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.

CGM

We convert continuous glucose data into personalized guidance that elevates traditional diabetes care standards.

Cardiometabolic

We address interconnected health conditions, turning digital engagement into measurable clinical improvements.

Diabetes complexities

We offer FDA-cleared solutions for T1 and T2 diabetes that simplify treatment while addressing daily health challenges.

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Artificial Intelligence

Artificial Intelligence (AI)

CGM & Real-Time Devices

Engagement

Healthcare Economics & Outcomes

Healthcare Models and Frameworks

Health Equity

Integrated Care Models

Outcomes: All

Outcomes: Blood Pressure

Outcomes: Glycemic

Outcomes: Weight

Population Health

Randomized Clinical Trial

Real World Evidence

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Article

Integrated Care Models

Outcomes: All

Outcomes: Glycemic

Real World Evidence

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Combining the High Tech with the Soft Touch: Population Health Management Using eHealth and Peer Support

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Poster

Integrated Care Models

Outcomes: All

Outcomes: Glycemic

Real World Evidence

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CONNECTING PATIENTS AND DIABETES EDUCATORS VIA A MOBILE PHONE AND WEB-BASED TECHNOLOGY SYSTEM: Content Analysis of Portal Messages

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Poster

Artificial Intelligence (AI)

Outcomes: All

Outcomes: Glycemic

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CGM-GPT: A Transformer Based Glucose Prediction Model to Predict Glucose Trajectories at Different Time Horizons

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.

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Outcomes: All

Outcomes: Blood Pressure

Real World Evidence

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Blood Pressure Improvement in People Using a Digital Health Solution for Comprehensive Diabetes Self-management

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Poster

Artificial Intelligence (AI)

Engagement

Health Equity

Outcomes: All

Outcomes: Glycemic

Real World Evidence

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Beyond Tracking: The Benefits of Contextual Annotation in a Diabetes Digital Therapeutic

Digital therapeutics can help patients manage chronic conditions by leveraging structured data like blood glucose, diet, and medication adherence, as well as unstructured data. The purpose of this study, presented at an American Diabetes Association session, was to develop a lexicon to characterize annotations made by patients using Welldoc's BlueStar® solution.

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Article

Population Health

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Are You Ready to Be an eEducator?

Health technology tools are redesigning clinical care and diabetes self-management education. In the article entitled, "Are You Ready to Be an eEducator?," the co-authors focus on the need for diabetes educators to move towards the implementation of digital health as a key delivery platform for diabetes education and care.

READ THE ARTICLE

Poster

Artificial Intelligence (AI)

Engagement

Healthcare Economics & Outcomes

Outcomes: All

Outcomes: Glycemic

Real World Evidence

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Applying the Glycemia Risk Index (GRI) to User Data from a Digital Health Tool Reveals Patterns of Engagement That Differ by Type of Diabetes

Welldoc® and the Carey Business School of Johns Hopkins University (formerly CHIDS at University of Maryland) continue to research how a digital health solution, combined with CGM data, can help individuals living with diabetes improve lifestyle factors and support better glycemic outcomes. In this study, we applied the Glycemia Risk Index (GRI) metric to assist with the basic clinical interpretation of CGM data. Our findings show that the average individual with type 1 and type 2 diabetes improved their GRI by 6 points during the first 14 days of use of the combined CGM and digital health tool. Individuals with type 2 diabetes that engage in digital health features like medications and lifestyle increased the probability of improved GRI, whereas individuals with type 1 diabetes saw improvement based on CGM wear time. The continued use of the GRI metric will help us demonstrate self-management and behavior outcomes of individuals using a combination of a digital health solution and CGM while extending support for diabetes populations.

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Article

Integrated Care Models

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An Effective Model of Diabetes Care and Education – Revising the AADE7 Self-Care Behaviors®

In this position paper from the American Association of Diabetes Educators (AADE), the authors evaluate the AADE7 within the framework of the advances in diabetes management as well as in diabetes self-management education and support.

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Poster

Artificial Intelligence (AI)

Engagement

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An Approach for Evaluating and Visualizing CGM Data in People with Diabetes

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.

