Lessons Learned From eMERGE II

1. Lessons Learned From eMERGE II David J. Carey, PhD Weis Center for Research Marc S. Williams, MD Genomic Medicine Institute Geisinger Health System

2. Why lessons learned? • Most accomplishments have been reported previously • Participation in eMERGE has contributed to fundamental changes in approach to research at Geisinger • Areas to discuss • Biobanking* • Consent for participation* • Phenotyping* • Genotyping/Sequencing • EHR implementation • Return of Results • Patient Engagement in research at Geisinger* |

3. Leveraging an Integrated Health System to Create a Translational Genomics Pipeline Clinical Use • Validated • Phenotypes • Clinical Data • Gene-Phenotype • Associations • Geisinger • Patients • Genomic • Data • Biobank Discovery

4. Biobanking: MyCode Project • Comprehensive patient and community engagement project • Central repository of blood, serum and DNA from consented participants • Broad inclusion criteria for participation; includes pediatric participants (added 2012) • Samples available for broad research use, including genetic analysis • Molecular data linkable to GHS clinical data • CLIA certification of the MyCode DNA biobank pending | • | | • 4 • 4

5. Consented MyCode Participants • As of 6/24/15 80,804 consented participants • Currently enrolling ~1,000 participants per week • 85.3% consent rate |

6. Consent for Participation • Consenting practices and policies based on patient focus group feedback and survey data • Opt-in consent and HIPAA authorization • Participants enrolled during outpatient visit to a GHS clinic (primary care or specialty) • Soon to pilot use of online and electronic consenting • MyCode protocol and consent modified in 2013 to explicitly permit return of medically actionable results • Participants consent to re-contact for follow-up research | • | | • 6 • 6

7. Phenotyping • Phenomic Analytics and Clinical Data Core provides a focal point for research use of GHS clinical data • models EHR, billing, and administrative data in Geisinger’s enterprise data warehouse and other data sources • extracts data for use by researchers in a manner consistent with approvals, and de-identifies data when necessary • develops and validates phenotypes based on this data • utilizes structured and unstructured data (e.g. via text searching or natural language processing) • Median length of EHR data for MyCode participants is 12 years, with median of 47 clinical encounters

8. ePhenotype Development and Validation Research Idea • Identify informative data elements • Inclusion/exclusion criteria • Diagnostic and procedure codes • Lab values • Radiology reports • Pathology reports • Dates • Visit type • Progress notes (NLP) • refine Initial query of EMR/CDIS Phenotype Algorithm • Case, control definitions • Excludes • refine Execute Algorithm vs EMR/CDIS • refine Chart Validation • PPV, NPV

9. Genotyping/Sequencing • Illumina Human OmniExpress Array • 3,149 samples • 733,202 SNP markers (MAF > 0.01) • Illumina HumanExome Array • 7,800 samples • 232,125 non-synonymous coding region SNVs • 12,459 splice site SNVs • 7,012 promoter SNVs • 5,325 tag SNPs • Illumina Human CoreExome Array • 9,684 samples • 264,909 tag SNPs • 244,953 exome SNVs • Whole exome sequence data • >31,000 samples | • | | • 9 • 9

10. EHR Implementation and Informatics • There sure are a lot of barriers • Typical IT Org Chart |

11. Solutions and infrastructure Research Informatics Clinical Informatics Chief Medical Informatics Officer under CCIO Portion of position charged with research implementation Clinical Informatics Fellowship Approved to start July 2016 Research component to training Developing genomics emphasis • Research/Clinical liaison • Research Informatics Core • Data • Bioinformatics • High Performance Computing • Research Informatics Recruitment • Multiple senior and junior faculty • Chief Research Informatics Officer |

12. Additional solutions • IT governance that includes input from research • Reorganization of informatics structure • Partnership with other organizations • Penn State • Ohio State • Others • Active participation in national informatics initiatives and organizations |

13. Return of Results • Listen to the voice of the participant |

14. Said they wanted any and all results pertaining to their health • Wanted the results returned to them and their clinicians at the same time • And wanted the results deposited in their electronic health records Revised MyCode Consent permitting return of results Majority of focus group participants • Approved by Geisinger’s IRB in October 2013 participant engagement significant change in consent policy |

15. qualitative and quantitative methods MyCode participant engagement integrating genomics in clinical practice Participant experiences of return of results Challenges concerning familial implications Challenges concerning pediatric participants |

16. Return of Results • Details presented tomorrow in workgroup update |

17. Patient Engagement • Need to move from patients as subjects to patients as partners |

18. the value of patient perspectives • Identification of outcomes important to patients • Provision of insight on patient decision making • Provision of expertise that clinicians and investigators do not possess: the expertise developed by patients in the course of their experience—of illness and of care • Input on language and cultural issues important in recruitment, dissemination, etc.

19. patient engagement in the process of research and discovery • From the definition of a research topic & the formulation of a study question through the identification of a study population & the selection of interventions, comparators, and outcomes to measure & through the conduct of the study & the analysis of results & culminating in the dissemination of research findings into clinical practice, researchers should ensure patient centered outcome research results accurately and effectively inform health decisions important to patients. |

20. Formation of a Working Group on Patient Engagement Revised Strategic Plan January 2014 Research Strategic Planning Retreat Recommend-ations Formulated and Presented • First “high-level” recommendation: Adopt concept of an enterprise-wide Learning Health System, reflecting a continuous cycle of integrated discovery, innovation, implementation, assessment, and reengineering in all aspects of the combined clinical and research mission, all carried out in the context of community engagement and impact. • Second “high-level” recommendation: Embrace engagement of and partnership with Geisinger patients and others in the Geisinger community and family, as fundamental to all activities of a true Learning Health System dedicated to the transformation of health and health care. |

21. Geisinger’s Engagement Framework • Continuum of engagement • Consultation and • Disclosure • Partnershipand • Shared Leadership • Involvement • Levels of Engagement Care Patients receive information about treatment and care Patients are asked about their preferences for treatment Treatment decisions are based on patient preferences, medical evidence & clinical judgment Care Improvement Patients are surveyed for their opinions about their care Patients serve as hospital advisors or on advisory groups Patients co-lead safety and quality improvement initiatives Patients support sharing of data, specimens Research and Discovery Patients are informed about discovery activities that utilize patient data Patients serve as co-investigators in discovery activities Patients serve as advisors to discovery initiatives • Adapted from “Patient Engagement.” Health Policy Brief. Health Affairs, February 14, 2013

22. strategies for patient engagement in research and discovery pre-engagement identifying patient partners & participants engaging hard to reach communities supporting patient partners & participants supporting patient partners in dissemination & implementation |

23. Existing and Needed Expertise • An Initial Assessment • June 2015 Advancing Patient Engagement in Research and Discovery @ Geisinger Assessment Framework (or model)