Why Long-Term Success in Healthcare Technology Is an Engineering Decision
Three products. One pattern. A decade of engineering platforms that scaled to over a million users without structural rewrites.
Key Takeaways
Long-term success in healthcare technology is not a marketing outcome but the result of engineering choices made from day one.
Three Klika builds anchor this story: a connected sleep device platform handling 1.2 billion sessions; a connected wellness device re-platformed in 9 months for a 55% per-unit cost reduction; and a tele-healthcare platform modernized to support multi-generation diagnostic hardware without service interruption.
Four engineering patterns that recur: architecture for evolution, parallel hardware-software development, automated validation early, and long partnerships that compound.
The same patterns apply outside MedTech; to insurance modernization, public health systems, and any regulated product that must outlast the decade it was built in.
What earns a healthcare product industry recognition isn’t the launch. It’s what the product does in the years after.
Most recognition stories get told as marketing wins. Namely, a press release, an award, or a case study filed away on a website. The engineering decisions that made the product possible - the architecture choices, the testing infrastructure, the firmware that kept working through multiple generations of hardware - rarely make the headline.
But those decisions are the story. They are what separates a product that scales to a million users from one that gets quietly rewritten eighteen months in. They are what make a partnership last eight years instead of one.
What follows is a walk-through of what we accomplished with our healthcare partners and the patterns that tie them together.
Engineering Longevity: How Does an IoT Health Product Stay in the Market for Eight Years
Most IoT health products don’t survive their own success. The firmware that worked for ten thousand devices breaks at a hundred thousand. Architecture that served one hardware generation can’t absorb the next. Not to mention, the data devices generate constantly, have no value without infrastructure to unify and make sense of it.
What looks like a product problem is almost always an engineering problem that was deferred.
A global leader in sleep innovation that utilizes biometric tracking and smart bed technology to improve physical and mental well-being has been in a technical partnership engagement with Klika for almost a decade. With over 70 engineers dedicated at peak, we worked across four distinct areas of work: firmware engineering, enterprise data platform, BI and cloud PaaS, and big data analytics with ML and deep learning. The decisions made in each of those areas are why the product is still in the market, still evolving, and still trusted by over a million users.
1.2 billion sleep sessions turned into a single, trusted view of customer health data
1 million+ customers received reliable sleep insights they could act on with confidence
8+ years of continuous innovation built on a trusted engineering partnership
The firmware decisions that kept the product trusted
The firmware layer was built in C, C++, and Assembly on FreeRTOS. It is important to have a real-time operating system for safety-critical consumer devices. In practical terms, this means the device behaves predictably at three in the morning on unit number 800,000 the same way it did on unit number one. Moreover, Over-the-air (OTA) firmware updates delivered via Yocto Linux meant the product could be patched and extended in the field without a hardware recall or a consumer action. For end users, this meant older devices kept working as new ones launched. Therefore, the investment users and the business had made in the hardware was protected across every generation. The EU Medical Device Coordination Group (MDCG) is explicit that post-market software updates for connected health devices must be managed in a controlled, traceable way.
We embedded Hardware-in-the-Loop (HiL) testing early into the development process. Thus, issues were caught before users felt them. This protected user trust and reduced the risk of silent device failures that drive churn in subscription health products.
Read more about the engineering work behind this product →
The data decisions that gave the business a competitive edge
The data layer tells the same story from a different angle. A platform generating over a billion sleep sessions has a problem that doesn’t exist at ten thousand. The data is only valuable if it can be unified, queried, and acted on in real time. Klika built the infrastructure to do that; processing sessions at volume and harmonizing first-party device data with third-party tracker data (Fitbit, Nest, Under Armour), each arriving in a different format from a different source. Thus, 1.2 billion sessions unified into a single source of truth provided the platform with real-time insights that are navigable for both technical and non-technical departments. Consequently, new features are grounded in actual behavioral and usage patterns. This is a data asset that competitors without the same engineering investment cannot easily replicate.
Learn more: Data-Driven Insights for Enterprise Growth Case study →
The partnership demonstrates not only technical capability but also its business impact across the organization. So, the engineering decisions made early and held to consistently are what allowed the product to hold market leadership for years, not quarters.
Engineering Agility: How to Re-Platform a Connected Health Device in 9 Months
Supply chain disruption doesn’t announce itself with much lead time. In this case, global memory shortages and rising component costs were putting pressure on a connected wellness product’s profitability and launch schedule.
9 months to deliver a complete platform transformation
55% reduction in per-unit costs while gaining supply chain independence
No degradation in customer experience during the re-platforming
Klika responded to the client’s problem by breaking the usual hardware-first dependency and launched on schedule without compromising core functionality. The decision that made nine months possible was not working faster. It was removing the reliance that makes hardware projects slow by default. In most device development cycles, firmware work can’t begin until hardware is finalized. Hence, every week of hardware uncertainty is a week of firmware standing still. Our team broke that dependency by developing and validating firmware in parallel with hardware design, running it against development kits and custom prototype boards before final hardware existed.
The outcome was a fully re-platformed system in production within nine months, with a 55% reduction in per-unit hardware cost and a reduced dependence on constrained components. The product launched on schedule, and the savings were structural, not one-time. Architectural decisions made during the re-platform gave the client flexibility to absorb future component changes without the same level of engineering effort.
