Abstract: Utilization of a CGM-Based Dashboard to Prioritize Distinct Cohorts of Youth with Type 1 Diabetes
B. Spartz1, K. Noland2, E. Dewit3, B. Lockee2, M. Clements3
1Children’s Mercy Hospital, Endocrinology, Kansas City, United States of America, 2Children’s Mercy Hospital, Endocrinology, KANSAS CITY, United States of America, 3Children’s Mercy Hospital, Endocrinology and Diabetes, Kansas City, United States of America
Background and Aims: A CGM-based population health dashboard, created by researchers at Stanford University, was adapted/adopted by Children’s Mercy (CM). The dashboard flags youth with T1D into risk categories: extreme lows >2%, no alerts, lows >4%, >15% drop-in time-in-range (TIR), TIR <65%, extreme highs >10%, >15% drop in wear time, insufficient data, extreme highs >3%. These categories help clinicians prioritize at-risk patients for proactive outreach to families for intensive support between standard-of-care clinic visits.
Methods: Utilizing Power BI for data visualization, the dashboard was implemented targeting biomarker-based risk groups. Modifications to filters within the dashboard allows a focus on specific cohorts of youth such as new-onset, new-to-technology, teens transitioning to adulthood, and lost-to-follow-up (LTF). Diabetes educators used the filters to identify patients that fell in risk categories within each cohort and provide a one-time outreach to provide problem-solving support.
Results: After an adjustment to dashboard filters, the percentage of appropriate patients populating in the dashboard increased from 56% to 100%. Although all patients populated accurately, not all were eligible for outreach due to TIR >80%, recent adjustments with the diabetes clinic, or no longer attending CM. Reachability varied with LTF, teens transitioning, new to CGM, and newly diagnosed respectively at 25%, 57%, 60%, and 66%.
Conclusions: The specificity of the dashboard helps clinicians identify cohorts of youth at risk for poor outcomes by providing an at-a-glance view of the T1D population using CGM. Future work will include additions to the dashboard to improve provider engagement and decrease clinic time spent reviewing data.