1. Arthur Davidson, MD, MSPH Denver Public Health adavidson@dhha.org Committee on Recommended Social and Behavioral Domains and Measures for Electronic Health Records National Academies of Sciences Building 2101 Constitution Avenue NW Washington, DC 20418 April 8, 2014 1 How to Create Information Systems With Data that Flow Both Ways Linking EHRs Between PH, Social Service Agencies, and Other Relevant Organizations

2. Agenda Conceptual Model Linkage for Public Health Surveillance Linkage for Public Health Intervention Next Steps

3. Linkage Conceptual Model Clinical Care (EHR) Clinical Care (EHR) Public Health Social Service Social Service Schools/ Childcare Quitline

4. Linkage for Public Health Surveillance (Colorado) Transparent, distributed data network – Modeled on the Mini-Sentinel (FDA) project and local experience with HMO Research Network (AHRQ) project Governance – Voluntary participation; unlike mandated reporting, data use agreements established/required Privacy – Minimal data necessary to achieve stated goal (de-identified to start) Technical – Infrastructure: 1) common data model, 2) emphasize data quality assessment, and 3) federated query tool

5. Linkage for Public Health Surveillance (Colorado) Colorado Health Observation Regional Data Service (CHORDS) – Provide a "laboratory" to develop and evaluate scientific methods to support public health surveillance – Afford Denver Metro and Colorado communities an opportunity to use existing EHR data systems for public health surveillance – Learn about barriers and challenges, both internal and external, to building a viable and accurate system of surveillance for public health events (e.g., conditions, behaviors and outcomes) – Build an event agnostic infrastructure for public health surveillance, quality assessment, and research

7. e.g., Kaiser Permanente CHORDS Registries Colorado Health Observation Regional Data Service Standard (Virtual) Data Warehouse CHORDS Query Service (PopMedNet ) PMN Secure federated query Current registry efforts: BMI CVD risk Tobacco use and SHS exposure Mental health Colorectal cancer Adult obesity PMN Client e.g., Denver Health Standard (Virtual) Data Warehouse PMN Client Secure federated query Authorize Authenticate

8. CHORDS Project SourceYears Colorado Clinical Translational Science Institute focused on regional informatics infrastructure for research; existing Cancer Center informatics expertise NIH 2008- 2018 Grant allowed initiation of virtual data warehouse (VDW) at Denver Health complementing Kaiser Permanente local expertise AHRQ 2010- 2012 Evaluation resulted in selection of Mini-sentinel PopMedNet model used FDA post-marketing surveillance 2012- 2013 BMI monitoring including social and environmental and data TCHF KP-CB 2011- 2013 Cardiovascular risk reduction - Community Transformation GrantCDC 2011- 2016 Tobacco use, second hand smoke exposure and cessationCDPHE 2012- 2015 Mental health and substance useAHRQ 2013- 2017 Capacity to Support Multiple Conditions

9. Link Clinical, Social and Environmental Data Across Multiple Delivery Systems Pre-CHORDS (e.g., weight status surveillance): BRFSS self-reported demographic, weight data Survey ~12,000 Colorado/year = 700 Denver/year Allows county-level estimates only CHORDS state: Combine measured BMI data from multiple institutions Include demographic data, residence location (geo-code) Link geographically aggregated BMI data (e.g. census tract) with social and environmental data Identify “place-based” interventions (e.g., social marketing, community resource development, and policy initiatives) Pilot features of local data sharing network

10. Types and Sources of Geo-coded Social and Environmental Data for Mapping DataSourceData type Grocery stores Reference USA Points, aggregated into census tracts Restaurants Reference USA Points, aggregated into census tracts Food Deserts (USDA definition) USDA Economic Research Council At census tract level Walkability (based on number of street intersections per unit area) Streetmap USA/ ESRI web distribution Points, aggregated into census tracts Green space/parks From wide variety of sources Polygons (areas), with points of park entrance Poverty American Community Survey Polygons (areas), aggregated into census tracts

11. Combined Measured BMI Data  Denver County (valid BMI) : – all ages: 184,644 (31%) – adults: 119,075 (26%) – children: 64,606 (51%)  Coverage varies widely: ‒> 50% for some communities ‒few with aberrant results CHORDS BMI Registry Description Children <18345,756 Adults364,889 Total Sample 710,645 Median Adult BMI by Year 200926.5 201026.5 201127.1 Median Child BMI Percentile 200962.9 201064.4 201165.4

12. Proportion of Children with a Valid BMI, Denver

13. Proportion of Adults with a Valid BMI, Denver

14. Proportion of Children with Obesity, Denver

15. Percent Overweight + Percent of Families in Poverty

16. Personal Prescription - example

17. Linkage Conceptual Model Clinical Care (EHR) Clinical Care (EHR) Public Health Social Service Social Service Schools/ Childcare Quitline

18. e-Referral between EHR and Quitline Goal: Efficient EHR-mediated e-Referral (including patient preferences) to Quitline and timely acknowledgement/status messages returned to and posted within the EHR. North American Quitline Consortium Consensus process with ~15 Quitline vendors/service providers Message requirements: – Content: define common data elements – Structure: HL7 2.x and c-CDA formats – Transport: sFTP, Direct, web-service (WSDL-SOAP)

19. What next? CHORDS Build out standard data model (i.e., add tables, required content/variables [IZ], extend time range) Conduct comprehensive data quality assessment Compare member-vs. visit-based denominator estimates Expand stakeholders (PH and clinical) Address duplicates Quitline Set e-Referral standards, assure meets PH needs Vet with EHRA and standards development organization Consider as model for PH related HIE and e-referral for other community-based services

20. Sustainability strategy  Enhance “event agnostic” distributed surveillance/research network (e.g., breadth of use cases and stakeholders, depth of content)  Study utility of system to multiple stakeholders (i.e., communities, elected officials, and individuals)  Facilitate incorporation of new social/environmental data (e.g., barriers and assets)  Standardize approach to geocoding and de-duplication  Target outreach and community-based interventions (i.e., policy, systems and environmental changes) to those who need them  Assess impact on health disparities reduction  Develop cadre of applied researchers and methods

21. Discussion/Questions?