The Prospects of Using Electronic Health Records for Genomics Research

The Prospects of Using Electronic Health Records for Genomics Research
Rex L. ChisholmAdam and Richard T. Lind Professor of Medical Genetics Dean for Research, Feinberg School of Medicine Northwestern University

Personalized Medicine

Genetic Components of Common/Complex Disease Identical Fraternal http://www.gig.org.uk/education4.htm

http://www.ageofpersonalizedmedicine.org/objects/pdfs/TheCaseforPersonalizedMedicine_11_13.pdf http://www.ageofpersonalizedmedicine.org/objects/pdfs/TheCaseforPersonalizedMedicine_11_13.pdf

Critical factors for Defining Genetic Contributions to Disease: A key to Personalized Medicine Methods to measure genetic variation in an individual human genome Large numbers of well phenotyped human genomes

Electronic Health Record A longitudinal electronic record of patient health information Your health story Diagnoses Laboratory Tests Xray results Medications Allergies Clinical Notes Environmental exposures (?) A critical resource for Personalized Medicine

Northwestern University’s Biobank: NUgene Launched in fall 2002 IRB approved Voluntary participation; informed consent process with study staff Collect biological specimens under a broad consent One-time questionnaire completed at time of enrollment Longitudinal medical information is captured from EMR data from affiliated medical institutions Secure application and database High-throughput phenotyping based on EHR and study data Recontact option for additional research Resource facilitating genetic research: providing samples and data for studies & supporting clinical investigators Developing plans for broad, institution-wide recruitment Genotype data returned to NUgene

Informed Consent Document Combined consent/HIPAA authorization allows: Blood sample to be drawn and DNA stored for length of study Access to EHR data on participants Questionnaire information to be obtained Future use of DNA sample for “genetic variation” research Access to de-identified DNA and medical information by third parties with approved research studies, including companies Recontact (optional) For additional information or enrollment in other studies To receive a study newsletter Most EHR not appropriately consented (special protections for mental health and ?! genetics)

Privacy Protections Patient identifiers are removed and replaced with a computer generated barcode Single link between the DNA sample and medical information remains in a secure database, called NOTIS Physicians are not informed of patient’s participation or results Certificate of Confidentiality Issued by the NIH to protect participant’s information involved in certain research projects from subpoena and other legal actions

BioVU– The Vanderbilt Opt-out DNA Bank Extract DNA from leftover clinical blood samples that have been de-identified (~50k per year) Create de-identified “Synthetic Derivative” of EMR (1M individuals) Enable DNA sample retrieval based on queries of clinical data Support prospective GWAS and genotyping panels

Phenotypic Data Warehouse NOTIS Data Flow Participant Enrollment Materials Medical Record Coding & Data Parsing Process Patient Identifiers MRNs SSNs Coded Data Phenotypic Engine Encryption NUgene Database Decryption

Over 9,300 participants are enrolled* (10,000 expected by end 2009) Male: 41% Female: 59% Median age: 48 Age distribution: 18 - 85+ Ethnic breakdown similar to census data for 6-county area Over half enrolled through primary care clinics Average participant has 29 distinct diagnoses (ICD9 non V codes) Overall participation rate is 25% Uptake rate is ~50% if physician mentions the study 90% of participants agree to be contacted for future research or additional health information Population Summary *As of 10/2009

Participant Race and Ethnicity 1 Based on NUgene participation from 10/31/02 – 1/6/09. 2 2000 Census data for the population ≥ 18 years of age. 3 Collar counties include: DuPage, Kane, Lake, McHenry, and Will. 4 Individuals may report more than one race, thus percentages > 100. 5 Unreported includes < 1% of NUgene participants.

Data Sources Questionnaire (self-report): Completed once, at time of enrollment: Demographic information Environmental exposures Medications Self-reported family and medical history for select conditions Electronic billing record data Retrospective and prospective diagnosis (ICD9) & procedure (CPT/ICD9CM) codes Electronic medical record data Retrospective and prospective: Medical history and diagnoses Lab tests and results Medications and therapies Family and social history Free text physician notes*

EHRs at the Northwestern Medical Center Operates a state of the art EHR system Well integrated systems Mix of commercially and internally developed systems Electronic data capture for virtually all aspects of inpatient and outpatient care, including Over 10 years of clinical data Anthropometric and clinical measures, prescriptions, diagnoses, lab measures, and clinical notes Using high quality, widely used commercial systems Cerner PowerChart – inpatient records EpicCare Ambulatory EHR – outpatient records PRIMES – inpatient registration and billing records IDX – outpatient registration and billing records

Top 15 Participant Reported Conditions (Questionnaire Data) *As of 1/6/2009

Participant Counts Per Selected 3-Digit ICD9s* * Includes codes from billing dx, encounter dx, problem list, med hx. **Based on participants with EMR data as of 7/8/08.

Top Laboratory Tests within Population * Based on participants with EMR data as of 7/8/08.

