An automated, workload-adjusted framework for continuous quality assessment in clinical radiation oncology

Quality assessment in radiation oncology is essential to ensure safe, timely, and effective care. Although multiple quality indicators have been proposed, their routine implementation is often limited by heterogeneous definitions, manual data collection, and the lack of adjustment for clinical workload and treatment complexity.

Within a national quality initiative, a structured revision of previously proposed quality indicators was performed. A survey among heads of Radiation Oncology departments was conducted to assess the relevance, feasibility, and management value of existing indicators. Based on survey results, a reduced set of prioritized indicators was selected and operationally defined for automated extraction from routine radiotherapy information systems. In parallel, a workload-based complexity stratification was developed, defining six levels for external beam radiotherapy and five levels for brachytherapy. Automated data extraction was implemented using commonly deployed clinical information systems, enabling continuous indicator monitoring without additional manual data entry.

Forty-one department heads participated in the survey, with strong support for reinforcing quality indicator use. The original set of 29 indicators was reduced to 17 prioritized indicators covering structure, process, and outcome domains. Process indicators related to treatment preparation times for conventional and special techniques showed the highest acceptance. Automated extraction resulted in a standardized quality report enabling routine monitoring of indicator performance and data quality. The workload-based complexity stratification revealed substantial heterogeneity across treatment techniques and enabled contextual interpretation of activity and performance.

This study presents an automated and workload-adjusted framework for continuous quality assessment in Radiation Oncology. By integrating prioritized quality indicators, workload-based complexity stratification, and automated data extraction, the proposed approach supports sustainable quality monitoring and facilitates meaningful inter-center comparison. Although developed within a national initiative, the methodological principles are broadly applicable to other healthcare systems and technologically complex oncological settings.