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Julian Upton is Pharmaceutical Executive's Online and European Editor. He can be reached at email@example.com
At this month's AMPLEXOR Life Sciences’ “Be The Expert” conference, Pharm Exec caught up with Steve Gens, who talked about the evolution of Regulatory Information Management (RIM) and offered some predictions for 2022.
At this month's AMPLEXOR Life Sciences’ “Be The Expert” conference in Dubrovnik, Croatia, Steve Gens of life sciences consultancy Gens & Associates talked about the evolution of Regulatory Information Management (RIM) and offered some predictions for 2022. Pharm Exec caught up with Steve for an update of the current and future state of RIM.
Steve Gens: We get our views from the extensive industry benchmarking we do and our report this year was based on the analysis of 69 companies. The main difference between are 2016 study and this year was a stall in the efficiency and data quality levels for the management of vital regulatory information. This was a surprise, because there's a tremendous amount of investment going on and we saw a good increase in both efficiency and data quality levels from 2014 to 2016. Part of the challenge is most companies have a history of disconnected information which is driven by having many software providers whose solutions do not integrated well with each other. This is starting to change as several providers are finally bringing a true end-to-end capability to the market, meaning global information is housed in one place.
The other key finding was a clear focus on connecting RIM to supply release, manufacturing product change control and clinical processes over the next three years. This was the case for large, mid-tier and smaller organizaitons. What this means is that RIM is being treated as an enterprise assest and global connectivity is a key driver.
So there's still a lot of work that industry has to do, particularly around improving the exchange of information with health authorities and collaboration partners. We started seeing investment pick up in regulatory around 2013 and belive we’re half way through a 10 year regualtory transformation period.
In our benchmark we looked at 15 technologies such as robotics, AI, blockchain, business intelligence that are being used in an innovative or disruptive way. What stood out for us is AI. A new part of our benchmark was a close review of the regulatory connection with product change control process, supply release, and quality management where a lot of complexity with life-cycle management of products resides. We envision AI really taking hold in this area over the next two to three years. What we see with AI is the machine will learn over time; we did that type of change before, these were the countries we had to submit, this was the cost of submission, this was the cost of the manufacturing change, does it make business sense? Instead of a manual three to four week process, this could get done in days. So that's one area where I think AI's going to be applied, and it's very exciting.
Key performance measures is one of the ways in which we assess different companies in our benchmark. We have twenty seven performance metrics we test comprising of three catatories; volume, quality, and cycle time metrics. Where industry continues to struggle is with cycle time measures, so perhaps it's the average time for a regulatory impact assessment, the time from receiving a health authority question to the actual responese for example. The other key area is measuring the data quality confidence for critical information areas such as registrations, health authority commitments, and submission documents. How accurate it is, how timely is it to be updated?
So what we see is the aspiration of industry to get to a higher performance level through using many of these measures in a formal continuous improvement program. We still think most regulatory organizations continuous improvement and metrics programs are immature, but it's an area of focus and opportunity for them over the next two years.
Life sciences is such a complex and highly regulated environment that it’s hard to compare. Some other highly regulated environments such as finance or energy might have something to offer generally, but not specially to regulatory information management system. One area of interest is how they are applying technologies like AI and robotics in a highly regulatory environment, that's where the learning could be.
Since 2007 we've been tracking outsourcing very closely and the question of talent recruitment is always there, especially in regulatory. The knowledge required in both the regulatory affairs and operations side is pretty significant. I kid around with my clients, "You just don't find those people hanging out at Starbucks", so attracting and retaining talent is very important.
Technology is not used so much to replace people, it’s more using automation to get more value from the limited people they do have. They tend to be highly credentialed, educated, or have a lot of experience on the operations side because those skills are very hard to get.
We looked at this recently and cut the data by just comparing the European-based companies (40% of the 69 companies had European headquarters) versus their North American and Japaneas peers.
What we found was actually really interesting. European headquartered companies were slightly higher in both efficiency and data quality levels. When we look at performance metrics, they tend to have a stronger program. So generally speaking European firms are ahead of the pack compared to other regions of the world.
When we look at our world-class and strong performance work that is part of the benchmark, we give the highest priority to data quality confidence levels. What we found in our research in 2014, 2016, and again in 2018, is that top performing companies do not have one provider or some magical system. They have different types of providers and they just get it done better.
A lot of this has to do with the data quality confidence of vital regualtory information. When companies or people are not confident in the data in their systems, they spend a substantial amount of time verifying that data before progressing an activity or making a decision. This impacts the local affiliate, regional, and central offices, and takes away a lot of productivity from your typical regulatory organization.
So if there's a priority focus, it should be on increasing the quality of regulatory information, be it data or documents.