Longitudinal evaluation of workflow optimization in radiotherapy: A 4-year retrospective study

Area of Science:

• Radiotherapy workflow optimization
• Medical physics and radiation oncology
• Healthcare operational efficiency

Background:

• Inefficiencies in radiotherapy workflows, including long patient wait times and suboptimal treatment planning, were identified in 2020.
• Targeted workflow improvements were implemented starting in early 2021 to address these critical deficiencies.

Purpose of the Study:

• To retrospectively evaluate the impact of radiotherapy workflow optimization on operational and clinical metrics from 2021 to 2024.
• To assess the effectiveness of implemented changes in improving radiotherapy delivery.

Main Methods:

• Analysis of data from 25,838 patients undergoing radiotherapy.
• Implementation of AI-assisted tumor contouring, standardized planning protocols, dynamic scheduling, automated quality control, and machine learning for predictive maintenance.
• Evaluation of key performance indicators including wait times, planning durations, and quality control efficiency.

Main Results:

• Total admission-to-treatment duration decreased by 30% (from 12.83 days to 8.78 days).
• Significant reductions observed in treatment planning preparation time (5.75 to 2.57 days) and duration (2.68 to 1.33 days).
• Decreased clinical review time (0.78 to 0.46 days) and plan transfer time (0.42 to 0.24 days).

Conclusions:

• Workflow optimization led to sustained improvements in radiotherapy efficiency and safety.
• AI-driven tools, standardized workflows, and data-driven management are crucial for advancing global radiotherapy practices.
• The study highlights positive impacts on patient satisfaction and overall care delivery.