diabetes Archives - Digital Health Wire

Easing the Data Burden in Diabetes Care

Posted on June 22, 2026June 22, 2026 by Partner Content

By Mark Clements, M.D., Ph.D. and Trisha Martinez , RN, BSN, MBA, Glooko

Health systems are not short on diabetes data. They are short on the time, workflows, and signal clarity needed to act on it.

That distinction matters. Continuous glucose monitoring (CGM), connected devices, remote uploads, insulin delivery data, and inpatient glucose trends are generating more information than ever before.

In Glooko’s latest Annual Diabetes Report, more than 60 billion CGM readings flowed through the platform in 2025, supported by more than 1 million active patients, 30,500 clinicians, 9,000 clinics, and a global footprint across 1,082 geographic locations.

The scale is no longer the story, but what health systems do with that scale is.

For leaders focused on digital transformation, the opportunity is to shift diabetes management from retrospective review to prioritized action.

Traditional measures such as Time in Range (TIR), Glucose Management Indicator (GMI), Time Below Range (TBR), and Time Above Range (TAR) remain essential, but they can obscure when risk occurs, how severe it is, and which patients need attention first.

Two patients may look similar by familiar metrics, yet one may face recurring overnight lows while another carries persistent daytime hyperglycemia.

The report’s overnight hypoglycemia analysis illustrates why this matters. Glooko’s model surfaced patients who appeared near target by common measures but had substantially higher overnight hypoglycemia exposure.

In validation across 586,549 patient weeks, the highest-risk group showed a 2.79x lift in identifying observed overnight hypoglycemia compared with baseline selection.

This is where digital transformation becomes clinical transformation: turning connected data into prioritized lists, cohort-level visibility, and workflows that help teams intervene between visits.

The same safety lens extends into the hospital. EndoTool provides an inpatient view of glycemic management, supporting individualized insulin dosing during complex episodes such as Diabetic Ketoacidosis (DKA) and Hyperosmolar Hyperglycemic State (HHS), renal impairment, steroid exposure, and changing nutrition status.

The Takeaway

The next chapter of diabetes care will not be defined by more dashboards. It will be defined by connected intelligence that helps health systems identify risk earlier, focus clinical attention, reduce cognitive burden, and support safer decisions across the hospital, clinic, and home.

Learn how Glooko is partnering with organizations like yours to transform diabetes care.

Digital Diabetes Management in the Hot Seat

Posted on March 27, 2024March 27, 2024 by Jason Barry

Digital diabetes management tools found themselves in the hot seat after a blistering study from the Peterson Health Technology Institute suggested that several leading solutions don’t provide significant clinical benefit, especially relative to their cost.

That conclusion naturally had many digital health advocates sharpening their pitchforks, but first let’s start by unpacking PHTI’s research and findings.

The analysis had two main endpoints: clinical effectiveness and economic impact. PHTI reviewed 1,100+ articles, including 120 submitted by the companies being evaluated, then offered ratings across three categories – remote patient monitoring, behavior / lifestyle modification, and nutritional ketosis. This chart summarizes the results beautifully.

On the clinical side, PHTI found that these tools deliver small reductions in HbA1c (0.23 to 0.60 percentage points) compared to usual care, as well as limited long term durability of the improvements.

On the economic side, PHTI concluded that each of the three product categories led to a net increase in spending, with total reimbursement and program investment exceeding any cost reduction from avoided care (the ketosis category carried an asterisk for its potential to cut costs over the long term).

Those findings led to plenty of blowback, including an excellent rebuttal from the Digital Therapeutics Alliance and a flurry of press releases from the companies in the report.

The DTx Alliance’s rebuttal centered around three primary issues:

The limited selection of solutions overlooks a large portion of the diabetes tools on the market, and that it’s misleading to give generalized conclusions based on a small sample when many products can demonstrably improve clinical and economic outcomes.

The report’s reliance on predictive models rather than actual cost studies overlooks real-world evidence, particularly concerning products like Dario, which has independent studies demonstrating reductions in both costs and hospitalizations.

It might have made sense for PHTI to list any of the nation’s 9,000 endocrinologists (diabetes experts) as authors or advisors. It’s tough to beat their pointed suggestion: “Had there been expertise in this evaluation, they may have considered the broader scope of diabetes management like reductions in hypo- and hyperglycemic events, in addition to the reduction of A1C levels.”

The Takeaway

Embracing debate is essential if the industry wants to improve, and even if there was room for improvement in PHTI’s methodology, it definitely succeeded in its goal of refocusing attention on the clinical and economic impact of digital innovations.

Social Needs Impact on Diabetes Management

Posted on August 13, 2023October 3, 2023 by Jason Barry

New research published in JAMA Network Open found that health-related social needs (HRSNs) have a major impact on both quality and utilization outcomes for people with type 2 diabetes.

Using self-reported data from a national sample of 21,528 Medicare Advantage beneficiaries with T2D, researchers found that the majority (56.9%) reported at least one HRSN, and that each one significantly affected outcomes.

Here’s a look at the odds ratio impact of HRSNs on diabetes medication adherence (proportion of days covered over 80%), statin adherence (PDC over 80%), and having controlled HbA_1c. Not too surprising to see that financial strain had the largest negative impact on HbA_1c control.

Food insecurity – Diabetes (0.93), Statin (1.02), HbA_1c (1.02)
Financial strain – Diabetes (0.91), Statin (0.91), HbA_1c (0.83)
Loneliness – Diabetes (0.85), Statin (0.79), HbA_1c (0.96)
Unreliable transport – Diabetes (0.80), Statin (0.80), HbA_1c (0.94)
Housing insecurity – Diabetes (0.78), Statin (0.96), HbA_1c (0.92)

Each of the five HRSNs also influenced hospital utilization, and it was interesting that food insecurity was identified as the factor with the largest impact on acute care usage. Here are the changes in utilization per 1,000 MA enrollees for avoidable hospitalizations, ED visits, inpatient encounters, and 30-day readmissions.

Food insecurity – Hospitalization (17.1), ED Visit (84.6), IE (30.4), Readmission (8.2)
Financial strain – Hospitalization (4.6), ED Visit (40), IE (6.8), Readmission (2.3)
Loneliness – Hospitalization (3.9), ED Visit (173), IE (-6.3), Readmission (-4.5)
Unreliable transport – Hospitalization (13.5), ED Visit (244.6), IE (41.8), Readmission (1)
Housing insecurity – Hospitalization (6.3), ED Visit (55.4), IE (16.1), Readmission (10.2)

The Takeaway

Type 2 diabetes affects more than a quarter of people above age 65 while costing the US health system over $200B each year, and this study underscores the significant role that HRSNs play in managing the disease. Much of the existing work around HRSNs relies on area-level social needs measures because patient-level data is hard to come by, but these findings suggest that collecting patient data is worth the effort to move the needle in diabetes care.

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