Phenotyping and EHR Data Challenges From a research (and clinical) perspective, there are several types of technical barriers to the use of electronic data: Lack of standardization in coding, vocabulary and nomenclature (including a common reference information model, common data types and common units) Lack of standardized messaging for transmitting data Lack of infrastructure capacity for inputting, storing, and retrieving data Lack of a “gold standard” for the validity of data contained in electronic systems Lack of use of algorithms to enhance the validity of data Findings and Recommendations from a Conference Sponsored by the Association of American Medical Colleges; October 30-31, 2002

Northwestern Data Flow into Standardized Data Repository

Implementation of Electronic Phenotype Algorithms Can EMR data be used effectively and efficiently to ID disease or quantitative phenotype cases and appropriate controls? Test cases: Type 2 Diabetes Asthma Conclusions: EMR data collected through routine clinical care appears to be adequate for defining phenotypic cases and controls Algorithm development and validation an iterative process Expect significant attrition Requires adoption of data standards “Missing Data” a challenge

Identifying Type 2 Diabetes Cases Patients w/o T1DM & w/o T2DM ICD9 Codes Patients with T2DM ICD9 Code 9.0% 90% Patients treated with insulin alone Patients treated with T2DM medication Patients without DM medication/insulin but abnormal lab* Patients treated with T2DM medication AND have an abnormal lab* Never on T2DM medication On T2DM medication in past 6.0% 0.5% 0.9% 2.2% T1DM Dx** OR <2 T2DM Dx dates No T1DM Dx** & >=2 T2DM Dx dates Type 2 Diabetes cases NUgene: 8,760 w/EMR data as of 7/31/09 EXCLUDE 0.8% 9.4% *Random glucose > 200 mg/dl, Fasting glucose > 125 mg/dl, hemoglobin A1c ≥ 6.5% **Encounter or problem list Dx only (all other Dx in this chart could also be from medical Hx)

Identifying Type 2 Diabetes Controls No ICD9 codes for DM, or any diabetes-related conditionsor procedures 6,637 (75.9%) > 2 clinic visits 4,860 (55.6%) Not Rx insulin, pramlintide or other DM meds, or diabetic supplies 4,837 (55.3%) > 1 reported glucose measurement 4,256(48.7%) No reported glucose > 110 mg/dl Nor hemoglobin A1C > 6.0% * EMR data and NUgene questionnaire 1,987 (22.7%) No reported family Hx of type 1 OR 2 diabetes* NUgene: 8,746 w/EMR data as of 07/28/09 1,501 potential T2D controls (17.2%)

Type 2 Diabetes Phenotype: Defining Controls Absence of data ≠ absence of condition May simply be inadequate data capture in EMR for individual Difference in comprehensiveness of data capture between sites Valid criteria for controls requires same data elements as case

NHGRI-funded national consortium formed to develop, disseminate and apply approaches to research that combine DNA repositories with electronic medical record systems for large-scale high-throughput genetic research Network institutions Group Health/University of Washington Marshfield Clinic Mayo Clinic Northwestern University Vanderbilt University Consortium also includes a focus on social and ethical issues such as privacy, confidentiality, return of genetic research results, and the interactions with the broader community Over 20,000 samples with Genome-wide SNP genotyping http://www.gwas.net

eMERGE Goals Test the ability to leverage EMRs and biorepositories for genomic research Evaluate validity & utility of EMR phenotype/exposure data for use in GWAS Develop & validate electronic algorithms for 1° and 2° phenotypes Conduct association studies of genome-wide data with EMR-derived phenotypes and deposit information into dbGaP Assess adequacy of existing consent for genomic technologies & data sharing Develop best practices for GWAS in the areas of electronic phenotyping, genomics & analytics, and ELSI topics

eMERGE: NUgene Community Engagement Programs Assess the appropriateness of our consent and consultation process for GWAS data dissemination Develop best practices for consenting participants Mixed method approach to community engagement regarding data sharing Focus groups regarding data sharing requirements with biorepository participants and the public Survey of IRB Professionals In Collaboration with investigators at University of Washington, Case Western Reserve, University of No Carolina, Duke Medical Center, and PRIM&R Consensus meetings of key professional stakeholders

eMERGE Community Engagement: Focus Groups Survey Results

eMERGE Community Engagement: Focus Groups Themes A Diverse Spectrum of Understanding of Genetic Research Both groups expressed misunderstandings and misinformation about genetic research Weighing Pros and Cons of Participation in Genetic Research participants indicated they would participate in genetic research despite barriers and potential risks The Role of Oversight Body Influences Participation: Credibility, Trust, and Research Integrity Matter Reputation and trust in the organization were important influences to participation Across all focus groups, participants expressed distrust of the government as an oversight body for genetic research data Questions and Concerns about Genetic Research Data Sharing and the NIH GWAS Policy participants in all groups expressed a need for more information about the NIH data sharing policy Not all participants were familiar with the NIH and its role Desire for more Information and Education about Genetic Research to clarify information, to reduce fears, to build trust, to become more comfortable with the information, and to provide what they “have the right to know.”

Summary Biobanks are increasingly playing a critical role in identifying associations between genetic variation, disease risk, drug efficacy and clinical outcomes Longitudinal mining of electronic medical records can be used to provide the most up to date phenotype associated with human biospecimens Research use can be an important driver of EHR quality Networks of EHR-linked biobanks that share samples and data have the potential to increase statistical power to detect genetic associations, population diversity in these studies, and overall research efficiency Improved methods to capture environmental exposure in EHR are needed Policy adjustments to use of health required to address current limitations

NUgene Governance Committee & Community Advisory Committee NU eMERGE investigators: Rex Chisholm Phil Greenland Abel Kho Bill Lowe Wendy Wolf Maureen Smith Geoff Hayes Pedro Avila Amy Lemke May Law Jen Allen Funding for eMERGE studies is provided by NHGRI (U01HG004609) NUgene team: Rex Chisholm, PhD (PI) Warren Kibbe, PhD (Co-founder) William Lowe, MD (Medical Dir.) Wendy Wolf, PhD Maureen Smith, MS, CGC Jennifer Allen Tony Miqueli Sharon Aufox, MS, CGC Nicole Sheehan Noah Goss Maribeth Miceli More information about NUgene: http://www.nugene.org Acknowledgements

The Prospects of Using Electronic Health Records for Genomics Research