To facilitate the delivery of world class advances in medical care through world class advances in hospital operations.
Improving the quality of patient care. Educating students, doctors, nurses, and hospital leaders. Sharing knowledge with the medical and academic communities.
We use machine learning, mathematical optimization, simulation, and a variety of statistical, probabilistic, and computational tools.
Andrew Ward, EE PhD ’20, presents “Personalized Diabetes Management Using Data from Continuous Glucose Monitors” at the American Diabetes Association Scientific Sessions 2019.
Intensive glucose control in patients with type 1 diabetes (T1D) is essential to prevent the development of vascular complications. However, in the United States only ~25% of children and adolescents with T1D meet long-term glucose control goals. Despite the high incidence and significant morbidity and mortality associated with T1D, few quantitative tools to personalize care models are available and deteriorating glucose control is often detected only retrospectively. A typical patient may check their blood glucose 4-10 times/day and receive feedback from their care team once every 3 months based on a single lab value (HbA1c) that represents glucose control over the past 3 months. Continuous glucose monitors (CGMs), which record blood glucose levels once every 5 minutes, offer an opportunity to use high-frequency data to identify and predict deteriorating glucose control.