Today, robotic process automation (RPA) registers strongly on the radar of Regulatory Affairs departments; many companies are talking about it or looking at it, if not actually already using it. But is it being used to its full potential? Amplexor’s Agnes Cwienczek separates the fact from the fiction and offers a pragmatic plan for life sciences firms to derive measurable benefits.
All things robotic or machine-operated can be confusing, especially as lines blur between straightforward automation of routine tasks, and systems that use some level of artificial intelligence to determine exactly what to do or the best way of doing it before progressing a task.
In Regulatory Affairs (RA), and especially in Regulatory Operations, the potential for RPA-enabled transformation of workload management is substantial due to the sheer volume and intensity of administrative throughput, so it is not surprising that companies are evaluating the potential for reliable, expedited help with this. And, certainly, this is where RPA comes into its own. Even without any real applied intelligence, systems can reduce the time taken to perform repetitive manual tasks – freeing up expensive talent to use their knowledge and skills more productively, while reliably processing work items that can invite error as the human brain grows tired.
Structured vs smart RPA
For taking over simple task execution, robotic process automation is a great place to start for RA functions looking to apply their resources in smarter ways and reduce risk and time to market. There is a good range of highly-structured, ruled-defined processes and tasks that lend themselves to this form of automation. These include the kinds of tasks once routinely outsourced to third-party service providers in pursuit of greater cost-efficiency.
Common operational use cases might include automated data entry; extracting data from Excel sheets for uploading into databases, importing documents, or archiving them; checking data or document quality; or parsing emails. In clinical regulatory operations, where there are hundreds of reports coming in from contract research organisations (CROs), potentially running to thousands of pages of information, an RPA tool can help take the strain from teams who otherwise would have to input data manually from these documents according to a given checklist of entries.
Lightening the laborious load
Creating or verifying hyperlinks between related data in documents is another gargantuan task that RPA tools can help with. Often, links to aid reader navigation are left until the end, as all documents are readied for submission – by which point the task can be overwhelming, potentially requiring tens of thousands of hyperlinks to be added.
Checking documents for submission-readiness according to defined criteria is another undertaking which RPA could transform. For instance, is the PDF file the right version, and does it have the right settings and bookmarks? Checking off documents prepared by scientific professionals against 10-20 criteria which could mean the difference between acceptance or rejection can be a full-time job, occupying whole teams who have to manually process hundreds of documents each day.
Processing and parsing emails is another strong use case for RPA – for instance, extracting standard data from routine documents such as standard agency approval letters. This is an example where AI-enhanced RPA can boost a tool’s potential, and the payback. AI-enabled RPA allows tools to cope with unstructured scenarios as well as fully standardised, highly-structured contexts where the parameters remain consistent and predictable. As agency approval letters can vary by country, an RPA tool with some degree of machine intelligence could help by first determining which country the letter has come from, and therefore where to look to extract the required information and how to interpret it before uploading the results into a regulatory system.
Quick win interim solution
As RPA use becomes more widespread, companies – especially the large players with very heavy workloads and a sizeable appetite for improved economies of scale – are becoming more mature in their application of the technology as well as more ambitious in their aims. Once they have tried out RPA and seen for themselves the tangible benefits, these organisations are beginning to think laterally about where they could take the technology next.
One trend that’s on the rise is the use of RPA as an agile, interim solution to deliver quick wins in parallel to larger-scale transformations of regulatory information management (RIM).
Where companies are impatient to deliver ROI and accelerate speed to market now, targeted RPA applications – turned around quickly and affordably – can readily demonstrate their worth and reaffirm the business case for regulatory digitalisation. Targeted RPA applications help to highlight what’s possible, inspiring investigation into more advanced use cases – and reassuring teams that automation isn’t a threat to their jobs, but rather the key to making them more interesting.
As companies progress with and become more serious about RPA, determining strong targeted use cases will be important. It will help to build credibility and confidence around the technology, and to break down fears about technology taking people’s work. Validation of RPA technology could conceivably be a challenge, particularly where systems are continuing to evolve using AI and machine learning. Niche, application-specific bots which execute repetitive tasks and are relatively restricted in their use and predictable in their performance, however, should not pose too much of a problem.
DIY versus managed RPA use
Beyond the considerations above, next decisions include to what extent companies develop and run their own RPA capabilities, or lean on third parties to create and operate the tools for them.
It is anticipated that some process automation tools will remove the need to rely so heavily on external services to improve operational cost-efficiency. In other cases, the use of RPA or standard systems offering the same capabilities for such automations will increasingly become a pre-requisite when choosing service providers – on the basis that third parties which have invested in next-generation processes can be expected to be operating at a level of superior economic advantage.
Standardised data management increases the opportunity
To maximise the opportunities for harnessing RPA technology, life sciences companies should be thinking about further standardising the way they capture, record and manage data. RPA bots are relatively easy to code; the bigger challenge is harmonising processes and channels and shoring up data quality so that automation can be applied easily and reliably.
The best approach as organisations survey and scope the potential is to look where the biggest pain points are; where tasks are executed according to check lists, or the most resource-draining or inefficient outsourcing relationships, and use this as the steer for RPA development or advancing with digitalisation.
Ultimately, every organisation needs to take big strides towards the digital world, and in life sciences – where progress is considerably behind that of other industries – RPA offers a great starting point.