Leveraging predictive analytics to target payer-led medication adherence interventions.

Abstract

This article examines how predictive analytics can enhance payer initiatives to improve medication adherence. Despite its known impact on health outcomes and costs, medication nonadherence remains a widespread and persistent challenge in health care. Although payers are increasingly involved in addressing nonadherence, traditional approaches typically lead to suboptimal results due to their reactive nature and generic intervention. With improved access to data and more sophisticated machine learning tools, there is a growing opportunity for payers to use predictive analytics to stratify and target members at high risk, predict potential primary and secondary nonadherence, and preemptively intervene with tailored solutions. The potential benefit of this approach includes prevention, not only resolution, of nonadherence and leads to improved health outcomes, reduced health care costs, and increased member satisfaction. The article also discusses potential caveats to consider, such as data sharing, bias mitigation, and regulatory compliance, when implementing predictive analytics in this context.