In a completely equitable world, we’d all be able to achieve our best health. Doctors would be available to people in rural, urban and suburban settings. People would all be able to find and use reliable information to make decisions about their health. Cost wouldn’t be an impediment to care. But we’ve long known that health disparities are widespread. Low health care costs or a lack of claims doesn’t necessarily indicate good health.
Organizations that have used claims data as an identification strategy have found their pool of potential participants skewed toward people with access to health care. They’ve also found them to be lacking in diversity. At ActiveHealth, our mission is to help people achieve their best health in body, mind and spirit. To do that, we need to know as much as possible about our members in order to provide a highly personalized experience. We use a multi-dimensional approach to identification that includes information from multiple sources, including self-reported data. We look at clinical markers, gaps in care and social risk factors during the identification. Then we craft a plan to address both clinical and non-clinical barriers to health improvement.
Since inception we’ve been confident that this multi-dimensional approach helps to limit identification bias. However, we run an analysis periodically to confirm it. Our most recent analysis shows that we continue to identify a rich, diverse pool of members for our clinical programs. Variance in identification for some conditions clearly align with higher prevalence of these conditions within populations. After adjusting for these higher rates of incidence, we are pleased to see that our identification rates are similar across groups of the population.
At ActiveHealth, we believe that conscientious use of data – first in identification and then in engagement – is critical to helping each person achieve their best possible health.
