Invited commentary: Observational research in the age of the electronic health record

Research output: Contribution to journalReview articlepeer-review

Abstract

Historically, clinical epidemiologic research has been constrained by the costs and time associated with manually identifying cases and abstracting clinical data. In this issue, Carrell et al. (Am J Epidemiol. 2014;179(6);749-758) report on their impressive success using natural language processing techniques to correctly identify cases of cancer recurrence among women with previous breast cancer. They report a 10-fold decrease in the need for chart abstraction, though with an 8% loss in case detection. This commentary outlines some recent history associated with the development of "high-throughput clinical phenotyping" of electronic health records and speculates on the impact such computational capabilities may have for observational research and patient consent.

Original languageEnglish (US)
Pages (from-to)759-761
Number of pages3
JournalAmerican journal of epidemiology
Volume179
Issue number6
DOIs
StatePublished - Mar 2014
Externally publishedYes

Keywords

  • clinical case retrieval
  • electronic medical records
  • high-throughput clinical phenotyping
  • natural language processing

ASJC Scopus subject areas

  • Epidemiology

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