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Purpose: The ambulatory patient experience is heavily influenced by wait times for provider care. Delayed patient visit start times may negatively affect overall satisfaction, and increased wait times affect the perception of the information, instructions, and treatment given by health care providers. Improving institutional practices overall requires the determination of the essential quality metrics that will make such an achievement possible. A protracted time leading up to the initiation of radiation therapy may promote poor satisfaction and perceived quality of care for both patients and referring providers alike, which may then create a barrier to patients being treated with radiation therapy. This institution piloted and sucessfully completed a study into improving the timeliness of initiation of patient radiation therapy for our patients. Methods and Materials: This work sought to identify inefficiencies in radiation therapy treatment planning to shorten the time each patient waited for treatment. We examined the time between simulation to the start of the first fraction of treatment. This period includes simulation, contouring, treatment planning, and quality assurance of the plan. Results: Before the study, the planning process would typically take 2 weeks. Target and organs-at-risk contouring were found to be the main inefficiency delaying treatment start dates. This delineating process includes drawing contours on radiologic images, typically computed tomography and magnetic resonance imaging. We focused on the time needed for the contouring process to be completed and took steps to increase efficiency. The length of time from simulation to contour approval was decreased by more than 60%, a reduction from an average of more than 4 days to less than 1.5 days. Overall planning time dropped from 2 weeks to less than 5 days. Conclusions: Process improvements and implementation of task-specific tools improved the timeliness of patient treatments, reducing the overall planning time from simulation to treatments to less than 5 days. Continuous monitoring and modification of these processes revealed that the successes achieved toward better quality of care have been sustained.

Purpose: Stereotactic magnetic resonance image–guided adaptive radiation therapy (SMART) is an emerging technique that shows promise in the treatment of pancreatic cancer and other abdominopelvic malignancies. However, it is unknown whether the time-limited nature of on-table adaptive planning may result in dosimetrically suboptimal plans. The purpose of this study was to quantitatively address that question through systemic retrospective replanning of treated on-table adaptive pancreatic cancer cases. Methods and Materials: Of 74 consecutive adapted fractions, 30 were retrospectively replanned based on deficiencies in planning target volume (PTV) and gross tumor volume (GTV) coverage or doses to organs-at-risk (OARs) that exceeded ideal constraints. Retrospective plans were created by adjusting dose-volume objectives in an iterative fashion until deemed optimized. The goal of replanning was to improve PTV/GTV coverage while keeping the dose to gastrointestinal OARs the same or lower or to reduce OAR doses while keeping PTV coverage the same or higher. The global maximum dose was required to be maintained within 2% of that of the treated adaptive plan to eliminate it as a confounding factor. A threshold of 5% improvement in PTV coverage or 5% decrease in OAR dose was used to define a clinically significant improvement. Results: Of the 30 replans, 7 obtained at least 5% PTV coverage improvement. The average increase in PTV coverage for these plans was 11%. No plans were clinically significantly improved in terms of OAR sparing. Changes in beam-on time did not show any correlation. Statistical analysis via a linear mixed-effects model with a nested random effect suggested that both GTV and PTV coverage were improved over SMART process plans by 0.91 cc (P = .02) and 2.03 cc (P < .001), respectively. Conclusions: Dosimetric plan quality of at least 10% of SMART fractions may be improved through more extensive replanning than is currently performed on-table. Further work is needed to develop an automated replanning workflow to streamline the in-depth replanning process to better fit into an on-table adaptive workflow.