(Sunday, 7/12/2020) [Eastern Time (GMT-4)]
Room: AAPM ePoster Library
Purpose: Cancer waits for nobody; the sooner a patient can be treated the likelihood for malignancies and tumor size will be more maintainable. Therefore, its pertinent that clinics are efficient in their task to provide the best care possible. However, with the multiple staff members involved, tasks can become quite challenging to track, and tasks can slow down due to the chaos of the system. Thus, by developing a system that provides feedback can reduce bottlenecks and provide insight into the planning process. The purpose of this study is to develop an integrated system that allows for real-time clinical status of patient plans and alerts clinical staff to delays.
Methods: Using the python language, a set of scripts were designed to read the MOSAIQ database information for currently active patients. Information was assimilated based on treatment type, attending, and required tasks. Based on task generation and due times, a set of stoplight colors is assigned to each active task that changes over time. Time calculations for each task item were limited only to the working hours of the clinic. Information was then displayed using python’s flask package as an interactive webpage.
Results: The whiteboard system has been active clinically for six months now and shown an overall average of 7% in time reduction for the overall treatment planning process. While the patient load has increased, each task set and department within radiation oncology was shown to have reduced their time to complete tasks over this period. Statistics for each task have been reported monthly to the administration and has allowed them to generate better policies and practices to alleviate more common issues.
Conclusion: The whiteboard tool is relatively new and has room for improvement and functional improvements, however it has already shown to be a worthwhile implementation into the clinic.
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