Author: Mrs. Neelam Singh
Volume: 01
First Online: 31 August 2024
Pages: 187-210
DOI:

Safety data generation is a fundamental process in pharmacovigilance, involving the collection, analysis, and interpretation of data related to the safety of drugs and medical products. This process is essential for identifying, evaluating, and mitigating risks associated with the use of medications. Safety data is generated through various sources, including clinical trials, post-marketing surveillance, spontaneous adverse drug reaction (ADR) reporting, and real-world evidence from healthcare settings. Advanced technologies such as electronic health records (EHRs), data mining algorithms, and artificial intelligence (AI) enhance the ability to detect safety signals and assess potential risks. The continuous generation and analysis of safety data support informed decision-making by regulatory authorities, healthcare providers, and pharmaceutical companies, ultimately ensuring the protection of public health. Robust safety data generation is critical for maintaining drug safety throughout the entire lifecycle of a product, from development to post-market use.