Development of a Rule-Based Knowledge Base for Digitalization of Standard Operating Procedures on Radiotherapy in Breast Cancer

Background: Traditional text-based standard operating procedures (SOPs) have limitations in today's data-rich environment, as they provide limited support for automated reasoning, contextual guidance, and real-time clinical decision-making. We developed a rule-based knowledge base to overcome these challenges through digitalization of SOPs on breast cancer radiotherapy. Methods: We designed and developed a web application with a relational database structured around Common Data Elements (CDEs), a rule-based inference engine, and a responsive user interface (UI). The system's information retrieval success was evaluated in a single-center study over 14 months, where nine radiation oncologists submitted clinical queries and provided feedback on the relevance of the results. Results: The knowledge base incorporated 103 specific information entries covering 8 main topics, structured around critical clinicopathological features such as tumor stage, receptor status, grading, and lymphovascular invasion. During a 14-month testing period, nine radiation oncologists submitted 62 distinct clinical queries (e.g., determining boost indication for a patient of certain age and tumor situation). Of these queries, 56.5% were successfully answered as indicated by the user. Analysis of unsuccessful queries revealed that the information was mostly present but inaccessible due to user-experience issues. Conclusion: Our rule-based knowledge base demonstrates the feasibility of transforming static SOPs into interactive, evidence-linked resources. While promising, substantial user experience barriers remain. Future enhancements will prioritize user-interface improvements, sustainable knowledge-updating mechanisms, and AI integration to strengthen the system for practical use.