Using artificial intelligence to improve capture of metastatic breast cancer (BC) status in Electronic Health Records (EHR)

Though an important prognostic feature in cancer, stage information is often missing from patient’s EHRs and unavailable in claims data. The primary objective of this study was to develop and validate an artificial intelligence model that classifies metastatic status in BC patients at their last observed timepoint (proxy for present-day) using previously collected, de-identified, retrospective EHR data. This model yielded high precision and recall, and thus could be an important tool for imputing missing stage information in EHRs. This could save substantial time and resources by quickly identifying patients for clinical trial enrollment or retrospective outcomes studies as compared to expert manual curation.