Why Is Healthcare Only Now Actually Working on Digital Biomarkers?

Posted in AI Healthcare

Tech is everywhere: wearables that monitor your heart around the clock, sensors that catch early signs of disease before you ever feel a symptom. Real-world health data flowing seamlessly into the hands of your care team. For years, it sounded like something that was always five years away.

That gap is closing, and the reason it’s closing now is more interesting than most people realize. It has very little to do with new gadgets. It has everything to do with the infrastructure around those gadgets finally catching up.

What Are Digital Biomarkers?

Digital biomarkers are objective, quantifiable, physiological, and behavioral data collected from portable, wearable, or implantable devices (e.g., smartwatches, apps). They provide continuous, real-time, and non-invasive insights into a person’s health, aiding in disease diagnosis, monitoring, and treatment efficacy. 

Revisit the last doctor’s appointment. They checked a few numbers, asked how you’d been feeling, and sent you on your way with a picture of your health at that one specific moment in time. That’s how medicine has largely worked for decades, periodic snapshots, taken in a clinical setting, that may or may not reflect what’s actually happening in your body on a normal Tuesday.

However, digital biomarkers work differently. They are measurable health signals: 

• Heart rhythm
• Glucose levels
• Walking patterns
• Sleep quality
• Breathing rate 

All collected continuously through devices you’re already wearing or carrying. A smartwatch, a sensor patch, or even your phone. Instead of a snapshot, you get a film reel. Instead of one data point every six months, you get thousands.

That continuous, real-world picture changes what’s possible in medicine. In drug development alone, the implications are significant. Slowly progressing conditions like Alzheimer’s or Parkinson’s are notoriously difficult to measure through traditional clinical assessments; a patient can appear stable in a thirty-minute office visit while showing meaningful decline in their daily life. 

Digital biomarkers can detect those subtle shifts far earlier and with far greater precision. Some trials using digital endpoints have managed to achieve the same statistical confidence with 50 to 70 percent fewer patients than they would have needed using conventional measures. That’s not a marginal improvement.

But none of that translates automatically into regulatory acceptance, clinical adoption, or reimbursement. A measurement that’s technically impressive still has to prove itself within systems that were built around a very different set of tools. That’s where the real story of digital biomarkers lives, not in the sensors themselves, but in the slow, essential work of making those sensors trusted, integrated, and paid for.

The gap was never about the technology.

The devices were never really the problem. Sensors became more accurate, more affordable, and more capable right on schedule. Consumer adoption of health wearables grew faster than almost anyone predicted. Over a billion people now wear some form of health-tracking device.

The problem was everything that surrounded those devices. Wearables generated data with no clear clinical home. Hospitals couldn’t easily pipe device readings into patient records. Insurance companies didn’t know how to reimburse any of it. 

And physicians were skeptical of numbers produced by consumer gadgets that lacked clinical validation.

The result was a frustrating in-between state. Innovation was real. Impact wasn’t. Patients wore things that produced numbers. Doctors didn’t act on those numbers. Nothing meaningfully changed.

That’s now shifting, because three things that were missing are starting to come together at the same time: regulatory clarity, reimbursement, and data infrastructure.

The Regulations

For most of the last decade, health tech companies building wearables and digital health tools faced a fundamental question with no clean answer: Does this count as a medical device?

If it did, a long and expensive FDA approval process followed. If it didn’t, products launched in a gray zone with vague wellness language, careful to avoid specific health claims. The legal uncertainty made it hard to build and harder to invest.

Earlier this year, the FDA published updated guidance that draws clearer lines. Non-invasive wearables that measure metrics like blood pressure, oxygen saturation, and glucose-related signals can operate as general wellness products, provided they don’t make diagnostic claims and include appropriate consumer safeguards. 

For AI-powered clinical decision support tools, there’s added flexibility where the recommended action is clear, and clinicians can independently verify the logic.

The FDA is not loosening; companies still need to navigate carefully. But the key shift is that there are now clear lanes to navigate toward. That clarity alone is enough to unlock investment and development that was previously sitting on the sidelines, waiting for certainty that never came.

Reimbursement

Regulatory approval means very little if there’s no payment pathway behind it.

For most of the last decade, reimbursement for digital health tools was sparse and inconsistent. Without billing codes, hospitals and clinics had little financial incentive to integrate wearable-derived data into care workflows, even when the clinical case was solid. Patients ended up paying out of pocket, or the tools were simply never adopted.

Medicare has significantly expanded coverage for remote patient monitoring, essentially the formal, reimbursable version of what wearables do. Private insurers have been following. New billing pathways have opened up for chronic disease management programs built around continuous digital data rather than periodic in-person check-ins.

The market is reflecting this momentum. The global digital biomarkers market is projected to reach $15.6 billion by 2030, driven in large part by this expanding reimbursement landscape. Healthcare systems respond to incentives, and the incentives are finally pointing in the right direction.

Data Infrastructure

For digital biomarkers to be clinically useful, the data they generate needs to travel from the device into a clinical system where a physician can actually see it and do something with it. 

For years, this was genuinely broken. Devices store data in proprietary formats. Electronic health records couldn’t accept wearable inputs. Different platforms couldn’t communicate with each other.

The broader adoption of FHIR integration, a standard for electronic health data exchange, has been one of the most consequential quiet developments in digital health. It’s the reason a reading from a glucose monitor can now flow into a structured, usable entry in a patient’s medical record, rather than disappearing into a separate app that no one in the care team ever checks.

Alongside interoperability, there’s been real progress in digital biomarker validation, the formal, evidence-based process of proving that a measurement is accurate, consistent across different populations, and clinically meaningful. Organizations like the Digital Medicine Society have developed structured validation frameworks that are increasingly becoming the standard. This work is what converts physician skepticism into physician trust, and physician trust is the final mile that digital health has always struggled to cross.

