AI and big data will continue to disrupt pharma sector, says survey

20 July 2021

Anna Begley / European Pharmaceutical Review

A GlobalData report has revealed the extent to which companies will be using AI and big data in drug discovery and development processes.

GlobalData’s latest report has revealed that artificial intelligence (AI) and big data will continue to disrupt the pharmaceutical sector, according to the healthcare industry professionals surveyed. The report, ‘Smart Pharma’, showed that 28 percent of companies will be using AI and big data to optimise drug discovery and development processes in the next two years, while 32 percent would be relying on big data to streamline sales and marketing.

Other findings included:

GlobalData’s Urte Jakimaviciute stated: “The pharmaceutical industry is data driven. With the increasing volume and complexity of data being generated by the sector from multiple sources, the need to organise and streamline information is a constant challenge.”

As such, GlobalData noted that the use of AI will continue to grow rapidly, especially considering the amount of data that can be mined from patient records and registries, real-world evidence, sales and marketing, and connected devices. AI can also be used to design treatment plans, develop drugs, or improve clinical trial outcomes, thereby making drug development cheaper and faster.

Furthermore, the increasing use of social and digital media tools among physicians and patients has contributed to increasing volumes and variety of information that companies can access, collect and analyse. “By obtaining data from diverse sources and leveraging the power of data analytics, pharma companies can get better insights into end users’ behaviour patterns, response to marketing campaigns, product performance, and upcoming industry trends which if comprehensively analysed and interpreted can result in improved marketing and sales,” Jakimaviciute said.

However, Jakimaviciute also added caution to the use of AI and emphasised the importance of data quality. “While big data and AI are often touted as the innovations that can improve nearly every element of the pharma value chain, integration and data quality remain core focuses. AI requires high-quality data, and the more data AI receives, the more accurate and efficient it can become. However, if companies do not have full visibility into data quality, they cannot trust the results that AI generates,” she concluded.

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