Impact of Quality Improvement Interventions on the Efficiency of Treatment Planning Timelines in a Modern Proton Therapy Clinic
Article
McConnell, KA, Valladares, M, Gutierrez, AN et al. (2026). Impact of Quality Improvement Interventions on the Efficiency of Treatment Planning Timelines in a Modern Proton Therapy Clinic
. 11(1), 10.1016/j.adro.2025.101895
McConnell, KA, Valladares, M, Gutierrez, AN et al. (2026). Impact of Quality Improvement Interventions on the Efficiency of Treatment Planning Timelines in a Modern Proton Therapy Clinic
. 11(1), 10.1016/j.adro.2025.101895
Purpose: Following the guidance of The American Association of Physicists in Medicine (AAPM) Medical Physics Practice Guideline (MPPG) 4a/4b, AAPM Task Groups 100/275, and American Society for Radiation Oncology's Safety is No Accident, our institution focused on quality improvement to streamline clinical workflows, enable complex treatments, standardize procedures, and positively evolve our practice in proton radiation therapy. A retrospective institutional analysis was completed to map interventions identified prior to data analysis that were likely to affect the evolution of the treatment planning timelines. Methods and Materials: Care Paths within our Oncology Information System were used to sequence and track clinical workflows since 2017. Data were mined between 2017 to 2023 to obtain the task's completion and expected completion dates. The task completion offset was calculated to measure the number of days late or early the task was completed. Five quality management interventions were mapped onto control charts for each task to identify the evolution of the practice with each intervention. Average time, SDs, and statistical significance before and after each intervention were also computed. Additionally, total treatment planning times were computed for each patient and histograms, average time, median time, and standard error of the mean were computed and compared by year. Results: Task completion offsets improved from being, on average, 1.59 to 2.63 days late to 0.06 to 2.25 days early, with control charts visually showing the reduction in mean value, reduction in SD, and ultimately, the processes falling more into control. Interventions 1, 2, and 3 showed the strongest overall statistical impact on task completion offsets. Overall, planning timelines improved from a median of 19 days to 11 days. More importantly, the distributions of overall planning time and spread of these times became Gaussian, demonstrating the characteristics of normalized activity patterns, with a reduction in variability. Conclusions: The interventions identified before data collection were well associated with the evolution of the treatment planning timeline data. When quantifying with control charts, there were noted decreased task completion offset variabilities across many examined tasks. Additionally, the data showed shortened overall planning timelines during the time that the complexity of protons plans was increased, newer delivery approaches were made available, and more complex clinical scenarios were incorporated.
