New Insights, New Efficiencies: 2022 Trends in Data Management

Remote working practices during the pandemic forced companies to adopt new technology and data platforms. Will this trend continue in 2022? Three experts from Veeva Systems, a cloud-computing company focused on pharmaceutical and life sciences industry applications, give their view on how improved data management will affect the sector.

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Clinical trials are not fully virtual, but hybrid models are becoming the norm

?Jim Reilly, Vice President, Vault R&D, and Quality, Veeva Systems

During the pandemic, the industry accelerated the adoption of decentralized trial capabilities to bring more trial elements directly to the patient. However, predictions that pharma would adopt a fully virtual model in which sites become less relevant have been proven wrong. Instead, the industry is moving to a hybrid model of trial execution with some decentralized elements. Sites (and the investigator) will continue to play a vital role as touchpoints for patient engagement and retention. However, as an increasing amount of clinical data is captured electronically, I expect to see faster trials that are better managed, leading to the faster delivery of new therapies.

Greater patient and site centricity will also drive further change in studies. Some of the decentralized clinical trials that ran in 2020 and 2021 weighed patients down by requiring them to log in and out of numerous digital applications to engage with healthcare providers or submit outcomes, while research sites felt burdened with a multitude of point solutions (applications or tools used to fix digital problems) that made it even more difficult to manage trials. Now, as they work to reduce the technology burden on patients, more sponsors are looking to minimize the digital applications and portals that they require sites to use. This will enable sites to spend less time on administrative functions, and more on patient safety and care.

Pharmacovigilance transformation will help improve safety

John Lawrie, Vice President, Vault RIM and Vault Safety, Veeva Systems

Companies are looking to modernize pharmacovigilance and the way they handle case intake and processing. For example, safety departments are taking a more proactive approach earlier in drug development and expanding beyond the traditional safety systems and processes, investigating more advanced technologies, like artificial intelligence (AI) and natural language processing, for signal detection, analysis, and management.

In 2022, I believe that automating manual processes and finding new ways to process information will be crucial to improving patient safety and maintaining compliance. AI, for example, can be applied to filter and clean terabytes of data. This will make the analysis of information easier to determine risk levels or identify new drug safety signals. Faster identification of adverse events and signals improves drug safety monitoring.

Simplifying data management systems, their validation, and ongoing maintenance will also be key to improving safety. Within the past year, I’ve seen more and more life sciences companies, from small innovators to large enterprises, modernizing pharmacovigilance data management?to streamline safety operations. These organizations are seeing benefits like better decision-making from real-time data access, more automation from workflows for key processes, and lower administrative burden on staff.

As the technology modernization trend continues this year, safety departments will focus on the opportunity to manage their end-to-end pharmacovigilance processes and information more holistically, in a more efficient and compliant manner.

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Clean, consistent data will help unlock value in drug development

Rik Van Mol, SVP, Development Cloud, Veeva Systems

?As R&D teams work to reduce point solutions, many are realizing the importance of data quality. On the clinical side, for example, patient data must be aggregated and cleaned if information from new sources, such as wearables, is to connect to clinical and clinical operations data. Companies that cannot do this will find it difficult to learn from past events to improve operations.

That’s why we expect to see more companies adopt data management tools that will automate and speed up this work by ingesting, aggregating, and cleaning data so that it’s easier to analyze, report on, and share.

GSK?and?Novartis?are among the companies focusing on data quality and moving toward real-time interactive dashboards, using a platform approach that simplifies data exchange. Instead of merely storing data in a data lake and then analyzing it, this approach deals with the disparity of data and their sources, whether input manually or sent from sensors. The result will be a single source of data that will improve cross-functional collaboration by allowing information to flow easily to different functions within drug development.

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?Do you have any thoughts on the future of R&D? Be sure to share them with us.