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Dashboard / AI Discovery / Source Document Extraction
Opportunity Surfaced
High Impact AI Agent

Source Document Extraction

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

Acrobat PDF reading (877 events) occurs during narrative and assessment work. Users manually read source documents to extract dates, events, and lab results. AI could pre-extract key entities from source PDFs and present structured data to case processors.

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 PDF/Acrobat events: 877
  • Total hours in PDF tools (pilot): 0.00 hrs
  • PDF reading co-occurs with narrative and assessment activities
  • Key entities (Dates, Events, Lab Results) could be auto-extracted

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 216 events Across 3 apps
2335834 198 events Across 3 apps
2349955 181 events Across 2 apps
2317182 180 events Across 1 apps
2355714 172 events Across 3 apps

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

  • Deploy AI document extraction to pre-process source PDFs on case intake
  • Auto-extract key entities: dates, adverse events, lab results, medications
  • Present structured extracted data alongside Veeva case form
  • Use OCR + NLP pipeline for scanned/handwritten source documents

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