Affiliation:
1. Division of Cardiovascular Medicine and Cardiovascular Institute Stanford University Stanford CA
2. Department of Cardiovascular Medicine Cleveland Clinic Foundation Cleveland OH
3. Department of Medicine Stanford University Stanford CA
4. Department of Biomedical Data Science Stanford University Stanford CA
5. Department of Surgery Stanford University School of Medicine Stanford CA
Abstract
Background
Statins are guideline‐recommended medications that reduce cardiovascular events in patients with diabetes. Yet, statin use is concerningly low in this high‐risk population. Identifying reasons for statin nonuse, which are typically described in unstructured electronic health record data, can inform targeted system interventions to improve statin use. We aimed to leverage a deep learning approach to identify reasons for statin nonuse in patients with diabetes.
Methods and Results
Adults with diabetes and no statin prescriptions were identified from a multiethnic, multisite Northern California electronic health record cohort from 2014 to 2020. We used a benchmark deep learning natural language processing approach (Clinical Bidirectional Encoder Representations from Transformers) to identify statin nonuse and reasons for statin nonuse from unstructured electronic health record data. Performance was evaluated against expert clinician review from manual annotation of clinical notes and compared with other natural language processing approaches. Of 33 461 patients with diabetes (mean age 59±15 years, 49% women, 36% White patients, 24% Asian patients, and 15% Hispanic patients), 47% (15 580) had no statin prescriptions. From unstructured data, Clinical Bidirectional Encoder Representations from Transformers accurately identified statin nonuse (area under receiver operating characteristic curve [AUC] 0.99 [0.98–1.0]) and key patient (eg, side effects/contraindications), clinician (eg, guideline‐discordant practice), and system reasons (eg, clinical inertia) for statin nonuse (AUC 0.90 [0.86–0.93]) and outperformed other natural language processing approaches. Reasons for nonuse varied by clinical and demographic characteristics, including race and ethnicity.
Conclusions
A deep learning algorithm identified statin nonuse and actionable reasons for statin nonuse in patients with diabetes. Findings may enable targeted interventions to improve guideline‐directed statin use and be scaled to other evidence‐based therapies.
Publisher
Ovid Technologies (Wolters Kluwer Health)
Subject
Cardiology and Cardiovascular Medicine
Cited by
5 articles.
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