TY  - JOUR
AU  - Elkin, Peter L
AU  - Mullin, Sarah
AU  - Mardekian, Jack
AU  - Crowner, Christopher
AU  - Sakilay, Sylvester
AU  - Sinha, Shyamashree
AU  - Brady, Gary
AU  - Wright, Marcia
AU  - Nolen, Kimberly
AU  - Trainer, JoAnn
AU  - Koppel, Ross
AU  - Schlegel, Daniel
AU  - Kaushik, Sashank
AU  - Zhao, Jane
AU  - Song, Buer
AU  - Anand, Edwin
PY  - 2021
DA  - 2021/11/9
TI  - 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
JO  - J Med Internet Res
SP  - e28946
VL  - 23
IS  - 11
KW  - afib
KW  - atrial fibrillation
KW  - artificial intelligence
KW  - NVAF
KW  - natural language processing
KW  - stroke risk
KW  - bleed risk
KW  - CHA2DS2-VASc
KW  - HAS-BLED
KW  - bio-surveillance
AB  - 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 CHA2DS2‑VASc ≥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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/11/e28946
UR  - https://doi.org/10.2196/28946
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34751659
DO  - 10.2196/28946
ID  - info:doi/10.2196/28946
ER  -