Chapter 13: AI and Automation in Pharmacovigilance and CDM
Synopsis
Author
Mr. Vijay Anand Kada,
PharmD Scholar, Department of Pharmacy Practice, Pydah College of Pharmacy, Patavala, Kakinada, Andhra Pradesh, India
Abstract
The operational scope of pharmacovigilance and clinical data management is currently undergoing a seismic shift driven by the exponential growth of data and the advent of cognitive computing. The transition from manual, labor-intensive workflows to an automated ecosystem is powered by Artificial Intelligence (AI) and Machine Learning (ML). Natural Language Processing (NLP) is revolutionizing case intake by automating the extraction of medical concepts from unstructured text, thereby allowing human experts to focus on complex medical assessment rather than data entry. Parallel to this technological evolution is the regulatory shift toward Risk-Based Quality Management (RBQM), a methodology codified in ICH E6(R2) that replaces 100% source data verification with intelligent, targeted monitoring of critical data points. The rise of Real-World Evidence (RWE) derived from "Big Data" sources such as Electronic Health Records and claims databases allows for the detection of rare adverse events and the monitoring of diverse patient populations previously excluded from research. This convergence of advanced analytics and automated processing creates a continuous learning healthcare system capable of handling the volume and velocity of modern safety data.
Keywords: Artificial Intelligence (AI), Risk-Based Quality Management (RBQM), Real-World Evidence (RWE), Natural Language Processing (NLP), Cognitive Automation
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