Pharma Evidence Intelligence Suite
Pharma Evidence Intelligence Suite uses advanced AI to discover, analyze, and synthesize clinical, real‑world, and regulatory evidence across the global literature and key data sources. It automatically surfaces relevant studies, extracts critical endpoints and safety signals, and generates traceable, regulator-ready insights to support drug development and medical affairs. This accelerates evidence generation, reduces manual review effort, and improves decision quality across the pharmaceutical R&D lifecycle.
The Problem
“Slow, inconsistent evidence review and safety coding delay regulatory and R&D decisions”
Organizations face these key challenges:
FDA accepts a limited number of RWE requests per cycle, so weak narratives create costly missed opportunities
Study design narratives are reviewed manually against complex and evolving FDA criteria
Clinical, real-world, and regulatory evidence is spread across many databases and document formats
Patient-reported ADR narratives are unstructured, ambiguous, and often contain multiple symptoms in one report
Manual MedDRA coding is slow, inconsistent, and difficult to scale during case volume spikes
Teams need regulator-ready traceability, confidence scoring, and audit logs for every recommendation
Cross-functional review cycles create bottlenecks between safety, epidemiology, regulatory, and medical affairs teams
Impact When Solved
The Shift
Human Does
- •Define evidence questions and review scope for development, safety, or medical affairs needs
- •Search literature and data sources manually, then collect and organize relevant studies
- •Read full texts and abstracts to extract endpoints, comparators, outcomes, and safety findings
- •Discuss findings across stakeholders and decide what evidence supports next actions
Automation
Human Does
- •Set evidence objectives, inclusion criteria, and acceptable use boundaries for each review
- •Validate high-impact findings, adjudicate ambiguous evidence, and handle exceptions
- •Approve evidence summaries, safety interpretations, and regulator-facing conclusions
AI Handles
- •Continuously scan approved literature and data sources to identify relevant new evidence
- •Rank, cluster, and triage studies by indication, population, endpoint, and relevance
- •Extract key study details, endpoints, safety signals, and real-world outcomes into structured summaries
- •Generate traceable evidence syntheses and draft regulator-ready insight packages with citations
Operating Intelligence
How Pharma Evidence Intelligence Suite runs once it is live
Humans set constraints. AI generates options.
Humans choose what moves forward.
Selections improve future generation quality.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Define Constraints
Step 2
Generate
Step 3
Evaluate
Step 4
Select & Refine
Step 5
Deliver
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
Humans define the constraints. AI generates and evaluates options. Humans select what ships. Outcomes train the next generation cycle.
The Loop
6 steps
Define Constraints
Humans set goals, rules, and evaluation criteria.
Generate
Produce multiple candidate outputs or plans.
Evaluate
Score options against the stated criteria.
Select & Refine
Humans choose, edit, and approve the best option.
Authority gates · 1
The system must not submit regulator-facing conclusions, evidence packages, or remediation recommendations without approval from a designated regulatory or medical affairs reviewer. [S1][S4]
Why this step is human
Final selection involves taste, strategic alignment, and accountability for what actually moves forward.
Deliver
Prepare the selected option for operational use.
Feedback
Selections and outcomes improve future generation.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Pharma Evidence Intelligence Suite implementations:
Key Players
Companies actively working on Pharma Evidence Intelligence Suite solutions:
Real-World Use Cases
AI quality-checking of RWE study design narratives against FDA selection criteria
An AI reviewer checks whether an RWE proposal clearly explains fit-for-use data, study design quality, and regulatory conduct so the sponsor can improve the package before submission.
Automated coding of patient-reported adverse drug reactions from free-text reports
An AI system reads patients’ written descriptions of side effects and automatically tags the adverse drug reactions, reducing manual review work for pharmacovigilance teams.