TITLE: Cancer Patient Scheduling

SPEAKER: Marty Puterman

ABSTRACT:

This talk will highlight research carried out by the CIHR (Canadian Institutes of Health Research) Team on Operations in Quality Cancer Care which I head up.  In this talk, I will focus on two very different scheduling applications, one applied study which uses multi-criteria discrete optimization to schedule chemotherapy patient’s daily appointments and a more fundamental study which uses approximate dynamic programming to determine effective patient scheduling rules for radiotherapy treatments.  A brief description of each topic follows:

1. Chemotherapy Appointment Scheduling Process Redesign: Manual booking practices in place at the start of this study limited effective demand management and resulted in last-minute rescheduling of appointments.  The consequence of this was heightened stress for patients and staff and operational challenges for the pharmacy and outpatient clinics. The implementation in June 2011 of more flexible booking procedures combined with a custom-built computerized scheduling program based on a multi-criteria discrete optimization model, has alleviated these problems by providing a reasonable timeframe to notify patients of their appointments.  This has reduced unnecessary changes to pre-booked appointments, supported the complex task of organizing the daily treatment schedule and balanced nurse and pharmacy workload. 

2. Dynamic Radiotherapy (RT) Appointment Scheduling:  This research sought to develop good policies for the dynamic scheduling of patients for radiation therapy.  A unique feature of this problem is that scheduling a patient means committing capacity over a course of treatments that can range from 1 to 28 days depending on cancer site and treatment protocol.  Further patients differ with respect to the degree of urgency for their treatment and which RT machines can deliver their therapy.  The practical problem motivating the research involved scheduling 11,000 patients per year on 9 RT machines. To address it, we formulated and solved a discounted infinite-horizon Markov decision process (MDP). We used an affine architecture to approximate the MDP value function and solved an equivalent linear programming model through column generation to obtain an approximate optimal policy for this problem. The benefits of the proposed method are evaluated by simulating its performance for a practical example based on data provided by the BCCA in which relative costs of delays were assessed by RT professionals.  We hope this research will provide the basis for development of a scheduling application.

Co-authors include Antoine Saure, Jonathan Patrick, Scott Tyldesley, John French, Pablo Santibanez, Ruben Aristizabal, Vincent Chow, Kevin Huang and Nancy Runzer

Bio:

Martin L. Puterman is Advisory Board Professor of Operations in UBC’s Sauder School of Business.  He was founder and director of the Centre for Operations Excellence (in Sauder), the UBC Centre for Health Care Management,  and the Biostatistical Consulting Service at BC Children’s Hospital. He is co-principal investigator of the CIHR Team for Operations Research in Quality Cancer Care.

 His research focuses on health care operations research especially pertaining to cancer care delivery and decision making, Markov decision processes and statistical modeling of golf performance and PGAtour structure.  He has consulted widely on health care operations, statistical modeling, inventory control, forecasting, operations management, program evaluation and management strategy.

He received the prestigious INFORMS Lanchester Prize for his book Markov Decision Processes.    He 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.   He has been an editorial board member of Mathematics of Operations Research, Operations Research, Management Science, Production and Operations Management, Manufacturing and Service Operations Management and The Journal of the American Statistical Association.

He received his PhD in Operations Research and an MS in Statistics from Stanford University and AB in Mathematics from Cornell.