What’s It All About?
Essentially, AI takes automation and makes it super-smart. It can be instructed to carry out highly complex tasks. An element of AI, machine learning, improves on that even further, finding better ways to approach certain tasks. So while humans struggle to map out every eventuality and possibility, and programme a system for that, an AI-based system can learn from what they have been told to create a powerful shortcut, almost as if they are actually thinking for themselves.
So taking, for example, a massive data set that would occupy a human team for weeks at a time, AI and machine learning can perform analyses and distil subtle trends that humans might easily overlook. Operationally, such tools can help companies navigate routine processes more promptly, thoroughly and efficiently, such as regulation and compliance. Not only will it do these tasks better, but it also frees up teams to use their skills elsewhere in the business.
Regulatory = Mundanity
This is why regulation is such a prime area for AI to add value in life sciences, where requirements are multiplying and changing all the time. These are a massive burden on regulatory affairs and quality teams and in fact, can also potentially slow companies’ time to market. Moves towards international standards, and deployment of sophisticated content management systems, go a long way towards alleviating the additional work involved and maintaining data quality. Yet, with each new regulatory initiative or submissions hoop that companies need to jump through, the business agility and creativity they are aiming for gets bogged down further.
The ideal for regulatory affairs teams would be that their product lifecycle content systems will make it more intuitive to manage data changes, document authoring and reviews, quality control, and submission. Currently much of this is managed via comprehensive rules, templates and workflow which help to streamline processes and ensure that the right data is used in support of the given requirement. But what if AI and machine learning could promote reliable shortcuts, and issue red flags or suggestions if rogue actions are taken, the wrong master data is used, or someone tries to alter approved ISO IDMP-compliant source content?
Earlier in 2017, Amplexor staged its annual BE THE EXPERT event, which gathered some of the most innovative thinkers from the world of life sciences for a series of presentations, seminars and keynote speeches. One of our speakers was René Kasan, of IT consultancy NNIT, who outlined his own personal take on AI’s potential as part of regulatory information management. He explored how it might help transform companies’ ability to consume and harness vast volumes of complex data, and unprecedented inter-connections between different data sources and systems - without compromising the integrity of the content, and with significant benefits to speed and reliability. And without time limits, because AI doesn’t get tired or slow down.
As well as freeing up skilled people’stime to do more satisfying and productive work, AI could alsoreduce the riskof dependence on a single person’s knowledge of how things aredone. Currently, if a highly skilled team member moves on, with expertise and experience in a particular area, that expertise moves with them, meaning there is often a productivity gap as their replacement gets up to speed.
It’s true that a number of life sciences firms are already harnessing more automation to streamline regulatory information processes. Annual surveys by Gens & Associates repeatedly show increasing sophistication in the industry’s approach to regulatory information management. But what is starting to appear on the agenda is AI, which has become one of the hottest of hot topics.
Organisations are waking up to AI’s potential to transform regulatory information and submissions management. These might not be the areas of improvement by AI that are seen most commonly in the press, but for life sciences firms it would be addressing an area of real need.