Clinical Research

Weight loss is a key factor in improving health outcomes for many cardiometabolic conditions, like diabetes. Building upon our previous AI-modeling research, Welldoc has now harnessed our AI to predict weight loss. Based on a large dataset of individuals with type 2 diabetes using our digital health solution’s food log, our AI models predicted at least 3% weight loss with high accuracy (93%). It also evaluated the effect of different nutrients on weight loss, as well as time-based correlations. Future research will dive deeper into nutrient- and time-of-day-based personalized digital nutrition coaching to better predict the likelihood and timing of achieving nutrition and weight loss goals for users. This work is integral in Welldoc’s ability to make our digital health solution for weight management even more targeted and accurate, as well as support weight loss-related outcomes across comorbid conditions in our platform.

In the latest issue of “Frontiers in Endocrinology,” Welldoc highlights key elements for designing and implementing successful diabetes digital health tools in clinical practice. We explore topics like the importance of regulatory oversight, looking beyond A1C, addressing technology literacy, and clinical integration to increase efficiency. Additionally, we outline the practical steps needed to become a digital health-ready practice. These insights are integral to Welldoc’s commitment to developing best-in-class digital health solutions and transforming the care continuum.

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.