Abstract 21: Comparison of Methods to Identify Atrial Fibrillation Patients from the Electronic Medical Record

Abstract

Background: Healthcare systems lack a validated method to identify specific patient populations for quality improvement and research. We compared the performance of 5 different methods, for identifying patients with atrial fibrillation (AF) from the electronic medical record (EMR). We also evaluated the impact of each method on apparent oral anticoagulant (OAC) treatment rates. Methods: We identified 22000 patients with at least one billing code for AF from 2010 to 2017. A training subset (n=786) was classified as having clinical AF present or absent by 2 clinicians, with a third for adjudication (reference standard). We constructed 5 different models to predict the presence of AF: regression based on a published Medicare model, regression based on a published Kaiser model, 2 regression models using additional EMR data, and a natural language processing (NLP) model. For NLP, the code scans each note for target AF-specific terms and negating modifiers (“e.g. no history of AF”). Patients with at least one positive reference for AF were classified as having AF. We calculated the total number of patients that would be included based on each model and resulting OAC treatment rates using on prescription orders. Results: The NLP method had the highest accuracy, sensitivity and NPV. The Kaiser model had the highest PPV at the expense of sensitivity. Among all 22000 patients, the Kaiser model resulted in the fewest patients (11512, 52%) and the NLP model resulted in the most patients classified as AF present (18212, 83%). OAC treatment rates varied from 50% to 62%, depending on which model was used for patient selection. Conclusions: Some of the most widely-used administrative data algorithms to identify AF patients appear to have limitations, which may be improved upon using NLP. Our findings have implications for EMR-based quality improvement and research. In future revisions, we will incorporate the CHA2DS2-VASc score and examine variations in apparent OAC treatment rates.