A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

Standardized taxonomies allow for consistency across settings and enhance research and analysis. This article describes the collaborative effort of developing a standardized taxonomy based on errors from two natural language processing (NLP) models. There was high variability in error types across approaches and electronic health record systems; continued research and refinement is still needed.