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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Poster

Artificial Intelligence (AI)

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.

Mansur Shomali, MD,CM, Abhimanyu Kumbara, MS, and Anand Iyer, PhD, MBA

Diabetes Technology Meeting (DTM)

October 2025

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Poster

Artificial Intelligence (AI)

Outcomes: All

Outcomes: Weight

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Food Detection from Continuous Glucose Monitoring Sensors Using Pretrained Transformer-Based Models

Nutrition is an essential component of managing chronic health conditions like diabetes and obesity. However, logging meals within health apps is time-consuming and cumbersome for many people. Welldoc continues to research opportunities to streamline this process through advanced AI.

‍‍In this research, we successfully developed a novel approach for automatedly detecting recent food intake solely based on continuous glucose monitoring (CGM) data and AI with an accuracy of 77.8%.‍

‍This research is a blueprint for the next generation of digital health. By enabling tools to gather consistent, complete dietary information without any input from the user, we are moving toward zero-effort self-management.

Abhimanyu Kumbara, MS, Junjie Luo, MS, Mansur Shomali, MD, CM, Anand Iyer, PhD, Gordon Gao, PhD

October 2025

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Poster

Artificial Intelligence (AI)

Outcomes: All

Outcomes: Weight

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Engagement With an AI-enabled Digital Health Tool by Individuals With Overweight or Obesity Enrolled in a Weight Management Program Differs by Use of Anti-obesity Medications

We investigated user engagement patterns within our digital health application, comparing individuals utilizing a weight management program both with and without anti-diabetic/anti-obesity medications (AOMs).‍

‍Studying engagement patterns is a key focus area for Welldoc as we continue to focus our research on further personalizing the weight management journey.‍

‍This research indicates that engagement can vary based on medication usage. We observed significantly higher engagement with the non-AOM group. This supports the idea that the need for tracking, guidance, and support needs may be more pronounced for individuals navigating their weight without these medications.

Mansur Shomali, MD,CM, Abhimanyu Kumbara, MS, and Anand Iyer, PhD, MBA

October 2025

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Poster

CGM & Real-time Devices

Healthcare Models and Frameworks

Outcomes: Glycemic

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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.

Jennifer Scarsi, RD, CDCES, Welldoc, Dawn McCarter, RN, BSN, CDCES, Allina Health Diabetes Education, Minneapolis, Minnesota, USA; Mary Brunner, MS, RD, CDCES Allina Health Diabetes Education, Malinda Peeples, RN, CDCES, FADCES; Columbia, MD, USA;Janice MacLeod, MA, RD, CDCES, FADCES, Janice MacLeod Consulting, Glen Burnie, MD

Association of Diabetes Care & Education Specialists (ADCES)

August 2025

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Poster

Artificial Intelligence (AI)

CGM & Real-time Devices

Outcomes: Glycemic

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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.

Junjie Luo, MS, Abhimanyu Kumbara, MS, Mansur Shomali, MD, CM, Anand Iyer, PhD, Gordon Gao, PhD

Machine Learning for Healthcare

August 2025

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Poster

Healthcare Economics & Outcomes

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.

Mansur Shomali, Simon Salgado, Siddharth Banyal, Anand Iyer

American Diabetes Association (ADA) 85th Scientific Sessions

June 2025

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Article

Artificial Intelligence (AI)

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Personalized Cardiometabolic Care Powered by Artificial Intelligence

This is a foundational paper on AI and healthcare, and what it takes to successfully deploy AI in a healthcare environment. We discuss foundational principles of data lake infrastructure, governance, multi-variate data sets and model monitoring.

Mansur Shomali, Abhimanu Kumbara, Janice MacLeod, Anand Iyer

Frontiers in Endocrinology

May 2025

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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.

