Clinical Safety and Pharmacovigilance, GSK, 980 Great West Road, Brentford, Middlesex, TW8 9GS, UK.
Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK.
Drug Saf. 2021 Feb;44(2):125-132. doi: 10.1007/s40264-020-01001-7. Epub 2020 Oct 7.
Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre-licensure. Reasons are provided on why adoption is challenging and we also provide a perspective on changes needed to accelerate adoption, and thereby improve patient safety. Last, we make clear that while technologies could be superimposed on existing pharmacovigilance processes for incremental improvements, these great societal advances in data and technology also provide us with a timely opportunity to reconsider everything we do in pharmacovigilance operations to maximise the benefit of these advances.
尽管在电子数据捕获、存储、链接和分析方面取得了巨大的技术进步，但对常规药物警戒的影响有限。我们讨论了人工智能、机器学习和自动化在药物警戒生命周期中的应用的新兴研究，包括许可前。提供了为什么采用具有挑战性的原因，我们还提供了对加速采用所需的更改的观点，从而提高患者安全性。最后，我们明确表示，虽然可以将技术叠加到现有的药物警戒流程上以实现渐进式改进，但这些在数据和技术方面的巨大社会进步也为我们提供了一个及时的机会，重新考虑我们在药物警戒操作中所做的一切，以最大限度地发挥这些进步的优势。
