Development and Evaluation of Aria Report-Based Initial Chart Check Tool 📝

Author: Ulrich Langner, Timothy Leech, Oleksii Semeniuk 👨‍🔬

Affiliation: Brown University Health 🌍

Abstract:

Purpose: To standardize and improve quality of the chart review process, while simultaneously increasing the efficiency of the physicist, by allowing them to allocate more time for other items.
Methods: The automated chart checker tool was developed using the RayStation scripting environment as well as the reporting tools in Aria. Following the TG-275 recommendations, the script verifies the fundamental failure modes in TPS plan data and the patient summary report, generated by the radiation oncology system (Aria). The generic physics summary Aria reports have been expanded beyond the machine parameters data to include comprehensive prescription and treatment delivery information. The script returns a summary PDF document, color-coding the match in critical nodes of information reviewed by the physics staff. It was tested on >600 patients, receiving various type of treatment, including 3D-CRT, IMRT and electron treatments.
Results: Utilization of the report was successful in catching the cases with incorrect prescription, patient information, and erroneous machine parameters as the clinical treatment plan, pushed to the record and verify system. While the treatment plan quality was clinically acceptable, the treatment delivery parameters and data transfer errors were found. The dominant failure mode were incorrect scheduled imaging and insufficient treatment time setting for the 21iX linac. While rare, erroneous monitor units, were also detected while other plan parameters were correct. While some errors could be caught during patient specific QA of IMRT plans, they can reach a patients treated with 3D-CRT, leading to treatment delays and potentially medical event.
Conclusion: The chart checker was found to be an essential clinical tool for quality control in preparation of radiation therapy treatments. In addition to verification of machine parameter propagation, it provides a fast fundamental insight on treatment plan quality and can also be used for staff training and improvement of clinic efficiency.

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