A few years ago, at an annual check up, I was told that I was prediabetic. My Hemoglobin A1C was 6.0 and despite my BMI being normal, exercising 5 times a week, and being relatively diligent in my diet, I had to figure out how to tackle this. Naturally my next question was, which CGM should I use to monitor my glucose levels? Surprisingly the answer was: a CGM isn’t prescribed for prediabetes, only diabetes. And that payors would not cover it.
With over 115 million Americans in the prediabetic range, and 20-30% converting to diabetes within 5 years (that is over 20 million people), why would we not use CGMs collecting data, to prevent a chronic and potentially severe disease?
Wearables such as Dexcom G7, have API solutions to integrate data directly into EHRs such as Epic Locus Health and Truveta. Abbott FreeStyle Libre does as well, as does Guardian.Medtronic This is a key issue to reduce clinician admin overload, workflow friction and make it easier for patients to discuss this information.
For other diseases as well, wearable data has helped to reduce morbidity. In a study at Cedars-Sinai, patients using Fitbit trackers with advanced cancer had less adverse events and hospitalizations the more steps they took. In another study looking at post operative patients, results were similar.
For diabetes and CGMs, the evidence seems clear:
The CONNECT Trial (June 2026): Presented at the American Diabetes Association Scientific Sessions, this randomized controlled trial followed 283 adults with Type 2 diabetes who were not on insulin. The Dexcom G7 intervention group saw an average A1c reduction of 1.6 percentage points.
A systematic review published in the International Journal of Behavioral Nutrition and Physical Activity that looked at 25 randomized clinical trials with almost 3,000 participants with and without diabetes, showed that using a CGM purely as a lifestyle modification tool drove a 0.28% drop in HbA1c.
So, the question is why are we not using CGMs and wearables more to reduce disease progression? And how can we better implement them in our healthcare system, allowing for clinician adoption and seamless workflow to be fundamental necessities?
What do physician leaders think?
I had a chance to speak with Dr Brennan Spiegel, MD, MSHS, Professor of Medicine, Public Health, and Biomedical Sciences and Director, Certificate Program in Applied Artificial Intelligence for Health Systems.
Question: What is the importance of RPM and wearable data for patient care?
“Patients spend 99.9% of their lives nowhere near the clinic. They’re at home, at work, in their communities, living their lives. If we’re going to truly understand health, we need a way to learn what happens between office visits, not just during them. That has always been the promise of remote patient monitoring.
For many years, we’ve had the ability to collect wearable data, but we’ve struggled to translate that information into meaningful clinical action. Today, advances in AI, combined with HIPAA-compliant data sharing and patient consent, are making that vision much more achievable. Rather than overwhelming clinicians with a constant stream of data, AI can help identify meaningful patterns and ‘hot spot’ patients who may need more timely attention. The goal isn’t more data—it’s better, more proactive care.”
Question: What are the challenges to integrating this into workflow, and how have you and your team overcome these?
“For more than a decade, Cedars-Sinai has been among the first health systems in the country to integrate wearable biosensor data directly into the electronic health record. Building the technical connection is an important first step, but it’s only the beginning.
The larger challenge is developing clinical workflows that make these data genuinely useful without creating additional burden for clinicians. That means determining which patients are most likely to benefit, which conditions are appropriate for remote monitoring, how information should be presented, and when it should trigger clinical action.
This remains an active area of innovation for our team. We’re focused on building evidence-based care models that integrate wearable data into routine clinical practice in ways that improve outcomes, enhance efficiency, and support clinicians rather than overwhelm them.”
Doctors, NPs, PA’s nurses and other healthcare professionals remain key in helping the patients navigate the data from wearables and RPMs. They must be not only included in implementation, but also far before that- in development and understanding the product too.
For me personally, as a patient (and physician( I ordered a CGM and started using it. And, it certainly did help me with insights- what spiked my glucose levels (sadly it was sourdough bread which I love) and what helped them stay stable (yoga!).
As we these technologies get better and help gather insights about better patient care, and help to prevent disease and disease progression, we must include all players. Optimal technology adoption occurs with education, engagement and collaboration.