Introduction: Statins are life-saving medications for patients with atherosclerotic cardiovascular disease (ASCVD), but women with ASCVD are persistently less likely to be prescribed statins than men. This study aims to use Natural Language Processing (NLP) to further elucidate patient and provider factors contributing to this disparity. Methods: The study cohort included patients with >2 ASCVD encounters between 2014 and 2021 within a multisite electronic health record (EHR) in Northern California. Data from a random sample of our cohort (N = 942) was manually annotated to develop a benchmark deep learning natural language processing (NLP) approach, Clinical Bidirectional Encoder Representations from Transformers (BERT); 80% of these were used for model training and 20% for testing. After reviewing structured EHR data (e.g. prescriptions, allergies), BERT was used to identify and interpret discussions of statins in clinical notes. Results: Of 88,913 patients with ASCVD (mean age 67.8 ± 13.1 years), 35,901 (40.4%) were women. Women were less likely to be prescribed statins (56.6% vs. 67.6%, p < 0.001). Only 18.6% of nonallergic patients without a statin prescription had a mention of statins in unstructured EHR text. Statin use through unstructured text was less likely to be identified among women than men (32.8% vs. 42.6%, p < 0.001). Reasons for statin nonuse did not significantly differ by gender (Figure). Conclusions: Women with ASCVD were less likely to be use statins, and this disparity became more pronounced with the inclusion of unstructured data. An NLP approach revealed actionable reasons for statin nonuse. Future studies should leverage these approaches to monitor and track statin adherence by combining structured and unstructured data in real-world populations.