Editorial: Computational methods and systems to support decision making in pharmacovigilance

Abstract

The World Health Organization defines pharmacovigilance (PV) as “the science and activities related to the detection, assessment, understanding, and prevention of adverse events or any drug-related problem.” (World Health Organization, 2023) Government agencies, clinical institutions, the pharmaceutical industry, and other entities manage PV programs as vital safeguards for supporting the early detection and analysis of safety signals. These rely primarily on expert judgment and concrete business practices specific to each organization (Ball and Dal Pan, 2022). Computational methods and decision-support systems may enable more timely, consistent, and comprehensive analysis and processing of real-world data (RWD) and may free up time for domain experts to focus on higher-value contributions. As a positive side-effect, their development can help standardize business practices by specifying the steps human reviewers follow. In this Research Topic issue, we primarily invited papers that assess contributions to PV which may improve efficiency in established business practices, minimize manual effort, and maximize the quality of human decision-making by enhancing existing processes transparently. We also welcomed case studies of method implementation and perspectives that might significantly contribute to the domain. Primary use cases in PV, such as case processing and prioritization or signal detection and evaluation, incorporate several steps that the complete decision-support system should augment. As data, user-related, and other challenges of the entire workflow vary, most efforts, to this date, have delivered solutions addressing more narrowly defined tasks. Historically, computational methods were initially proposed and deployed to enable signal detection and analysis in large databases using only structured data. More recently, several studies have presented methods for processing unstructured free texts, prioritizing case reports for clinical and regulatory review, and improving data quality in spontaneous reporting systems. For example, Painter et al. conducted ameta-analysis of a recent scoping reviewonmachine learning in PV (Kompa et al., 2022) and found that the pharmaceutical industrymainly appliedmachine learning to processRWDand socialmedia. They also highlighted the need to develop consistent systems that can learn and incorporate humanin-the-loop mechanisms and called for best practices for adopting and validating these systems. To build effective solutions, existing workflows and business practices must be understood. These are rarely documented in stepwise algorithmic forms that can easily be translated into OPEN ACCESS