High-Volume Manufacturing: The Moment of Truth

One of the most important parts of developing a new medical device is making sure it is properly labeled. Labeling the device involves a variety of details, including instruction in its proper use, requirements to safely operate the device, storage and maintenance, and installation requirements. All devices are required to have a Unique Device Identification (UDI) that identifies the general type of device and a specific product identifier. Common symbols are used to communicate properties that occur over a variety of devices. While the standards in the US, the EU, and Canada all vary slightly, the basic principals remain the same.

For some devices, this is a fairly straightforward process, especially with devices that are similar to ones already developed. Others take more time, because they are based on a new type of device or a device type that was not regulated before. Labeling is a very precise process, and the complications that result from new safety or security concerns can jam up the works. As an example, it was only in January that AI systems and other forms of machine learning became covered under medical device labeling regulations. Since AI systems “learn” through use and change accordingly, the device may not resemble what was originally on the label. So in addition to what is on the label itself, these devices require a “Predetermined Change Control Plan” that extrapolates what the device can learn from AI or machine learning and how those changes could affect the current label. Not surprisingly, the final date where these regulations will finally take place is several years in the future. To help assist those in the field, the FDA has planned several public workshops on the label regulations.

A growing number of devices involve digital components that transmit data or other forms of information to a central or cloud-based system. This is a new wrinkle for labeling, because that data is protected under privacy regulations and needs to be kept secure in transit as well as in the device itself. A lot of the technology itself is not new, but since the previous version of the devices had no cybersecurity requirements, the labels don’t have those security measures listed. Labels are currently in the process of being revised for these measures, but in the wake of the COVID pandemic many of those requirements have been loosened. A new cybersecurity director was recently placed in the FDA, which may help keep labels up to date in this field.

Another complication is that many digital medical devices are intended to be used at home by the general public, and they are not often familiar with the symbols and other markings on labels that those who work in the medical field are. It may work out better if those symbols on those devices should be defined or used in a way that makes their meaning clear. There are some proposals with the FDA that suggest that any potential security risks should be spelled out in either the label or device documentation.

Loftware Spectrum software aims to take the complexity out of making medical device labels. Not only does it provide templates that can be re-used for similar devices, the system can be updated once new label requirements are in place and any edits to the label can be tracked from the beginning. Spectrum has the potential to make all of the new labeling requirements and details much simpler to implement.