Abhimanyu Kumbara MS, MBA, Mansur Shomali, MD, CM, Siddharth Banyal, MS, and Anand Iyer, PhD, MBA

Conference on Health IT and Analytics (CHITA)

April 2025

Explore the presentation

Article

CGM & Real-time Devices

Outcomes: Glycemic

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Safety of a Novel CGM-Informed Insulin Bolus Calculator Mobile Application by People with Type 1 and Type 2 Diabetes

Individuals with diabetes relying on basal-bolus insulin regimens often struggle with precise bolus dose adjustments. Current Continuous Glucose Monitoring (CGM) systems offer limited guidance, often relying on basic trend arrows. Building upon Welldoc’s extensive research on connecting digital health to CGM, Welldoc has developed a novel CGM-informed insulin bolus calculator. This advanced technology leverages sophisticated algorithms to analyze trend arrows and exercise factors, delivering real-time, personalized insulin dose recommendations.

This article outlines the results from a 30-day prospective clinical trial with participants with type 1 and type 2 diabetes. Participants experienced improved glycemic control and reduced diabetes distress, particularly for type 2 diabetes. Key findings include a notable Time in Range (TIR) improvement of approximately 3 points from 68.4 to 71.8% (N=54, P=0.013).

Welldoc’s CGM-informed insulin bolus calculator represents a significant advancement in diabetes management. By empowering individuals with diabetes to achieve better glycemic control and reduce the burden of the disease, our technology underscores the transformative potential of digital health when integrated with CGM.

Mansur Shomali, MD, CM, Colleen Kelly, PhD, Abhimanyu Kumbara, MS, Anand Iyer, PhD, Jean Park, MD, Grazia Aleppo, MD

Diabetes Spectrum

November 2024

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Article

Artificial Intelligence (AI)

Engagement

Integrated Care Models

Population Health

Real World Evidence

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Digital Health and AI: RDN Path to Success

Cardiometabolic digital health solutions, which address conditions like diabetes, are increasingly being integrated into clinical care. These solutions can provide personalized artificial intelligence (AI)-driven self-management support for individuals and treatment insights for clinicians. Understanding how to effectively integrate these technologies into an individual’s daily experience and the clinician’s workflow is essential. Successful implementation of these solutions can improve reach, access and outcomes for health and operational efficiencies at the individual and population levels. This article discusses these solutions and shares real-world examples of strategies for integrating them into clinical practice.‍

‍Thank you to Cutting Edge Nutrition and Diabetes Care for providing open access to our article. To purchase and read the full issue, please visit the journal’s website.

Jennifer Scarsi, RDN, CDCES, Malinda Peeples, MS, RN, CDCES, FADCES

Cutting Edge Diabetes Nutrition and Care

November 2024

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Article

Health Equity

Outcomes: Blood Pressure

Outcomes: Glycemic

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Comorbidities And Reducing InEquitieS (CARES): Feasibility of self-monitoring and community health worker support in management of comorbidities among Black breast and prostate cancer patients

Black individuals with cancer often face poorer health outcomes compared to other racial groups in the U.S., including a higher prevalence of cardiometabolic comorbidities, like diabetes and high blood pressure. A study published in Contemporary Clinical Trials Communications explores the potential of digital health tools to address these health disparities.‍

‍The study investigated the feasibility of incorporating the Welldoc cardiometabolic digital health app to improve blood pressure and/or blood glucose levels in Black individuals with breast or prostate cancer. Participants in this six month study used a home-monitoring device and the Welldoc app to track their health metrics weekly, with support from a community health worker.‍

‍While the study findings were modest, they suggest that digital health tools may be beneficial in helping individuals manage their overall health during cancer treatment. Further research is needed to optimize the integration of cardiometabolic health and digital health tools into cancer care, aiming to improve patient outcomes and reduce health disparities.

Laura C. Schubel, MPH, Ana Barac, MD, Michelle Magee, MD, Mihriye Mete, PhD, Malinda Peeples, MS, RN, Mansur Shomali, MD, Kristen E. Miller, DrPh, Lauren R. Bangerter, PhD, Allan Fong, MS, Christopher Gallagher, MD, Jeanne Mandelblatt, MD, Hannah Arem, PhD

Contemporary Clinical Trials Communications

October 2024

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Poster

Artificial Intelligence (AI)

CGM & Real-time Devices

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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.

