The technologies driving minimally invasive and precision healthcare require the integration of robotics, imaging, sensors, and data-driven technologies. MedTech is evolving from siloed products and devices to increasingly multi-functional and integrated digital systems. One study estimated that the global market for connected medical devices is expected to grow from $26.5 billion in 2021 to $94.2 billion by 2026.

Now more than ever, device manufacturers are faced with significant challenges in bringing these connected solutions to market. What are the reasons? Development lifecycles are slow and expensive, device architectures are complex and inflexible, and the need for increased cybersecurity poses significant and ongoing hurdles. The solution lies in a new generation of devices powered by intelligent software data flow.

The traditional approach to software communication architecture is message-centric, where messages are passed between applications. This approach may be manageable for simple systems that do not require high performance or distributed data-sharing across different devices and data sources. A message-centric architecture (typically point-to-point, or server-based) becomes problematic for larger and more complex systems.

Next-generation medical devices are increasingly integrating diverse data sources across intelligent applications, components, and networks. With a message-based design, complex dependencies are introduced as application software that must be developed to configure, package and send data to specific computing nodes and distributed applications. As the system evolves with new features, significant redesign is often required to interoperate with new devices and data interfaces. In addition, since the application is tasked with managing the mechanics of sending messages, this approach constrains the performance, reliability, and security of the system.

On the cybersecurity front, regulatory agencies are raising expectations for cybersecurity in device designs. Device manufacturers are now expected to demonstrate a cybersecurity architecture across the device ecosystem. This includes comprehensive threat assessments of data flows that may be dependent on the use case and operational state of the system. These activities must be incorporated into a comprehensive secure development lifecycle that ensures robust security controls and connectivity performance across distributed applications and networks.

For MedTech manufacturers, these challenges translate to time-consuming and expensive development lifecycles, and risks to regulatory approval. In a message-centric architecture, the software and resources required to design, develop, verify, and maintain these systems grows over time. Software teams are tasked with maintaining the messaging infrastructure, and the learning curve for product teams is steep.

From a business perspective, companies need to be both innovative and nimble. Success in this competitive industry depends on managing the complexity of technology integration and moving faster to deliver a pipeline of flexible and innovative solutions. New approaches to software communication architectures are needed to address the challenges of data sharing across applications and systems.

Data-Centricity: The Solution for Intelligent Data Flow

How can device manufacturers address the complexities of data flow? By designing software communications around the data instead of messages. Product teams are increasingly turning to the Data Distribution Service (DDS) standard, where data is shared based on the definition of data types, and the specification of data flow is independent of applications and their network locations. This concept is called Data-Centricity.

A data-centric communication architecture is inherently modular and completely decentralized, with no central server. The DDS software framework, instead of the application, manages the data flow across applications, enabling highly secure and scalable connectivity. This architecture enables future-proof systems as applications evolve with new data flow requirements. DDS was specifically designed for real-time data sharing, and is used across industries in next-generation safety-critical systems.

From a cybersecurity perspective, data-centricity means a zero-trust approach that focuses on securing the data in motion, not messages or “trusted” network perimeters. Known data structures are only shared with authorized applications that need the data.

Incorporating data centricity early in the system design process enables device manufacturers to holistically design secure, flexible, and interoperable data flow across the system. This drives reference architectures that are scalable across computing platforms, as well as legacy, future, and third-party system interfaces. At the same time, product teams can focus on the development and evolution of clinical applications instead of infrastructure.

Data-centricity provides the foundation for connected medical devices, applications, and systems to work together in real-time. Intelligent data connectivity simplifies and accelerates the flexibility and scalability of digital solutions that are transforming health care.