ESPE Abstracts

Optimal management of type 1 diabetes requires precise insulin administration to maintain glucose within optimal glucose ranges. Insulin dosage regimens are complicated by day-to-day variability in insulin requirements to maintain near-normoglycemia, which can cause considerable excursions in glucose levels. Digital decision support systems have been developed to help healthcare professionals facilitate timely, more frequent and adequate insulin dose adjustments, either in person or remotely, and thus improve glycemic control for individuals with diabetes. This field includes a wide range of algorithms to guide diverse aspects of diabetes management and insulin dosing adjustments, including tools to use in times of physical activity and eating, various insulin bolus calculators for meal-time dosing; and advanced algorithms to guide insulin dosing adjustments for individuals using insulin pumps, multiple daily injections, and automated insulin delivery. Decision support systems for healthcare providers and also individuals living with diabetes are becoming a subject for intensive research, development, and adoption in routine clinical practice. Several decision support systems have been introduced in the past years, some using advanced data science methods, including machine learning and artificial intelligence. In this presentation, we will present contemporary data on decision support systems used to optimize glycemic control in individuals living with diabetes.