Pharmacovigilance case page
Healthcare
Modernising pharmacovigilance operations with AI
“The biggest shift was operational. We reduced manual burden across intake and review without compromising compliance, which gave the team more capacity for high-value safety work.”
VP, Drug Safety Operations
CLIENT
European Specialty Pharmaceutical Company (NDA)
INDUSTRY
Healthcare
HEADQUARTERS
Europe (NDA)
USE CASES
AI Case Intake Engine
MedDRA Coding Assistance
Workflow Monitoring Dashboard
Automated Deadline Alerting
Est. 45-55%
Reduction in manual intake workload
Est. $1-2M
Annual savings
Company facts
Organisation scale
Approx. 25 pharmacovigilance specialists; approx. 14,000 ICSRs processed annually across 30+ global markets
Operating environment
Multi-market specialty pharma, EU-regulated, GxP-compliant validated case management systems; AI layer operates at data exchange boundary
Primary challenge
Manual adverse event processing consuming specialist capacity, with no integrated operational layer across intake, coding, and submission
An introduction to European Specialty Pharmaceutical Company (NDA)
This client manages commercial pharmaceutical products across multiple European markets, with pharmacovigilance obligations spanning an established portfolio and several pipeline compounds. Safety case volumes had grown substantially over the preceding years, driven by expanded market presence and increased reporting requirements, and the manual workflows built for a smaller operation were no longer adequate at scale.
Individual case processing was handled largely by hand: intake, assessment, MedDRA coding, and submission preparation consumed a disproportionate share of specialist time. Safety professionals with deep regulatory expertise were spending the majority of their working hours on administrative processing rather than clinical judgement.
The company needed operational capacity, not more headcount, and not a new regulatory system. They needed the existing team and infrastructure to work at a scale the business had already grown into.
ARGUS Operational Audit
AItillery analysed case intake processes, MedDRA coding workflows, team workload distribution, and regulatory reporting timelines.
The audit surfaced two distinct operational problems: a manual case processing burden consuming safety specialist capacity, and a fragmented system architecture that prevented scalable oversight. ARGUS ranked all identified opportunities by estimated efficiency gain and compliance risk reduction, and produced a sequenced implementation plan. The two challenges below represent the highest-priority interventions, where operational impact was highest and implementation risk was contained.
Challenge One: Manual case processing in a high-volume safety environment
The Problem
Adverse event intake was handled manually: safety staff transcribed information from source documents (emails, literature, patient reports) into the case management system by hand. For a team processing thousands of cases annually, this alone consumed a disproportionate share of specialist time.
MedDRA coding was labour-intensive and inconsistent. Specialists selected codes manually from a hierarchy of over 80,000 terms, with no AI-assisted suggestion layer to accelerate or standardise the process. Coding quality varied by individual, creating downstream QC overhead.
Workflow monitoring was fragmented. There was no real-time view of case queue status, team workload distribution, or deadline proximity. Supervisors relied on manual check-ins to understand where bottlenecks were forming.
QC review cycles were extensive because errors introduced at intake and coding stages propagated through to submission preparation, requiring multiple rounds of correction that added days to processing timelines.
The Solution
AItillery implemented an AI Safety Operations Platform with four components. The platform was designed to operate at the data exchange boundary of the client's validated case management system: ingesting structured exports and returning AI-generated recommendations that specialists action within the validated environment, preserving GxP compliance and Computer System Validation integrity throughout.
AI Case Intake Engine: NLP-driven extraction of structured adverse event data from unstructured source documents, auto-populating case fields and reducing manual transcription to a review-and-confirm workflow rather than a data entry task.
Medical Coding Assistance: AI-generated MedDRA coding suggestions ranked by confidence score and presented to the specialist for review, reducing average coding time per case while retaining full human accountability for every submitted code.
Workflow Monitoring: a live case queue dashboard surfacing workload distribution, processing velocity, and regulatory deadline proximity across the full team, replacing manual supervisor check-ins with continuous operational visibility.
QC Assistance: automated pre-submission checks flagging likely inconsistencies between intake data, coding selections, and submission fields, shifting error detection upstream and reducing the volume of corrections required at the final review stage.
The Result
The new operating model reduced manual intake workload by an estimated 45–55%, accelerated ICSR processing by an estimated 35–45%, and introduced AI-assisted classification across 70–85% of intake volume. The remaining volume comprised complex multi-source cases, literature-derived reports, and edge-case presentations requiring full specialist handling without AI suggestion. Human review and regulatory sign-off were retained across all volume: the system surfaces structured recommendations, not autonomous decisions.
Head of Pharmacovigilance
Challenge Two: Connecting fragmented safety operations into one operational layer
The Problem
Safety data was distributed across disconnected systems: adverse event intake lived in one tool, MedDRA coding workflows in another, and regulatory submission tracking in a third. No single view of case status existed across the full processing lifecycle.
The absence of an integrated operational layer meant that efficiency gains from Challenge One automation remained partially trapped: faster case processing still fed into a fragmented downstream environment that created coordination overhead at every handoff point.
Reporting timelines depended on manual coordination between systems. When a deadline was at risk, there was no automated escalation or visibility mechanism to surface the risk before it became a compliance exposure.
The Solution
AItillery built an Operational Intelligence Layer connecting the AI Safety Operations Platform from Challenge One with the downstream reporting and compliance infrastructure:
System Integration Layer: structured data flows connecting the intake engine, coding workflow, and regulatory submission tools into a single coordinated environment, eliminating the manual handoff steps that created throughput bottlenecks between Challenge One outputs and final submissions.
Automated Deadline Alerting: rules-based escalation logic triggering notifications when case processing timelines approached regulatory submission thresholds, replacing manual tracking with proactive risk visibility.
Unified Case Status Dashboard: a single operational view aggregating intake, coding, QC, and submission status across all active cases, giving the safety team real-time visibility into pipeline position and deadline exposure.
The Result
Reduced outsourcing dependency and improved operational throughput generated estimated annual savings of $1.0M–$2.0M, with three contributing components. CRO case processing reduction was the largest: the client had been outsourcing approximately 25–35% of annual case volume to external CROs at a blended rate of $150–$270 per case, reflecting the complexity profile of their specialty pharma portfolio.
Repatriating that volume as internal capacity was freed from manual intake work generated $525k–$1.3M in direct outsourcing cost reduction. Reduced overtime across the specialist team contributed an estimated $200k–$400k. Improved specialist utilisation, with time previously spent on administrative processing now redeployed to higher-value medical review and signal detection work, contributed an estimated $150k–$300k, valued at the marginal cost of equivalent external medical review expertise.
At a typical ARGUS engagement investment, the first year of operational savings alone represents a return of twenty times or more, with savings recurring annually as the repatriated volume and redeployed specialist capacity become the new operational baseline.
VP, Drug Safety Operations
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