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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Patient Safety

Background:

  • Radiation therapy (RT) involves complex processes with potential for deviations.
  • Ensuring RT process reliability is crucial for patient safety outcomes.
  • Incident learning systems offer valuable data for process analysis.

Purpose of the Study:

  • To develop an innovative method for quantifying, visualizing, and predicting RT process reliability.
  • To utilize data from a voluntary incident learning system to improve patient safety.
  • To establish a metric for proactive identification of potential deviations in RT.

Main Methods:

  • Analysis of 111 reported deviations across 159 mapped RT process steps.
  • Calculation of trip rates and fail-to-catch-rates (FCR) for quality assurance (QA) controls.
  • Application of logistic regression to predict FCRs and quantify RT process reliability.

Main Results:

  • Predicted upper 95% likelihood of uncaught deviations post-treatment planning: 10.26%.
  • Predicted upper 95% likelihood of uncaught deviations before first treatment: 0.0052%.
  • Predicted upper 95% likelihood of uncaught deviations during treatment delivery: 0.0276%.

Conclusions:

  • RT process reliability can be effectively predicted and visualized using incident learning system data.
  • Implementation as a safety metric can help RT clinics proactively manage patient safety.
  • Findings can guide future standardization and continuous improvement of RT QA programs.