Rising case volume outpaces manual intake and processing capacity, putting compliance timelines at risk.
As products and reporting channels multiply, individual case safety report (ICSR) volume can outpace the capacity to intake, code, and process cases manually — putting regulatory timelines and quality at risk. AI-driven case processing automates intake, data extraction, and coding so safety teams can clear backlog and focus on assessment.
How AI helps clear an adverse-event case backlog
Case-processing AI works on the individual case safety reports (ICSRs) that arrive from spontaneous reports, call centers, patient support programs, and other channels. It reads the incoming source documents — often a mix of forms, emails, and free text — and extracts the structured fields a case requires: patient and reporter details, suspect products, events, dates, and narrative. Some tools also propose MedDRA coding for events and indications, and draft an initial narrative for a safety professional to review. The intent is to remove keystrokes and lookups from high-volume, repetitive intake so trained staff spend their time on medical assessment rather than transcription. The AI extracts and suggests; a qualified case processor or safety physician still confirms every field, adjusts the coding, and remains accountable for what enters the safety database and what is reported to regulators.
What to evaluate before buying case-processing AI
Extraction accuracy on your own source documents is the deciding factor, and it varies with document type and language, so ask to test a tool on a representative sample of your real intake — messy PDFs and free-text emails, not clean sample forms. Probe how it handles the fields that are hardest to parse reliably, such as causality-relevant narrative detail, dates, and product identification, and how it flags low-confidence extractions for human review rather than passing them through silently. Because pharmacovigilance is regulated, examine the audit trail, validation documentation, and how the system fits GxP and data-integrity expectations. Also weigh workflow fit: how the tool connects to your safety database and case-management system, how review and correction are captured, and whether it can learn from the corrections your team makes.
How teams typically get started
A low-risk entry point is running the tool in parallel on a defined slice of incoming cases — for example, one channel or one product — while the existing process continues, then comparing the extracted and coded output against what the team produces manually. That measures real accuracy on your documents, shows where human review is most needed, and builds an internal evidence base before the tool sits in the compliance-critical path.
AI Use Cases That Address This Problem
Case Processing Automation
Signal Detection & Aggregate Reporting
Frequently asked questions
How does AI help with a growing adverse-event case backlog?
It automates the repetitive parts of individual case safety report processing — reading source documents, extracting the required fields, and proposing MedDRA coding and a draft narrative — so trained staff can focus on medical assessment instead of data entry. The tool suggests; safety professionals verify every case and stay accountable for what is recorded and reported.
Can AI decide causality or make reporting decisions on its own?
No, and it should not be asked to. Causality assessment and reporting decisions are medical and regulatory judgments that qualified safety professionals own. Case-processing AI speeds intake and structuring so those experts have clean, complete cases to assess faster; accountability for the assessment stays with people.
Is automated case processing compliant with pharmacovigilance regulations?
It can be, but compliance depends on how the system is validated, documented, and controlled, not on the algorithm alone. Look for tools built for GxP environments with full audit trails, human review of extracted data, and validation evidence, and confirm the workflow keeps a qualified person accountable for each reported case.
What should we ask a case-processing vendor in a demo?
Ask them to run your real, messy source documents — not curated samples — and show extraction and coding accuracy, how low-confidence fields are flagged for review, how the audit trail and validation documentation work, how it integrates with your safety database, and whether it improves from your team's corrections over time.
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.