Abstract
: The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information. However, EHRs are designed primarily for clinical care, not research, so reuse of clinical EHR data for research purposes can be challenging. Difficulties in use of EHR data include: data availability, missing data, incorrect data, and vast quantities of unstructured narrative text data. Structured information includes billing codes, most laboratory reports, and other variables such as physiologic measurements and demographic information. Significant information, however, remains locked within EHR narrative text documents, including clinical notes and certain categories of test results, such as pathology and radiology reports. For relatively rare observations, combinations of simple free-text searches and billing codes may prove adequate when followed by manual chart review. However, to extract the large cohorts necessary for genome-wide association studies, natural language processing methods to process narrative text data may be needed. Combinations of structured and unstructured textual data can be mined to generate high-validity collections of cases and controls for a given condition. Once high-quality cases and controls are identified, EHR-derived cases can be used for genomic discovery and validation. Since EHR data includes a broad sampling of clinically-relevant phenotypic information, it may enable multiple genomic investigations upon a single set of genotyped individuals. This chapter reviews several examples of phenotype extraction and their application to genetic research, demonstrating a viable future for genomic discovery using EHR-linked data.
Highlights
Introduction and MotivationTypical genetic research studies have used purpose-built cohorts or observational studies for genetic research
Unavailability may result from clinics that are slow adopters, have very high patient volumes, or have specific workflows not well accommodated by the electronic health record (EHR) system [25]
EHRs have long been seen as a vehicle to improve healthcare quality, cost, and safety
Summary
Typical genetic research studies have used purpose-built cohorts or observational studies for genetic research. Rare diseases may take a significant time to accrue in these datasets Another model that is gaining acceptance is genetic discovery based solely or partially from phenotype information derived solely from the electronic health record (EHR) [6]. Both study designs share costs for obtaining and storing DNA Another advantage of EHR-linked DNA databanks is the potential to reuse genetic information to investigate a broad range of additional phenotypes beyond the original study. This is true for dense genetic data such as generated through genome-wide association studies or large-scale sequencing data. Another example is the Kaiser Permanente Research Program on Genes, Environment and Health, which has genotyped 100,000 members with linked EHR data [8]
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