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Suzanne Bakken , PhD, RN, FAAN, FACMI School of Nursing and Department of Biomedical Informatics Columbia University

Informatics-Enabled Community Engagement in the Washington Heights Informatics Infrastructure for Comparative Effectiveness Research (WICER) Project. Suzanne Bakken , PhD, RN, FAAN, FACMI School of Nursing and Department of Biomedical Informatics Columbia University. 1R01HS019853, R01HS022961.

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Suzanne Bakken , PhD, RN, FAAN, FACMI School of Nursing and Department of Biomedical Informatics Columbia University

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  1. Informatics-Enabled Community Engagement in the Washington Heights Informatics Infrastructure for Comparative Effectiveness Research (WICER) Project Suzanne Bakken, PhD, RN, FAAN, FACMI School of Nursing and Department of Biomedical Informatics Columbia University 1R01HS019853, R01HS022961
  2. Community Engagement in Context of the Learning Health Systems Building a strong fabric of trust among stakeholders through communication and demonstration of value Principles as a foundation for fabric of trust: Build a shared learning environment Engage health and health care, population and patient Leverage existing programs and policies Difficulty of building a strong fabric of trust among racial and ethnic minorities Low participation rates in research studies Low participation rates in biobanks Limited use of information technologies for health-related purposes.
  3. Goals of WICER Comprehensive understanding of the Washington Heights/Inwood population Facilitate research with this population Demonstrate infrastructure capabilities for comparative effectiveness research
  4. Washington Heights/Inwood 5 zip codes: 10031, 10032, 10033, 10034, 10040 Represents significant issues in health care disparities
  5. WICER Components Research data warehouse - Wilcox Research data explorer (RedX) and I2B2 for viewing research data warehouse – Wilcox Informatics support for recruitment and integrating clinical and research workflows – Weng Comparative effectiveness studies – Bigger, Feldman (Visiting Nurse Service of New York) Community survey – Bakken, Boden-Albala, Fleck
  6. WICER Data In patient Clinical Encounters Out patient Clinician Clinician Clinician Clinician Integration Platform ResearchData Access Community Members Home care RedX ResearchData Warehouse Long term Data Access Expert Coordinator Coordinator Trial Patient Research Encounters Survey Researcher Local Databases
  7. Community Engagement Survey design and implementation Returning data to community Role of informatics
  8. Community Survey Demographics Including socio-economic status Anthropometric measures and vital signs Blood pressure, height, weight, waist circumference Surveys (incorporate selected PROMIS measures) including Nutrition Physical activity Social role performance Health and illness perceptions Self health assessment Depression Medication adherence Quality of life Health literacy Baseline and follow-up approximately 1 year apart Some overlap with BRFSS and NYC Community Health Survey – Yoon eGEMS publication
  9. Survey Populations Community 3,940 Ambulatory Clinics 1,200 Community Outreach Center 800
  10. Survey Design and Implementation CTSA-funded Columbia-Community Partnership for Health (CCPH) Free community blood pressure screening and education at CCPH Focus groups to inform survey content Data collection by bilingual community health workers from Washington Heights/Inwood in homes, community organizations, and local businessesas well as CCPH and clinics Incorporation of snowball sampling methods Compensation for participant time with incentives of value to residents (e.g., grocery coupons) Asked about top health concerns
  11. Building a Strong Fabric of Trust Participation rates in research studies – increased enrollment rates for WICER referrals to other studies Participation rates in biobanks – exceeded biospecimen collection goals Use of information technologies for health-related purposes– only 7% had used information technology for health purposes, but most participants agreed to linkage of survey data to clinical data
  12. Returning Data to the Community Survey respondents Community-based Organizations
  13. Returning Survey Data to Participants Established WICER visualization working group that includes investigators from a variety of disciplines including nursing, medicine, public health, biomedical informatics, engineering, human factors, communication, art, fashion, theater Creation of infographics of varying levels of complexity based on cognitive tasks Focus groups regarding preference and acceptability Basic research regarding comprehension Applied research related to actionability in consumer-facing and provider-facing applications Process reported in Arcia et al. AMIA2013
  14. Key Lessons from Focus Groups More is more Culture matters
  15. Risks of High Blood Pressure Your Blood Pressure Stroke Cerebrovascular accident Vision loss Hypertensive retinopathy Heart attack Myocardial infarction Kidney failure Renalfailure Blood vessel Damage Atherosclerosis
  16. Days with Adequate Physical Activity in the Last Month 15 22 25 Jill Jones 54 years old 50-60 year-old females nationwide 50-60 year-old females in Washington Heights
  17. Physical Activity Jill Jones 54 years old 50-60 year-old females nationwide 50-60 year-old females in Washington Heights Pictorial bar graph Image from http://advgraphic2.blogspot.com/2011/01/icons-of-graphic-design.html
  18. Physical Activity
  19. Community-based Organizations Focus groups Elicitation of information needs and desired formats
  20. Informatics Approaches Simple topic modeling Community web site as mechanism of return of survey data to respondents Electronic Tailored Infographics for Community Engagement, Education, and Empowerment (EnTICE3)
  21. The EnTICE3 Framework and Sample Infographic
  22. EnTICE3 Requirements Construct individual- and community- level visualizations adapt to change reusable, generalizable “change the color & size”, “replace star icons with banana icons”, “change the age group from 18-35 to 18-25”, ... “same bar graph, but using different survey variables”, “generate the bar graph for a different dataset”, “display this chart in another website”, “a different institution wants a similar visualization”
  23. EnTICE3 Requirements Construct individual- and community- level visualizations adapt to change iterative approach prototype → production reusable, generalizable modular, generic services web standards (HTML, HTTP) web components (encapsulated interactive templates)
  24. Governance Infrastructure Structures and processes that control data Query and extract Store new data Access control Audit access Underlies EnTICE3and other components
  25. Conclusions Significant barriers to research participation exist for populations at high risk for health disparities Community engagement approaches essential to reach such participants Ethical imperative to return data to those that contribute it in a way that is comprehensible and actionable for improving health Important component of a learning health system Requires continued engagement and iterative refinement
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