Document Detail

An Evaluation of the NQF Quality Data Model for Representing Electronic Health Record Driven Phenotyping Algorithms.
MedLine Citation:
PMID:  23304366     Owner:  NLM     Status:  In-Data-Review    
The development of Electronic Health Record (EHR)-based phenotype selection algorithms is a non-trivial and highly iterative process involving domain experts and informaticians. To make it easier to port algorithms across institutions, it is desirable to represent them using an unambiguous formal specification language. For this purpose we evaluated the recently developed National Quality Forum (NQF) information model designed for EHR-based quality measures: the Quality Data Model (QDM). We selected 9 phenotyping algorithms that had been previously developed as part of the eMERGE consortium and translated them into QDM format. Our study concluded that the QDM contains several core elements that make it a promising format for EHR-driven phenotyping algorithms for clinical research. However, we also found areas in which the QDM could be usefully extended, such as representing information extracted from clinical text, and the ability to handle algorithms that do not consist of Boolean combinations of criteria.
William K Thompson; Luke V Rasmussen; Jennifer A Pacheco; Peggy L Peissig; Joshua C Denny; Abel N Kho; Aaron Miller; Jyotishman Pathak
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Publication Detail:
Type:  Journal Article     Date:  2012-11-03
Journal Detail:
Title:  AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium     Volume:  2012     ISSN:  1942-597X     ISO Abbreviation:  AMIA Annu Symp Proc     Publication Date:  2012  
Date Detail:
Created Date:  2013-01-10     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101209213     Medline TA:  AMIA Annu Symp Proc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  911-20     Citation Subset:  IM    
Northwestern University, Chicago, IL;
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