Clinical Research

Welldoc is actively developing generative-AI models specific to continuous glucose monitoring (CGM) value prediction. Our GPT-like AI modeling approach, based on a vast CGM dataset for individuals with type 1 and type 2 diabetes, has achieved state-of-the-art performance in predicting short-term CGM trajectories, at five-minute intervals, up to two hours in advance. Specifically, when predicting one of the 5 glucose categories (VeryLow, Low, InRange, High, VeryHigh), the model demonstrated an overall accuracy of 94% in predicting CGM values within 30 minutes. The absolute prediction accuracy to predict glucose values within 5mg/dL and 10 mg/dL error range in 30mins was 62% and 80% respectively. This exceptional accuracy highlights the model's potential to provide valuable insights into the dynamic interplay between blood sugar levels, lifestyle factors, and interventions. By leveraging these insights, personalized, automated coaching can be developed to optimize diabetes management and improve individual outcomes. This work is integral to Welldoc’s Advanced AI and our ability to transform the care continuum.

Welldoc is pioneering the use of AI to revolutionize diabetes management. Our groundbreaking general purpose transformer based CGM research represents a significant leap forward in predicting glucose levels for individuals living with both Type 1 and Type 2 diabetes.

This research is the first in a series outlining Welldoc's novel methodology towards predicting future continuous glucose monitoring (CGM) glucose levels with high accuracy. The poster presents Welldoc's state-of-the-art AI models, which can predict glucose trajectories at 30-, 60- and 120-minute intervals for both type 1 and type 2 diabetes populations with ~ 50% less root mean square error, when compared to existing benchmark studies. Welldoc's GPT model, CGM-GPT, was trained only on CGM data sets from individuals living with Type 1 and Type 2 diabetes, and reflects the advanced opportunity to develop sophisticated large sensor models (LSM) by leveraging the vast data available via real-time sensors.

We are committed to further refining our models by incorporating additional data sources and exploring expanded applications. This ongoing research will drive the development of innovative diabetes management solutions. Stay tuned for additional information on our exciting transformer based CGM research.

Cardiometabolic digital health solutions, which address conditions like diabetes, are increasingly becoming integrated into clinical care models. Inclusion of AI-driven, personalized coaching can empower individuals towards better self-management and healthy habits, while also providing data-driven, actionable insights to drive optimized clinical decisions, increased clinical efficiency, and scalability of cardiometabolic health programs.

Allina Health, a nonprofit health system that cares for individuals, families and communities throughout Minnesota and western Wisconsin, collaborated with Welldoc to launch an integrated Diabetes Self-Management Education and Support (DSMES) program. This poster delves into the real-world learnings across this multi-year initiative. Allina's Certified Diabetes Care and Education Specialists (CDCES) share critical factors in standing up an integrated digital-first program and valuable insights demonstrating how digitally enabled programs can drive improved reach, access, health outcomes and operational efficiency. Learn how Allina Health overcame challenges, demonstrated patient engagement, and enhanced operational efficiency throughout this impactful initiative.

Welldoc continues to research how AI-driven personalized digital health solutions can motivate and support better engagement, self-management and overall health. This Welldoc study analyzed digital health engagement patterns among individuals with type 1 and type 2 diabetes. Individuals who utilized the app to set goals and participate in simple health challenges, focused on building better habits, engaged with the app up to 8x more than those who did not. This indicates how breaking down health goals into manageable and how personalized steps can significantly increase digital health engagement and foster heathier routines.

Continuous glucose monitoring (CGM) has emerged as an important tool to help people with diabetes manage food, activity, and insulin dosing. CGM measures Time in Range (TIR) and has become a key metric for clinical practice.

Building upon Welldoc’s research on the impact of CGM + digital health, this analysis sought to correlate the glycemic metrics, time in tight range (TITR) and glycemic risk index (GRI), to level of engagement with the digital health tool. Highlights from the study include further understanding of TIR improvement, particularly for those with type 2 diabetes, and initial findings specific to engagement with a digital health insulin calculator feature. Welldoc continues research in this area to drive further integration of GIR and TITR into AI-driven digital health solutions, to impact user engagement and cardiometabolic health.

This research presents a novel artificial intelligence (AI) framework for building dynamic user profiles, based on interactions with digital health app features. This framework makes it possible to identify individual-level use-patterns and develop advanced AI models to accurately predict and effectively influence health outcomes.

In this research, Welldoc analyzed continuous glucose monitoring (CGM) + My E-Diary for Activities and Lifestyle (MEDAL) data, as collected within Welldoc’s cardiometabolic digital health platform to determine features and use patterns which influence future weight loss. This study proposes a novel AI framework to build dynamic user profiles based on how users interact with digital health apps. The researchers analyzed continuous glucose monitoring (CGM) and MEDAL data collected within Welldoc's digital health platform, to find patterns that could predict future weight loss. Continued research in this area will ultimately drive the development of next generation, personalized digital health solutions that further impact the user experience, individual behaviors and overall health outcomes.