1. From a clinician’s perspective, what are the most critical gaps you still see between what wearable hardware can reliably measure and what clinicians actually need for confident medical decision-making?

Emanuel Tkach: The critical gap is between what wearables measure and what clinicians need to confidently act. Most devices provide trend data — heart rate, activity, sleep — that’s useful for context but insufficient for diagnosis or treatment decisions. We need more than correlation; we need validated, prospective evidence that specific device metrics reliably predict clinically meaningful outcomes in defined patient populations. For example, does AF burden from this wearable actually predict stroke risk in atrial fibrillation patients? Does respiratory rate deviation trigger actionable intervention protocols in heart failure?

Without peer reviewed clinical validation showing that a metric changes management and improves outcomes, physicians treat wearables as supplementary signals, not diagnostic tools. Until that evidence exists, we’re left with “interesting data” rather than “actionable clinical intelligence.” To close this gap, developers must design studies that answer not just “Can we measure this?” but “Does measuring this change outcomes in a way that matters to patients and payers?”

2. When translating clinical requirements into hardware specifications, where do engineering teams most often underestimate complexity - particularly in sensor design and signal integrity?

Lisa Voronkova: We've learned this lesson the hard way on projects requiring 8-10 hours of continuous skin contact. In the lab, signals look clean and stable. But after a few hours on real skin, sweat accumulates, natural oils build up, and impedance starts drifting. The sensor that worked perfectly on the bench suddenly loses signal quality. Movement adds another layer of complexity. 

Our approach now is validating prototypes on actual users early in development, rather than waiting until clinical trials to discover these issues.

3. At what stage in wearable device development do misalignments between clinical ambition, regulatory requirements, and commercial feasibility typically begin to create serious downstream risk?

Jordan Foster: The misalignment typically surfaces around the pivotal study design stage. Teams lock in clinical endpoints that satisfy the FDA's "reasonable assurance" bar but completely miss what payers actually need for coverage decisions - real-world outcomes, cost data, and comparative effectiveness. By the time you're celebrating FDA clearance, you realise you have an 18-month evidence gap before any major health system will actually buy the device. That's when commercial timelines collapse.

4. Medical-grade wearables must perform accurately under motion, sweat, and long-term use. Which hardware design decisions most directly determine whether a device can achieve clinically reliable sensor accuracy?

Lisa Voronkova: We've worked with many optical sensors; PPG sensors are everywhere now in wearables. What we've seen is that skin tone, body hair, and even tattoos create serious signal interference. Algorithms can compensate for some of this, but when you need reliable clinical-grade data, the physical sensor design matters enormously. How the sensor contacts the skin, the pressure distribution, the angle of the light path… all these mechanical details determine whether you get a usable signal or just noise, especially during movement.

5. How tolerant are clinicians of data variability from wearables, and where does acceptable signal noise cross the line into clinical risk?

Emanuel Tkach:  Clinician tolerance for data variability depends entirely on the clinical consequence of error. For screening or wellness applications, we accept higher noise because the risk of acting on a false positive is low, maybe an unnecessary follow up test. But when wearable data drives treatment decisions — adjusting anticoagulation, titrating heart failure meds, triggering emergency response — error tolerance drops dramatically.

The line crosses when measurement noise approaches or exceeds the minimal clinically important difference for that parameter. At that threshold, we’re making decisions based on artifact rather than physiology, which becomes a patient safety issue. In practice, that means we demand higher accuracy, lower variability, and robust validation for any metric that directly influences therapy, compared to metrics used only for risk stratification or patient engagement. For clinical adoption, the burden is on the device to prove that its noise profile is small enough that clinicians can trust it to guide care, not just monitor trends.

6. As algorithms increasingly compensate for noisy signals, where should hardware engineers draw the line and resist the “fix it in software” approach?

Lisa Voronkova: There's a limit to what software can fix. 

