Adverse event review is the most time-critical activity in clinical safety monitoring, and the consequences of delays or missed signals are severe. Safety reviewers working under expedited reporting timelines need to rapidly identify cases that meet regulatory reporting criteria, assess signal patterns across the case series, and prepare clinical narratives that accurately characterise the safety profile. When adverse event data is linked to ontological concept identifiers, every one of these tasks becomes faster and more reliable.
Beyond MedDRA Coding
MedDRA provides a standardised coding hierarchy for adverse events, and coding to MedDRA is a regulatory requirement. Its limitation as an analytical tool is that it was designed primarily for regulatory reporting rather than clinical reasoning: the hierarchy reflects regulatory grouping conventions that do not always align with clinical or mechanistic relationships. Ontological enrichment supplements MedDRA coding by linking each adverse event to a broader clinical knowledge graph: the disease mechanisms it may represent, the organ systems it involves, the drug classes associated with similar events in the literature, and the patient factors that modify its probability. This richer representation enables safety queries that MedDRA coding alone cannot support.
Semantic Queries for Safety Signal Detection
With ontologically linked adverse event data, a safety reviewer can ask: "show me all cases with any renal adverse event in patients who also received an ACE inhibitor" — where "renal adverse event" expands to include all MedDRA terms within the renal and urinary system organ class, and "ACE inhibitor" expands to include all drugs in that pharmacological class in the study's concomitant medication data. This query, which might take hours to construct and execute manually, runs in seconds against an ontologically indexed safety database.
Narrative Generation Support
Ontologically linked adverse event data also supports faster narrative generation. When preparing a case series narrative for a periodic safety update report, a reviewer needs to characterise the population, the events, their severity, their outcomes, and the time-to-onset distribution. An AI assistant grounded in the ontologically linked safety database can generate a first-pass narrative that accurately represents these characteristics, drawn from structured data rather than from pattern-matching over text, substantially reducing the time from data analysis to narrative completion.
