Big Data Phenomics in the VA. Outline
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1 Big Phenomics in the VA Mary Whooley MD Director, VA Measurement Science QUERI San Francisco VA Health Care System University of California, San Francisco Kelly Cho PhD MPH Phenomics Lead, Million Veteran Program VA Boston Health Care System Harvard Medical School Academy Health Annual Research Meeting June 27, 2017 Outline Importance of data standardization and interoperability PCORnet and the Observational Medical Outcomes Partnership (OMOP) Common Model Million Veteran Program (use case) Coding algorithms for computable phenotypes 2 1
2 3 entry coding Big are Messy organization harmonization analysis 4 2
3 VA Information Systems Technology Architecture (VistA) VA hospitals and clinics 5 Example: How can we identify uncontrolled diabetics? 3
4 Logical Observation Identifiers Names and Codes Example: How can we identify uncontrolled diabetics? 4
5 VA Corporate Warehouse Tables 9 entry coding Big are Messy organization harmonization analysis 10 5
6 Outline Importance of data standardization and interoperability PCORnet and the Observational Medical Outcomes Partnership (OMOP) Common Model Million Veteran Program (use case) Coding algorithms for computable phenotypes
7
8 to present 16 million unique patients 11 million w/ at least one encounter 5 million deaths 3 billion procedures 2.5 billion conditions 973,000 providers Abstract presented Nov 2015 Am Medical Informatics Assoc 8
9 Mapping to Observational Medical Outcomes Partnership (OMOP) Common Model Query using the same SQL code SQL = Structured Query Language Observational Outcomes Partnership (OMOP) Common Model Implementations > 600 million patients worldwide 18 9
10 Outline Importance of data standardization and interoperability PCORnet and the Observational Medical Outcomes Partnership (OMOP) Common Model Million Veteran Program (use case) Coding algorithms for computable phenotypes
11 Million Veteran Program (MVP) National VA research initiative aiming to enroll one million users of the VHA in an observational cohort Over 500,000 patients already enrolled Blood collection for genotyping and storage Access to electronic medical record Goal is to create database of genomic, military exposure, lifestyle and electronic health information Currently enrolling at >50 VHA Facilities Principal Investigators: John Concato MD MS MPH J. Michael Gaziano MD MPH 22 11
12 Genome wide association study (GWAS): identify genotype(s) associated with specified phenotype Strength of association with computable phenotype Chromosome (genotype) Genome wide association study (GWAS): identify genotype(s) associated with specified phenotype Strength of association with computable phenotype gene (on chromosome 6) linked with specified phenotype Chromosome (genotype) 12
13 Outline Importance of data standardization and interoperability PCORnet and the Observational Medical Outcomes Partnership (OMOP) Common Model Million Veteran Program (use case) Coding algorithms for computable phenotypes 25 What is a computable phenotype? Electronic Health Record Structured data ICD9/10 codes CPT codes Prescriptions Lab results Vital signs + Unstructured data Visit notes Signs/symptoms Smoking/alcohol Employment Radiology reports Discharge summary Pathology reports = Computable Phenotype 26 13
14 Phenotype Algorithms Phenotype Methods Owner Atrial Fibrillation CPT Codes, ICD 9 Codes, Natural Language Processing Vanderbilt Dementia ICD 9 Codes, Medications emerge Univ Washington Heart Failure CPT, ICD 9 Codes, Labs, Meds, Natural Language Processing emerge Mayo Coronary Disease CPT Codes, ICD 9 Codes PCORI MidSouth CDRN Sleep Apnea CPT Codes, ICD 9 Codes Beth Israel Deaconess Type 2 Diabetes ICD 9 Codes, Labs, Medications emerge Northwestern Venous Thromboembolism CPT, ICD 9 Codes, Vital Signs Natural Language Processing emerge Mayo Electronic Health Record Mart Training Set Predicted Cases + Non cases Validation Set 1. Identify cases and non cases (often requires chart review) 2. Iteratively refine & test classification algorithm 3. Validate final algorithm (probabilistic approach) 28 14
15 J Am Med Inform Assoc 2013 Genome Medicine MVP Phenomics Group Mission: 1) to provide a phenotyping framework for MVP Phenomics Science 2) to manage and coordinate resources for MVP phenotyping projects 3) to play a leading role towards Mapping the Human Phenome Organization: Kelly Cho PhD MPH Scott DuVall PhD Jackie Honerlaw RN MPH Kevin Malohi BS Mai Nguyen PhD Anne Ho MPH David Gagnon MD PhD Lead, MVP Phenotyping Lead, MVP VINCI Collaboration Manager, Phenomics Core Manager, VINCI Services Manager, MVP Analytics Lead, MVP Management Lead, Biostatistics and Science 30 15
16 Summary Big Phenomics in the VA Big data are messy VA EHR data have been mapped to national VA Corporate Warehouse (CDW) CDW data have been transformed to OMOP Common Model Million Veteran Program actively using these data Phenotype algorithms can be shared at PheKB.org
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