  While natural language processing (NLP) of unstructured clinical narratives
holds the potential for patient care and clinical research, portability of NLP
approaches across multiple sites remains a major challenge. This study
investigated the portability of an NLP system developed initially at the
Department of Veterans Affairs (VA) to extract 27 key cardiac concepts from
free-text or semi-structured echocardiograms from three academic medical
centers: Weill Cornell Medicine, Mayo Clinic and Northwestern Medicine. While
the NLP system showed high precision and recall measurements for four target
concepts (aortic valve regurgitation, left atrium size at end systole, mitral
valve regurgitation, tricuspid valve regurgitation) across all sites, we found
moderate or poor results for the remaining concepts and the NLP system
performance varied between individual sites.
