sharpn high-throughput phenotyping (htp) november 18, 2013
TRANSCRIPT
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SHARPn High-Throughput
Phenotyping (HTP)November 18, 2013
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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
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High-Throughput Phenotyping from EHRs
DataTransformTransform
EHR-driven Phenotyping Algorithms – The Process
PhenotypeAlgorithm
Visualization
Evaluation
NLP, SQL
Rules
Mappings[eMERGE Network]
©2013 MFMER | slide-3
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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]
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High-Throughput Phenotyping from EHRs
DataTransformTransform
Algorithm Development Process - Modified
PhenotypeAlgorithm
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 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)
[Welch et al., JBI 2012; 45(4):763-71]
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High-Throughput Phenotyping from EHRs ©2013 MFMER | slide-6
[Thompson et al., AMIA 2012]
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High-Throughput Phenotyping from EHRs ©2013 MFMER | slide-7
[Li et al., AMIA 2012]
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High-Throughput Phenotyping from EHRs
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High-Throughput Phenotyping from EHRs
Phenotype Modeling and Execution Architecture (pheMA): New 4-year NIH R01
©2013 MFMER | slide-10
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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
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High-Throughput Phenotyping from EHRs
Plan for Aims 2 & 3: National Library of Computable Phenotyping Algorithms
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