A Quantitative Framework for Evaluating the Performance of Algorithm-Directed Whole-Population Remote Patient Monitoring for Type 1 Diabetes Care

IntroductionClinics continue to adopt remote patient monitoring for type 1 diabetes (T1D) and care models shaped by algorithmic CGM data analysis. No clinic-facing quantitative framework currently exists to track the impact of such algorithm-directed care on patient outcomes and clinical workload. The Teamwork, Targets, Technology, and Tight Control (4T) Study provides precision, whole-population care enabled by algorithms that use continuous glucose monitoring (CGM) data to direct clinician attention to patients with deteriorating glucose management. MethodsWe used data from the 4T Pilot (n=133) and 4T Study 1 (n=135), in which algorithms use CGM data to identify youth with T1D meeting criteria for clinical review and potential clinician contact. Through iterative data analysis and interviews with diabetes educators and clinicians, we identified metrics for reviewing and revising clinical workloads, glucose management, and timeliness of care. For each metric, we developed an interactive dashboard to provide clinical and administrative leaders with an overview of the program. ResultsThe metrics to track clinical workload were the total number of youths: (1) in the program, (2) in each study, and (3) cared for by each clinician. The metrics to track glucose management were the number of youths meeting each criterion for review: (4) in total, (5) for each clinician, and (6) for each study. The metric to track timeliness of care was (7) the number of days since meeting criteria for clinical review. When presented at weekly program leadership meetings, the metrics facilitated data-driven decision making about clinical and operational components of the program. ConclusionWe propose a novel quantitative framework for diabetes care teams to supervise and enhance algorithm-directed whole-population T1D care. As the role of algorithms grows in directing clinical effort and prioritizing patients for care, this framework may help clinics track clinical workload, patient outcomes, and the timeliness of care.