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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
[email protected]
Why study chronic disease risk
factors present in the environment &
community?
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
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
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:
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Clinical care
Inform clinical quality improvement
Design public health interventions
Facilitate further research
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)
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.
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
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:
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Census Demographic
Tapestry Segmentation
Consumer Spending
Business Summary and Location
Retail Market Place
Multi-Level Modeling and Data Mining of Disease Risk,
Disparity, and Health Outcome Quality
Outcomes =
Patient
Clinician
Clinic
Community
Factors +
Factors +
Factors +
Factors
Asthma
Age
Age
Location
Census Block Group:
Diabetes
Gender
Gender
Capabilities
Poverty
CVD / CHF
Race/ethnicity
Certifications
Processes
Education level
Immunizations
Co-morbidities
Graduation
Obesity
Medications
Hypertension
Education
Smoking
Literacy
Safety / crime
Alcohol
Language
Psycho-demographics
A1c level
Insurance
Restaurant mix
LDL
Urban / Rural
Fast food sales
HDL
Census Block Group
Fresh fruit & vegetable
sales / consumption
date
Years of practice
Built environment:
Traffic
Recreation / parks
BP
Hospitalizations
Public Health Program
Information
Health Care Process factors
(e.g, time to
repeat follow-up)
Electronic Health Record & Hospitalization Data
Census / ESRI BA Data
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)
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
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
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
Collaborative Effort
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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
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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
What have we learned so far?
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
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