Innovative Education Method for a More Effective, Faster, and Valued Training in Radiation Therapy Treatment Planning

Area of Science:

• Medical Physics
• Radiation Oncology
• Health Professions Education

Background:

• Automation in radiation therapy necessitates updated competencies for radiation technologists (RTTs).
• Traditional training methods face challenges in adapting to evolving technological demands.
• Lean management principles offer a framework for optimizing training processes.

Purpose of the Study:

• To develop and pilot test an innovative training method for lung cancer treatment planning (TP) based on lean management principles.
• To enhance the decision-making skills of RTTs in TP through a structured, peer-reviewed environment.
• To evaluate the impact of the new training method on TP quality, trainee experience, and training efficiency.

Main Methods:

• A novel training method was implemented for lung cancer TP, emphasizing a stable environment and critical decision-making rationale.
• Trainees (six students, six RTTs) were required to justify their TP decisions to peers.
• TP quality was assessed using dose metrics for organs at risk and target volumes; perceived experiences were surveyed at baseline, post-peer session, and 6 months later; training throughput time was measured.

Main Results:

• Initial RTTs showed greater variability in dose metrics but lower mean doses to the heart and esophagus compared to students.
• Post-training, TP quality (interquartile range of doses) became similar between RTTs and students, with no significant differences in mean organ doses.
• Trainees reported positive experiences (scores of 7-9/10), and training throughput time decreased from 12 to 3 months, with sustained TP quality at 6 months.

Conclusions:

• The lean management-based training method effectively improved RTT training for lung cancer TP.
• The method significantly reduced training time while maintaining high-quality treatment planning outcomes.
• This approach holds potential for enhancing training efficiency in complex decision-making scenarios within radiation therapy.