Junjie Luo, Abhimanyu Kumbara, Anand K. Iyer, Mansur E. Shomali, and Guodong “Gordon” Gao

INFORMS Annual Meeting

October 2024

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Poster

Artificial Intelligence

CGM & Real-time Devices

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Evaluating Perplexity and Glucose Level Impact on State-Of-The-Art Generative Pre-trained Transformer (GPT) Model to Predict Glucose Values at Different Time Intervals

Welldoc continues to research how generative-AI models can be used in combination with data from real-time devices like continuous glucose monitors (CGM) to predict metrics like glucose risk indicator (GRI) and future health outcomes. Here, Welldoc developed a state-of-the-art GPT model to predict CGM trajectory at different time horizons and across two different prediction contexts. One particular context included model perplexity, which is an industry-standard metric of how well a model can predict a sample or next value in a sequence. The data show higher prediction uncertainty as the glucose profile complexity increases. This work supports Welldoc’s efforts to further optimize our diabetes solution, making the experience for individuals even more targeted and personalized.

Junjie Luo, Abhimanyu Kumbara, Anand K. Iyer, Mansur E. Shomali, and Guodong “Gordon” Gao

Diabetes Technology Meeting (DTM)

October 2024

See the Poster

Poster

Artificial Intelligence (AI)

Outcomes: Weight

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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.

Catherine Brown, Anand Iyer, Abhi Kumbara, Maxwell Ebert

Food & Nutrition Conference & Expo (FNCE)

October 2024

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Poster

Artificial Intelligence (AI)

CGM & Real-time Devices

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Evaluating a State-of-the-art Generative Pre-trained Transformer Model to Predict Continuous Glucose Monitoring Values at Different Time Intervals

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.

Mansur Shomali, MD, CM, Junjie Luo, MS, Abhimanyu Kumbara, MS, Anand Iyer, PhD, Gordon Gao, PhD

European Association for the Study of Diabetes (EASD)

September 2024

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Article

Artificial Intelligence (AI)

CGM & Real-time Devices

Engagement

Integrated Care Models

Outcomes: Blood Pressure

Outcomes: Glycemic

Outcomes: Weight

Population Health

Real World Evidence

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The Critical Elements of Digital Health in Diabetes and Cardiometabolic Care

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.

Mansur Shomali, Pablo Mora, Grazia Aleppo, Malinda Peeples, Abhimanyu Kumbara, Janice MacLeod, Anand Iyer

Frontiers in Endocrinology

September 2024

READ ТHE ARTICLE

Poster

Integrated Care Models

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

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.

Dawn McCarter, RN, BSN, CDCES Program Manager Allina Health Diabetes Education; Jennifer Scarsi RD, CDCES Clinical Digital Solutions Specialist

Association of Diabetes Care & Education Specialists (ADCES)

August 2024

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Poster

Engagement

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Use of Personalized Goals and Challenges in a Digital Health Tool to Amplify Patient Engagement with the ADCES7 Self-Care Behaviors®

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.

Catherine Brown, Anand Iyer, Abhi Kumbara, Mansur Shomali, Malinda Peeples

Association of Diabetes Care & Education Specialists (ADCES)

August 2024

SEE THE POSTER

Poster

Artificial Intelligence (AI)

CGM & Real-time Devices

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.

Junjie Luo, MS, Abhimanyu Kumbara, MS, Mansur Shomali, MD, CM, Anand Iyer, PhD, Gordon Gao, PhD

Machine Learning for Healthcare (MLHC)

August 2024

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Poster

CGM & Real-time Devices

Outcomes: Glycemic

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Use of a Digital Health Tool to Support People with Diabetes Who Inject Bolus Insulin Improves the Glycemia Risk Index and Time in Tight Range

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.

Mansur Shomali, MD, Maxwell Ebert, MPH, Anand Iyer, PhD, Jean Park, MD, and Grazia Aleppo, MD

American Association of Clinical Endocrinology (AACE)

May 2024

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Presentation

Artificial Intelligence (AI)

Outcomes: Weight

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Using an Automated, Real-time Data Enabled Feature Engineering Process to Predict Future Weight Outcomes

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.

