Abstract Body

Diabetes centers, healthcare systems, and individuals with diabetes generate many types of data about diabetes-related outcomes and about self-management behaviors, comorbid medical conditions, and clinical care-related events. Yet only a small fraction of these data are used by clinicians regularly for decisionmaking. Risk-based management protocols can help diabetes centers to improve both the quality and cost-efficiency of care. These protocols may be driven by biomarkers of risk extracted or derived from electronic health records, diabetes self-management devices (or the cloud services that receive their data), and digital patient reported outcomes platforms; protocols may alternately be driven by forecasting of negative outcomes via Artificial Intelligence/Machine Learning approaches. The participation by diabetes centers in Learning Health Networks, with data sharing to a central data repository, can accelerate Big-Data-driven quality-improvement of care delivery. The presenter will review examples of risk-based management approaches using each technique, including novel biomarker-based risk indices like the 6 Habits of self-management, the Diabetes Care Index, and the Risk Indexes for Poor Glycemic Control and for Diabetic Ketoacidosis (RI-PGC and RI-DKA, respectively). The presenter will further examine the current state of algorithms and AI/ML to manage population health in diabetes clinics, including a population health dashboard to reduce deterioration in glycemic control in the post-diagnostic period for type 1 diabetes, a precision medicine project for type 2 diabetes incorporating multiple -omics biomarkers, and the Rising T1DE Alliance, which seeks to implement multiple ML models to predict outcomes in clinical care, and to test remote patient monitoring along with multiple digital and behavorial health interventions to improve those predicted outcomes via a risk-based management approach.

Abstract Body

The COVID-19 pandemic required the urgent deployment of a telehealth approach to deliver diabetes care across regions and across the lifespan. A number of observational studies have documented the use of telehealth in people with type 1 and type 2 diabetes from childhood through the older adult population. While the pandemic brought multiple inconveniences to all of us, it permitted health care delivery systems and providers to utilize remote care delivery in a previously unprecedented manner. As a result, a number of these observational studies have a demonstrated that provision of telehealth services maintained needed care processes and some studies have even demonstrated either maintenance or potential improvement in glycemic outcomes.

Data from the Joslin Diabetes Center offer observational information on how telehealth was deployed either via telephone or video modalities in various age groups. In addition, these data help us to evaluate the utility of telehealth services in different segments of the population living with diabetes, for example, according to age or modality of their diabetes treatment. Finally, telehealth services provided opportunities even to initiate diabetes treatment in those newly diagnosed and to implement changes in diabetes management for those with established diabetes, including the implementation of advanced diabetes technologies. These issues will be discussed in the symposium.