Introduction: Care providers continue to adopt platforms that enable remote patient monitoring (RPM) for type 1 diabetes (T1D) care through the algorithmic analysis of continuous glucose monitor (CGM) data. No provider-facing quantitative framework is available to track how the use of such platforms impacts patient care and healthcare provider workload. We propose such a framework.
Methods: We used data from four sub-studies (n=127, 137, 136, 95) of whole-population algorithm-enabled RPM for T1D in Stanford’s pediatric Teamwork, Targets, Technology, and Tight Control (4T) Study. We used HbA1c measurements, CGM data, time studies of care providers, and secure messages sent by care providers to patients and families. We iteratively analyzed data and interviewed care providers to identify metrics relevant to whole-clinic and whole-population patient care and healthcare provider workloads. We developed an interactive dashboard to facilitate monitoring these metrics.
Results: The metrics identified were: the numbers of patients (1) in the overall program, (2) in each sub-study, (3) cared for by each care provider, (4) meeting criteria for review or care in each clinical category, (5) in each clinical category by care provider, (6) in each clinical review category by sub-study, and (7) each patient’s most recent date of being eligible for review by care providers.
Conclusion: We propose the first quantitative framework for healthcare organizations to supervise and enhance algorithm-enabled whole-population T1D care. As the role of digital care platforms continues to increase, this framework may help clinics manage and improve their programs.
J. Kurtzig: None. D. Scheinker: None.
