TOP 10 GLOBAL PHARMA

The Challenge

A leading multinational pharmaceutical company's medical review team was drowning. Every week brought a fresh stack of promotional materials, reference documents, clinical study summaries, and compliance statements—each demanding line-by-line verification against strict internal SOPs.

The process was slow, inconsistent, and exhausting:

• Long cycles: A full MLR review cycle averaged 12-13 days, and first-pass approval rates hovered around 50-60%—meaning nearly half of all submissions needed rework
• Uneven results: Review quality depended on who happened to be assigned—the same material could get different verdicts from different reviewers within the same team
• Reviewer exhaustion: Content volume kept climbing, and the team was consumed by repetitive checks, leaving no room for strategic discussions or cross-functional alignment on evolving review boundaries
• Routine checks piling up: With 34 decision checkpoints across 8 review dimensions, reviewers spent most of their time on repetitive verification instead of applying their medical expertise where it mattered most

The team needed a way to enforce their existing SOPs at scale—without replacing their medical judgment.

The Solution

ZENO built an AI-powered pre-screening system that plugs into the client's existing MLR workflow—not as a replacement, but as a standardized quality checkpoint before human review.

ZENO's medical, product, prompt engineering, and development teams worked closely with the client to deliver a tailored solution:

1. Intelligent Pre-Screening Engine

The system analyzes submitted content across 8 review dimensions (promotional claims, reference accuracy, off-label risks, clinical data interpretation, format compliance, and more) using 34 pre-configured decision checkpoints mapped to the client's SOPs.

The workflow follows four steps:

• Analyze → The system parses content against compliance rules automatically
• Display → Results are shown with clear status: approved, flagged, or rejected
• Moderate → Flagged items are categorized and routed for human review
• Explain → Each flag includes a specific reason, so reviewers know exactly what needs attention

Content goes from "draft" to "pre-screened" instantly—reducing noise before it ever reaches a human reviewer.

2. Seamless Integration

The system sits as a standardized quality checkpoint within the client's established MLR pipeline. No workflow overhaul needed. Reviewers see fewer false starts, and first-pass rates improve as a natural result.

3. Enterprise Deployment

• Deployed on AWS infrastructure with enterprise-grade reliability
• Supports multiple private deployment configurations
• Full data sovereignty and security compliance

The Results

The AI pre-screening system delivered measurable impact on the client's MLR workflow:

What stood out:

• Fewer rework cycles: With first-pass rates climbing above 70%, the team spends less time on back-and-forth revisions and more time on content that actually needs attention
• Faster time-to-market: Review cycles cut by nearly a week—from two weeks to seven days—accelerating material launches
• Reviewers focus on higher-value work: The pre-screening system handles routine checks, freeing medical reviewers for boundary cases, therapeutic area discussions, and cross-functional alignment
• Enterprise-ready from day one: Private deployment keeps data secure while delivering AI-powered pre-screening capabilities

"The AI pre-screening system took the noise out of our review pipeline. We went from spending most of our time on routine checks to having our reviewers work on what they were actually trained for."

— Medical Review Team Lead, Global Pharma

What's Next

Building on the success of the initial deployment, the client is expanding the system:

• More therapeutic areas: Rolling out across additional disease areas and product portfolios
• Continuous improvement loops: Integrating feedback mechanisms that sharpen review accuracy over time

With 34 pre-configured decision checkpoints and a scalable AI architecture, the foundation for enterprise-wide medical content review automation is in place.