which, in the initial dataset, are true cases.13,14 On the other hand for common diseases, where the datasets will include thousands or tens of thousands of subjects, electronic algorithms are clearly required to extract true cases and controls. Resources such as the eMERGE’s Phenotype Knowledgebase (PheKB.org) and i2b2 (informatics for integrating biology and the bedside) present phenotype definitions to investigators using EHRs for discovery research.15–18 The development of electronic phenotyping algorithms at one institution has been followed by the evidence that such algorithms perform well across multiple institutions, often with different EHR architectures.10 Further, phenotype algorithms for complex diseases such as rheumatoid arthritis, type II diabetes, and hypothyroidism that incorporate multiple dimensions of information – billing codes, natural language processing (NLP) of free text, medications, etc – perform better than simple structured data such as billing codes.11
