Antoine Sauré

Dynamic multi-appointment patient scheduling for radiation therapy

Seeking to reduce the potential impact of delays on radiation therapy cancer patients such as psychological distress, deterioration in quality of life and decreased cancer control and survival, and motivated by inefficiencies in the use of expensive resources, we undertook a study of scheduling practices at the British Columbia Cancer Agency (BCCA). As a result, we formulated and solved a discounted infinite-horizon Markov decision process for scheduling cancer treatments in radiation therapy units. The main purpose of this model is to identify good policies for allocating available treatment capacity to incoming demand, while reducing wait times in a cost-effective manner. We use an affine architecture to approximate the value function in our formulation and solve an equivalent linear programming model through column generation to obtain an approximate optimal policy for this problem. The benefits from the proposed method are evaluated by simulating its performance for a practical example based on data provided by the BCCA.

The use of discrete-event simulation modelling to improve radiation therapy planning processes

BACKGROUND AND PURPOSE The planning portion of the radiation therapy treatment process at the British Columbia Cancer Agency is efficient but nevertheless contains room for improvement. The purpose of this study is to show how a discrete-event simulation (DES) model can be used to represent this complex process and to suggest improvements that may reduce the planning time and ultimately reduce overall waiting times. MATERIALS AND METHODS A simulation model of the radiation therapy (RT) planning process was constructed using the Arena simulation software, representing the complexities of the system. Several types of inputs feed into the model; these inputs come from historical data, a staff survey, and interviews with planners. RESULTS The simulation model was validated against historical data and then used to test various scenarios to identify and quantify potential improvements to the RT planning process. CONCLUSIONS Simulation modelling is an attractive tool for describing complex systems, and can be used to identify improvements to the processes involved. It is possible to use this technique in the area of radiation therapy planning with the intent of reducing process times and subsequent delays for patient treatment. In this particular system, reducing the variability and length of oncologist-related delays contributes most to improving the planning time.

A Quantitative Analysis of the Relationship Between Radiation Therapy Use and Travel Time

PURPOSE To model and quantify the relationship between radiation therapy (RT) use and travel time to RT services. METHODS AND MATERIALS Population-based registries and databases were used to identify both incident cancer patient and patients receiving RT within 1 year of diagnosis (RT1y) in British Columbia, Canada, between 1992 and 2011. The effects of age, gender, diagnosis year, income, prevailing wait time, and travel duration for RT on RT1y were assessed. Significant factors from univariate analyses were included in a multivariable logistic regression model. The shape of the travel time-RT1y curve was represented by generalized additive and segmented regression models. Analyses were conducted for breast, lung, and genitourinary cancer separately and for all cancer sites combined. RESULTS After adjustment for age, gender, diagnosis year, income, and prevailing wait times, increasing travel time to the closest RT facility had a negative impact RT1y. The shape of the travel time-RT1y curve varied with cancer type. For breast cancer, the odds of RT1y were constant for the first 2 driving hours and decreased at 17% per hour thereafter. For lung cancer, the odds of RT1y decreased by 16% after 20 minutes and then decreased at 6% per hour. Genitourinary cancer RT1y was relatively independent of travel time. For all cancer sites combined, the odds of RT1y were constant within the first 2 driving hours and decreased at 7% per hour thereafter. CONCLUSIONS Travel time to receive RT has a different impact on RT1y for different tumor sites. The results provide evidence-based insights for the configuration of catchment areas for new and existing cancer centers providing RT.

The Appointment Scheduling Game

This paper describes the appointment scheduling game ASG, an easy to use teaching tool that reveals the challenges in managing advance patient scheduling systems, and also provides an introduction to simulation and decision analysis. In addition to describing the game, the paper provides recommendations on how to play it, student questions and suggested answers, and a Markov decision process MDP formulation. The ASG simulates a system in which daily patient appointment requests, which are characterized by their urgency level, arrive randomly. Daily service capacity is limited. Students playing the game assume the role of a scheduling clerk who must assign appointment dates to these requests without knowing future demand. They are left to discover the need for performance metrics, data collection, and strategy formulation. An attractive feature of the game is that it requires only a printed one-month calendar, multicolored poker chips, and a standard six-sided die. Although the game is primarily aimed at undergraduate and graduate operations students, it also can be used to introduce a range of MDP concepts to advanced operations research students. The game has been used successfully in several courses at the University of British Columbia including “Managing Health Care System Operations” MBA, “Managing Patient Flow” executive MBA in healthcare and “Logistics and Operations Management” undergraduate. It has also been used by colleagues at the University of Ottawa, McGill University, and the University of Michigan.

Advance Patient Appointment Scheduling

This chapter describes the use of the linear programming approach to approximate dynamic programming as a means of solving advance patient appointment scheduling problems, which are problems typically intractable using standard solution techniques. Starting from the linear programming approach to discounted infinite-horizon Markov decision processes, and employing an affine value function approximation in the state variables, the method described in this chapter provides a systematic way of identifying effective booking guidelines for advance patient appointment scheduling problems. Two applications found in the literature allow us to show how these guidelines could be used in practice to significantly increase service levels for medical appointments, measured as the percentage of patients booked within medically acceptable wait times, and thus to decrease the potential impact of delays on patients’ health.