title: Next-generation phenotyping of electronic health records.
abstract: The national adoption of electronic health records (EHR) promises to make an unprecedented amount of data available for clinical research, but the data are complex, inaccurate, and frequently missing, and the record reflects complex processes aside from the patient's physiological state. We believe that the path forward requires studying the EHR as an object of interest in itself, and that new models, learning from data, and collaboration will lead to efficient use of the valuable information currently locked in health records.
authors.affiliation: Biomedical Informatics, Columbia University, New York, NY 10027, USA.hripcsak@columbia.edu
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
authors.forename: David J
authors.initials: DJ
authors.lastname: Albers
mesh_terms: D035843:Biomedical Research / Q000706:statistics & numerical data; D057225:Data Mining / Q000379:methods*; D004059:Diffusion of Innovation; D057286:Electronic Health Records / Q000458:organization & administration / Q000639:trends*; D006801:Humans; D008962:Models, Theoretical; D011786:Quality Control; D014481:United States