Real-Life Examples

It’s one thing to describe the infrastructure shifting. It’s another to see what that actually looks like in practice.

Parkinson’s Disease 

Merck conducted a longitudinal clinical study called WATCH-PD, which used composite digital biomarkers to track motor function in Parkinson’s patients, combining wearable sensor data with traditional clinical endpoints. 

The results showed that the digital composite endpoint showed more than twice the effect size of the standard clinical rating scale. In practical terms, that means the same clinical finding could be detected with 73% fewer patients in a trial. 

Digital tools offer the ability to quantify Parkinson’s symptoms with more objectivity, precision, and frequency than any traditional scale, and that changes the economics and speed of drug development entirely.

Cancer Patients and the Apple Watch

Kaiser Permanente Northern California ran a study in which 50 cancer patients actively receiving treatment used an Apple Watch integrated with a mobile app for 28 days. The study tracked physical activity as a digital biomarker, specifically daily step counts, and found meaningful associations between activity levels and clinical events, including disease progression. 

Patients were monitored from their own homes, reducing the burden on both patients and clinical infrastructure, while generating richer and more continuous data than scheduled in-clinic visits would ever capture.

Duchenne Muscular Dystrophy 

EU regulators accepted a digital endpoint for efficacy in clinical trials for Duchenne muscular dystrophy, a significant milestone for the field. The endpoint is measured by small sensors worn on the ankles and wrists and captures stride velocity in real-world conditions. 

Traditional outcome measures for DMD required burdensome in-hospital timed tests, which were particularly difficult for patients with more severe disease. The sensor-based approach captures real-world functional ability continuously and far less intrusively, and regulators accepted it as a valid primary trial endpoint. It signals that digital measurements are moving from supporting evidence into primary evidence in some of the most demanding regulatory environments in medicine.

Continuous Glucose Monitoring 

What started as a tool for insulin-dependent diabetes management has become one of the clearest proof points that continuous monitoring changes behavior in ways that periodic testing cannot. 

Roche has been expanding CGM into clinical trial applications, with active work on algorithm development for broader metabolic health contexts. The ability to see in real time how a specific meal, a stressful meeting, or a poor night’s sleep affects blood sugar,  for people who don’t have diabetes, is opening an entirely new category of preventive health management.

But why does this matter for patients?

Healthcare is largely reactive. You feel unwell, you seek care, you get treated. That model handles crises reasonably well. 

What it does poorly is catch the slow, quiet, symptom-free deterioration that precedes most serious conditions, the atrial fibrillation that’s been present for months before a stroke, the metabolic dysfunction that develops over years before a diabetes diagnosis, the motor changes in Parkinson’s that are measurable long before a patient or physician notices anything wrong.

Digital biomarkers are the foundation of a different model, one where health is something you monitor continuously rather than check occasionally. Where the distance between an early warning signal and a clinical response shrinks from months to days. 

Where the data your doctor works from reflects your real life, not just the forty-five minutes you spent in a waiting room.

That shift doesn’t replace clinical judgment. Wearables and sensors extend the reach of medicine into everyday life, but they don’t substitute for the expertise and relationship of a physician. But they fill a gap that has always existed: the vast space between appointments where most of what matters to your health is actually happening.

Closing Note 

Every year for the past decade, someone has announced that this is the year digital health finally delivers on its promise. What’s genuinely different today is that the shift isn’t coming from one new technology or one regulatory change. It’s coming from the convergence of several enabling conditions at the same time: clearer regulation, expanding reimbursement, more interoperable data systems, maturing validation science, and a deepening clinical evidence base across a growing range of diseases.

The market is also sorting itself. The era of being rewarded simply for having an interesting wearable concept is over. What earns attention and investment now is demonstrated clinical value, economic justification, and the ability to integrate into the workflows where clinical decisions actually get made.

That bar is higher. But clearing it is exactly what distinguishes a genuine medical advance from a consumer trend. The foundation is being built on evidence that holds up in the most demanding clinical and regulatory environments in the world. And for the patients managing chronic conditions, families watching for early signs of disease, physicians trying to make better decisions with better data, that progress is what matters most.

Frequently Asked Questions

What are examples of digital biomarkers?

Digital biomarkers are health signals captured through everyday devices. Wearables like smartwatches track heart rate variability, SpO2, sleep patterns, and skin temperature, while continuous glucose monitors provide real-time metabolic data. Smartphones contribute behavioral signals, typing speed, voice cadence, and GPS mobility patterns that correlate with conditions like depression, cognitive decline, and Parkinson’s disease. In clinical settings, smart inhalers, ingestible sensors, and AI-analyzed retinal scans extend biomarker capture into medication adherence and disease prediction. 

What is the new biomarker for Alzheimer’s?

pTau217. Studies reveal that elevated plasma or cerebrospinal fluid pTau217 levels strongly reflect AD-related brain tau and Aβ pathologies.

Which companies specialize in remote patient monitoring solutions?

Top remote patient monitoring (RPM) companies providing specialized software and services include Folio3 Digital Health, Medtronic, Philips, etc. 

Which apps offer digital biomarker tracking for mental health?

Digital biomarker tracking for mental health involves using smartphone sensors, wearables, and AI to analyze data. This includes voice, movement, and screen usage to monitor, diagnose, or predict shifts in mental health conditions. Check out Folio3 Digital Health’s work – Neuroworld. Our gamified brain and mental health assessment app for cognitive rehabilitation and brain & mental health management via targeted exercises, neuropsychological assessments, and goal tracking. It boosts user engagement and retention through dynamic AI agents and themed islands that target different wellness areas, promoting overall psychological well-being.

About the Author