The foundation of our clinical rigor
Proven science drives everything we do. Through rigorous research, we demonstrate how our health technology delivers real results: better patient outcomes, greater value for healthcare systems, and more advanced AI capabilities. Key areas include:
Our AI transforms complex health data into personalized insights and guidance when individuals need it most.
We convert continuous glucose data into personalized guidance that elevates traditional diabetes care standards.
We address interconnected health conditions, turning digital engagement into measurable clinical improvements.
We offer FDA-cleared solutions for T1 and T2 diabetes that simplify treatment while addressing daily health challenges.
Patient-Generated Health Data Enhance Clinical Care for People with T2D Using a Digital Health Tool
Optimal management of people with Type 2 diabetes often requires collaboration among healthcare providers and educators. This study, presented at the Association of Diabetes Care & Education Specialists annual event, demonstrates how patient-generated health data enhances clinicians' ability to modify treatment plans.
Personalized cardiometabolic care powered by artificial intelligence
Technology-Enabled Diabetes Self-Management Education & Support
Older Adult Self-Efficacy Study of Mobile Phone Diabetes Management
Nutritional Analysis and Advanced Artificial Intelligence (AI) Predicts Weight Loss for People with Diabetes
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.
Mobile diabetes intervention study: testing a personalized treatment/behavioral communication intervention for blood glucose control
Mobile Prescription Therapy: The Potential for Patient Engagement to Enhance Outcomes
Mobile Messaging: It’s More Than Texting. A Mobile Message Taxonomy that Utilizes the Capacity of Mobile Technology
Mobile Diabetes Intervention for Glycemic Control: Impact on Physician Prescribing
Mobile Diabetes Intervention for Glycemic Control in 45- to 64-Year-Old Persons With Type 2 Diabetes
Methods, Analysis, and Insights from a State-Of-The-Art Large Glucose Model
Proactive diabetes self-management requires accurate glucose value prediction and in-the-moment AI-driven coaching based on those predictions, all made possible by raw data from continuous glucose monitors (CGM).
Here, Welldoc builds upon our prior AI models that used CGM data only and expands to a new Large Glucose Model (LGM), which uses both CGM values and time series inputs to predict glucose trajectories at 30mins, 60mins and 2-hour time horizons. Results were analyzed across different Type 1 and Type 2 diabetes population subgroups (time of day, age group and total engagement levels) within a mobile diabetes management application.
This work will allow Welldoc to power new cardiometabolic focused capabilities and innovations in enhanced AI-driven personalization. Welldoc continues to drive this type of research to develop novel solutions leveraging data from real-world sensors, like CGM, and provide deep insights into subgroup level patterns and differences.
Measures derived from patient-generated health data provide insights on glycemic control beyond A1C for people with type 2 diabetes
Meet the Newly Revised AADE7 Self-Care Behaviors® Up Close
The AADE7 Self-Care Behaviors® (AADE7) provides a robust framework for self-management of diabetes and other related conditions. In this poster presented at ADCES 2020, the authors review AADE7 and make recommendations on behavior names.
Lessons From a Community-Based mHealth Diabetes Self-Management Program: “It’s Not Just About the Cell Phone”
Integration of the Glucose Management Indicator (GMI) into the electronic health record through a diabetes-cardiometabolic digital health app
Health systems are increasingly integrating digital health solutions to provide personalized support to patients and timely insights to clinicians. The Allina Health system,, has partnered with Welldoc to integrate Welldoc’s FDA-cleared cardiometabolic digital app into their diabetes program. The Welldoc App syncs with continuous glucose monitoring (CGM) devices. This integration enables the system to capture a new metric, Glucose Management Indicator (GMI) to help support patients with diabetes. GMI is a calculated value based on CGM data that provides an estimate of a person's average blood sugar (A1C) over a shorter period. This is valuable because GMI can show changes in glucose levels faster than a traditional A1C test, which is helpful for both patients and clinicians. The poster outlines the GMI as a new metric for success in diabetes and key aspects of health system-health tech collaboration in diabetes.Key Takeaways:
- New Metric for Success: Welldoc is the first to utilize GMI as a quality metric within a digital health solution. The GMI is a calculated value used to estimate A1C based on CGM data. It provides a unique value in that it can be reported in a shorter time period (10-14 days) and allows for faster observation of glucose changes. The GMI's inclusion as a quality metric in the 2025 HEDIS measures recognizes the value of CGM data in assessing diabetes management.
- The power of health system-health tech collaboration: Allina and Welldoc partnered to effectively integrate the Welldoc solution into Allina’s diabetes program, which included workflow, eHR integration and incorporating data into clinical interventions and treatment plans.
