Using Artificial Intelligence With Natural Language Processing to Combine Electronic Health Record's Structured and Free Text Data to Identify Nonvalvular Atrial Fibrillation to Decrease Strokes and Death: Evaluation and Case-Control Study.

BACKGROUND: Nonvalvular atrial fibrillation (NVAF) affects almost 6 million Americans and is a major contributor to stroke but is significantly undiagnosed and undertreated despite explicit guidelines for oral anticoagulation. OBJECTIVE: The aim of this study is to investigate whether the use of semisupervised natural language processing (NLP) of electronic health record's (EHR) free-text information combined with structured EHR data improves NVAF discovery and treatment and perhaps offers a method to prevent thousands of deaths and save billions of dollars. METHODS: We abstracted 96,681 participants from the University of Buffalo faculty practice's EHR. NLP was used to index the notes and compare the ability to identify NVAF, congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, stroke or transient ischemic attack, vascular disease, age 65 to 74 years, sex category (CHA2DS2-VASc), and Hypertension, Abnormal liver/renal function, Stroke history, Bleeding history or predisposition, Labile INR, Elderly, Drug/alcohol usage (HAS-BLED) scores using unstructured data (International Classification of Diseases codes) versus structured and unstructured data from clinical notes. In addition, we analyzed data from 63,296,120 participants in the Optum and Truven databases to determine the NVAF frequency, rates of CHA2DS2­VASc ≥2, and no contraindications to oral anticoagulants, rates of stroke and death in the untreated population, and first year's costs after stroke. RESULTS: The structured-plus-unstructured method would have identified 3,976,056 additional true NVAF cases (P<.001) and improved sensitivity for CHA2DS2-VASc and HAS-BLED scores compared with the structured data alone (P=.002 and P<.001, respectively), causing a 32.1% improvement. For the United States, this method would prevent an estimated 176,537 strokes, save 10,575 lives, and save >US $13.5 billion. CONCLUSIONS: Artificial intelligence-informed bio-surveillance combining NLP of free-text information with structured EHR data improves data completeness, prevents thousands of strokes, and saves lives and funds. This method is applicable to many disorders with profound public health consequences.

Use of Chronic Medications Among Patients with Non-Valvular Atrial Fibrillation.

BACKGROUND: Frequency of administration (once daily versus more than once daily) is believed to be an important consideration affecting drug choice. OBJECTIVE: The aim of this study was to describe the characteristics of patients with non-valvular atrial fibrillation (NVAF) and the extent to which they take chronic medications, other than anticoagulants, more frequently than once daily. METHODS: Using data from a large, national database of health insurance claims, patients with a diagnosis of NVAF between 1 July 2008 and 30 September 2011 were identified, along with their prescription medications, to determine the proportion of patients taking chronic medications more than once a day. Prescription medications, co-morbidities, and CHADS2 and CHA2DS2-VASc scores were evaluated. CHADS2 assesses the risk of stroke in NVAF patients with the following risk factors: Congestive heart failure, Hypertension, Age ≥75 years, Diabetes mellitus, and history of prior Stroke or transient ischemic attack. The CHA2DS2-VASc score adds the following risk factors to the CHADS2 score: Age 65-74 years, Vascular Disease, and Sex Category (Female). RESULTS: Overall, 324,172 patients with NVAF with mean CHADS2 and CHA2DS2-VASc scores of 1.51 and 3.08, respectively, were included in the study. Of these patients, 299,716 (92.5 %) took chronic medications, with an average of 6.9 medications per patient, and 215,527 (66.5 % of all patients or 71.9 % of those taking chronic medications) took medications more than once per day. CONCLUSION: Use of chronic medications other than anticoagulants is common among patients with NVAF, and medications are typically taken multiple times per day. The average number of medications per patient and multiple therapeutic classes prescribed underscore the clinical complexity of NVAF patients. Hence, the choice of a once daily anticoagulant versus a more than once daily anticoagulant may be less relevant in a real world NVAF population in terms of a potential convenience benefit.