Translating Digital Buzzwords to Real Value for Medical Devices (post)


Tech-Clarity research shows that the majority of manufacturers believe that digitalization is important or critical to achieving their business strategy (see figure). Over one-third say that it’s critical. Digitalization in medical device design and manufacturing has significant promise, but what does it actually mean?

Medical device companies, already struggling with how to address critical initiatives like UDI and the Case for Quality, are inundated with information about “digitalization” accompanied with a host of buzzwords including Digital Twins, Digital Threads, and more. How do they really apply to the design, approval, production, and lifecycle management of medical devices? We’ll try to put it into practical terms.

Digital Thread

Let’s start with the digital thread. That’s probably the most straightforward concept and one that provides tangible value. The digital thread, in its simplest definition, is the collection of information used to define, engineer, and develop a product. Ideally it moves beyond the manufacturer into the in-service part of the equipment lifecycle. It offers a view of the digital continuity of the life of the device.

The value for medical device companies, in a nutshell, is end-to-end traceability. It’s the connection between everything from patient needs and early requirements through the patient experience, including post market surveillance requirements required by the EU MDR. It provides a broad base of information that allows medical device companies to analyze and learn from history, for example tracking down root causes for a CAPA.

The digital thread also clearly supports regulatory requirements related to these needs including the DHF and UDI, including all of the local variants of the regulations. In addition, the digital thread also provides an integrated source of data that can be used to prepare submission documentation. With the digital thread as the trusted data source, medical device companies can automate much of the process to generate these crucial reports.

Digital Twin

One of the key contributors to the digital thread is the Digital Twin. The digital twin has multiple definitions, but it starts at the core as a complete, digital model of the device. It incorporates a holistic view of the design to a level of granularity that companies can accurately simulate and predict device performance and behavior. There is clear overlap with the digital thread in this part of the definition.

The value of the digital model for medical device companies is a cohesive view of the device. It allows people from various disciplines to contribute their part of the design and see it in the context of the whole product. It allows engineers to analyze and optimize performance early in the product lifecycle, catching errors and improving performance in silica before physical prototypes are developed. As regulatory bodies get more comfortable with simulation data, the digital twin may also play a big part in reducing the length and cost of clinical trials. The digital thread also delivers quality management and regulatory value including the development of the DMR.

Connected Digital Twin

The value of the digital twin expands dramatically when it goes beyond device production and into usage. Some would say a digital twin without connectivity isn’t a complete twin, but regardless of definitions a digital model is highly valuable. But a connected one adds significantly greater value.

The rise of the IoT adds a new dimension to the value available from the digital twin. Companies can collect real-world device performance and associate it back with the intention of the design model. This can help identify issues where devices are not performing as designed, and may provide an early indicator of a potential variance.

A second aspect of the digital twin is identifying differences between predicted and actual device performance where the device is operating as designed, but not as intended. In these cases, there are gaps in the simulated performance of the digital model that can be addressed to improve simulations and understanding of how devices perform in the field.

Medical device companies gain significant value from the connected digital twin. It rounds out the information in the DMR and supports a more robust data set for UDI. It could also be used early in the lifecycle to help support clinical trials, as well as other regulatory demands throughout the lifecycle.

Digital Twin of the Plant

Another aspect of the digital twin is creating digital twins of the equipment used to produces devices. Companies can create fully functioning models of machines, lines, and plants to design, simulate, and optimize production. As with the digital twin of the product, connecting the digital twin provides even greater value. In this case, it may include the IIoT in addition to the IoT.

The digital twin of the plant helps medical device companies validate production methodology, SOPs, and set critical control points to improve control, reduce variability, and improve quality. It can also be used to validate process control intent with regulatory bodies. These twins, along with the digital twin of the device (which should be integrated) can also help automate regulatory submissions. Again, we see significant overlap in the use of digital tools in the medical device industry.


The final piece of the puzzle we’ll discuss today is data analytics. Life sciences companies have been using analytics in multiple aspects of their business for quite some time. The digitalization of the underlying information in the design, engineering, manufacturing, and use of the device dramatically expands the opportunities. Companies now have a much broader data set to analyze and transform into intelligence.

Medical device companies can leverage big data analytics in conjunction with the digital twin and digital thread to identify trends and correlations previously hidden in non-digital or non-integrated data sets. Analytics can be used in multiple phases of the device lifecycle, from identifying process control parameters drifting toward spec limits to analyzing adverse event data in the field. This can make existing processes better, for example being able to more quickly identify root causes for CAPAs. It can also support newer requirements like post market surveillance in the EU and the upcoming shift to focusing on patient outcomes. It can also benefit patients, for example predictive analytics may be able to identify potential failures prior to their occurrence and prevent adverse events. Finally, additional insights may create a new source of innovation that leads to new and better treatment options.


The last area to discuss is the IoT. We’ve already mentioned it while discussing the earlier digital topics, it’s hard not to given it’s significant potential to change the relationship between medical devices, the manufacturer, healthcare professionals, payers, and the patient. But there is much more to this topic, so we’ll save this for a later post.

Our Take

Digitalization of the medical device industry takes the value of new technologies and techniques and extends them to improve both company profitability and patient outcomes. In the end, the buzzwords represent new capabilities with real potential to help medical device companies innovate, drive rapid product design, speed approvals, improve quality, and achieve higher levels of compliance. These are all important for them to continue their mission to improve patient welfare in today’s complex healthcare environment.

You can find more information about digitalization for medical devices from our sponsor, Siemens PLM.

You can also find more information from Tech-Clarity on digitalization in the medical device industry please see our The Digitalization Opportunity for Medical Device Companies (video) or Digitalization in the Medical Device Industry (animation).