Atrial fibrillation is a simple example. For other conditions, combinations of multiple types of data within an EHR, including structured data (such as billing codes) as well as natural language processing approaches to analyze unstructured data (such as clinical notes), may be required to identify “true cases”.11,12 The longitudinal follow-up inherent in the EHR may assist in phenotype algorithm development: whether a patient with joint pain has rheumatoid arthritis or another related condition may become clearer over time. Longitudinal follow-up is an especially desirable feature in individualized medicine, where the questions are often which subsets of patients with disease X will go on to develop complication Y, or which subsets of patients exposed to drug A will go on to develop outcome B.