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Poster

Artificial Intelligence (AI)

Engagement

Health Equity

Integrated Care Models

Outcomes: All

Outcomes: Glycemic

Real World Evidence

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A Real-Time CGM-Enabled Digital Health Tool Highlights a Relationship Between Sentiment and Diabetes Distress in People Using Bolus Insulin

Welldoc continues to focus our research on advancing digital health through connection with real-time connected devices, such as continuous glucose monitoring (CGM). Building on prior clinical research, which showed an improvement in glucose outcomes and reduction in diabetes distress with the use of an app-based CGM-informed insulin bolus calculator, this study demonstrates qualitatively how these same individuals interacted with the technology and how it made them feel. People with type 1 and type 2 diabetes who inject bolus insulin often find their diabetes to be overwhelming. Here, Welldoc found there was a significant proportional connection between those who felt less distress about their diabetes and felt positively about the technology, versus those who felt more distressed. This research reinforces the power of combining digital health with CGM in not only supporting individuals with a more personalized digital health solution that they enjoy using, but also providing clinicians with additional tools to support people with diabetes distress management.

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Article

Integrated Care Models

Outcomes: All

Outcomes: Glycemic

Population Health

Real World Evidence

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A Systematic Review of Reviews Evaluating Technology-Enabled Diabetes Self-Management Education and Support

Since the introduction of web-enabled mobile devices, technology has been increasingly used to enable diabetes self-management education and support. This timely systematic review summarizes how currently available technology-enabled diabetes self-management solutions impact outcomes for people living with diabetes.

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Presentation

Artificial Intelligence (AI)

Integrated Care Models

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A Proposed Foundational Architecture for AI-powered Digital Health Platforms

Advancements in artificial intelligence (AI) are providing a wealth of opportunities for improving clinical practice and healthcare delivery. In this clinical presentation, we discuss principles that govern the responsible adoption of AI capabilities in healthcare to complement, not replace, the clinician.

Leveraging AI to drive better health care is complex, requiring diligence in operationalizing extensive and diverse data sets, clinical evidence, data governance, interoperability and a data intelligence platform that ensures privacy, security and scalable application in real-world settings. Examples are shared from Welldoc's digital health platform using generative AI features with the goal of transforming the care continuum from prevention through diagnosis, treatment, and ongoing management, including efficient acute care interventions when needed.

EXPLORE THE PRESENTATION

Poster

Artificial Intelligence (AI)

Outcomes: All

Outcomes: Weight

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A Probability State Transition Matrix can be Used to Estimate Weight Loss for Individuals with Overweight or Obesity Enrolled in an AI-enabled Digital Health Weight Management Program

At Welldoc, our goal is to leverage these deep insights to build more personalized capabilities. By analyzing engagement across different clinical segments, we are developing AI models that can better predict individual needs and tailor digital coaching to truly support every unique path.

‍‍In this research, we studied the probabilities of weight loss, stratified by starting body mass index (BMI) band. We developed a probability state transition matrix to estimate the weight loss effect of a digital health tool on groups of individuals with and without an anti-obesity medication (AOM) in a weight management program.‍

‍In general, for users with starting BMI <36, use of the digital health tool proved to be an effective mechanism to lose weight without an AOM. When combined with engagement patterns, this research is foundational to developing AI-based weight loss prediction capabilities, based on both starting BMI level and the level of digital health engagement.

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Article

Healthcare Economics & Outcomes

Outcomes: All

Outcomes: Glycemic

Population Health

Real World Evidence

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A Payer Digital Health Study Shows Scalable Approach to Cost Savings and Outcomes

Health plans must find a scalable solution that reduces healthcare use and costs among those with chronic conditions. This study from Aetna/CVS Health, Welldoc, and LifeScan explores health plan strategies that take advantage of digital health relative to outcomes.

