Digital Healthcare Pharmacovigilance in India: Adverse-Event Reporting, Data Interoperability, and AI-Assisted Safety Signal Detection

The field of pharmacovigilance deals with timely data acquisition, coding, evaluation, and analysis of adverse drug reactions (ADRs) to medicines, vaccines, and medicinal products. The digital environment in India is evolving, and PvPI works through adverse reaction monitoring centers, while Indian Pharmacopoeia Commission has developed Adverse Drug Monitoring System (ADRMS) web portal in August 2024. While digital reporting can avoid bureaucratic hurdles, the worthiness of such platforms would be determined by the completeness of reports, consistency in language use, traceability, handling of duplicate reports, and the process that leads from the report to the evaluation stage by experts. This narrative review will cover the Indian reporting environment, the technical function of Individual Case Safety Reports (ICSRs) and E2B(R3), and possible contributions of machine-assisted technologies to case acquisition and safety signal prioritisation. It does not intend to compare AI models or provide a survey of clinical artificial intelligence. Rather, it reviews how the quality of digital reporting and data standards impact the safe implementation of such automation. The academic literature suggests that natural language processing and machine learning techniques can be used for extraction, coding, duplicate detection, and prioritization; however, the effectiveness of their performance depends on the quality of the source and context of use. Spontaneous report information continues to be hypothesis generating and does not prove causality on its own. A synthesis specific to the particular workflow process, where each automation step is coupled with validation needs, human responsibility, and metrics of process performance, is suggested. The analysis results in the conclusion that India’s best defense is to build structured data capturing and interoperability and only then consider automation.