Modeling Impactability With Predictive Analytics Can Drive Better Outcomes in Whole-Person Care

Whole-person interventions can improve health outcomes, reduce costs, and increase patient satisfaction by identifying and addressing the root causes of non-compliance and unhealthy behaviors — which often stem from undiagnosed or underdiagnosed behavioral health or from substance-use issues driven by SDoH.

Feeding data into a predictive analytics model can generate propensity scores or predictive values for individuals within a member population, giving payers and providers an impactability measurement for a patient or member. As more data goes into the predictive analytics engine, an impactability measurement becomes more accurate and more multidimensional.

This data creates an additional layer of intelligence that can be put back into the predictive analytics engine to further guide care-management and whole-person intervention strategies for an individual, creating a multidimensional, ever-evolving measurement of their impactability and needs.