Automation in signal management in pharmacovigilance—an insight. Issue 4 (18th December 2020)

• Record Type: Journal Article
• Authors: Wadhwa, Diksha; Kumar, Keshav; Batra, Sonali; Sharma, Sumit
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 4(2021)
• Publication Date: 2020-12-18
• Subjects: automation -- signal management -- regulatory -- VigiBase -- eudravigilance -- pharmacovigilance; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa363
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Drugs are the imperial part of modern society, but along with their therapeutic effects, drugs can also cause adverse effects, which can be mild to morbid. Pharmacovigilance is the process of collection, detection, assessment, monitoring and prevention of adverse drug events in both clinical trials as well as in the post-marketing phase. The recent trends in increasing unknown adverse events, known as signals, have raised the need to develop an ideal system for monitoring and detecting the potential signals timely. The process of signal management comprises of techniques to identify individual case safety reports systematically. Automated signal detection is highly based upon the data mining of the spontaneous reporting system such as reports from health care professional, observational studies, medical literature or from social media. If a signal is not managed properly, it can become an identical risk associated with the drug which can be hazardous for the patient safety and may have fatal outcomes which may impact health care system adversely. Once a signal is detected quantitatively, it can be further processed by the signal management team for the qualitative analysis and further evaluations. The main components of automated signal detection are data extraction, data acquisition, data selection, and data analysis and data evaluation. This system must be developed in the correct format and context, which eventually emphasizes the quality of data collected and leads to the optimal decision-making based upon the scientific evaluation.