New Technologies in Electronic Pharmacovigilance Systems for Marketing Authorisation Holders

Emergent ways to obtain information on the safety of medicinal products give relevance to the implementation of new information technologies into big data analysis in pharmacovigilance.
The aim of the study was to systematise data on the use of information technologies for pharmacovigilance process automation and identify problems and limitations that may arise when introducing the technologies.
Materials and Methods: the authors analysed literature on the subject matter and the practical experience of Flex Databases with the development of the electronic system for pharmacovigilance data processing designed for marketing authorisation holders.
Results: using the electronic pharmacovigilance system by Flex Databases as an example, the authors demonstrated the feasibility of basic, robotic, and cognitive automation and artificial intelligence technologies for data processing. Automation technologies allow the users to streamline information entry, process and analyse data, create reports and metrics, timely submit the reports and metrics to regulatory authorities, and manage risks and safety signals; they also help specialists in decision making. Artificial intelligence technologies (a wide range of technologies including machine learning, neural networks, and automatic natural language processing) are used to collect safety reports, amongst other things, through real-world clinical data analysis; prepare summary reports; and manage risks and safety signals. Moreover, human involvement is necessary only at certain stages, particularly to process the data on exceptional cases and to analyse the results in an expert capacity.
Conclusions: there is demand for process automation and artificial intelligence technologies at all stages of collection and analysis of pharmacovigilance information, from receiving a safety report to submitting it to regulatory authorities and identifying a safety signal. The deployment of the technologies within pharmacovigilance systems helps to increase the amount of data processed, among other things as a result of the inclusion of real-world clinical data into the search process. As the technologies reduce the degree of human involvement into routine processes of data collection, entry, verification, and analysis, the likelihood of errors reduces as well, whereas the quality and accuracy of the obtained results improve.

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