Extending an Analytics Program to Support the Social Determinants of Health (SDOH) and Improve Outcomes

Mar 08, 2022

Extending an Analytics Program to Support the Social Determinants of Health (SDOH) and Improve Outcomes

Written by Tim Lehmann

Category: Analytics

From the U.S. Department of Health and Human Services, the Social Determinants of health (SDOH) are:

1. Economic Stability
2. Education Access and Quality
3. Health Care Access and Quality
4. Neighborhood and Built Environment
5. Social and Community Context

When I was a population health committee member for a health care system in Loma Linda, California, we had a guest speaker who was a pediatrician and researcher who saw firsthand the impact of the SDOH. Interstate 10 was the only thing dividing the communities of Loma Linda and San Bernadino, yet the life expectancy of residents in Loma Linda was 10 years higher than the national average while residents in San Bernardino were living well below that average. The researcher’s main point was “It’s not your genetic code but your zip code that best determines your life expectancy”. Understanding this variation and the data collection needed to support a team focused on SDOH requires an analytics program to align with the goals for improved outcomes. To identify the most at risk populations and prescribe what is needed for the communities served, organizations must commit to internal collaboration and utilizing analytics to support this program.

The three keys to extending analytics for SDOH revolve around People, Process and Technology.

People: Extending or creating a governance program that is focused on SDOH is key. Members of this work group should include:

1. An existing population health leadership team that includes clinician champions
2. Case management
3. Analytics joined at the hip with the EHR application team
4. Finance
5. Quality / patient safety
6. Patient transport

Each of these members represent key groups that have expertise in the data domains or processes that control how data is collected and analyzed. Key goals of this work group are to 1) collaborate to ensure roadblocks are removed for data collection 2) proactively manage data quality.

Process: A key goal of governance is to streamline the process to initiate or alter work flows to capture the data needed to identify at risk populations tied to the factors of SDOH. Resources are assigned from the application and analytics team to review, design, and implement data collection and then publish data for the analysts and clinical teams to use and measure improvements over time. Establishing a base line and outcome improvements is key and quality teams have expertise in establishing and utilizing existing metrics.

Technology: Finally, the key to extending an analytics program for SDOH should be focused on data integration and data quality management. Different systems and opportunities to identify your at-risk members starts when the patient arrives and is registered. Work streams and other instruments must be altered or developed to be tailored to capture data or flag a member for follow up. This requires a pragmatic approach with a robust analytics architecture to support integration and data streaming and advanced analytics to develop algorithms that blend demographic data, real time patient clinical data, and financial data. Data quality management is another key capability that is focused on master data management and reference data management to ensure data between the different systems is usable and maintainable. The initial steps are small but a long-term vision to implement fully over two to three years is the right mindset.

Blending the right People, Process and Technology will allow the full capture of data and analysis that can address identifying patients at risk for not showing for appointments, in need of financial assistance, access to education programs, or proactive interventions with improved nutrition. Your strategic planning and marketing teams can use the information to implement mobile health care units or select optimum locations for community clinics. With the right data, the possibilities for improvements are endless.

This also requires a robust enterprise analytics architecture vision and strategy. I will expand on this more in a future topic for extending your Analytics program to support SDOH.