1. UW MED – PHINEX University of Wisconsin Medical Record– Public Health Information Exchange Wisconsin’s Clinical EMR – Public Health Data Exchange Pilot Theresa Guilbert, MD, MS Project PI University of Wisconsin-Madison Department of Pediatrics tguilbert@wisc.edu

2. Why study chronic disease risk factors present in the environment & community?

4. Multi-Level Approach A multilevel approach that includes an ecological viewpoint may help to explain heterogeneities in chronic disease expression across socioeconomic behavioral, and geographic boundaries that remain largely unexplained Improved knowledge regarding disease disparity is important in order to develop intervention strategies

5. Overall Hypothesis Data exchange between UW Dept Family Medicine (DFM) clinics and the Wisconsin State Division of Public Health (DPH) and subsequent linking of these data to public databases on geographical, environmental, socioeconomic, and demographic profiles will highlight areas of disparity and discover novel chronic and communicable disease risk factors

6. Rationale By having such a large clinical data set and using sophisticated spatial and multivariate modeling and data mining tools, areas of healthcare disparities will be highlighted New information about risk factors will be discovered to guide: –Clinical care –Inform clinical quality improvement –Design public health interventions –Facilitate further research

7. Specific Aims Establish a health information exchange between DFM clinics the DPH using a HIPAA privacy rule compliant limited data set – All Personal Health Identifiers are removed except for gender, ethnicity/race, birth year/month, dates of service, zip code, and census block group –This approach has been proved by the UW IRB Determine areas and populations of chronic and communicable disparity through collaboration with the UW Applied Population Laboratory (APL)

8. Specific Aims GIS and spatial analyses of population trends to chart areas of disparity and geographic characteristics of those communities that can lead to hypotheses regarding etiology Assess novel environmental and community risk factors by matching CBG coded EHR to its community level demographic and socioeconomic characteristics using data bases available through the APL and DPH.

9. Specific Aims Use multivariate (logistic and Poisson regression, fixed and random effects regression modeling) and data mining techniques at DPH to create predication models that specify risk factors associated with asthma among many environmental and community based factors from the census and commercial databases Using statistical clustering techniques analyze and determine prominent within patient disease co-morbidity groupings and determine the individual and community risk predictors of these clusters

10. Specific Aims DPH operates the Public Health Information Network (PHIN), a secure, web based system: –Advanced statistical and GIS modeling services –SAS Business Intelligence Server/Enterprise Miner –ESRI ArcGIS server Available community level databases include: –Census Demographic –Tapestry Segmentation –Consumer Spending –Business Summary and Location –Retail Market Place

11. Multi-Level Modeling and Data Mining of Disease Risk, Disparity, and Health Outcome Quality Outcomes =PatientClinicianClinicCommunity Factors + Factors AsthmaAge LocationCensus Block Group: DiabetesGender CapabilitiesPoverty CVD / CHFRace/ethnicityCertificationsProcessesEducation level ImmunizationsCo-morbiditiesGraduation Built environment: ObesityMedications date Traffic HypertensionEducationYears of practice Recreation / parks SmokingLiteracy Safety / crime AlcoholLanguage Psycho-demographics A1c levelInsurance Restaurant mix LDLUrban / Rural Fast food sales HDLCensus Block Group Fresh fruit & vegetable sales / consumption BP Hospitalizations Public Health Program Information Health Care - Process factors (e.g, time to repeat follow-up) Electronic Health Record & Hospitalization DataCensus / ESRI BA Data

12. Data Sets Public Health – Behavioral Risk Factor Surveillance System 2004-2009 Clinical – UW Family Medicine & UW Hospitals and Clinics (demographics, diagnoses, problem lists, laboratory test results, vital signs, procedures, medication lists) Community Data – ESRI geo-coded data (CBG)

13. ESRI Data Bases Fresh Fruit & Vegetable Consumption Index Milwaukee & Suburbs – Census Tracts Color Ramp Grey –Lowest White-Low Cream-Medium Yellow-High Red-Very High Source: ESRI / BLS Consumer Expenditure Survey

14. Fresh Fruit & Vegetable Consumption Index With Individual Store Location / Sales Volume Milwaukee & Suburbs – Census Tracts Color Ramp Grey –Lowest White-Low Cream-Medium Yellow-High Red-Very High Circle size = store sales volume Source: ESRI / BLS Consumer Expenditure Survey

15. Disparity in Dane County? ~50% of K-12 students in Madison schools are economically disadvantaged (> 70% in some) 50% of the kids in the Madison Metropolitan School District are of racial/ethnic minority groups (poor access to care) Disparity does not always correlate with poverty –Falk Elementary School (West Madison) has 9% children with asthma and a 65% poverty rate with 70% minorities –Mendota Elementary School (North Madison) has 22% children with asthma and a 70% poverty rate with 74% minorities

16. Collaborative Effort Brian Arndt-UW DFM Bill Buckingham-UW APL Tim Chang-UW Biostats Dan Davenport-UW Health Kristin Gallager-UW Pop Health Theresa Guilbert (PI)-UW Peds Larry Hanrahan-DPH David Page-UW Biostats Mary Beth Plane-UW DFM David Simmons-UW DFM Aman Tandias-DPH Jon Temte-UW DFM Kevin Thao-UW DFM Carrie Tomasallo-DPH

17. What have we learned so far?

18. Presenters Brian Arndt MD-UW Family Medicine UW MED- PHINEX Diabetes & Obesity Use Case Clinician Lead Estimating the Prevalence of Diabetes in Wisconsin Kevin Thao-UW Family Medicine The Prevalence of Type 2 Diabetes Mellitus in a Wisconsin Hmong Patient Population

19. Presenters Carrie Tomasallo, PhD, MPH-Wisconsin Division of Public Health Wisconsin Asthma Program Estimating Wisconsin Asthma Prevalence Using Clinical Electronic Health Records and Public Health Data