Room: AAPM ePoster Library
Purpose: To develop a real-time digital whiteboard system to track, record, and evaluate time frames associated with clinical radiation oncology treatment planning processes. The goal of the system is to find ?common issues/delays, aid in advanced planning with anticipation of problems, and inform recommendations for task completion time goals for planning in prospective individual cases.
Methods: We developed a digital whiteboard software using an R environment employing the Shiny package library for web applications. The planning workflow was divided into six abstracted stages and each patient was graphically displayed in its current stage. Patient stages were updated automatically on completion of quality check lists (QCLs) in our Mosaiq instance(Elekta, Sweden). Time-stamped moves between planning stages were recorded. Whiteboard logs were merged with diagnostic factors extracted from Mosaiq database and evaluated visually for trend and statistically for significance.
Results: Since implementation, the Whiteboard has become an essential clinical tool for tracking patients and checking planning status. Examining the merged time data with data extracted from the oncology information system (OIS), we found trends in the overall plan completion time with respect to diagnostic and prescriptive factors. Treatment intent, number of prescriptions, and nodal involvement were the main factors influencing overall time to plan completion. We found that overall planning time is specific to disease site and palliative cases take half as long to plan as curative ones. Among all sites, planning time increased linearly with the number of nodes involved. The same was true for the number of prescriptions.
Conclusion: The developed Whiteboard establishes the utility of real-time task-tracking tools in the radiotherapy planning process. The results provide data-driven evidence which adds justification for practice change implementations and workflow refinement. Specifically, this information could be used to optimize patient scheduling and minimize bottlenecks in the planning process.