Data Strategy

Health Equity is the next frontier of quality improvement.

The CQI portfolio contains some of the richest data on patient care in the country. For the last 25 years, the CQIs have relied on an approach that uses granular data collection, analysis, and transparency to develop and implement practice improvements to support a learning health system. 

MSHIELD was founded on the principle that we can build on this approach to incorporate attention to social needs across the portfolio. With a health equity lens, MSHIELD can give you the tools to ensure all patients can have the best possible outcomes. ​

Achieving health equity requires attention to structure, process, and outcomes. 

There is a growing evidence base showing that interventions addressing social determinants of health are associated with better health outcomes, lower costs, or both. Identifying patients at highest risk for high-cost and low-value care due to unmet social needs is critical to the success of effective interventions. It can be challenging, however, to know that you have fully explored your data with a health equity lens, and balanced your interpretation and analysis with humility.

When building research or participating in quality improvement work, it is essential to incorporate a health equity lens, but it’s not always clear how to do this and do it right. There is no one right answer, but:

are all important steps in understanding how your work intersects with health equity. Ideally equity-focused research and QI enables action toward achieving health equity rather than solely description of disparities.

Framework for Equity-Focused Research and Quality Improvement

MSHIELD supports CQIs to harmonize multiple modes of data to identify and address health inequities.

MSHIELD uses data to identify quality improvement goals that improve equity of outcomes by supporting CQIs to build, explore, and analyze Health Equity Dashboards.

Start with exploring your current data to see what sociodemographic data are already available. Then analyze your outcomes in stratified analyses by sociodemographic data, such as by race/ethnicity. If you have zip codes or other geographic data available, consider whether to integrate area-level measures of social determinants of health. The ultimate goal is to synthesize these data and stratified analyses into a health equity dashboard or report that allows your CQI to regularly consider how outcomes may differ for important sub-populations of patients. 

Contact our team member for more information:

MSHIELD Health Informatics Specialist

E: kirchm@med.umich.edu

P: 734-764-9864