Leveraging Digital Innovation for Efficiency, Productivity and Insight

The digital and automation age is upon us and life sciences companies have enormous scope for process improvement by tapping into new capabilities.

More effective use of resources and improved outcomes are key drivers for life sciences companies. These goals can be achieved through using better analytics to derive greater insights from regulatory data, bringing products to market faster through more streamlined submission processes, and leveraging technology and expertise to remove manual tasks from regulatory teams.

During discussions at the DIA in June, business leaders articulated their view that while there is no way to get around the time frames and requirements mandated by the regulatory authorities, they want to be able to control what’s in their own space. This is where automation and machine learning can have a profound effect.

Automation improves efficiency and productivity in several ways.

From a macroscale perspective, automation can vastly improve how stakeholders search for and find information. Typically, the way users search for information is hit and miss. Embedding automation tools in technology can assist with the information-retrieval process. For example, voice recognition capabilities could allow users to quickly search for content in a more seamless way.

At a microscale level, consider the time-consuming process of importing documents, which is typically done one at a time. Now imagine an automation feature, enabled through metadata, that groups documents in folders and simply uploads whole folders, saving users hundreds of steps.

Another way to drive greater efficiency is through better insight into data and content.

Content management solutions, such as FirstDoc and FirstPoint, store millions of documents that are integral for a submission and for supporting products on the market. But many of these documents are unstructured, which can make it more difficult to conduct analytics.

Being able to turn that unstructured data into structured content would make it much easier for users to carry out reporting and analytics. And this could help provide useful future insights. For example, if a new drug application took a company six months to prepare, having access to how long each process took and what the holdups were would give that company insight into how long a similar application might take. The ability to predict outcomes allows companies to use their time more efficiently.

Connecting the dots

Automation and artificial intelligence also create the opportunity to connect data from various streams — clinical studies, manufacturing, regulatory, etc. — to derive insights on potential new drug candidates. This can be achieved by applying insights to information that already exists within the company to determine the type of values researchers want to extract from the data.

For example, this might entail taking laboratory information management systems and coupling them with content management solutions to assess which molecules have been looked at in the past and whether these might be valuable for future drugs in the product pipeline.

The same type of information connection could apply to manufacturing. By accessing solutions that manage the supply chain, manufacturing can assess downstream processes, such as how long various manufacturing plants took to produce a drug, which had the fewest changes in its life cycle and which had the fewest problems. This could help companies make decisions on which facilities might be best suited to certain products.

With automation and artificial intelligence, it is possible to extrapolate insights across the product life cycle — from clinical, to regulatory, to manufacturing, and to sales and marketing.

Over the coming months, I will explore some of these scenarios and address opportunities for gleaning greater insights, and for improving efficiency and productivity. Find out more about the next-generation strategy essential for life sciences transformation.


Sharad Khusal is head of DXC’s Life Sciences Build team.

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