New mobile app from Medtronic, Sugar.IQ™, applies AI technology from IBM Watson Health to help people with diabetes make more informed decisions.
Self-driving cars may not be here yet, but artificial intelligence is being used today to help patients with diabetes to manage their glucose. IBM announced its advancement in using artificial intelligence (AI), machine learning, and analytic technologies to address the data-driven obstacles of diabetes, as presented at the American Diabetes Association’s (ADA) 78th Scientific Sessions. Through IBM Watson Health’s ongoing partnership with Medtronic, the companies announced the commercial availability of Sugar.IQ™ with Watson, an app that aims to give people insights to help manage their diabetes. They also announced findings from three data presentations at ADA, including real-world data underscoring the value of machine learning and analytic tools in diabetes.
AI machine learning and analytics are changing the way people manage their health today, and the launch of the Sugar.IQ app with Watson is an indication of where the future of disease management is headed.
The average patient with diabetes has to make more than 180 decisions daily based on careful monitoring of their glucose, nutritional intake and activity level.1 The Sugar.IQ diabetes assistant works exclusively with the Guardian™ Connect Continuous Glucose Monitoring (CGM) system and continually analyzes glucose, insulin, food, and other data from the past and present to give users a better understanding of how lifestyle choices, medications, and multiple daily injections impact diabetes management. Using Watson, the Sugar.IQ™ app gives people with diabetes powerful, personalized insights to help them make more informed decisions to better manage glucose levels and keep them in the target range.
At ADA, new data presented in an oral session by Medtronic demonstrated the utility of Sugar.IQ in a real-world setting (Abstract: Real-World Assessment of Sugar.IQ with Watson—A Cognitive Computing-Based Diabetes Management Solution). The study found that people with diabetes using the Sugar.IQ app spent 36 more minutes per day in healthy glucose range compared to before they used the app. This included 30 minutes less time in hyperglycemia (>180 mg/dL) and 6 minutes less time in hypoglycemia (<70 mg/dL). This represents more than 9 additional days in a year that a person with diabetes is spending in a healthy glucose range.
During ADA 2018, Watson Health also spotlighted findings that its technologies helped researchers reach.
First, investigators for the ADA and IBM found that 31 percent of patients with type 2 diabetes stopped using their medications within 3 months, a figure that climbed to 44 percent by six months and 58 percent by a year. IBM said the study, which was comprised of more than 324,000 patients, suggests healthcare needs interventions to solve this problem and prevent associated problems.
Next, ADA and IBM researchers compared patient outcomes from the company’s claims data to examine how machine learning can help identify potential therapeutic benefits. They found that certain medication classes for type 2 diabetes are “associated with fewer instances of cardiovascular events, including heart failure, heart attacks, and strokes.”
Watson Health also unveiled findings from two presentations at ADA that highlighted insights from real world evidence gleaned from IBM claims data. Investigators from the ADA and IBM Watson Health presented a retrospective analysis in an oral presentation, which found a significant number of type 2 diabetes (T2D) patients are not compliant with their medications after one year (Abstract: Evaluation of Treatment Persistence in Individuals with Type 2 Diabetes in a Real-World Setting). The study included data for 324,136 adults (mean age 55 years, 46% women) from the Truven Health MarketScan® Commercial and Medicare Supplemental Databases who were diagnosed with type 2 diabetes between 2013 and 2016. After three months, 31% of patients had discontinued their diabetes medications altogether; by six months, the number increased to 44%, and by one year 58% of patients had stopped treatment. This study suggests interventions are needed to improve adherence and avoid gaps in therapy that can be associated with serious outcomes for patients.
Separately, a study conducted by scientists from the ADA and IBM Watson Health found that applying machine learning tools can identify potential therapeutic benefits of certain T2D medication classes by comparing health outcomes reported in IBM claims data (Abstract: A Real-World Evaluation of the Association between Cardiovascular Outcomes and T2D Therapy). This analysis, presented during a poster session, demonstrated that certain T2D medications classes are associated with fewer instances of cardiovascular events, including heart failure, heart attacks and strokes, demonstrating the power of machine learning techniques to help practitioners develop new insights that can positively affect patient care.
- AI, machine learning, and analytics are changing the way people manage their health.
- Sugar.IQ helped to keep blood sugars in normal ranges for 9 extra days over a period of a year.
- The study included data for 324,136 adults (mean age 55 years, 46% women) and showed that after one year, 58% of patients had stopped their treatment and suggest interventions are needed to improve adherence.
American Diabetes 78th Scientific Sessions June 24, 2018