Authoring periodic safety reports is a heavy lift
Category: Pharmacovigilance & Safety
Aggregate reports like PSURs and DSURs pull data from many sources on fixed schedules, and compiling and writing them consumes scarce specialist time.
Periodic aggregate safety reports — such as PSURs/PBRERs and DSURs — summarize a product's safety over a defined period and are due on fixed regulatory schedules. Compiling them means pulling case data, exposure estimates, and prior assessments from several sources, tabulating them consistently, and writing a coherent benefit-risk narrative, which consumes scarce medical-writing and safety-science time. AI approaches help by assembling and tabulating the inputs and drafting routine sections so specialists can focus on interpretation and the benefit-risk assessment.
How AI helps produce periodic aggregate safety reports
A large part of aggregate reporting is assembly: gathering the cases, line listings, summary tabulations, and exposure figures for the reporting period, formatting them consistently, and carrying forward standing information about the product. AI and automation help here by pulling data from the safety database and related sources, generating the standard tables, and drafting the descriptive, boilerplate, and data-summary sections of the document from that content. Language models can turn structured inputs into readable draft prose and keep terminology consistent across a long report. What these tools do not do is form the benefit-risk judgment. Assembling data and drafting routine text frees specialists to concentrate on the analytical and interpretive sections — signal evaluation, benefit-risk conclusions — that require medical and scientific reasoning. Drafts must be checked against the source data because generated text can be fluent yet wrong, and a qualified author owns the final report and its conclusions.
What to evaluate before buying aggregate-reporting AI
Accuracy and traceability of the assembled data come first: a report is a regulatory document, so every table and figure must be correct and reconcilable to the source, and every drafted statement must be verifiable against the underlying data. Ask how a tool sources and reconciles its numbers, how it handles the specific report types and templates you produce, and how it keeps a generated narrative anchored to real content rather than plausible-sounding filler. Because output feeds a regulated submission, examine validation, version control, and audit trails, and how human edits are captured. Weigh workflow fit too — how the tool connects to your safety database and document systems, whether it supports your templates and regional expectations, and whether it genuinely reduces effort once the necessary review of generated content is accounted for.
How teams typically get started
A sensible entry point is using the tool to assemble the data and draft the routine sections for a single upcoming report while authors write it as usual, then comparing the drafted tables and text against the finished document. That shows how much of the assembly and boilerplate it handles reliably, where generated content needs correction, and whether the time saved survives the review the output requires — all before it is relied on for a submission-critical report.
AI Use Cases That Address This Problem
- Signal Detection & Aggregate Reporting
Frequently asked questions
What are periodic aggregate safety reports?
They are scheduled summaries of a product's safety over a defined period — such as PSURs/PBRERs for marketed products and DSURs for products in development — that pull together case data, exposure, and prior assessments into a benefit-risk narrative for regulators. They are due on fixed timelines, which makes their recurring authoring workload significant.
How does AI help with aggregate report authoring?
It automates much of the assembly — gathering cases, generating standard tables and line listings, and pulling exposure and standing product information — and can draft the routine descriptive and data-summary sections. That frees specialists to focus on signal evaluation and the benefit-risk assessment, which require human medical and scientific judgment.
Can AI write the benefit-risk assessment itself?
It should not be relied on to. Drafting boilerplate and summarizing data is very different from forming a benefit-risk conclusion, which is a medical and scientific judgment a qualified author owns. Generated text can read well while being inaccurate, so every drafted section must be verified against the source data before it enters a report.
What should we ask an aggregate-reporting vendor?
Ask how the tool sources and reconciles the data in its tables, whether every drafted statement traces back to verifiable source content, which report types and templates it supports, how validation, version control, and audit trails work, how it integrates with your safety database and document systems, and whether it saves time once review of the generated content is counted.