title: Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.
abstract: 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.
  
  
  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.
  
  
  RESULTS
  Using CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria.
  
  
  CONCLUSIONS
  End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
authors.forename: Jyotishman
authors.initials: J
authors.lastname: Pathak
authors.forename: Kent R
authors.initials: KR
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authors.forename: Calvin E
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authors.lastname: Beebe
authors.forename: Steven
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authors.lastname: Bethard
authors.forename: David C
authors.initials: DC
authors.lastname: Carrell
authors.forename: Pei J
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authors.lastname: Chen
authors.forename: Dmitriy
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authors.lastname: Dligach
authors.forename: Cory M
authors.initials: CM
authors.lastname: Endle
authors.forename: Lacey A
authors.initials: LA
authors.lastname: Hart
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Haug
authors.forename: Stanley M
authors.initials: SM
authors.lastname: Huff
authors.forename: Vinod C
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authors.lastname: Kaggal
authors.forename: Dingcheng
authors.initials: D
authors.lastname: Li
authors.forename: Hongfang
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authors.lastname: Liu
authors.forename: Kyle
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authors.lastname: Marchant
authors.forename: James
authors.initials: J
authors.lastname: Masanz
authors.forename: Timothy
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authors.lastname: Miller
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Oniki
authors.forename: Martha
authors.initials: M
authors.lastname: Palmer
authors.forename: Kevin J
authors.initials: KJ
authors.lastname: Peterson
authors.forename: Susan
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authors.lastname: Rea
authors.forename: Guergana K
authors.initials: GK
authors.lastname: Savova
authors.forename: Craig R
authors.initials: CR
authors.lastname: Stancl
authors.forename: Sunghwan
authors.initials: S
authors.lastname: Sohn
authors.forename: Harold R
authors.initials: HR
authors.lastname: Solbrig
authors.forename: Dale B
authors.initials: DB
authors.lastname: Suesse
authors.forename: Cui
authors.initials: C
authors.lastname: Tao
authors.forename: David P
authors.initials: DP
authors.lastname: Taylor
authors.forename: Les
authors.initials: L
authors.lastname: Westberg
authors.forename: Stephen
authors.initials: S
authors.lastname: Wu
authors.forename: Ning
authors.initials: N
authors.lastname: Zhuo
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
mesh_terms: D000465:Algorithms; D035843:Biomedical Research; D016494:Computer Security; D057225:Data Mining; D057286:Electronic Health Records / Q000592:standards*; D006801:Humans; D008491:Medical Informatics Applications; D009323:Natural Language Processing; D010641:Phenotype; D012984:Software; D018875:Vocabulary, Controlled
keywords: Electronic health record; Meaningful Use; Natural Language Processing; Normalization; Phenotype Extraction