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Key points from ZDNET
- Wearables collect tons of health data, but much of it isn’t useful to doctors.
- The current healthcare model, designed for occasional visits, can’t easily handle continuous data streams.
- Some physicians believe AI might offer a solution to this challenge.
Dr. David Kao, a heart specialist, is no stranger to patients arriving at appointments with reams of data from their wearable devices.
On a Wednesday morning in late May, a patient brought her smart band stats to show him — just like many before her.
“About 70% of that data has no real medical meaning to me — it’s made up by the device maker,” explained Kao, who teaches cardiology at the University of Colorado School of Medicine. “But there were two or two key details in there that were genuinely helpful — insights we’d have missed if she hadn’t been wearing the gadget.”
This scene has played out repeatedly across the U.S. for over ten years: both patients and doctors are overwhelmed by the flood of numbers from wearable tech.
“It’s like standing under a firehose of information,” Kao said. “Often, I have to research parts of it just to form a rough response. And there’s no digital tool yet that helps summarize or guide a clinician on how to act on any of it.”
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More than 30% of U.S. adults now own a fitness or wellness wearable, reports show. As these gadgets have become widespread, so has the flood of personal health metrics — heart rate, blood pressure, sleep quality, stress levels, oxygen levels, and more. We’ve never had more data about our own bodies.
Despite the marketing hype suggesting these devices will lead to healthier, optimized lives, the truth is far messier — especially for patients and doctors trying to make sense of what the numbers actually mean.
Constant data, outdated systems
If you only see the doctor when something’s wrong or for yearly check-ups, you’re part of an “episodic care” model — and it wasn’t built to handle real-time health data.
“Physicians may see value in this data, but their tools, systems, and staffing simply aren’t ready to receive or act on it,” said Ream Shoreibah, a marketing professor at the University of Alabama at Birmingham.
Shoreibah works with a research team that recently published findings in *The Journal of Consumer Affairs* about the friction between patients, their data, and healthcare providers.
One major obstacle they identified is getting wearable data into electronic health records (EHRs) — the digital charts doctors already use.
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Fitting wearable data into an EHR is tough for several reasons. First, it requires two separate tech platforms — one from the wearable company, one from the hospital — to communicate securely. There also needs to be a reliable way to make sure the data reaches the right patient’s file, explained Dr. Ida Sim, a physician and computational health expert at UCSF and UC Berkeley, where she co-leads their joint precision health program.
“It’s the Wild West out there,” she said.
And even if data could flow smoothly into EHRs today, doctors still juggle multiple accounts and log-ins to view records from different wearable brands — each showing data in its own format.
On top of that, rules around data governance are unclear. Providers must decide: which data to keep, which to discard, and for how long?
Is there value in storing your heart rate readings every five minutes for three months — or forever?
As Sim pointed out, wearables often use scores like “recovery” or “strain” that don’t translate easily into clinical practice. Some doctors doubt whether they can trust these numbers at all.
Shoreibah’s study raised the same concerns: “This creates a dilemma for doctors — ignoring wearable data may upset patients who care deeply about their health, but acting on inaccurate readings could lead to harm.”
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Doctors and patients alike would feel more confident if wearable makers offered clearer validation — perhaps through FDA clearance or independent testing — along with more transparency about how metrics are calculated.
“We don’t know the raw inputs, we don’t know how it’s processed — we just get a number, a label, and a neat-sounding explanation that may or may not be scientifically sound,” Sim said.
Staying hopeful amid the data deluge
Dr. Kenneth Civello, a heart rhythm specialist at Our Lady of the Lake Regional Medical Center in Louisiana, remembers when the Fitbit launched in 2009 — the start of patients bringing in their own data. One moment stands out: an older woman walked in with her iPad loaded with health stats.
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“The pattern looked exactly like atrial fibrillation,” he recalled. “That’s when I started believing in these devices.”
Civello didn’t instantly embrace all wearable data — he’s both a fan and a critic. But it shaped his vision for the future. For instance, if a patient needs blood pressure monitoring while going about their day, having a smartwatch means they don’t have to stop and measure it manually — or forget altogether.
And yes, wearables have saved lives. Over the years, users have shared stories of Apple Watches flagging dangerous heart rhythms and other emergencies.
Clinical wearables like continuous glucose monitors are already feeding data into heart records. In cardiology, tracking patients remotely isn’t new — even those without wearables sometimes show up with blood pressure logs scribbled on napkins or printed charts.
While this feels chaotic, doctors like Civello remain optimistic. Companies are working to fix the integration gap. In 2025, Samsung acquired Xealth, a care platform that connects with Epic — the nation’s largest EHR provider. Civello believes this could simplify how Samsung health data flows into patient charts.
And if someone cracks the EHR puzzle, Civello thinks AI could be game-changing — helping doctors sort through the avalanche of digital health data and create clearer, more actionable insights.
And if someone can solve the EHR challenge, Civello is convinced AI tools will be vital in helping doctors process the “data tsunami” and generate more meaningful, usable health insights.
Tailored, individualized healthcare is on the horizon. According to him, large language models will play a key role by accessing your medical history to produce summaries, though having your doctor actively involved remains essential. He also pointed out that existing regulations and policies for LLMs handling medical information are still in development, particularly since current laws like HIPAA don’t currently cover consumer-facing AI chatbots and smart devices.
Encouragingly, Kao shared that the University of Colorado is actively researching solutions to bridge these regulatory and technical gaps.
The core challenge centers around how to link the practical electronic health record system with intelligent tools—such as devices or apps—that can absorb and analyze data from external wearable devices, arrive at clinically useful insights, and integrate those refined findings back into the patient’s health record for the treatment team to consider.
Sim highlighted efforts underway with an open-source initiative named JupyterHealth, which tackles the data integration issue without requiring all infrastructure to be centralized under one corporate entity.
“Health benefits all of society, and we need to treat it accordingly—not as purely a market-driven venture,” Sim explained.
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Recommendations and emerging best practices are also taking hold. In March, the American Academy of Neurology offered guidance tailored for neurologists using wearable technologies.
“Doctors already face an ever-growing body of medical knowledge. Having written recommendations can help clinicians grasp the fundamentals of new technologies, review their limitations, and promptly address potential patient concerns,” noted Dr. Sarah M. Benish, neurologist and lead author of the American Academy of Neurology publication on wearables.
Sim emphasized that even as more individuals embrace wearable health gear, it’s important to remember extensive, clean-synthesized data doesn’t instantly translate to improved health predictions. Understanding and treating human health involves far more complexity than fixing mechanical issues with, for example, your car’s carburetor.
For Kao, some patient encounters involve managing expectations when he regrettably cannot use their large volumes of tracked personal data.
“Admirably, patients are increasingly interested in learning more about their own bodies and responses.”
