How can Robotic Process Automation transform Regulatory Affairs?

by Agnes Cwienczek | Oct 29, 2020 | Blog


Robotic Process Automation (RPA) is increasingly seen as a solution to laborious, repetitive, resource-draining document and data processing.

Unsurprisingly, it is on the radar of Regulatory Affairs (RA) departments who are talking about it or looking at it, if not actually already using it. But what do they mean by RPA and are they looking in the right places for process optimisation and transformation?

In 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 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

RPA 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, such as data or content entry; extracting data from Excel sheets for uploading into databases, importing documents, or archiving them.

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.

There are many advanced scenarios which can be include in the list, such as document hyperlinking, dossier compilation and data extraction from unstructured sources.

RPA as a bridge during RIM transformation

RPA can be 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 can help to highlight what’s possible, understanding which automations are of added value in a future RIM, 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.

Building a good, solid regulatory intelligence database is a good example of a next-generation RPA use case. A blended approach of RPA-extracted data and human insights can result in a powerful resource with wide-reaching benefits in accelerating and improving the quality and success rates of global submissions.

Managed RPA versus a DIY approach

Companies can develop and run their own RPA capabilities, or engage third parties to create and operate the tools for them. It is expected that some process automation tools will eliminate the need to rely so heavily on external services to improve operational cost-efficiency. In other cases, the use of RPA or RIM 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.

Standardisation increases opportunity

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/for advancing with digitalisation.

At the end of the day, every organisation needs to take big strides towards the digital future, and in life sciences – where progress lags behind other industries – RPA or RIM systems with high automation capabilities offer a great launchpad.

Agnes Cwienczek

Agnes Cwienczek

Senior Life Sciences Consultant

About the author

Agnes Cwienczek has been in the position of Senior Life Sciences Consultant for Amplexor since May 2017. In her role as Life Sciences Consultant, Agnes is part of the Product Management team. Her main responsibilities are the contribution to the development and enhancement of the Amplexor Life Sciences Suite, supervision of the Life Sciences Consultants as well as the provision of business process and data management expertise to Amplexor clients in Regulatory Information Management, Document Management, and Submission Management. Before joining Amplexor, Agnes worked at Merck KGaA in Global Regulatory and Quality Assurance, where she was acting System and Process Owner for all regulatory owned systems, providing global leadership for the management of submission documents, regulatory data and archiving within the Merck Biopharma organization. She was responsible for implementing and maintaining the regulatory applications and data management strategy and roadmap, and ensure business operation of applications under her responsibility, and to lead a global team of regulatory operations experts. Agnes received her master’s degree in Information Management from the University of Koblenz-Landau.