Operations

Over the past 25 years, we had the privilege of working with many household name clients around the world. Supporting them on their analytics journey has presented us with the opportunity to help them get the most out of optimization and to learn that each client has his own challenges and preferences of decision making. We want to share examples of how we have seen companies elevate decision making with analytics and optimization in their day-to-day operations and generate tremendous value for their business. Observations about key drivers for success in becoming an analytics driven company that enjoys a competitive advantage will be shared as well as some stimulating case studies and lessons learned.


Gertjan de Lange is a member of the leadership team at AIMMS, an innovative analytics and optimization technology company. Gertjan has been with AIMMS since 1995 and worked with many different customers and partners in to enable the successful use of the AIMMS optimization technology. As SVP Business Analytics Gertjan is an active evangelist for the use of analytics, and specifically the use of optimization to potential users and research analysts in and outside the typical Operations Research community. Gertjan is and has been responsible for the overall product strategy since 2010 to assure the boundaries of AIMMS are pushed and the user experience is enhanced with every release. Gertjan holds an MSc degree in Applied Mathematics (OR) of University of Twente.

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Ronald Dravenstott

Geisinger Medical Center

Geisinger Health System Uses Predictive Modeling to Mitigate the Effect of No-Shows

No-shows, patient appointments that are scheduled but not completed, cost Geisinger Health System (GHS) over $20M annually. GHS has created and implemented a No-Show Predictive Model (NPM), an Artificial Neural Network-based model that identifies patients likely to no-show and enables proactive targeted interventions. The NPM and targeted intervention have been integrated with the Electronic Health Record and tested through a randomized controlled trial which showed a 24.9% relative reduction in the no-show rate. Subsequent to the randomized controlled trial, GHS has expanded the NPM to 40+ clinics. The impacts of the NPM are: 1) former no-showing patients receive care, 2) patient access to care is improved, and 3) clinics operate more efficiently. The NPM targeted interventions are projected to prevent over 5,000 no-shows annually.


Ronald Dravenstott has been an Operations Research Practitioner at Geisinger Health System (GHS) since 2011. He received his Master of Science (2012) and Bachelor of Science (2009) in Industrial and Systems Engineering from Ohio University. In addition to his work on no-shows to outpatient appointments, while at GHS Mr. Dravenstott has improved predictions for surgical case durations, implemented a short-term inpatient bed demand forecasting tool, and developed an Emergency Department discrete-event simulation model. Prior to joining GHS, Ron was a research assistant at Ohio University developing manufacturing cost estimation software for General Electric Aircraft Engines, Electric Power & Water Systems.

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Martin Puterman

University of British Columbia Sauder School of Business

Using Analytics to Improve Access to Cancer Care

Operations research methods have been used extensively to address challenges facing British Columbia Cancer Agency management when striving to deliver high quality and timely care to cancer patients. While focusing on cancer care, principles and analytic strategies described in my presentation apply equally well to other healthcare and service systems. Reducing wait times for initial oncologist consultations required accounting for demand variability, the multiplicity of cancer types and urgency levels, oncologist specialization mix and downstream demand generated by initial appointments. Optimization and simulation models showed the impact of managerial levers on relevant performance metrics and produced recommendations to reduce wait times. Providing acceptable notification lead times for chemotherapy appointments lead to an in depth study of chemotherapy unit operations and booking practices and the creation of Chemo SmartBook, an integer programming based chemotherapy appointment scheduling system that is currently used at four regional BCCA centers.


Martin L. Puterman is Professor Emeritus at UBC’s Sauder School of Business. He regularly communicates with professional audiences through his numerous consultancies, and his regular interactions with senior health care managers. For the past 5 years he has taught a highly regarded course on health care operations to Executive and full time MBA students.

He was founder and director of the UBC Centre for Health Care Management, the Centre for Operations Excellence at UBC and the Biostatistical Consulting Service at BC Children’s Hospital. He has consulted widely on health care operations and planning, statistical modeling, logistics, inventory control, forecasting, operations management, program evaluation and management strategy.

