Study in Colorado maps correlations between social risk factors and emergency department use, including the "superutilizers."
Overreliance on geographic or population averages to quantify and measure social determinants of health (SDOH) can send healthcare organizations down a costly and inefficient path when it comes to designing interventions to improve outcomes and reduce costs. Because these averages tend to mask the discrete patient experiences, their use can result in programs that fail to fully address the barriers representing the greatest potential for improving health outcomes — and healthcare organizations’ returns on investment.
A better approach is to first understand the unique fingerprint of risk within a defined patient population. This knowledge enables identification of optimal intervention opportunities and estimation of those efforts that will produce the greatest return.
This is the approach that the Colorado Hospital Association took when it sought to identify strategies that would help its members reduce costs by addressing emergency department (ED) super-utilization and readmission rates. The resulting analysis identified a strong correlation between these areas and SDOH.
A statewide analysis was conducted by hospital association in partnership with Carrot Health. Claims data from CHA’s On Demand Hospital Information Network were used to identify patient trends and utilization metrics. This information was coupled with Carrot Health’s consumer behavior data, enabling an industry-first analysis of healthcare utilization across Colorado that also established the relationship between social risk and its impact on ED utilization, admissions and readmissions.
The Carrot Health Social Risk Grouper®(SRG) was also used to score patient risk across a Colorado population of about 2.5 million adults who had a hospital interaction within the previous two years. Developed to help understand, identify, measure, and quantify social barriers and circumstances in which people live, the SRG provides scores on every adult in the U.S. These are composites driven by behavioral, social, economic, and environmental components encompassing 11 social risk categories. The categories allow healthcare organizations to understand their patient population’s fingerprint of risk and potential barriers to care.
A correlation between social risk factors and ED super-utilization was identified at aggregate and individual levels. Analysis found that the higher the SRG score, the higher the rate of ED superutilization.
Specifically, the southeastern part of Colorado was found to have a higher level of risk, while the wealthier mountain and more populated front range areas had lower risk. The analysis also determined that individuals in the top decile of risk for food insecurity superutilized the ED at 2.86 times the rate of the general population.
To visualize the relationship between readmissions and SDOH, the Colorado Hospital Association constructed an interactive dashboard to identify patients who have been readmitted within 30 days, and for whom SDOH were contributing factors to overall risk. This allows to identify areas of increased social risk (Figure 3) and members to examine patient populations by ZIP code, payer, race/ethnicity and SDoH risk groups and draw insights within each population.
For example, utilizing the dashboard allows hospitals to identify specific service areas and determine where there is increased social risk. Hospitals can also compare against peers, and identify risks and improvement opportunities by specific geographies, payers, and ethnicity.
Every individual and each population have a unique fingerprint of risk. Where one cohort may need food security, another may benefit more from housing stability. In this analysis, specific population subsets — individuals aged 18-35 and those not on Medicare — tended to show a greater correlation and predictive power between social determinants and adverse health outcomes.
This unique fingerprint of risk can help identify where the highest opportunity is for intervention, and which efforts will produce the greatest impact to improve the health of individuals and communities and improve costs. Assessing the risk of a community before investing in a one-size-fits-all intervention can help justify the appropriate investment, and the potential savings opportunity.