SHARPn High-Throughput Phenotyping (HTP) November 18, 2013

High-Throughput Phenotyping from EHRs Electronic health records (EHRs) driven phenotyping EHRs are becoming more and more prevalent within the U.S. healthcare system Meaningful Use is one of the major drivers Overarching goal To develop high-throughput semi-automated techniques and algorithms that operate on normalized EHR data to identify cohorts of potentially eligible subjects on the basis of disease, symptoms, or related findings both retrospectively and prospectively ©2013 MFMER | slide-2

High-Throughput Phenotyping from EHRs Data Transform EHR-driven Phenotyping Algorithms – The Process Phenotype Algorithm Visualization Evaluation NLP, SQL Rules Mappings [eMERGE Network] ©2013 MFMER | slide-3

High-Throughput Phenotyping from EHRs Key lessons learned from eMERGE Algorithm design and transportability Non-trivial; requires significant expert involvement Highly iterative process Time-consuming manual chart reviews Representation of “phenotype logic” is critical Standardized data access and representation Importance of unified vocabularies, data elements, and value sets Questionable reliability of ICD & CPT codes (e.g., billing the wrong code since it is easier to find) Natural Language Processing (NLP) plays a vital role ©2013 MFMER | slide-4 [Kho et al. Sc. Trans. Med 2011; 3(79): 1-7]

High-Throughput Phenotyping from EHRs Data Transform Algorithm Development Process - Modified Phenotype Algorithm Visualization Evaluation NLP, SQL Rules Mappings Semi-Automatic Execution ©2013 MFMER | slide-5 Standardized representation of clinical data Create new and re-use existing clinical element models (CEMs) Standardized representation of clinical data Create new and re-use existing clinical element models (CEMs) Standardized and structured representation of phenotype definition criteria Use the NQF Quality Data Model (QDM) Standardized and structured representation of phenotype definition criteria Use the NQF Quality Data Model (QDM) Conversion of structured phenotype criteria into executable queries Use JBoss® Drools (DRLs) Conversion of structured phenotype criteria into executable queries Use JBoss® Drools (DRLs) [Welch et al., JBI 2012; 45(4):763-71]

High-Throughput Phenotyping from EHRs ©2013 MFMER | slide-6 [Thompson et al., AMIA 2012]

High-Throughput Phenotyping from EHRs ©2013 MFMER | slide-7 [Li et al., AMIA 2012]

[Endle et al., AMIA 2012]

High-Throughput Phenotyping from EHRs

Phenotype Modeling and Execution Architecture (pheMA): New 4-year NIH R01 ©2013 MFMER | slide-10

Modeling Reviewing Evaluation One algorithm modeled by two individual MAT & QDM experts Measures reviewed by three individual domain experts (Comparison for the two versions of the measure) QDM & MAT extension Measure Authoring Tool Gold Standards eMERGE phenotypes: T2DM; Resistant Hypertension; Hypothyroidism; Cataracts; Diabetic Retinopathy; PAD; Dementia; VTE; Glaucoma; Ocular Hypertension Continuous variable phenotypes QRS duration from ECG; Lipids (inc. HDL); Height; RBC; WBC Standards to validate and compare the created measures: Measure how concise of the measure is (more concise is better) Measure is true to the algorithm Measure how much existing values sets and measures are re-used Measure how much time it took to implement in MAT Measure how many rules in the MAT version vs. Word document Considerations: how experienced the person was w/ MAT to start, and for ea. phenotype as gain more experience make note of it how well the person knew the phenotype to start Plan for Aim 1: Evaluation of Quality Data Model

High-Throughput Phenotyping from EHRs Plan for Aims 2 & 3: National Library of Computable Phenotyping Algorithms ©2013 MFMER | slide-12