Read more about the full re-platforming engagement →
Engineering Resilience: How to Modernize a Clinical Platform Without Breaking It
Connected devices are only part of the healthcare engineering picture. The platforms that clinicians and patients interact with directly carry their own class of engineering challenges. Modernizing a platform clinicians rely on in real time is delicate. The system has to keep running while it evolves, because downtime or instability can affect both clinical operations and patient care.
Klika worked with a tele-healthcare startup operating a network of physical diagnostic pods. They were locations where patients interact with diagnostic hardware directly, and clinicians manage consultations remotely in real time. The platform needed to support multiple generations of diagnostic devices across locations, each with different capabilities and integration requirements.
Multi-generation hardware support
Real-time clinical operations
Platform built to keep evolving
To solve that, our team built a hardware abstraction layer that helped the platform work across device versions without breaking legacy compatibility. This made it possible to introduce new hardware revisions without rewriting the core clinical workflows built on top. The team also implemented a RabbitMQ-based messaging layer with dead-letter handling and retry mechanisms to ensure reliable data exchange between devices, clinicians, and backend services. The provider dashboard was redesigned with real-time call routing, giving clinicians the operational visibility they needed to manage patient interactions. Moreover, Datadog observability gave the engineering team monitoring coverage to identify and address issues before they affected clinical operations. Automated CI/CD pipelines and regression testing reduced release risk, enabling the platform to evolve continuously without compromising reliability.
The result was a platform that could support new hardware generations, support real-time clinical workflows at scale, and keep evolving without adding unnecessary fragility.
Learn more: Mission-Critical Tele-Healthcare Modernization Case Study →
Engineering Patterns That Make Healthcare Products Last
We gave examples of three engagements with different companies and problems. However, the same four engineering decisions appear consistently, and their absence is visible in the products that don’t last.
Architecture for evolution, not just the launch
Every product is under pressure to ship. The engineering decisions that feel optional under that pressure are the ones that determine whether the product can absorb a new hardware generation, a new data source, or a regulatory change without a rewrite. The sleep device ran for eight years across multiple hardware generations because the firmware architecture treated evolution as a requirement, not an afterthought. The wellness device re-platform succeeded in nine months because platform-independent drivers meant software development didn’t block on hardware decisions. The tele-healthcare platform could onboard new diagnostic device generations because a hardware abstraction layer was part of the design, not a retrofit.
Early automated validation
Testing infrastructure is often treated as something to be built once the product is “done.” The products in this post inverted that sequence. HiL testing on the sleep device firmware caught integration failures between software and hardware before they reached production. Critical when a defect affects hundreds of thousands of devices simultaneously. Automated CI/CD across the tele-healthcare platform meant that changes could be delivered continuously without the manual release risk that mission-critical systems cannot carry. The principle holds across domains: the later you build validation infrastructure, the more expensive it becomes and the more risk you accumulate in the meantime.
Parallel development, not sequential handoff
The traditional model (design the hardware, then write the firmware, then build the software) creates dependencies that compress timelines and concentrate risk. In the wellness device re-platform, running hardware and firmware development concurrently meant that component decisions and software decisions could be made independently, reducing the single-point-of-failure risk that sequential delivery creates. In the sleep device partnership, sustained parallel work streams across firmware and data platform engineering meant the product and its analytics infrastructure evolved together, neither waiting on the other.
Long partnerships compound
When a technical partnership is established and maintained, it leads to building on the engineering decisions of those before. The team’s familiarity with the codebase, the product context, and the client’s operational realities meant that each new phase started from a higher baseline than a new engagement would. In regulated, long-lived products, that compounding effect is not a soft benefit but a structural engineering advantage.
Pattern | Smart Bed Platform | Wellness Device | Tele-healthcare Platform |
|---|---|---|---|
Architecture for evolution | ✓ | ✓ | ✓ |
Parallel hw/sw development | ✓ | ✓ | — |
Automated validation early | ✓ | ✓ | ✓ |
Long partnership | ✓ | — | ✓ |
Implications for the Next Generation of Healthcare Products
The products being built now in healthcare and health tech, such as AI-augmented diagnostics, connected monitoring platforms, and modernized public health infrastructure, will face the same pressures that the mentioned products faced. Hardware generations are bound to change. Data volumes will scale past initial estimates, and regulatory requirements will evolve. As EU AI Act compliance deadlines approach, industry groups such as MedTech Europe continue to emphasize interoperability, digital simplification, and the need for healthcare technology to adapt to regulatory and technical change over time.
At the same time, the bar for trust will continue to rise. Writing in Forbes, Klika CEO Edin Deljkic highlighted two prerequisites for that trust: confidence in the accuracy of health data and confidence that it is properly secured and protected. As connected devices and AI become more deeply embedded in healthcare, meeting those expectations will require engineering foundations capable of supporting reliability, security, and compliance as products evolve.
The companies that absorb those pressures without structural rewrites will be the ones that made the right engineering decisions at the beginning. That applies whether you are building firmware for a wearable device, modernizing a clinical platform, or laying the data foundation for AI features that don’t yet exist. The patterns are the same. The cost of skipping them compounds in the same direction.
Engineer for the decade, not the next quarter. The recognition takes care of itself.
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