Achieving level patient flow in an outpatient oncology clinic

ABSTRACT

In this article, we model an oncology clinic and treatment center as a multi-stage system of interdependent stochastic processes and illustrate the mechanisms leading to excessive patient waiting times. Insights from studying the simulations of the clinic's operations point to potential improvement of the system through the planning of patient appointments to achieve level patient flow. We construct and test a novel algorithm to calculate patient appointment times so that waiting is significantly reduced. This algorithm sets patient appointments according to the series of process steps that each patient must follow and the service rates that each of these process steps can provide.

KEYWORDS:

• Chemotherapy
• care delivery process
• healthcare management
• oncology
• outpatient scheduling

Acknowledgments

We are grateful to the Rossy Cancer Network, Mitacs, and Chronometriq for their support in this project.

Notes

1 NORM, WEIB, BETA, GAMM denote the Normal, Weibull, Beta, and Gamma probability distributions, respectively.

2 The number of patients of type P in each time slot is derived from subtracting the number of patients of type PR1+PR2+PR3 in each time slot from the number of patients that could be seen by the oncologist in each time slot (since scheduling practice fully books the clinic hours of each oncologist).

3 BETA, GAMM, LOGN denote the Beta, Gamma, and Lognormal probability distributions, respectively.

4 Pharmacist validation service time was measured where the pharmacist could complete the work without interruption due to missing information.

Additional information

Funding

Mitacs(10.13039/501100004489, IT05261)