One of the best examples of the power of predictive analytics is medication adherence. According to the Annals of Internal Medicine, non-adherence impacts the U.S healthcare system $100-$289B annually; 20-30% of that is related to prescriptions that are never filled and 50% for medications that are not taken as prescribed to treat chronic diseases. Payers, drug manufacturers and policy makers are paying the price, but Predixion can help make a difference.

Here’s how it works:

• Patient data is introduced to Predixion Insight to segment overall disease populations and identify non-adherence geographically
• The Predixion model then uses segmented populations to risk stratify patients for adherence potential, indicating groups at highest risk
• Population characteristics inform appropriate patient care paths, and geographic density of certain populations can target specific hospitals or physicians
• Intervene appropriately and efficiently based on Predixion Insight’s analysis of risk indicators





The Last Mile of Analytics™ in action: this is a care coordinator’s view of a predictive application built using the Predixion platform to predict & intervene on medication adherence. In this example, the care coordinator can see all diabetics in California, organized into groups based on machine learning. Age, income and insurance type all influence the risk of non-adherence. Now, the care coordinator can easily see who is at risk and intervene to improve outcomes.

Predixion Insight can leverage big data to identify patients at risk on non-compliance along with leading indicators to reduce that risk. Overall, Predixion’s powerful predictive analytics platform is helping life sciences companies address many challenges by delivering actionable insights to reduce risks, streamline product delivery, and improve profitability and patient outcomes.

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