Using Clusters Based on Social Determinants to Identify the Top 5% Utilizers of Health Care

Abstract

This article extends prior work that used only social determinants to create clusters that are labeled using an external measure of average total expenditures. In this article we show that these clusters can identify a reasonable percentage of the top 5% utilizers of health care and compare two methods of clustering (PAM and k-means). We include two independent cohorts to show the consistency of the use of clusters across cohorts. We find that the three clusters with the highest average total expenditure (labeled from the intial studies) identify approximately 40% of those who are among the top 5% utilizers and from 25% to over 50% of the expenditures of the top 5% utilizers for each of the three cohorts. By identifying characteristics of individuals who are consistently in the top 5%, third-party payors and other stakeholders have a better opportunity to prospectively apply effective interventions. Social determinants such whether the individual is not working, on food stamps, or homeless are more frequent in those top 5% utilizers compared to the overall population.