AMIA Annu Symp Proc - Modeling and executing electronic health records driven phenotyping algorithms using the NQF Quality Data Model and JBoss? Drools Engine.

Tópicos

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Resumo

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.

Resumo Limpo

increas adopt electron health record ehr need formal represent ehrdriven phenotyp algorithm recogn time recent propos qualiti data model nation qualiti forum nqf provid inform model grammar intend repres data collect routin clinic care ehr well basic logic requir repres algorithm criteria phenotyp definit qdm align meaning use standard ensur clinic data algorithm criteria repres consist unambigu reproduc manner howev phenotyp definit repres qdm structur execut readili exist ehr rather human interpret subsequ implement requir step process address need current studi investig opensourc jboss drool rule engin automat translat qdm criteria rule execut ehr data particular use apach foundat unstructur inform manag architectur uima platform develop translat tool convert qdm defin phenotyp algorithm criteria execut drool rule script demonstr execut real patient data mayo clinic identifi case coronari arteri diseas diabet best knowledg first studi illustr framework approach execut phenotyp criteria model qdm use drool busi rule manag system

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