Cutting Through Compliance Demands
An obvious area where AI and machine learning can help here is in managing matters of regulatory compliance – where requirements are multiplying and changing all the time. Not only does this increase the burden on regulatory affairs and quality teams; it also potentially slows 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 appears to become further out of reach.
In everyday life, AI has begun to transform the way people interact with diverse information and achieve end results. Over breakfast, thanks to AI-enabled ‘personal assistants’, they can check their various message sources, get a precis of the news, peruse their diary, search for travel options and make bookings, without touching a keypad or mouse. They simply speak their requests to a voice-enabled user interface (Siri, Alexa, Google Assistant or Cortana), and an AI-enabled ‘assistant’ does the rest – instantly interacting with all the different applications and performing the various analyses and transactions, returning its results before the user has taken a second bite of their toast.
It may feel like a leap now, but there is no reason to suppose regulatory affairs teams couldn’t enjoy a similarly unencumbered user experience when managing health authority submissions. The ideal is 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?
René Kasan, a visionary speaker from IT consultancy NNIT, outlined his own personal take on AI’s potential as part of regulatory information management at Amplexor’s recent annual conference. 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’s time to do more satisfying and productive work, AI could also reduce the risk of dependence on a single person’s knowledge of how things are done. (If a highly skilled team member moves on, there is usually a productivity gap as their replacement gets up to speed.) It’s easy to see how companies would benefit: it is getting harder to attract experienced skills for important regulatory roles6 as demand increases, yet the pressures of the job take their toll.
Could Alexa Automate Regulatory Information Preparation?
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. Although exploiting AI in particular is something that is only now appearing on their radar, AI and machine learning have become hot topics, and events and conference sessions on the theme are well attended.
We can speculate about a number of ways AI could transform regulatory information and submissions management transformation – improving the process of planning, structuring, authoring, publishing and archiving content.
Example scenarios might include using AI to monitor and determine which content elements of a submission are routinely included, so that they become a structural component in their architecture. AI could also help ensure referential integrity, so that the correct, approved master content is reliably drawn on every time, and that protected sources (e.g. ISO IDMP data) cannot be tampered with, without a formal change request.
Document and dossier authoring and reviews could be streamlined as AI capabilities learn to spot content that has been changed frequently in the past. Drawing on this knowledge, the system could propose changes as a document is being put together, saving rounds of redrafting. Alternatively, the user could ‘ask’ the system what the implications would be if they made a particular change to content, and have the Alexa-style voice interface list all the ramifications. An intuitive, user-friendly interface combined with smart, machine-based deductions could save a lot of clicks, system navigation and time.
AI could reinforce compliance and content quality along the supply chain, too, helping to restrict what country affiliate representatives are able to do with content. Where there have been quality violations, AI could provide the analysis and insight so teams can act and prevent repeated issues. Related to this, the technology could help identify and avoid common submission queries, to prevent delays in getting products approved. Similarly, it might support strategic decisions about which health authorities/ markets to target first.