Patient safety narratives are written by hand from structured case data — thousands of times per program — making narrative drafting one of the most repetitive writing tasks in pharma.
Every serious adverse event case needs a narrative: a structured prose account of the patient, the event, the treatment, and the outcome, assembled from data that already exists in structured form in the safety database. Writing them by hand — consistently, under reporting deadlines, at the volume a large program generates — consumes a striking share of safety and medical-writing capacity. Narrative generation is one of the most established uses of language technology in pharma precisely because the task is so structured: the facts are known, the format is conventional, and the volume is high.
How AI helps with narrative drafting
Narrative generation works from the structured case data — demographics, medical history, event terms, drug administration, outcomes — and composes it into the conventional narrative structure, applying an organization's preferred phrasing and formatting standards. Because the content comes from database fields rather than open-ended generation, this is among the most controllable applications of language technology: the facts are fixed, and the tool's job is accurate, consistent composition. Generated drafts also gain consistency across writers and across time, which reviewers and regulators read thousands of narratives against. The safety professional still owns the case: medical judgment about causality, the clinical coherence of the account, and any interpretation beyond the recorded facts remain human work. The draft removes the transcription layer — re-keying database fields into sentences — not the medical review.
What to evaluate before buying narrative generation
Fidelity to source data is the non-negotiable: every fact in the generated narrative must trace to a field in the case record, with nothing added, inferred, or smoothed over. Test with complex cases — multiple events, concomitant medications, ambiguous timelines — where composition is genuinely hard, and check how the tool represents missing data: an honest draft says information was not reported rather than papering over the gap. Review your organization's narrative conventions against the tool's output, since a draft that ignores house style creates editing work instead of removing it. Operationally, examine integration with your safety database and workflow — where drafts appear, how reviewer edits are captured, and what the audit trail records — and how the tool handles versions when a case is updated with follow-up information.
How teams typically get started
The standard pilot is retrospective and low-risk: generate narratives for a set of closed cases whose human-written versions already exist, and have experienced safety writers compare the two — factual accuracy first, then completeness, then how much editing the draft would have needed. Because the ground truth is unambiguous, this produces a clear quality read quickly. Teams that proceed typically deploy on routine cases first, keeping complex or medically sensitive cases with human drafters while confidence and conventions settle.
AI Use Cases That Address This Problem
Case Processing Automation
Frequently asked questions
Is narrative generation reliable enough for regulatory reporting?
Narrative generation from structured case data is one of the more mature applications of language technology in pharma, because the facts come from database fields rather than open generation. Reliability on your cases should still be verified retrospectively against narratives your team already wrote, and every generated narrative still goes through the same medical review as a human draft.
What happens with complicated cases?
Complex cases — multiple events, long medication lists, unclear sequences — are where tools differ most, so they belong at the center of your evaluation rather than its edges. Many teams route complex or sensitive cases to human drafters and use generation for the routine volume, shifting the boundary as measured quality allows.
Does this replace safety writers?
It changes what they spend time on. The repetitive composition work — turning database fields into conventional prose — is what generation removes; case assessment, medical coherence review, and judgment about what a case means remain with the safety team, which also reviews and approves every generated narrative.
How should we measure a narrative-generation pilot?
Retrospectively, against your own closed cases: factual accuracy field by field, honest handling of missing information, adherence to your narrative conventions, and the editing effort a draft would actually have needed. Closed cases give you unambiguous ground truth and zero exposure while you measure.
U.S. FDA went live with FAERS II adverse-event reporting powered by LifeSphere MultiVigilance
ArisGlobal announced the go-live of the U.S. FDA's Adverse Event Reporting System (FAERS II), an electronic safety-reporting platform powered by its LifeSphere MultiVigilance software — the federal drug-safety regulator running adverse-event intake on ArisGlobal technology as part of the FDA's technology modernization efforts.