Data day

As the transition from the traditional focus on documents to data accelerates, regulatory teams may not have all the skills they need to operate in and benefit from a data-first world.

Even in today’s world, most life sciences regulatory teams currently still think and work in terms of documents, paragraphs and sentences when putting together collateral for marketing authorisation and variations submissions. Yet it is data, rather than pre-prepared dossiers, that is moving into central focus now.

That’s as stakeholders across the life sciences and healthcare ecosystem realise that a data-first approach to collecting, managing and communicating product information will be the most efficient and reliable way to maintain a consistent, definitive, current and high-quality record of a product entering or on the market. One that can be interpreted and used in a wide range of use cases, by the broadest possible range of people (from regulators to clinicians, pharmacists and ultimately patients).

Professionals in a range of roles are now used to converting their particular information, such as the medicinal product’s clinical properties, chemical composition or information for patients, in the narrative form. But are they ready to adopt new, more structured ways of dealing with such information at the source? Or is there an expectation that the regulatory role will effectively assume the burden of data extraction and data entry assistance for them?

Given that this data-centric approach will be the new reality before long, the question for existing product information managers/regulatory teams is whether their skill sets now need to be refreshed to reflect the new ways of working (first, data and document sets needing to be carefully aligned, then a direct flow of good data to the regulators).

Smart innovation

So where are companies with all of this today? With the exception of very large pharma organisations with the budget and people resources to have already started exploring the wider possibilities, most companies still lack awareness both of the wider potential and of the work ahead of them in building the right capabilities.

At one level, this is about how they manage product information so that (a) it fulfils the demands of new identification of medicinal products structured data requirements, and (b) becomes sufficiently reliable to form a foundation for not only product registrations and their maintenance, but all sorts of other processes too.

On another level, the opportunity extends to leveraging reporting and analytics to smart effect – first to help users fill gaps and increase the quality of the data; then with a more strategic emphasis, even using AI-assisted tools to investigate scope for process improvement (based on insights into how data is currently being managed and where recurring patterns are emerging).

It can be tempting to imagine that IT is going solve all of this, and that by default users will be swept along on the journey. Yet failure to adapt internal regulatory capabilities, and to cultivate new data skills, is likely to severely compromise regulatory affairs’ data-based progress.

Of course, having efficient and user-friendly solutions that have been built not just with additional data fields to satisfy IDMP – but also with an appreciation for what new data-centric process management models will mean for life sciences regulatory and other teams – will be vital.

Equally, the teams involved will need help in adapting to the demands of IDMP. They will need guidance, support and help with validation to ensure that the right data is being entered in the right way, and that any gaps or issues are spotted and flagged.  And, given the huge weight of new responsibilities that will be placed on this critical ‘source of product truth’, it also follows that an additional layer of quality checks will be needed to cement confidence in the new bank of structured data.

As teams look to use this ‘live’ data to build reports, they will need help understanding how to make the most of analytics and of pre-built dashboards, too.

Sight and sound

As basic data interrogation becomes more commonplace and comfortable, teams will need to be able to transition towards more advanced analytics. In the familiar document-centric world, the scope for deep-diving into the incorporated information, and extracting new insights and value from those documents, was minimal.

Once the source of information becomes structured data, the scope to analyse it using smart tools improves sharply. It is at this point that teams can start to apply AI-assisted tools, and interrogate broader sets of structured data, to discover subtler inconsistencies, gaps or errors in data that may have slipped through manual reviews and controls.

Moreover, teams can start to look at the efficiency of data capture and management processes, to see whether alternative approaches might be more effective to enhance the availability of good data for all.

For every user with a role to play in shaping the data, this work needs to be as simple and as user-friendly to achieve as possible, enabled by intuitive tools. If users are not brought along on the journey from this earliest point, anything that comes afterwards will be in vain (as the reliability of the data will be compromised from day one).

Once teams are comfortable with working with data, and are confident in its quality - because they are adept at the process of capturing, enriching and managing it – regulatory operations can start to be more ambitious in their next-level plans.

This takes them deeper into the realm of data science, as they start to harness AI-enabled tools to interrogate the data for signs of how this could be improved, and where entire data-based processes could benefit from a new, streamlined approach.

Yet it is here that existing teams are most likely to find that they lack the appropriate skills and will need to bring on board new talent in the form of qualified data discovery professionals. In pharma regulatory Operations, data scientists do not currently exist – or not in a widespread capacity.

Pilot episode

Regulatory teams face a real challenge when it comes to developing the optimum combination of domain, tool and data discovery knowledge. As a starting point, teams must look to build software and domain knowledge, and grow data science capabilities through collaborative team-building and targeted training and skills transfer.

A practical approach is to introduce new skills adoption step by step, across a pilot initiative that targets regulatory’s biggest pain points, or the most complete source of existing data such as existing product information.

From the beginning, it is important that regulatory teams understand that the shift to data will bring benefits far beyond immediate operational improvements. Teams need leaders who can see and communicate the wider potential of a data-driven ecosystem and their plan for exploring this.

So, in the first instance, it may be necessary to start by supporting leaders as they build their awareness and knowledge. With a vision and leadership in place, the benefits of a data-first future are there for the taking.