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
Clinical Data
Validated
Phenotypes
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
Research/Clinical liaison
Research Informatics Core
Data
Bioinformatics
High Performance Computing
Research Informatics Recruitment
Multiple senior and junior faculty
Chief Research Informatics Officer
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 |

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
surveys
focus groups
semi-structured interviews
deliberative engagement forums
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. the vantage point of the patient design conduct analysis dissemination
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. January 2014 Research Strategic Planning Retreat
Formation of a Working Group on Patient Engagement
Recommend-ations Formulated and Presented
Revised Strategic Plan
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
Partnership and
Shared Leadership
Levels of Engagement
Involvement
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
Research and Discovery
Patients are informed about discovery activities that utilize patient data
Patients support sharing of data, specimens
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. Advancing Patient Engagement in Research and Discovery @ Geisinger
Existing and Needed Expertise
An Initial Assessment
June 2015
Assessment Framework
(or model)