- Document AI can read adverse event reports from email, PDF, scanned forms and handwriting, and return the case fields your safety database expects.
- An assistant can suggest coding terms from the verbatim and your coding conventions, with the source text beside each suggestion, and your coders accept or change every term.
- The platform can search case narratives with citations and group similar patterns into a brief for signal review, alongside the statistics your safety database already runs.
- A person stays in every decision: case processors confirm the case, and a medical reviewer assesses seriousness and causality, decides on any signal and signs.
Your reporting clock starts before anyone reads the report
Adverse event reports reach you from call centers, sales teams, partners, literature, patient programs and forms, and many arrive as scans or free text. Your case processors key the same fields by hand before a medical reviewer ever sees the case, while the reporting clock runs.
Coding is the next bottleneck: two coders can map the same verbatim to different terms, and a missed or duplicated case is a quality finding waiting to happen. The earliest hint of a safety issue often sits in the narrative, not the coded fields, and your safety physicians need a way to read across hundreds of narratives without losing the source of each finding.
How pharmacovigilance case intake works
Step 1: Read and extract the case
H2O AI Super Agent moves each report through intake, checks and coding, and hands a draft case to your team. Document AI extractors read each source with intelligent character recognition, including handwriting, tables and stamps. You define the case fields as a schema: reporter, patient, suspect product, event verbatim, dates and seriousness criteria. Extractor output carries no confidence score of its own, so your processor reviews every extracted case before it is saved.
Step 2: Check for duplicates
The agent compares the new case with existing cases to flag a possible duplicate before it moves forward.
Step 3: Suggest coding terms
An assistant can suggest coding terms for each verbatim, drawing on your coding conventions and the cases your team has already coded, and shows the source text beside each suggestion. Your coder accepts or changes every term, and your licensed coding dictionary stays the reference.
Step 4: Search narratives for signals
The platform indexes case narratives and answers questions such as which cases describe this event after a dose change, with each answer citing the cases it came from. It groups narratives that describe similar patterns, even when they were coded differently, and drafts a signal review brief that runs beside the statistics in your safety database.
Step 5: Review and sign
Your case processor confirms the case, and your medical reviewer assesses seriousness, expectedness and causality and signs. The agent does not submit anything to a health authority.
No claim of formal validation
We make no claim of GxP, 21 CFR Part 11 or FDA validation. The platform supports validated workflows; the validation is yours.