Children’s Mercy Kansas City and Joslin Diabetes Center announce project with Cyft technology to apply predictive analytics to proactively identify and manage at-risk individuals
June 9, 2017 – Children’s Mercy Kansas City, Joslin Diabetes Center, Cyft Inc., and The Leona M. and Harry B. Helmsley Charitable Trust have created a new learning health system to improve the care of individuals diagnosed with type 1 diabetes (T1D). Starting in mid-2017, Children’s Mercy and Joslin will deploy machine learning-enabled solutions to proactively manage health outcomes in patients with T1D at two independent diabetes clinics. Utilizing technology pioneered by Cambridge, Mass.-based Cyft, the project will work to optimize aspects of diabetes management by supplying novel information to clinical staff at the point of treatment.
Four industry leaders in care and innovation will guide the project as they seek to create, evaluate, and deploy predictive models at the two selected clinics:
- Mark Clements, MD, Ph.D., Medical Director for the Pediatric Clinical Research Unit at Children’s Mercy Kansas City
- Sanjeev Mehta, MD, MPH, Joslin Diabetes Center’s Chief Medical Information Officer and Director of Quality
- Leonard D’Avolio, Ph.D., CEO and founder of Cyft and Assistant Professor at Harvard Medical School and Brigham and Women's Hospital
- Susana Patton, Ph.D., CDE, Associate Professor of Pediatrics at the University of Kansas Medical Center
Machine learning is an entirely new approach to health analytics because it can generate robust insights from unstructured and imperfect data; such as the free text notes found throughout electronic health records. Validated by over a decade of research and clinical applications, Cyft technology will employ machine learning and natural language processing as well as device signal processing to analyze multiple data sources and create predictive models for use by health professionals. These models will detect and alert caregivers to opportunities to intervene in the care of patients at risk for deterioration in their health outcomes.
The three-year project is funded by a grant from the Helmsley Charitable Trust, a foundation that seeks to improve lives by supporting exceptional efforts in the U.S. and around the world.
“Advancing care for type 1 diabetes has traditionally been difficult as we are working to better understand the impact of clinical and sociodemographic risk factors on outcomes, while also incorporating these insights into patient management strategies,” said Mark Clements, MD, Ph.D. “Due to the development of machine learning technologies we can now make these data points immediately useful to individuals who are delivering care, not just those conducting research. This project aims to not only prove we can generate accurate type 1 diabetes learning models, but also use this information to proactively improve health outcomes and impact the wider type 1 diabetes community.”
T1D affects roughly 1.5 million people in the U.S., with more than 18,000 new cases diagnosed among youth in the United States in 2008-2009, per the American Diabetes Association. T1D is the second most prevalent chronic disease of childhood after asthma. Studies show that proactive care for glycemic control early in the course of the disease has a persistent influence on long-term clinical outcomes, making management of the disease during childhood paramount to reducing life-long risks for those living with T1D.
“For individuals living with T1D, we have learned much about risk factors for suboptimal health outcomes, but there remain significant opportunities to proactively identify and engage our patients who are at risk for future deterioration. Predictive analytics holds promise in this area as well as in the identification of novel clusters of patient factors that could identify high-risk patients,” said Sanjeev Mehta, MD, MPH. “This learning health system will further our goal of leveraging the power of our data to identify and proactively support patients who are at risk for clinical deterioration to positively impact their health and general well-being.”
“We’re increasingly seeing the effective use of predictive analytics to solve challenges in our healthcare system and this project represents the next step in that evolution,” said Leonard D’Avolio, Ph.D. “We can no longer be a ‘wait and see’ industry. Instead we’re pulling real insights from disparate data sources and using these to inform clinical care. We’re thrilled to partner with these leading institutions to serve such a critical patient population, and believe that the work this new learning health system will accomplish could fundamentally change how we care for people with T1D and their families.”