Atrial fibrillation (AF) is common and increases stroke risk and mortality. Many knowledge gaps remain with respect to practice patterns and outcomes. Electronic medical records (EMRs) may serve as powerful research tools if AF status can be properly ascertained. We sought to develop an algorithm for identifying subjects with and without AF in the EMR and compare it to previous methods. Using a hospital network EMR (n = 5,737,846), we randomly selected 8,200 subjects seen at a large academic medical center in January 2014 to derive and validate 7 AF classification schemas (4 cases and 3 controls) to construct a composite AF algorithm. In an independent sample of 172,138 subjects, we compared this algorithm against published AF classification methods. In total, we performed manual adjudication of AF in 700 subjects. Three AF schemas (AF1, AF2, and AF4) achieved positive predictive value (PPV) >0.9. Two control schemas achieved PPV >0.9 (control 1 and control 3). A combination algorithm AF1, AF2, and AF4 (PPV 88%; 8.2% classified) outperformed published classification methods including >1 outpatient International Statistical Classification of Diseases, Ninth Revision code or 1 outpatient code with an electrocardiogram demonstrating AF (PPV 82%; 5.9% classified), ≥1 inpatient International Statistical Classification of Diseases, Ninth Revision code or electrocardiogram demonstrating AF (PPV 88%; 6.1% classified), or the intersection of these (PPV 84%; 7.4% classified). When applied simultaneously, the case and control algorithms classified 98.4% of the cohort with zero disagreement. In conclusion, we derived a parsimonious and portable algorithm to identify subjects with and without AF with high sensitivity. If broadly applied, this algorithm can provide optimal power for EMR-based AF research.