Real-World Evidence Inclusion in Guideline-Based Clinical Decision Support Systems: Breast Cancer Use Case

Abstract

Adopting Clinical Decision Support Systems (CDSS) in clinical practice has shown to benefit both patients and healthcare providers. These CDSS need to be updated when new evidence, data, or guidelines arise since up-to-date evidence directly impacts physician acceptance and adherence to these systems. To this end, in previous studies, methodologies have been developed to update CDSS content by taking advantage of machine learning (ML) algorithms. Modifications in the domain knowledge require a reviewing and validation process before being implemented in clinical practice. Hence, this paper presents a methodology for including real-world evidence in an evidence-based CDSS for breast cancer use case. Decision trees (DT) algorithms are used to suggest modifications based on the analysis of retrospective data, which clinical experts review before being implanted in the CDSS. This way, our methodology allows to combine clinical knowledge from both guidelines and real-world data and enrich the domain clinical knowledge with real-world evidence.