Exponential growth of health-related data collected by digital tools is a reality within pharmaceutical and medical device research and development. Data generated through digital tools may be categorized as relevant to efficacy and/or safety. The enormity of these data requires the adoption of new approaches for processing and evaluation. Recognition of patterns within the safety data is vital for sponsors seeking regulatory approval for their new products. Nontraditional data sources may contain relevant safety information; early evaluation of these data will help to determine the product safety profile. Advanced technologies have allowed the development of digital tools to screen these data, which in some situations are classified as software as a medical devices and subject to clinical evaluation and post-marketing surveillance. Artificial intelligence may help to reduce or even eliminate noise from within these data, allowing safety experts to focus on the most pertinent evidence. We propose a data typology and provide considerations on how to define adverse events within different types of data, even where no human reporter exists. Proposals are made for the automation of screening processes. We consider validation aspects to support solutions that are proven to produce reliable results, and to deliver trusted outputs to stakeholders.
Keywords: adverse event; automation; computer-system validation; digital data; information systems; pharmacovigilance; technology; typology.
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