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Three Residency Programs’ Lessons Learned about Disparities from a Deep Dive into Our Population Data
Aurora UW Family Medicine Faculty
  • Carla Kelly, Aurora Health Care
  • Will Lehmann, Aurora Health Care
  • Jeffrey Stearns, Aurora Health Care
  • Deborah Simpson, Aurora Health Care
  • David B. Thompson, Aurora Health Care
  • Jonathan Blaza, Aurora Health Care
  • Sara OMeara, Aurora Sinai Medical Center
  • Molly K Lepic, Aurora Health Care
  • Abel H. Irena, Aurora Health Care
  • Jasmine Wiley, Aurora Health Care
  • Kushal Patel, Aurora Health Care
  • Richard J Battiola, Aurora Health Care
  • Abiy A Gesese, Aurora Health Care
  • Tracy Greiten, Aurora Health Care
  • Shelby L Pischke, Aurora Health Care
  • Jacqueline Gisch, Aurora Health Care
  • Rebecca Eberhardt, Aurora Health Care
Aurora Affiliations

Aurora St. Lukes Medical Center

Aurora Sinai Medical Center

Graduate Medical Education


Family Medicine

Ob Gyn

Internal Medicine

Publication Date
Presentation Notes
1 of 7 abstracts selected for platform presentation at AIAMC Annual Meeting


To deliver person-centric, best-in class health care we must transition to value-based care. As part of managing this transition, we must identify at risk populations – those with disparities in clinical measures - by leveraging our existing data sets to provide actionable data to inform how we manage these populations. Currently our health care system provides clinical quality metrics to support providers’ ability to engage in continuous improvement. This data is complimented by provider’s knowledge of the literature, which consistently identifies certain populations, often using the REAL-G categories, as at risk. For example, hypertension has well established risk factors including age, gender, and race: HTN increases through early middle age; women are more likely to develop HTN > 65; HTN is more common among blacks. However, our current clinical quality data does not normally provide detailed clinical/service level population specific metrics (e.g., REAL-G specific data) limiting providers’ ability to understand the clinical quality disparities in their patient populations.

Hypothesis/Aim Statement

To identify actionable disparity gaps for quality improvement through detailed analysis of selected clinic level quality metrics by REAL-G Categories (Race, Ethnicity, Age, Language, Gender).


Three residency programs participating in the Alliance of Independent Academic Medical Center’s National Initiative V (AIAMC-NIV) identified a current system-wide quality metric that was not at/above system goal: Family Medicine - colorectal cancer (CRC) screening; Internal Medicine – diabetes; and Ob/Gyn - postpartum readmission for hypertension. Through a partnership between Graduate Medical Education (GME) and Service Quality leaders, a retrospective analysis of selected quality metrics was undertaken to determine if there were disparities using REAL-G categories over a 12-month period (12.2014-11.2015). Each residency team then reviewed the data to identify the largest disparities by REAL-G category for quality improvement.


The largest disparities in our clinics/service areas were sometimes consistent with the literature (e.g., 65% of African American DM Patients > HbA1cs compared to 70% of White-Hispanic and 71% White-Non Hispanic) but not always! For example the largest CRC screening disparity was not race, ethnicity or gender (


Diving into our clinical quality metrics using REAL-G categories, provided the actionable data needed in each of our three residency programs to plan disparity targeted improvement cycles.

Document Type
Oral/Podium Presentation
Citation Information

Kelly CJ, Lehmann W, Stearns J, Simpson D, Thompson DB, Blaza J, O’Meara SM, Lepic MK, Irena A, Wiley J, Patel K, Battiola R, Gesese AA, Greiten T, Pischke SL, Gisch J, Eberhardt R. Three Residency Programs’ Lessons Learned about Disparities from a Deep Dive into Our Population Data. AIAMC Annual Meeting. March 30– April 1, 2017. Amelia Island, FL.