01 PROBLEM STATEMENT

Safety Signals are Discovered too Late in the Case Accumulation Curve

A pharma company managing post-market pharmacovigilance across multiple products found that safety signals were identified only after significant case accumulation often months after the signal was statistically detectable. MedWatch reports and case narratives from Argus Safety or Oracle AERS arrived in PDF and unstructured text. Pharmacovigilance physicians manually reviewed cases looking for signal patterns, but this reactive approach meant signals were flagged only after they became obvious not when they first emerged. Regulatory agencies were increasingly requiring proactive AI-assisted PV, and the company had no systematic signal detection capability. The cost of a missed safety signal legal liability, market withdrawal, and patient harm was unacceptable. PV operational cost was rising because every case required manual triage and CIOMS report generation consumed physician time.

02 current challenges

What the Pharma Company was Struggling with

03 SOLUTION OVERVIEW

STAR’s Approach – AINE Adverse Event Signal Detector

STAR Systems deployed the AINE Adverse Event Signal Detector, ingesting MedWatch reports and case narratives from Argus Safety or Oracle AERS API. MedWatch, EudraVigilance, and VigiBase literature feeds are integrated. MedDRA API standardizes adverse event terminology automatically. The system flags safety signals earlier than manual review detecting patterns in case accumulation that warrant pharmacovigilance physician investigation. Regulatory submission API integration for ICSR electronic filing. CFR 21 Part 11 compliant audit trail on IBM PowerVS for inspection readiness. Pharmacovigilance physician reviews all AI flagged signals before regulatory action no auto-submission. STAR provides weekly signal volume report and recalibrates the model quarterly on client case data.

04 WORKFLOW PROCESS

Step-By-Step: How Safety Signals are Detected Proactively

Step 1 – Case Ingestion: MedWatch reports and case narratives ingested from Argus Safety or Oracle AERS API. MedDRA standardization applied automatically.

Step 2 – Literature Feed: MedWatch, EudraVigilance, and VigiBase literature feeds integrated. Global adverse event data incorporated for signal context.

Step 3 – Signal Detection: AI flags safety signals earlier in case accumulation curve. Patterns detected before they become statistically obvious to manual review.

Step 4 – Physician Triage: Pharmacovigilance physician reviews AI-flagged signals. Decides whether signal warrants regulatory action or continued monitoring.

Step 5 – CIOMS Generation: Structured CIOMS report auto-generated for physician review. PV operational cost reduced – AI handles case triage and report drafting.

Step 6 – Audit Trail: CFR 21 Part 11 compliant audit logging on IBM PowerVS. Every AI-flagged signal and physician decision captured for FDA inspection

05 KEY FEATURES

What the Detector Does

Proactive Signal Detection:

AI flags safety signals earlier in case accumulation curve than manual review. Patterns detected when they first emerge – not months later after obvious

Argus Safety and Oracle AERS Integration :

MedWatch reports and case narratives ingested from Argus Safety or Oracle AERS API automatically. No manual data entry or PDF extraction.

Global Literature Feed Integration:

MedWatch, EudraVigilance, and VigiBase literature feeds incorporated. Global adverse event context used for signal detection accuracy.

MedDRA Term Standardization:

Adverse event terminology standardized via MedDRA API automatically. Enables cross-product and cross-geography signal detection consistency.

Structured CIOMS Report Generation:

CIOMS reports auto drafted for physician review. PV operational cost reduced – AI handles case triage and report drafting work.

CFR 21 Part 11 Audit Trail:

Complete audit logging on IBM PowerVS for FDA inspection readiness. Every AI-flagged signal and physician decision captured.

06 BUSINESS OUTCOMES

What Changes After Go-Live

07 REAL-WORLD SCENARIO

A Day in the Life – Before and After

08 ROI AND VALUE JUSTIFICATION

Why this Numbers Work

09 NEXT STEPS