He received the prestigious INFORMS Lanchester Prize, is an INFORMS Fellow and recipient of the Canadian Operations Research Society (CORS) Award of Merit, the CORS Practice Prize and the INFORMS case prize.

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Christopher Stromblad

Geisinger Health System

StratBAM: Geisinger Health System’s Strategic Bed Analysis Model

How do executives best utilize their most expensive resources and deliver value in a highly variable environment? At Geisinger our high level decision makers are faced with this question when deciding how many beds are needed to ensure the right care at the right time for our patients. Studies have shown that elongated emergency room wait times until inpatient admission (>6 hours) are associated with an increased risk of mortality. In addition, the initial cost of an inpatient high acuity bed can be as high as $1 million, emphasizing the need for an analytic approach to support all bed capacity decisions. This presentation will detail how our team:

  • Analyzed patient flow data (>70,000 inpatients) from our electronic health record
  • Understood the detailed patient placement processes and the market forecast methods
  • Developed, validated, and applied StratBAM
  • Transformed a decision making process to be strategic, objective, data driven, and robust


Christopher Thomas Strömblad is a Senior Operations Research Modeler at Geisinger Health System’s Division of Clinical Innovation and a research associate at the Geisinger Center for Healthcare Systems Re-Engineering. At Geisinger, Chris has optimized outpatient clinic and physician scheduling with the objective of improving access to care and seeing more patients using Mixed-Integer Programming. Chris has also provided strategic decision support to multi-million dollar inpatient bed capacity challenges at times of hospital expansion and renovation.

Prior to Geisinger Chris worked at Accenture Copenhagen as part of a team of consultants and developed an IT-Vision for Save The Children Denmark.

Chris serves as a Councilor at the INFORMS Health Applications Society (HAS) and as Chair for the INFORMS HAS Practitioner Engagement Committee.

Chris has a Master of Science Dual Degree in Industrial Engineering and Operations Research from The Pennsylvania State University and a Bachelor of Science in Engineering Mathematics from The Technical University of Denmark.

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Aysegul Topcu

Biogen Idec, Inc.

Risk Analytics in a Global Biopharmaceutical Supply Chain

Biopharmaceutical companies are exposed to a diverse set of risks in operating their supply chains, whether it is dealing with supply shortages, regulatory compliance, legal issues, natural disasters, financial, quality or safety challenges. These risks can not only waste companies’ resources but also threaten the patients’ life by disrupting the supply of medicines in many ways. As a result, to remain viable, companies are looking for ways to design and conduct a consistent risk management process to manage risk. This is usually an ongoing process of (1) risk assessment, (2) risk treatment, and (3) communication and awareness. In this talk, I’ll talk about:

  • Identifying the risk metrics faced in a global biopharmaceutical supply chain,
  • Transforming the data about the risk metrics into a consistent form that can be used to make decisions around prioritization,
  • Taking the information obtained from risk assessment and formulating strategies, plans and actions to bring risks to an acceptable level,
  • Formalizing the lessons learned and using tools to capture that knowledge in a reusable form that can be shared with others.


Aysegul Topcu, Ph.D. is the Manager of Operations Research at Biogen Idec, Global Supply Chain. She received her B.S. and M.S. degrees in Industrial Engineering from the University of Wisconsin – Madison, and her Ph.D. degree in Industrial Engineering from Northeastern University. Before joining Biogen Idec, she was a Senior Operations Researcher at Structured Decisions Corporation where she was responsible to provide operations research expertise to companies in service industries and was in charge of developing mathematical models for complex logistical operations. In addition to her industry experience, she has also held adjunct teaching faculty appointments at Boston College and Worcester Polytechnic Institute. In 2012, Dr. Topcu was nominated and attended the INFORMS Conference on Business Analytics and Operations Research as a participant of the Richard E. Rosenthal Young Researcher Connection.

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