Electronic medical records for genetic research: results of the eMERGE consortium. Academic Article uri icon

Overview

MeSH

  • Clinical Trials as Topic
  • Data Collection
  • Genome-Wide Association Study
  • Genomics
  • Humans
  • Phenotype

MeSH Major

  • Electronic Health Records
  • Genetic Research

abstract

  • Clinical data in electronic medical records (EMRs) are a potential source of longitudinal clinical data for research. The Electronic Medical Records and Genomics Network (eMERGE) investigates whether data captured through routine clinical care using EMRs can identify disease phenotypes with sufficient positive and negative predictive values for use in genome-wide association studies (GWAS). Using data from five different sets of EMRs, we have identified five disease phenotypes with positive predictive values of 73 to 98% and negative predictive values of 98 to 100%. Most EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format. We identified natural language processing as an important tool to improve case identification rates. Efforts and incentives to increase the implementation of interoperable EMRs will markedly improve the availability of clinical data for genomics research.

publication date

  • April 20, 2011

has subject area

  • Clinical Trials as Topic
  • Data Collection
  • Electronic Health Records
  • Genetic Research
  • Genome-Wide Association Study
  • Genomics
  • Humans
  • Phenotype

Research

keywords

  • Journal Article

Identity

Language

  • eng

PubMed Central ID

  • PMC3690272

Digital Object Identifier (DOI)

  • 10.1126/scitranslmed.3001807

PubMed ID

  • 21508311

Additional Document Info

start page

  • 79re1

volume

  • 3

number

  • 79