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Transforming Residency Education in a Department of Family Medicine and Community Health Brian Arndt, MD Kirsten Rindfleisch, MD 9.19.2012.

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Presentation on theme: "Transforming Residency Education in a Department of Family Medicine and Community Health Brian Arndt, MD Kirsten Rindfleisch, MD 9.19.2012."— Presentation transcript:

1 Transforming Residency Education in a Department of Family Medicine and Community Health Brian Arndt, MD Kirsten Rindfleisch, MD

2 You Tell Us … What’s a “Community Health Practice Profile?” ↓ A live, interactive, web-based portal combining clinical data from the EHR with multiple sources of public data and GIS mapping Will allow clinicians and learners to guide educational curriculum and serve as the infrastructure for focused practice based health improvement projects But what should “populate” the tool? …

3 3 What Drives Quality? Environment Social and Economic Health Behaviors Patient Factors Clinical Care

4 Digging a Little Deeper 4 EHR Community Data Analytics Use Case Definitions Extract Criteria Geo-Coding Linkage Transform Use Case Definitions Extract Criteria Geo-Linkage Transform Descriptive Tabular Maps Predictive Modeling Multivariate Longitudinal Data Mining

5 Community Data – ESRI Database ~6,000 Variables at Census Block Group ItemDescription Demographic Net Worth, Income, Age, Labor Force etc. 2,000 variables Segmentation Data ESRI Tapestry -neighborhood socioeconomic and demographic composition Business Location, total $$, # number of employees, # businesses by SIC & NAICS. Consumer Spending 750 variables - what consumers buy. Total & average $$ by household, Spending Potential Index (SPI = local / national) Market Potential 2,000 items of goods, services, and attitudes measuring probable demand Retail Market Place Direct comparison between retail sales and consumer spending 5

6 Diabetes Risk and A1c Control Outcomes =Health Behaviors Patient Factors + Clinical Care Factors + Physical Environment, Social, and Economic Factors Diabetes Risk A1c Control Age Gender Race BMI Smoking Depression Insurance Census Block Group ClinicCensus Block Group: Poverty Economic Hardship Index Social Vulnerability Fresh Fruit & Vegetable Consumption Electronic Health Record DataCommunity Level Data 6

7 Uncontrolled A1c and Diabetes Prevalence High Poverty High Economic Hardship High Social Vulnerability Low FF&V Consumption High T2DM Prevalence Higher Uncontrolled A1c

8 8 HBA1c Control – T2DM by Clinic Worse Control Better Control Adjusted control changes ranks from A, C, D, B to D, C, A, B Referent Group: Clinic A

9 Brainstorming OB: what elements of the built environment are prominent near our patients who experience excessive weight gain in pregnancy? Immunizations: do unvaccinated children cluster in certain elementary schools? Asthma: site-specific data on ER and urgent care utilization for patients with passive tobacco smoke exposure Chronic pain: include the residents’ own profile of patients with low back pain, treatment used, treatment duration (acute → chronic)

10 Brainstorming Diabetes group visit: pull up a map showing participants’ neighborhoods, nearby options for low-cost exercise, CSA drops, links to insurance reimbursement for gym, CSA, etc HM: clinic scheduler could show a patient a map including bus routes with nearby hospitals and clinics where a screening mammogram could be scheduled Home visits: go where a certain group of patients cluster geographically (see R2/3 lecture topics and ideas) Other thoughts? (both practical and pie in the sky)


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