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.