When your raw signal is buried in noise, say 30-40% of what you're measuring is actually artifact, algorithms start guessing rather than measuring. We've seen teams spend months trying to write smarter filters when the real problem was inadequate shielding or poor sensor contact mechanics.

It's tempting to think "we'll fix it in software," but some problems need hardware solutions. Better analogue filtering and mechanical stability upfront save months of frustration later.

7. Battery life remains one of the biggest constraints in continuous monitoring. What power management trade-offs most often determine whether a medical wearable succeeds or fails in real-world deployment?

Lisa Voronkova: Perhaps the most common mistake we see in hardware teams - they collect data at rates far higher than the clinical application actually requires. Maybe you're sampling ECG at 500Hz when 250Hz would work fine for your use case, or streaming data continuously over Bluetooth when batching every few minutes would suffice. The approach that's worked for us is starting with the clinical question: what's the minimum data we need to answer it reliably? And then building the power architecture around that, rather than maximising what's technically possible.

8. From a patient adherence and clinical workflow standpoint, how disruptive are frequent charging requirements, and how should this influence hardware design priorities?

Emanuel Tkach: Charging frequency directly impacts patient adherence, which determines whether we get any clinically useful data at all. Devices requiring daily charging see systematic dropout, especially in older or chronically ill populations who already manage complex medication regimens. We lose sleep data when patients charge overnight, miss arrhythmia detection during those gaps, and end up with fragmented datasets that can’t support robust clinical conclusions. Clinical programs designed around continuous monitoring fundamentally break when patients spend 15–20% of time off device.

From a clinical workflow perspective, designs targeting at least 5–7 days between charges maintain adherence trajectories that support meaningful insights, rather than fragmented, unusable datasets. That should be a first order design constraint, not an afterthought. To further reduce time off device, hardware teams should also build in practical adherence supports: clear alarms or reminders to charge, and designs that allow for easy battery swaps or replacement. Even better, where feasible, inductive (wireless) charging can eliminate the need to remove the device at all, preserving continuity of monitoring and minimizing gaps in data.

9. Extended wear introduces biocompatibility challenges that short trials often fail to reveal. What material or enclosure design decisions most commonly become problems during long-term use?

Lisa Voronkova: Adhesive chemistry surprised us on a long-term monitoring project. Short-term ISO testing looked perfect, but around day 10-12 of continuous wear, users started reporting skin reactions. Turns out certain adhesive compounds leach slowly under body heat and perspiration, and that cumulative exposure causes problems you'd never catch in 24-hour testing. Now we insist on biocompatibility data that matches the actual intended wear duration. It's the difference between what works in a controlled study and what works when real patients wear your device for weeks.

10. How do comfort, skin reactions, and wearability impact patient compliance, and why are these human factors still underestimated in early hardware development?

Emanuel Tkach: Comfort and skin reactions create an adherence cliff that completely undermines clinical utility. A technically perfect sensor that patients stop wearing after two weeks delivers zero value. We see this constantly: initial enthusiasm, then complaints about rash, irritation, or simple discomfort, then the device ends up in a drawer. Human factors are underestimated because engineering teams often optimise for bench performance, while clinical reality is that an 80% accurate device worn consistently beats a 95% accurate device worn sporadically.

For real world impact, wearables must be designed for the patient’s life, not just the lab. That means prioritising comfort, biocompatibility, and ease of use from day one, because if patients don’t wear it, the data doesn’t exist. This includes thoughtful choices about materials, adhesive chemistry, weight, and form factor, validated not just in short trials but in real world use over weeks. From a clinical standpoint, a device that’s slightly less accurate but worn reliably is far more valuable than a “perfect” sensor that patients abandon.

11. How do regulatory standards such as ISO 13485 and IEC 60601 practically shape hardware architecture decisions for medical-grade wearables?

