title: Modeling and executing electronic health records driven phenotyping algorithms using the NQF Quality Data Model and JBoss® Drools Engine.
abstract: With increasing adoption of electronic health records (EHRs), the need for formal representations for EHR-driven phenotyping algorithms has been recognized for some time. The recently proposed Quality Data Model from the National Quality Forum (NQF) provides an information model and a grammar that is intended to represent data collected during routine clinical care in EHRs as well as the basic logic required to represent the algorithmic criteria for phenotype definitions. The QDM is further aligned with Meaningful Use standards to ensure that the clinical data and algorithmic criteria are represented in a consistent, unambiguous and reproducible manner. However, phenotype definitions represented in QDM, while structured, cannot be executed readily on existing EHRs. Rather, human interpretation, and subsequent implementation is a required step for this process. To address this need, the current study investigates open-source JBoss® Drools rules engine for automatic translation of QDM criteria into rules for execution over EHR data. In particular, using Apache Foundation's Unstructured Information Management Architecture (UIMA) platform, we developed a translator tool for converting QDM defined phenotyping algorithm criteria into executable Drools rules scripts, and demonstrated their execution on real patient data from Mayo Clinic to identify cases for Coronary Artery Disease and Diabetes. To the best of our knowledge, this is the first study illustrating a framework and an approach for executing phenotyping criteria modeled in QDM using the Drools business rules management system.
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: Dingcheng
authors.initials: D
authors.lastname: Li
authors.forename: Cory M
authors.initials: CM
authors.lastname: Endle
authors.forename: Sahana
authors.initials: S
authors.lastname: Murthy
authors.forename: Craig
authors.initials: C
authors.lastname: Stancl
authors.forename: Dale
authors.initials: D
authors.lastname: Suesse
authors.forename: Davide
authors.initials: D
authors.lastname: Sottara
authors.forename: Stanley M
authors.initials: SM
authors.lastname: Huff
authors.forename: Christopher G
authors.initials: CG
authors.lastname: Chute
authors.forename: Jyotishman
authors.initials: J
authors.lastname: Pathak
mesh_terms: D000465:Algorithms; D003324:Coronary Artery Disease / Q000175:diagnosis; D003920:Diabetes Mellitus / Q000175:diagnosis; D057286:Electronic Health Records; D005103:Expert Systems; D006801:Humans; D051188:Knowledge Bases; D012984:Software