Utilization of Computable Phenotypes in Electronic Health Record Research: A Review and Case Study in Atopic Dermatitis

Abstract

Querying electronic health records databases to accurately identify specific cohorts of patients has countless observational and interventional research applications. Computable phenotypes are computationally executable, explicit sets of selection criteria composed of data elements, logical expressions, and a combination of natural language processing and machine learning techniques enabling expedited patient cohort identification. Phenotyping encompasses a range of implementations, each with advantages and use cases. In this paper, the dermatologic computable phenotype literature is reviewed. We identify and evaluate approaches and community supports for computable phenotyping that have been used both generally and within dermatology and, as a case study, focus on studied phenotypes for atopic dermatitis.