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Dashboard / AI Discovery / AI-Assisted MedDRA Coding
Opportunity Surfaced
Medium Impact AI Agent

AI-Assisted MedDRA Coding

Opportunity Lifecycle

1
Surfaced
2
Accepted
3
Remediating
4
Remediated
Status persists in your browser. In production, these actions notify team members, trigger workflows, and begin value-realization monitoring.
Calibrating — continuous monitoring active awaiting more data The pattern behind this finding has been detected, but the sample size within the 19-day pilot window is insufficient to produce a confident savings estimate. The agent continues to collect evidence — savings projections will appear as more data accumulates.

Automation Readiness Score

41
Medium
Pattern Frequency 0 hrs/yr (17 users)
Decision Complexity Judgment-heavy, probabilistic
Data Structure Unstructured judgment
Cross-App Scope Cross-application workflow

Description & Data Evidence

MedDRA browser lookups (54 events) occur outside Veeva during case processing. Users switch to desktop MEDDRABROWSERWIN or tools.meddra.org to search for terms, then return to Veeva to enter codes. An in-Veeva AI-powered term suggestion could eliminate this swivel-chair pattern.

Self-Evaluation Scores

The platform grades each finding on four dimensions (1–5 scale). Low scores flag findings that need more data or clearer remediation before acceptance.

Overall 3/5
Actionability 3/5
Specificity 3/5
Remediation Alignment 3/5

Key Findings

  • Total MedDRA lookup events: 54
  • Total hours spent in MedDRA tools (pilot): 0.00 hrs
  • Lookups happen outside Veeva = classic swivel-chair pattern
  • Could be replaced by AI-powered in-Veeva term suggestion

Case Evidence

Specific case IDs pulled from the pilot data where this pattern is most pronounced. In production, clicking a case opens its full event timeline.

Case ID Signal Context
2353948 208 events MedDRA lookups with Veeva activity
2349955 180 events MedDRA lookups with Veeva activity
2317182 180 events MedDRA lookups with Veeva activity
2355714 170 events MedDRA lookups with Veeva activity
2326107 161 events MedDRA lookups with Veeva activity

Validation Questions

0 of 3 answered
Before accepting this opportunity, work through the questions below with the relevant subject-matter experts. Your answers lock in the acceptance criteria and — when you toggle Share with Pyze — inform how our agents surface similar patterns in the future.
1
What is the quality bar for AI output to be accepted without rework by analysts?
Defines the accuracy target for the GenAI component.
2
Which case types or scenarios are the highest-risk for AI hallucination or error?
Identifies where human-in-the-loop is required even after AI assistance.
3
Is there existing ground-truth data (gold-standard cases) that can be used to benchmark AI performance?
Determines whether we can measure AI accuracy objectively pre-deployment.

Remediation Ideas

  • Integrate AI-powered MedDRA term suggestion directly in Veeva Safety
  • Use NLP to auto-suggest MedDRA preferred terms from adverse event descriptions
  • Provide ranked term suggestions with confidence scores
  • Train on historical coding decisions for organization-specific patterns

Implementation Roadmap

Effort
Large
Timeline
3-6 months
Primary Owner
AI Platform + Regulatory
Dependencies
  • Model selection + procurement
  • Regulatory validation plan
  • Human-in-loop workflow design

How Risk-Adjusted Savings Is Calculated

The risk-adjusted number is the annual savings multiplied by a composite factor of four independent dimensions. Each dimension is rated High (1.0×), Medium (0.8×), or Low (0.5×). See full methodology.

Detection 40% weight
Medium
Confidence the agent-detected pattern is real
Feasibility 25% weight
Medium
Ease of building the remediation
Adoption 20% weight
Medium
Likelihood users change workflow
Compliance 15% weight
Low
Simplicity of PV validation path
0 hrs × 0.76 = 0 hrs / year
At 1,000 users: 0 hrs / year · $0.0M