Industry Perspective on Artificial Intelligence/Machine Learning in Pharmacovigilance

Abstract

TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC) surveys on the use of intelligent automation in pharmacovigilance processes. MCs increased the number and extent of implementation of intelligent automation solutions throughout Individual Case Safety Report (ICSR) processing, especially with rule-based automations such as robotic process automation, lookups, and workflows, moving from planning to piloting to implementation over the 3 survey years. Companies remain highly interested in other technologies such as machine learning (ML) and artificial intelligence, which can deliver a human-like interpretation of data and decision making rather than just automating tasks. Intelligent automation solutions are usually used in combination with more than one technology being used simultaneously for the same ICSR process step. Challenges to implementing intelligent automation solutions include finding/having appropriate training data for ML models and the need for harmonized regulatory guidance.

Fig. 1

Changes in TransCelerate’s Technology Survey from 2020 to 2021. In 2020 and 2021, TransCelerate member companies were asked to assess their activities in the adoption of intelligent automation technologies for the ICSR management process based on the planning, piloting, and implementation phases. ICSR management processes are represented on the y-axis and automation technologies used in the survey are represented on the x-axis. Results on the x-axis are grouped by the planning, pilot, and production phases. Blue indicates fewer responses when 2021 results were compared with 2020 results, and orange indicates more responses in the 2021 survey compared with 2020. Deeper/more intense colors indicate more difference in responses in 2021 compared with 2020. Response values ranged from − 14% (darkest blue) to + 35% (darkest orange), with white indicating 0% change. ICSR Individual Case Safety Report, QC quality check, RPA robotic process automation, ML machine learning, OCR optical character recognition, NLP natural language processing, NLG natural language generation

Fig. 2

Heatmap of effort-benefit-risk associated with the use of intelligent automation in ICSR processes. In the 2021 survey, the member companies were asked to assess, for each activity in the ICSR management, what risk, effort, and benefit is associated with the implementation of IA. This matrix displays the averages of process step data displayed in Table 1 of the associated effort, benefit, and risk aggregated for the three main process blocks (intake, case processing and case reporting). The selected color schema indicates high values in red and low values in blue. The higher or lower the response was, the more intense the color is shown in the matrix. ICSR Individual Case Safety Report, IA intelligent automation

Fig. 3

TransCelerate’s Technology Survey Results 2021. In 2021, TransCelerate member companies were asked to assess their activities in the adoption of intelligent automation technologies for the ICSR management process based on the planning, piloting, and implementation phases. ICSR management processes are represented on the y-axis and automation technologies used in the survey are represented on the x-axis. Results on the x-axis are grouped by the planning, pilot, and production phases. The more a technology is used, the more intense the color is displayed in the matrix. Response values range from 0% (white) to 61% (darkest orange). QC quality check, RPA robotic process automation, ML machine learning, OCR optical character recognition, NLP natural language processing, NLG natural language generation