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Poster

Healthcare Economics & Outcomes

Outcomes: All

Outcomes: Glycemic

Real World Evidence

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A Novel Economic Analysis Applied to Innovative Diabetes Digital Health Intervention Demonstrates Significant Financial Benefits

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Poster

Artificial Intelligence (AI)

Engagement

Real World Evidence

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A Novel Approach to Assess Patient Burden Using Data from a Digital Therapeutic for Type 2 Diabetes Predicts Glucose Outcomes

Digital therapeutics can help patients manage chronic conditions while presenting opportunities to assess and possibly alleviate certain treatment burdens. The purpose of this study, presented at the American Psychological Association, was to determine how digital therapeutics can assess patient burden associated with diabetes outcomes.

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Poster

Healthcare Economics & Outcomes

Outcomes: All

Outcomes: Weight

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A Novel Approach to Estimating Cost Savings and Return on Investment (ROI) for Weight/BMI Changes with Digital Health

Obesity is a costly condition. Welldoc is able to shift the BMI curve through digital health engagement alone – translating to significant ROI and cost savings. Here, we presented a modeling tool to show the ROI and economic impact of shifting individuals with obesity into a lower BMI band and our ability to do so through AI-driven digital health.‍Key Takeaways:

~25% of individuals with obesity successfully shifted to a BMI below 30
$1,527 reduction in estimated average cost per person at 6 months

This was achieved without GLP-1 medications, highlighting how AI-driven digital health can be used in multiple ways to support weight management – as a standalone solution, or a precursor or adjunct to more costly treatment pathways.

SEE ТHE POSTER

Poster

Artificial Intelligence (AI)

Engagement

Integrated Care Models

Outcomes: All

Outcomes: Glycemic

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A Data Science Framework for Mobile Health–Engagement and Outcomes

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Poster

Artificial Intelligence

Engagement

Health Equity

Outcomes: All

Outcomes: Glycemic

Population Health

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A Digital Health Solution with a CGM-informed Insulin Calculator Reduces Diabetes Distress in Individuals with Type 1 and Type 2 Diabetes

A digital health solution that assists people with diabetes self-management and insulin dosing may reduce diabetes distress, particularly around their treatment regimen and interpersonal relationships.

SEE THE POSTER

Article

Engagement

Integrated Care Models

Outcomes: All

Outcomes: Glycemic

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A Framework for Optimizing Technology-Enabled Diabetes and Cardiometabolic Care and Education

This article published in The Diabetes EDUCATOR presents a framework for optimizing technology-enabled diabetes and cardiometabolic care and education using a standardized approach. The featured approach leverages the expertise of the diabetes care and education specialist, the multiplicity of technologies, and integration with the care team.

READ THE ARTICLE

Poster

Artificial Intelligence (AI)

Engagement

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A Novel Automated AI Method for Detecting and Classifying CGM Patterns

SEE THE POSTER

Poster

Integrated Care Models

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A Dynamic Duo: Virtual DSMES and a Digital App, a New Model for Self-Management Education

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Study

Engagement

Healthcare Economics & Outcomes

Integrated Care Models

Outcomes: All

Outcomes: Glycemic

Population Health

Real World Evidence

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A Mobile App to Improve Self-Management of Individuals With Type 2 Diabetes: Qualitative Realist Evaluation

In this study from the Journal of Medical Internet Research, a Web-based solution was evaluated for improving self-management in type 2 diabetes (T2DM) patients. The study aims to identify key combinations of contextual variables and mechanisms of action that explain for whom the solution worked best and in what circumstances.

VIEW THE STUDY

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Welldoc's BlueStar® may be covered under one or more of the following United States Patents: 8838513; 9064040; 9536053; 9754077; 9824190; 9858394; 9881136; 10152675; 10176893; 10192638; 10672509; 10748658; 10818389; 10839951; 10846607; 10854337; 10860943; 10872686; 10937535; 10978207; 11004558; 11147480; 11211156; 11298054; 11344232; 11357427; 11361238; 11361854; 11361857; 11521727; 11527314; 11538582; 11544610; 11564600; 11587660; 11587681; 11651845; 11699511; 11735300; 11798670; 11894142; 12009086; 12011262; 12046345; 12062421; 12279864; 12283381; 12293841; 12381005; and 12417831. This website is provided to satisfy the virtual patent marking provisions of various jurisdictions including the America Invents Act and 35 U.S.C. §287(a). Additional patents may be issued or pending in the US and elsewhere.