- Eye on Quality of Care: The incorporation of GMI as a quality metric is essential for maintaining high Health Plan Ratings and Star ratings for value-based care. This integration helps health systems meet their quality goals and ultimately improves care for people with diabetes.
Interactive Digital Health: Engaging Healthy Behaviors & Clinical Outcomes
Incorporating smartphone technology into self-management activities for people with type 2 diabetes may improve patient outcomes. This study evaluates the impact of the FDA-cleared One Touch Reveal Plus® app powered by Welldoc's BlueStar® on BG control through a pre-post study of glucose control, participant engagement, and health care utilization and cost.
Integration of a mobile-integrated therapy with electronic health records: lessons learned.
Integrating The 2017 National Standards For Diabetes Self-Management Education And Support Into A Technology-Enabled Population Health Diabetes Care And Education Framework
Improving Diabetes Self-Management with a Mobile App: Preliminary Results of a Pilot Program at a Safety Net Hospital System
For patients with type 2 diabetes, self-management remains a significant challenge. In recent years, the use of digital health technologies for chronic disease management has increased. In this study presented at the ADA 2020 Virtual Conference, patients with uncontrolled type 2 diabetes were analyzed using BlueStar® by Welldoc to determine mean changes in A1c.
Implementation Strategy for a Digital Health Tool Influences User Engagement
Published by the American Diabetes Association publication, this study by Welldoc authors examined the company's BlueStar® used in three different models: a healthcare provider model, a diabetes educator model and a health plan model to demonstrate how the implementation strategy has the potential to influence user engagement and outcomes.
Impact of a Combined Continuous Glucose Monitoring–Digital Health Solution on Glucose Metrics and Self-Management Behavior for Adults With Type 2 Diabetes: Real-World, Observational Study
In this journal article, Welldoc builds upon prior research focused on advancing chronic care through the use of digital health and real-time connected devices. This study examines the impact of combing real-time continuous glucose monitoring (RT-CGM) with an AI-driven digital health solution on helping individuals with type 2 diabetes to improve their glycemic metrics like time in range (TIR).
Individual Differences in Educational Engagement with a Digital Health Solution
This research, conducted by Welldoc in collaboration with the Robert H. Smith School of Business Center for Health Information and Decision Systems, evaluated the performance of a Welldoc automated method for detecting significantly adverse glucose events and, further, classifying those events by level of severity.
Impact of Food on A Transformer Based Glucose Prediction Model to Predict Glucose Trajectories at Different Time Horizons
In this research, Welldoc and Johns Hopkins Carey Business School leveraged dense, real-time glucose data from Continuous Glucose Monitoring (CGM), similar to how fitness trackers collect biometric signals. Our work shows that by combining this data with food intake, we can significantly improve the accuracy of future glucose predictions. This takes Welldoc one step closer to the real world application of reliable prediction and prevention of overnight hyperglycemia.Why It Matters:
- Smarter Predictions: We developed a "large health model" (LHM) that uses AI to analyze dense data from CGMs and food intake data. This model is more accurate in predicting future glucose levels over longer periods, with a greater improvement for the 2-hour interval when food's impact is highest. This improved accuracy is critical to establishing trust in the next generation of AI driven digital health coaching capabilities.
- Better Health Outcomes: This work establishes a clear pathway for integrating various types of health data into AI models to enhance their predictive power. The ultimate goal is to apply this improved accuracy to real-world clinical challenges, such as reliably predicting and preventing overnight hyperglycemia.
- A New Approach: Welldoc’s research goes beyond existing models by not only collecting data but also using it to predict future biometric values and offer automated coaching based on those predictions. This is a novel approach that leverages the power of AI to provide actionable insights for.
Identifying Digital Health Habits Correlated with Improved Blood Glucose Control
This research, conducted by Welldoc in collaboration with the Robert H. Smith School of Business Center for Health Information and Decision Systems, evaluated the performance of a Welldoc automated method for detecting significantly adverse glucose events and, further, classifying those events by level of severity.
Human vs. Machine: A Comparative Study on CGM Event Detection and Classification
This research, conducted by Welldoc in collaboration with the Robert H. Smith School of Business Center for Health Information and Decision Systems, evaluated the performance of a Welldoc automated method for detecting significantly adverse glucose events and, further, classifying those events by level of severity.
Let’s partner to elevate cardiometabolic health.
Contact our sales team to talk about a solution for your enterprise or request a demo to see Welldoc’s predictive AI in healthcare in action.
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