Jordan Foster: ISO 13485 and IEC 60601 fundamentally dictate your bill of materials and architecture decisions. The moment you choose applied part classification or decide on home-use capability, you're locked into specific isolation requirements, IP ratings, and EMC specifications that directly impact component costs and form factor. Teams that treat these as "later" problems end up with expensive redesigns when they discover their consumer-grade architecture can't pass 60601-1-11 testing.

12. For engineering teams new to regulated medical devices, which aspects of hardware documentation, verification, and traceability tend to be the most challenging?

Lisa Voronkova: The traceability matrix feels bureaucratic at first - just another documentation requirement. But here's why it matters: it's the map showing that every clinical need connects to a specific design decision, which connects to a verification test, which has documented results. When someone asks, "how do you know this feature actually works as intended?" you need that chain of evidence. We build it as we go now. It actually makes design reviews faster because everyone can see which requirements still lack verification tests.

13. What distinguishes wearable medical devices that successfully achieve reimbursement and market adoption from those that are technically strong but commercially unsuccessful?

Jordan Foster: The devices that win commercially solve the reimbursement equation early, not as an afterthought. They design pivotal trials with payer-relevant endpoints - hospital readmissions, procedure time, complications - not just technical performance metrics. They understand that FDA clearance opens the door, but without a clear CPT/HCPCS coding strategy, a coverage pathway through CMS (ideally NTAP for high-cost devices), and health economics data showing cost-offset, you're selling on hope rather than a business case. Strong devices get bought; devices with reimbursement strategies get scaled.

14. Looking ahead, what must wearable hardware developers do differently to ensure these devices evolve from consumer-inspired tools into clinically trusted medical instruments?

Emanuel Tkach: Wearable developers must shift from “interesting data” to “actionable clinical intelligence.” This means three things: First, demonstrate that your metrics predict outcomes that matter, hospitalisations, decompensation, disease progression, not just correlate with other measurements. Second, design for workflow integration, not standalone dashboards; data must flow into existing EHR systems with clear clinical decision support, so clinicians can act on it without extra steps.

Third, build programs around devices: pairing continuous monitoring with structured care pathways, escalation protocols, and human response teams. Technology alone doesn’t change outcomes; integrated programs with validated clinical pathways do. To earn trust, hardware must be paired with robust evidence, seamless integration, and a clear clinical role — not just technical novelty. For medtech teams, that means thinking less about specs and more about how the device fits into a patient’s journey and a clinician’s workflow.

15. From your respective roles in clinical practice, hardware engineering, and regulatory-commercial strategy, what is the single most underestimated hardware decision in medical wearable development that ultimately determines whether a device succeeds or fails in real-world healthcare adoption?

Emanuel Tkach: The most underestimated hardware decision is how early the team confronts the trade offs between clinical ambition, technical feasibility, and real world usability. At kickoff, clinical teams often want month long battery life, continuous high fidelity monitoring, a slim form factor, and medical grade accuracy all at once. But power density, signal quality, and size fundamentally conflict.

The critical decision is choosing which constraints to prioritise (e.g., battery life vs. sampling rate, signal fidelity vs. comfort) and which to deprioritise, before the design is locked in. It’s far easier to adjust requirements early than to spend a year chasing an impossible spec and then fail in pivotal studies or real world use. The hardware choices that ultimately determine success are not just about performance, but about aligning specs with what clinicians will use, patients will wear, and payers will reimburse.

This discussion highlights that clinically trusted wearable health technologies are built not by algorithms alone, but by disciplined hardware design, rigorous validation, and real-world usability. Accuracy, biocompatibility, battery life, regulatory alignment, and reimbursement strategy are foundational to clinical adoption.

We thank our panelists Lisa Voronkova, Jordan Foster, and Emanuel Tkach, MD, for sharing your experience-driven insights from the front lines of medical device development. 

And thank you to our readers for engaging with this important discussion on the hardware realities shaping the next generation of clinically trusted wearable technologies.

--AHHM Issue 71--