The week in health tech, explained in plain English.Get the free newsletter →
October 24, 2026

Takeaways from the most recent news in the technology and policies shaping healthcare.

Health IT

Dexcom Trial Backs Wider Use of Glucose Monitors

Dexcom has new randomized controlled trial data suggesting continuous glucose monitors (CGMs) help a broader swath of people with diabetes, not just those who depend on insulin, according to STAT News. The finding matters because reimbursement and clinical guidelines have historically reserved CGMs for insulin-using patients, leaving most people with type 2 diabetes without coverage.

If the evidence holds up, it could push payers and providers to widen access to wearable glucose sensors for patients managing their disease through diet, oral medications, or lifestyle changes. That expansion would significantly grow the addressable market for Dexcom and rivals like Abbott, whose devices are increasingly marketed to non-insulin users and even the prediabetic and wellness crowd.

The report ran alongside STAT's coverage of Apple's annual developers' conference and broader wearables momentum, a signal that consumer-grade health tracking and clinical-grade monitoring continue to converge. The open question is whether real-world outcomes and cost savings justify paying for CGMs at population scale.

Sources

More in Health IT

Health IT

Epic Opens Its Platform Wider to AI and Outside Developers

Epic used its Open@Epic developer conference to open its platform wider to outside AI and apps while reminding developers that the resulting traffic runs on hospitals' infrastructure.

Why it matters: As the dominant EHR vendor, Epic's decision to open its platform shapes how and where clinical AI gets deployed across most U.S. hospitals.

Health IT

After AI Go-Live, Who Owns Monitoring? Often No One

Health systems scrutinize AI before go-live, but post-deployment monitoring responsibilities often remain undefined, leaving no clear owner when models drift.

Why it matters: Unmonitored AI models can quietly degrade in clinical settings, putting patient safety at risk when no one is accountable for watching performance after launch.