Daily outpatient chemotherapy appointment scheduling with random deferrals

Abstract

In this paper we propose a heuristic approach that computes the order in which patients will be treated in an ambulatory chemotherapy center. Each patient follows an individual treatment plan that fixes dates for series of drug injections separated by recovery periods. The daily care process has three steps: consultation with the oncologist, drug preparation in the pharmacy and drug injection in medical beds. The facility closes after the last injection. As drug injection varying considerably in duration -from 15 minutes to 6 hours -bad schedules lead to excessive overtime. In addition, after the consultation the oncologist may decide to cancel the injection because of a weak patient's health condition. In the current setting of the chemotherapy facility we work with, First Come First Served policy controls the care process. In this study, we propose to compute a common priority list of patients for consultation and injection phases. A unique list of patients is a simple tool used by nurses to manage the flow of patients and to react to uncertain events. A GRASP algorithm is developed to compute optimized list of patients in few seconds as the operating planning context requires. Two objectives are considered; the closing time and the overworking time of the facility. Numerical experiments show that our GRASP is able to quickly reach near optimal solutions and that list of patients policy performance is comparable to more complex scheduling policies. Benchmark data sets are built based on historical data of the French chemotherapy facility, ICL in Saint-Étienne.

Master chemotherapy planning and clinicians rostering in a hospital outpatient cancer centre

“…An optimisation model to define the sequence of patients to be scheduled for chemotherapy infusion is presented in Garaix et al (2020). The model considers the uncertainty in the deferral due to the patient inability to receive the treatment.…”

Multi-stage appointment scheduling for outpatient chemotherapy unit: a case study

“…Woodall et al [41] used simulation based optimization to find the optimal nurse schedules with the objective of minimizing expected waiting times. Garaix et al [7] proposed a heuristic approach to schedule a list of patients for three main phases: consultation, drug preparation and injection phases. Two objectives are considered; the closing time and the overworking time of the clinic.…”

“…We notice that the literature chemotherapy scheduling mostly focuses on one phase especially the injection phase and its human and material resources management. To the best of our knowledge, only one paper similarly considered three main phases [7]. However, they did not consider the same assumptions and assumed that an adequate number of nurses is always available where the nurse capacity is a fundamental constraint in our study.…”

Improving chemotherapy infusion operations through the simulation of scheduling heuristics: a case study

“…Yokouchi et al (2012) also suggests an appointment scheduling system based on infusion times but stop short of providing a heuristic scheduling rule. A few studies suggest ordering patients by decreasing infusion time is a good target sequence (Garaix, Rostami, & Xie, 2018;Suss, Bhuiyan, Demirli, & Batist, 2017), though Garaix et al (2018) is limited by strong assumptions such as "the pharmacy is not a bottleneck and drugs arrive on time," there is no idle time in an oncologist's schedule, and all patients arrive on time.…”