Process modeling radiation oncology clinic workflow from therapeutic simulation to treatment: Identifying impending strain and possible treatment delays.

Abstract

39 Background: The administration of safe, high-quality radiotherapy requires the systematic completion of a series of steps from CT simulation, physician contouring, dosimetric treatment planning, pre-treatment quality assurance, plan verification, and ultimately treatment delivery. Nevertheless, due consideration to the cumulative time required to complete each of these steps is often not given sufficient attention when determining patient start date. On one hand, the nature of cancer therapy relies on timely treatment delivery. On the other hand, overly ambitious treatment turnaround times can lead to staff burnout, and result in medical errors. We sought to better understand how changes in patient volume could impact turnaround time through a simulation-based study. Methods: We developed a process model workflow for a single physician, single linear-accelerator clinic that simulated arrival rates and processing times for patients undergoing radiation treatment using AnyLogic Simulation Modeling software (AnyLogic 8 University edition, v8.7.9). We varied the new patient arrival rate from 1 to 10 patients per week to understand the impact treatment turnaround times from simulation to treatment. We utilized processing time estimates for each of the required steps as was determined in a departmental focus group study. We assumed an eight hour workday, and that contouring, treatment planning, verification, and treatment delivery followed the following distributions, respectively (in hours): triangular (2, 4, 16); triangular (8, 16, 60); triangular (0.25, 0.5, 1). Results: Altering the number of patients simulated per week from 1-10 patients resulted in a corresponding increase average processing time from simulation to treatment from 4 to 7 days. The maximum processing time for patient from simulation to treatment ranged from 6-14 days. To compare individual distributions, we utilized the Kolmogorov-Smirnov (K-S) statistical test. We found that altering the arrival rate from 4 patients per week to 5 patients per week resulted in a statistically significant change in the distributions of processing times (p = 0.03) and resulted in a corresponding increase in the maximum patient processing time from approximately 6 days to 12 days, or from approximately 1 week to 2 weeks. Conclusions: The results of this simulation-based modeling study confirm the appropriateness of current staffing levels to ensure timely patient delivery while minimizing staff burnout. However, sustained increases in volume could require increased staffing to ensure timely treatment delivery while minimizing staff burnout. Simulation modeling can help guide staffing and workflow models to ensure timely treatment delivery.