Chapter 13: Mining Electronic Health Records in the Genomics Era

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.

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“…[16][17][18] 3) Meticulous interpretation of results. Our algorithm performed very well in the RPDR; however, upon application to a substantially larger database, we discovered a limitation of the algorithm that was produced by a change in the ICD-9 coding method.…”

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“…Here, text mining may be employed alongside structured information to better define groups 24,25 . Structured information on patients requiring specialist care has been collected systematically by the NHS since 1981 through Hospital Episode Statistics in England, the Scottish Morbidity Record and Patient Episode Data for Wales.…”

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“…The following information is generally available in all EHRs are: laboratory results, billing data, medication records, and test details [12]. In most of the cases laboratory results and billing data are in the structured "name-value pair" data.…”