Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence

Abstract

Clinical guidelines integrate latest evidence to support clinical decision-making. As new research findings are published at an increasing rate, it would be helpful to detect when such results disagree with current guideline recommendations. In this work, we describe a software system for the automatic identification of disagreement between clinical guidelines and published research. A critical feature of the system is the extraction and cross-lingual normalization of information through natural language processing. The initial version focuses on the detection of cancer treatments in clinical trial reports that are not addressed in oncology guidelines. We evaluate the relevance of trials retrieved by our system retrospectively by comparison with historic guideline updates and also prospectively through manual evaluation by guideline experts. The system improves precision over state-of-the-art literature research strategies while maintaining near-total recall. Detailed error analysis highlights challenges for fine-grained clinical information extraction, in particular when extracting population definitions for tumor-agnostic therapies.