title: An evaluation of the NQF Quality Data Model for representing Electronic Health Record driven phenotyping algorithms.
abstract: 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.
authors.affiliation: Northwestern University, Chicago, IL, USA.
authors.forename: William K
authors.initials: WK
authors.lastname: Thompson
authors.forename: Luke V
authors.initials: LV
authors.lastname: Rasmussen
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Pacheco
authors.forename: Peggy L
authors.initials: PL
authors.lastname: Peissig
authors.forename: Joshua C
authors.initials: JC
authors.lastname: Denny
authors.forename: Abel N
authors.initials: AN
authors.lastname: Kho
authors.forename: Aaron
authors.initials: A
authors.lastname: Miller
authors.forename: Jyotishman
authors.initials: J
authors.lastname: Pathak
mesh_terms: D000465:Algorithms; D035843:Biomedical Research; D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D010641:Phenotype; D011381:Programming Languages; D011786:Quality Control