Population-Level Personalized Diabetes Management Facilitated by Analyses of Continuous Glucose Monitor Data and Telehealth Visits (Preprint)

BACKGROUND Continuous glucose monitors (CGM) are recommended as standard of care by the American Diabetes Association for individuals with type 1 diabetes on insulin. These devices generate glucose readings every 5-15 minutes and use cloud-based platforms to share data. This remotely reviewed data can be used by members of diabetes care team to provide remote care. OBJECTIVE To design an automated tool that facilitates timely, personalized, population-level guidance for glucose management through asynchronous telehealth. METHODS Using CGM data from six clinical trials and two observational datasets, we developed manufacturer-agnostic algorithms to generate generic (e.g., mean glucose (MG) > 170mg/dL) and personalized (e.g., MG increased by >10mg/dL) flags. We developed and deployed an automated tool in a pediatric type 1 diabetes clinic, measured sensitivity for identifying who may benefit from telehealth, and measured the time saved reviewing data with the use of the tool. RESULTS The eight cohorts contained 1,365 patients with 30,017 weeks of data collected by seven types of CGMs. In the cohort with the highest MG, 81.3% (26 of 32) and 3.1% (1/32) of people had a generic and personalized flag every week, respectively. In the clinic, on average, 57.2% of patients were flagged per week, corresponding to a sensitivity of 98.6% and a 42.8% reduction in the time required to review data. CONCLUSIONS The automated analysis of CGM data may help identify people requiring guidance on glucose management while reducing the workload for care providers. The rules-based approach provided fully interpretable representations of patient status relative to the latest guidelines. When deployed in a clinic, an automated tool to generate flags identified 98.6% of patients who would benefit from asynchronous telehealth contact while reducing the time required to review patient data by 42.8%. Guideline-based population health management may become more accessible through the use of automated tools.