Junjie Luo, Abhimanyu Kumbara, MS, MBA, Anand K. lyer, PhD, MBA, Mansur E. Shomali, MD, CM, Guodong (Gordon) Gao, PhD

Conference on Health IT and Analytics (CHITA)

May 2024

EXPLORE THE PRESENTATION

Article

Engagement

Population Health

Real World Evidence

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Public-Private-Industry Learning Network: Digital Health Expands the Reach and the Role of the Diabetes Care and Education Specialist

Welldoc collaborated with the state of Montana and the Montana Diabetes Digital Health Learning Network (MDDHLN) to integrate digital health into their diabetes program. The intent was to scale services to better support Montana’s rural, frontier communities. This population typically has limited access to health resources and can benefit from innovative tools, like digital health platforms, to help better self-manage their diabetes. This article highlights the many learnings and best practices to efficiently and effectively implement novel cardiometabolic care models that integrate in-person, virtual and digital capabilities.

Malinda Peeples, MS, RN, CDCES, FADCES, Marci Butcher, RD, CDCES, FADCES, Jennifer Scarsi, RD, CDCES, Deb Bjorsness, MPH, BC-ADM, RD, CDCES, Melissa House, MBA, and Janice Macleod, MA, RD, CDCES, FADCES

ADCES in Practice

April 2024

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Poster

Outcomes: Blood Pressure

Real World Evidence

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Use of a Diabetes Digital Health Solution Leads to Improvements in Cardiometabolic Outcomes

In this research, Welldoc demonstrates the potential for digital health solutions focused on diabetes to also address a broader set of cardiometabolic outcomes. A multi-condition approach could help health plans, health systems and care teams to better manage the complexities associated with diabetes, while also addressing other cardiometabolic health comorbid conditions. This work can also support better understanding of digital health engagement patterns and the impact to specific health outcomes, ultimately contributing to the development of predictive models and advanced artificial intelligence capabilities.

Abhimanyu Kumbara, MS, Anand Iyer, PhD, and Mansur Shomali, MD, CMO

Advanced Technologies & Treatments for Diabetes (ATTD)

March 2024

SEE THE POSTER

Poster

Artificial Intelligence (AI)

CGM & Real-time Devices

Healthcare Economics & Outcomes

Outcomes: All

Outcomes: Glycemic

Population Health

Real World Evidence

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Using Early Engagement Data from a Digital Health Solution to Predict Future Glycemia Risk Index (GRI)

In this real-world analysis, Welldoc builds upon prior research to show early engagement data from a digital health solution can predict the future Glycemic Risk Indicator (GRI) metric* in people with type 1 or type 2 diabetes using a continuous glucose monitoring (CGM) device. This research could enable care teams to use early digital health engagement data to build more personalized and optimized care plans for individuals, and supports Welldoc's efforts in developing next-generation digital health solutions that can improve efficiencies and health outcomes.

Reference: Klonoff DC, et al. A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings. J Diabetes Sci Technol. 2023 Sep;17(5):1226-1242. doi: 10.1177/19322968221085273. Epub 2022 Mar 29. PMID: 35348391.

Junjie Luo, MS, Abhimanyu Kumbara, MS, Anand Iyer, Ph, Mansur Shomali, MD, and Gordon Gao, PhD

November 2023

SEE THE POSTER

Poster

Artificial Intelligence (AI)

CGM & Real-time Devices

Outcomes: Blood Pressure

Outcomes: Weight

Population Health

Real World Evidence

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The Use of a Digital Health Tool with AI-coaching for Patients Enrolled in a Virtual Diabetes Program is Associated with Improvements in Weight and Blood Pressure

In this real-world analysis, Welldoc shows how AI-driven digital coaching can provide personalized, whole-person support for individuals with multiple cardiometabolic conditions, like diabetes, hypertension, and obesity, and amplify the benefits of visits and communication with providers. These outcomes demonstrate how overall cardiovascular risk can be reduced in a high-risk population in a scalable manner, and support Welldoc’s efforts in developing next-generation digital health solutions that can improve health outcomes and increase access to care.

Mansur Shomali, MD, CM, Abhimanyu Kumbara, MS, MBA, and Anand Iyer, PhD, MBA

Cardiometabolic Health Congress (CMHC)

October 2023

SEE THE POSTER

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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.