Clinical Implementation of a Digital QA Management Platform for Monthly Quality Assurance Testing In a Radiotherapy Department
Abstract
Purpose
To show the clinical implementation process of a digital quality assurance (QA) management and analysis platform for monthly testing of modern linear accelerators empathizing workflow standardization, automated analysis and reduced reliance on manual documentation.
Methods
The platform was deployed through the creation of an inventory of seventy nine (79) clinical assets, including linear accelerators, HDR equipment, QA phantoms, ionization chambers, electrometers and related measurement equipment. These assets were assigned into a monthly QA test list comprising mechanical, dosimetric and image-based evaluations, with tolerance criteria to support automated pass/fail assessments and report generation. The ability to associate variable names to each test allowed exploring the use of scripting to expand or modify predefined test templates and integrate specific dosimetric calculations (RadFormation test), using parameters traceable to the calibration of the instruments. For image-based work flows, preconfigured modules with system-registered phantoms were used, after selecting the appropriate phantom and importing the scan images, relevant slices and target objects were automatically identified for analysis.
Results
The platform enables monthly QA documentation and organization within a single digital workflow, providing traceability across tests, instruments and calibration status. Retrieval of prior results for comparison and historical review was streamlined, reducing omissions during routine execution and decreasing dependence on spreadsheets and paper records. These features improved consistency across consecutive monthly QA sessions and supported structured review and internal oversight of recorded outcomes.
Conclusion
Implementation of a digital QA platform improved the consistency of monthly QA by standardizing tasks, tolerances and documentation. Structured access to historical records supported trend identification and more informed clinical decision-making. This approach is well suited for environments with multiple systems and high operational workload.