Artificial Intelligence and the Future of the Drug Safety Professional

The healthcare industry, and specifically the pharmacovigilance industry, recognizes the need to support the increasing amount of data received from individual case safety reports (ICSRs). To cope with this increase, more healthcare and qualified professionals are required to capture and evaluate the data. To address the evolving landscape, it will be necessary to embrace assistive technologies such as artificial intelligence (AI) at scale. AI in the field of pharmacovigilance will possibly result in the transformation of the drug safety (DS) professional’s daily work life and their career development. Celgene’s Global Drug Safety and Risk Management (GDSRM) function has established a series of work activities to drive innovation across the pharmacovigilance value chain (Celgene Chrysalis Fact Sheet. https://www.celgene.com/newsroom/media-library/chrysalis-fact-sheet/, 2018). The development of AI in pharmacovigilance raises questions about the possible changes in DS professionals’ lives, who may find themselves curious about their future roles in a workplace assisted by AI. We discuss the current state of pharmacovigilance and the DS professional, AI in pharmacovigilance and the potential skillsets a DS professional may require when working with AI. We also describe the results of research conducted at Celgene GDSRM. The objective of the research was to understand the thoughts of pharmacovigilance professionals about their jobs. These results are provided in the form of aggregated responses to interview questions based on a 12-part questionnaire [see the Electronic Supplementary Material (ESM)]. A sample of six DS professionals representing various areas of pharmacovigilance operations were asked a range of questions about their backgrounds, current roles and future expectations. The DS professionals interviewed were, overall, enthusiastic about their job roles potentially changing with AI enhancements. Interviewees suggested that AI would allow for pharmacovigilance resources, time, and skills to shift the work from a volume-based to a value-based focus. The results suggest that pharmacovigilance professionals wish to use their qualifications, skillsets and experience in work that provides more value for their efforts. Machine learning algorithms have the potential to enhance DS professionals’ decision-making processes and support more efficient and accurate case processing.