Artificial intelligence–enabled analysis of unstructured EHR data for identification of actionable HER2-low breast cancer phenotypes

ConcertAI Precision360 is a US nationwide, validated, AI-powered platform that utilizes advanced language models to deliver insights from structured and unstructured machine-curated EHR clinical data. This study focused on a subset of Precision360 breast cancer patients diagnosed on or after 1/1/2014 with HER2 results within 180 days of initial diagnosis from both 1) EHR-derived, native, structured data and 2) AI-curated data from unstructured EHR clinical notes. HER2 results as defined by the American Society of Clinical Oncology/College of American Pathologists guidelines within 180 days of initial diagnosis were included and classified as positive (IHC 3+ or IHC2+/FISH positive), low (IHC 2+/FISH negative or IHC 1+) negative, equivocal, or unknown.

HER2 status was available in both structured and unstructured/AI-curated data sources for 27,126 patients. HER2-low rates for structured, unstructured/AI-curated, and the combined data sources of both were 10%, 28%, and 34%, respectively. HER2-negative rates were 73%, 56%, and 52%, while HER2-positive status did not vary between the three groups (13%).

AI-based curation of unstructured clinical notes reclassified a subset of patients, increasing the number of HER2-low patients and reducing those labeled as HER2-negative. Unstructured clinical documentation contained substantially more detailed and higher-resolution HER2 information than structured EHR fields alone. This approach can increase the identification of patients eligible for HER2-targeted therapies