A Standards-based Semantic Metadata Repository to Support EHR-driven Phenotype Authoring and Execution.
This study describes our efforts in developing a standards-based semantic metadata repository for supporting electronic health record (EHR)-driven phenotype authoring and execution. Our system comprises three layers: 1) a semantic data element repository layer; 2) a semantic services layer; and 3) a phenotype application layer. In a prototype implementation, we developed the repository and services through integrating the data elements from both Quality Data Model (QDM) and HL7 Fast Healthcare Inteoroperability Resources (FHIR) models. We discuss the modeling challenges and the potential of our system to support EHR phenotype authoring and execution applications.
Mayo Clinic College of Medicine, Rochester, MN, USA.
Guoqian
G
Jiang
Mayo Clinic College of Medicine, Rochester, MN, USA.
Harold R
HR
Solbrig
Mayo Clinic College of Medicine, Rochester, MN, USA.
Richard
R
Kiefer
Northwestern University, Chicago, IL, USA.
Luke V
LV
Rasmussen
Vanderbilt University, Nashville, TN, USA.
Huan
H
Mo
Vanderbilt University, Nashville, TN, USA.
Peter
P
Speltz
Northwestern University, Chicago, IL, USA.
William K
WK
Thompson
Vanderbilt University, Nashville, TN, USA.
Joshua C
JC
Denny
Mayo Clinic College of Medicine, Rochester, MN, USA.
Christopher G
CG
Chute
Mayo Clinic College of Medicine, Rochester, MN, USA.
Jyotishman
J
Pathak
D016208:Databases, Factual / Q000592:standards*; D057286:Electronic Health Records / Q000592:standards*; D017408:Guidelines as Topic; D057208:Health Level Seven / Q000592:standards*; D008498:Medical Record Linkage / Q000592:standards; D009323:Natural Language Processing; D012660:Semantics; D014481:United States; D018875:Vocabulary, Controlled