Labelling and the art of Data Quality for UDI

By Graham Francis, Channel Marketing Manager, Kallik

In my previous UDI blog, I talked about the dynamic nature of labelling and artwork content. I also suggested that there could be a difference between what is classed as ‘master data’ and what might be a single source of truth’. In this, my third and final blog reflecting back on the IPQC UDIs and Traceability conference in Brussels last month, I’ll be discussing how adopting best practice for managing labelling and artwork related content can bring early business value to UDI projects.

Before we do that, let’s return to some of the discussion points from the event around data quality. We heard from the FDA that the biggest issue in the US is the quality of data submitted to the GUDID. Granted that out of the 65 data attributes uploaded to the GUDID for each product, only 12 or possibly 13 are related to labelling content, but inaccuracies in product nomenclature could at best lead to expensive product recalls and at worst, patient injury and huge fines.

It was also said by one prominent speaker that UDI is a company-wide project. The point made here being that UDI is not something that can be successfully implemented by one department alone, it needs executive level sponsorship with cross-functional collaboration and clear ownership.

So accepting that UDI is not simple, let’s turn our attention back to data quality in the context of labelling. Data quality is characterised by levels of accuracy, completeness and consistency (or standardisation). Having the wrong name on the wrong product is not a data quality issue, but having a misspelt product name or one obfuscated by spurious characters is. So what can be done to minimise these risks and why the attention paid here to a subset of UDI attributes rather than the majority?

Well, when I questioned the FDA at the conference as to whether any penalties had been so far levied on organisations submitting inaccurate and/or incomplete data to the GUDID, their response was a simple “no.” They said they recognised the enormity of the task for some companies (particularly as we move through Class II to Class I devices) and are preferring to take a supportive rather than a vengeful approach. This would not be the same for inaccurately labelled products – this we can be sure of!

It is understandable why the majority of the industry is focused on getting submission data right first as the EU MDR calls for all attributes relating to every class of product to be uploaded to Eudamed by 26 May 2020. But like the FDA, plus given the enormity of the task facing manufacturers selling into the EEA, it’s likely the EMA will go for a ‘soft’ rather than ‘hard’ enforcement.

However, the problem the industry is trying to solve is not ‘just’ submitting the right data in the ‘right’ format to either the GUDID and/or Eudamed database, it’s much broader than this. It requires (as one speaker from the industry asserted) corporate level sponsorship, executive oversight and buy-in from all stakeholders.

So back to labelling and artwork. At Kallik, we see UDI as an opportunity to jump start implementation of best practice for UDI compliance. By first getting to grips with discovering, standardising and nominating stewardship of each of the attributes that constitute a product label, you’ve taken the first steps towards implementing a robust data governance framework. This will also provide ‘advance standing’ for broader UDI compliance.

Everyone from regulatory through to supply chain has a role to play in labelling, so why not start here and use this as a pilot for rolling out the policies and procedures needed for the broader suite of attributes required for UDI? The great thing about taking this approach is that it will demonstrate clear productivity gains for labelling and artwork processes and potentially de-risk your broader UDI project.

None of the above requires you to rush out and invest in an IT solution may in the end turn out as not being fit for purpose. A better approach is to first look at where your priorities lie, understand who owns your data and what needs to be done to make it UDI ready. Focusing first on the subset of data required for labelling is likely to make this much more manageable. If you do decide to explore this as an approach, we here at Kallik be delighted to support you throughout your journey.

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