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Published on: June 11, 2012
A New Technology-Enabled Care Model for Pediatric Type 1 Diabetes.
David Scheinker1,2,3, Priya Prahalad1, Ramesh Johari4
1Associate Professor, Pediatrics, Stanford University, Stanford, California, USA.
Stanford improved pediatric type 1 diabetes care with a new telemedicine model called TIDE. This technology reduced hemoglobin A1c levels and provider screen time, enhancing patient outcomes.
Area of Science:
- Endocrinology
- Digital Health
- Pediatric Care
Background:
- Pediatric type 1 diabetes (T1D) care in the U.S. faces challenges including suboptimal patient outcomes and limited access to continuous glucose monitoring (CGM) for publicly insured individuals.
- Current care models often rely on fixed-schedule appointments rather than on-demand interventions, and provider access to CGM data is inefficient.
Purpose of the Study:
- To develop and implement a technology-enabled, telemedicine-based care model for pediatric patients with newly diagnosed T1D.
- To improve access to CGM technology and facilitate timely, data-driven interventions.
Main Methods:
- Development and deployment of the Timely Interventions for Diabetes Excellence (TIDE) platform for automated CGM data analysis and as-needed patient contact.
- Utilizing philanthropic funding to provide CGM access for publicly insured patients.
- Conducting a pilot study (4T study) integrating TIDE with CGM use in new-onset T1D patients.
Main Results:
- The pilot study showed a 0.5%-point reduction in hemoglobin A1c compared to historical controls.
- Provider time spent reviewing patient data decreased by 86% with the TIDE system.
- The TIDE system was expanded to 300 patients and made available as open-source software.
Conclusions:
- The TIDE platform and telemedicine approach represent a successful model for improving pediatric T1D care.
- This model enhances patient outcomes, increases provider efficiency, and improves access to essential diabetes technology.
- Future plans include scaling the TIDE system to support approximately 1,000 patients and integrating data from additional sensors.
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