AI Algorithm May Predict Medication Adherence in Patients with MS

With chronic health conditions, such as multiple sclerosis (MS), medication or therapy adherence is key to treatment success and delayed disease progression. However, according to the World Health Organization, an estimated 50% of patients with chronic conditions are nonadherent with their treatments or medications. Nonadherence contributes to treatment failure and raises healthcare costs by the way of increased symptom management and hospitalizations.

Swoop, a New York-based consumer health data company, has launched a first-of-its-kind targeting algorithm designed to predict the likelihood of patients becoming nonadherent with a given treatment or drug within the next 30 days. The methodology, called Predictive AI Adherence Targeting, applies machine learning (ML) and artificial intelligence (AI) approaches to de-identified real-world data from more than 300 million patients to identify those most likely to stop their medication or treatment plan within the next 30 days. The company says it is compliant with the U.S. Health Insurance Portability and Accountability Act (HIPPA).

The goal is for healthcare providers (HCPs) and pharmaceutical companies to be able to use this data to connect with patients at risk for nonadherence at a crucial point before they stop their treatment. Swoop reports that its Predictive AI Adherence Targeting accurately predicted 92% of patients with MS who became nonadherent in the next 30 days.

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