Robust optimization of a radiotherapy pretreatment preparation workflow

Area of Science:

• Medical Physics
• Operations Research
• Radiotherapy

Background:

• Radiotherapy departments face strain due to increasing cancer incidence, staff shortages, and high burnout rates.
• Clinical implementation of operations research (OR) tools in radiotherapy is limited due to a lack of robustness against patient-specific task duration variations.
• Optimizing radiotherapy processes is crucial for improving efficiency and reducing staff burden.

Purpose of the Study:

• To develop and evaluate a method combining OR tools for robust pretreatment preparation scheduling in radiotherapy.
• To balance expected average patient preparation time (Fmean) with the risk of working overtime (RoO).
• To optimize resource deployment in a one-stop shop (OSS) outpatient clinic setting.

Main Methods:

• Developed a genetic algorithm to generate optimized sequences for radiotherapy pretreatment preparation tasks.
• Used a decoder to translate sequences into schedules and evaluate fitness parameters Fmean and RoO.
• Evaluated the method in various settings of an one-stop shop (OSS) outpatient clinic.

Main Results:

• The proposed approach reduced the expected Fmean of the current clinical schedule by 37% without increasing RoO.
• Experiments provided insights into trade-offs between Fmean, RoO, shift length, patient volume, and staff composition.
• Demonstrated the potential for significant workflow optimization in radiotherapy.

Conclusions:

• Operations research tools can optimize radiotherapy resources through robust pretreatment workflow scheduling.
• The developed method offers a promising approach for improving efficiency and reducing burnout in radiotherapy departments.
• Further exploration of scheduling optimization is supported for various clinical settings beyond radiotherapy.