Enhancing Radiation Oncology Physics Chart Review through Automation and Data Validation
Abstract
Purpose
The medical physics chart check process is a critical safety step in radiation therapy, serving as one of the final validations of a patient’s treatment plan prior to delivery. Despite its importance, studies have shown that a significant proportion of treatment planning errors may go undetected due to the complexity of plan data, time constraints, and reliance on manual review. To address these challenges, this project explored the use of Python programming to automate portions of the physics chart check process.
Methods
A Python-based application was developed to compare treatment plan data exported from the MOSAIQ patient information system and the Monaco treatment planning system. The software parses RTP and DICOM files, validates key plan parameters, and presents results through a graphical user interface that highlights discrepancies and automates selected pass/fail checks. Additional features were implemented to support deeper data analysis, including visualization of gantry, collimator, couch, and multi-leaf collimator parameters. The software offers statistical analysis for the MU distribution through the control points. This is helpful in predicting PSQA requirement for the plan. Current version of the software is installed on the server and can be accessed by any node within the hospital network.
Results
Preliminary clinical testing demonstrated that approximately 40–60% of chart check tasks could be automated or flagged using the tool, reducing repetitive manual comparisons while maintaining physicist oversight. Users reported improved efficiency and clarity in identifying plan inconsistencies, although the software’s capabilities are limited by the scope of data available for export from clinical systems.
Conclusion
Overall, this project demonstrates that Python-based automation can meaningfully support the medical physics chart check process by improving efficiency, consistency, and data visualization. With further development and integration, this tool has the potential to enhance patient safety and reduce workload in clinical radiation oncology environments.