Electronic medical records for genetic research: Results of the eMERGE consortium

Abel N. Kho, Jennifer A. Pacheco, Peggy L. Peissig, Luke Rasmussen, Katherine M. Newton, Noah Weston, Paul K. Crane, Jyotishman Pathak, Christopher G. Chute, Suzette J. Bielinski, Iftikhar J. Kullo, Rongling Li, Teri A. Manolio, Rex L. Chisholm, Joshua C. Denny

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number79re1
JournalScience translational medicine
Volume3
Issue number79
DOIs
StatePublished - Apr 20 2011
Externally publishedYes

ASJC Scopus subject areas

  • Medicine(all)

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