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

Abstract

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.

Effectiveness of Using the FreeStyle Libre® System for Monitoring Blood Glucose during the COVID-19 Pandemic in Diabetic Individuals: Systematic Review

“…It is important to consider that AI is not a solution, but a relevant and alternative tool to assist in the solution [ 21 ]. The purpose of AI is to guide a response, improve care, and save lives [ 22 ]. AI was seen in the context of the COVID19 pandemic as a “force multiplier”, since the world was facing a great challenge, in which it was necessary to carry out large-scale and short-term activities [ 4 ].…”

Bridging the Gap Between Artificial Intelligence Research and Clinical Practice in Cardiovascular Science: What the Clinician Needs to Know

“…A study of >63 million individuals applied NLP to free-text data combined with structured electronic health record data, and correctly detected 3,976,056 further non-valvular AF cases, compared with using structured data alone. 7 Evidently, introducing AI-based detection methods into clinical use could help clinicians screen a vast number of arrhythmia cases that may otherwise have gone undetected and, with appropriate treatment, reduce the likelihood of adverse outcomes in these patients.…”

CDS-Compare: A Web Application for Machine Learning Assisted Curation of Clinical Order Sets

“…Each order set consists of one or numerous medication orders,. We wanted a way to compare all of the order sets by the orders which they contained, therefore we applied High-Definition Natural Language Processing (HD-NLP), a well-tested, high-speed system that processes clinical data and extracts clinically relevant terms in a standardized form, e.g., SOLOR, SNOMED-CT, RxNorm, and LOINC [7,8]. Processing of the study order sets with HD-NLP resulted in 860 unique SNOMED-CT, RxNorm, and LOINC codes across the 1,293 order sets present in our dataset, and every order was mapped to at least one code.…”