Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium
Published in Journal of the American Medical Informatics Association, 2013
Recommended citation: Jyotishman Pathak, Kent R Bailey, Calvin E Beebe, Steven Bethard, David S Carrell, Pei J Chen, Dmitriy Dligach, Cory M Endle, Lacey A Hart, Peter J Haug, Stanley M Huff, Vinod C Kaggal, Dingcheng Li, Hongfang Liu, Kyle Marchant, James Masanz, Timothy Miller, Thomas A Oniki, Martha Palmer, Kevin J Peterson, Susan Rea, Guergana K Savova, Craig R Stancl, Sunghwan Sohn, Harold R Solbrig, Dale B Suesse, Cui Tao, David P Taylor, Les Westberg, Stephen Wu, Ning Zhuo, and Christopher G Chute. 2013. Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium. In Journal of the American Medical Informatics Association. https://academic.oup.com/jamia/article/20/e2/e341/2909250
Abstract:
Research objective To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. Materials and methods Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems—Mayo Clinic and Intermountain Healthcare—were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine …