Invited commentary: Observational research in the age of the electronic health record.
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.
Christopher G
CG
Chute
D001943:Breast Neoplasms / Q000175:diagnosis*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D009323:Natural Language Processing; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis*
clinical case retrieval; electronic medical records; high-throughput clinical phenotyping; natural language processing