Handling Rising Data Rates and Volumes in Medical Systems: What Works in Practice

Medical systems are changing fast. What used to be a manageable amount of data is turning into systems that must process high-frequency signals and large volumes of data, while AI-based applications are now being introduced and rely on that same data. At the same time, expectations remain unchanged: systems must react in real time, behave predictably, and remain reliable under load.

That combination is where many systems start to struggle.

From what we’ve seen across projects, the root cause is rarely the application itself. It’s the layer underneath: how data is exchanged between different parts of the system. Every modern architecture needs a way to move sensor data and results between parts of the system. When this foundation is not designed for large data streams, the same problems keep appearing:

• CPU load grows unexpectedly

• Latency becomes unpredictable

• Systems behave differently under real load than during testing

What Is Often Underestimated

A common instinct is to build the communication layer in-house. It promises full control and a perfect fit for the system.

In practice, this quickly turns into a complex task. Efficient data exchange is not just about moving data from A to B. It requires handling memory, timing, and system behavior under load in a reliable way. Many teams realize too late that they are no longer focusing on their actual product, but on building and stabilizing infrastructure.

Another common approach is to rely on standard communication mechanisms or technologies that were originally designed for network communication, such as gRPC. These are proven tools, but they are not optimized for continuously moving large amounts of data within a single system.

The result is often unnecessary overhead. Data is copied multiple times, CPU usage increases, and timing becomes less predictable. This is usually not a problem in early prototypes, but it becomes visible as soon as systems scale.

Choosing a commercial communication stack can reduce initial effort, but often introduces new challenges such as increased complexity, reduced flexibility, and less control over performance-critical parts of the system.

What Works in Practice

Other industries have been dealing with these challenges for years. In robotics and automotive, systems routinely process large data streams under strict timing requirements.

One key lesson is that the communication layer should not be treated as an afterthought. It is a core part of the system architecture.

The approach that works best in practice is to build on proven solutions and combine them with the right expertise.

In many cases, this foundation comes from mature open-source technologies. These are developed by experts, used across many real systems, and continuously improved over time.

A good example is iceoryx2, an open-source communication technology designed for efficient data exchange. Instead of repeatedly copying data, it allows data to be shared in a way that keeps overhead low and system behavior predictable, even with large data volumes.

Originally developed in automotive systems, this approach is increasingly relevant for medical devices as well. It helps ensure that communication does not become the limiting factor as systems grow.

A Practical Takeaway

As data rates continue to increase, the way data is handled inside a system becomes a critical factor.

The most effective strategy is not to build everything from scratch and not to rely on generic solutions that are not designed for this kind of workload. It is to build on proven foundations and work with experts who understand how to apply them in real systems.

This is exactly what we demonstrate at MedtecLIVE: what changes when the foundation is designed for high data rates from the beginning, and how systems behave once communication is no longer the bottleneck.