Process Modeling a Radiation Oncology Clinic Workflow From Therapeutic Simulation to Treatment: Identifying Impending Strain and Possible Treatment Delays

• Published in: Advances in radiation oncology 2023-11, Vol.8 (6), p.101261-101261, Article 101261
• Main Authors: Chowdhry, Varun Kumar, Simpson, Natalie Catherine
• Format: Article
• Language: English
• Subjects: Research Letter
• ISSN: 2452-1094; 2452-1094
• DOI: 10.1016/j.adro.2023.101261

The administration of safe, high-quality radiation therapy requires the systematic completion of a series of steps from computed tomography simulation, physician contouring, dosimetric treatment planning, pretreatment 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. We set out to understand the systemic dynamics as to how varying patient arrival rate can affect treatment turnaround times using Monte Carlo simulations. 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 the 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 effect of treatment turnaround times from simulation to treatment. We used processing-time estimates determined in prior focus studies for each of the required steps. Altering the number of patients simulated from 1 per week to 10 per week resulted in a corresponding increase in average processing time from simulation to treatment from 4 to 7 days. The maximum processing time for patients from simulation to treatment ranged from 6 to 12 days. To compare individual distributions, we used the Kolmogorov-Smirnov 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 = .03). The results of this simulation-based modeling study confirm the appropriateness of current staffing levels to ensure timely patient delivery while minimizing staff burnout. Simulation modeling can help guide staffing and workflow models to ensure timely treatment delivery while ensuring quality and safety.