AI and big data will continue to disrupt pharmaceutical sector, according to healthcare industry professionals surveyed by GlobalData
AI and big data will continue to disrupt pharmaceutical sector, according to healthcare industry professionals surveyed by GlobalData
- Artificial intelligence (AI) and big data will play a major role in optimizing pharmaceutical drug discovery and development process, as indicated by 24% and 25% of global healthcare industry professionals, respectively
- 23% of the surveyed healthcare industry professionals confirmed that their companies were currently using in AI to enhance drug discovery and development process with 28% expecting to continue to implement/start using this technology in the next two years
- 27% of survey respondents believed that big data/analytics would play a major role in optimizing marketing and sales
- More than 30% of global healthcare industry professionals were currently using big data/analytics in marketing and sales process and expected to use it in the next two years
- The implementation of AI in sales and marketing is believed to reach a bigger uptake in the next few years, up by 4% from a current use rate (18%)
The COVID-19 pandemic has given businesses an unprecedented opportunity to implement technology-fueled changes to the way they operate. AI and big data/analytics have been identified by healthcare industry professionals as the top technologies that will transform pharmaceutical drug discovery and development processes, as well as marketing and sales, according to a survey by GlobalData, a leading data and analytics company.
In a GlobalData’s latest report, ‘Smart Pharma’, healthcare industry professionals revealed that 28% of companies will be using AI and big data/analytics to optimize drug discovery and development processes in the next two years, while 32% would be relying on big data/analytics to streamline sales and marketing.
Urte Jakimaviciute, Senior Director of Market Research at GlobalData, comments: “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 organize and streamline information is a constant challenge.”
AI and big data are linked in healthcare. GlobalData notes that use of the former will continue to grow rapidly – especially when considering the amount of data that can now be mined from patient records and registries, real-world evidence, sales and marketing, and connected devices. It can also be used to design treatment plans, develop drugs, or improve clinical trial outcomes.
Jakimaviciute continues: “In the drug discovery and development process, the effective analysis of big data can enhance research and development (R&D) productivity and effectiveness through early and more targeted problem solving and decision-making mechanisms. By supporting the analysis of big data, AI has the potential to rapidly accelerate R&D timelines, making drug development cheaper and faster.”
The COVID-19 pandemic gave a significant boost to data-generating areas of healthcare such as wearables, electronic healthcare records, remote patient monitoring and mobile apps. The increasing use of social and digital media tools among the physicians and patients have also contributed to increasing volumes and variety of information that the companies can access, collect, and analyze.
Jakimaviciute adds: “By obtaining data from diverse sources and leveraging the power of data analytics, pharma companies can get better insights into end-users’ behavior patterns, response to marketing campaigns, product performance, and upcoming industry trends which if comprehensively analyzed and interpreted can result in improved marketing and sales.
“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 remains 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.“