Clinical Trial Intelligence
An FDA-compliant strategic intelligence layer for clinical trial operations. 9 build phases. 18 real oncology trials. AI governance with hallucination detection. Built in 12 hours. Reviewed by senior medical researchers for analytical quality in their domain.
The Problem
Clinical trials generate millions of data points across dozens of sites, hundreds of investigators, and thousands of subjects. Safety signals, enrollment trends, protocol deviations, competitive movements, regulatory shifts — the information exists, but nobody synthesizes it intelligently.
Trial sponsors rely on static reports delivered weeks after the data was generated. By the time a safety signal is detected or an enrollment shortfall is identified, the window for intervention has often closed. The intelligence layer between raw trial data and strategic decision-making simply does not exist in most organizations.
Nine Phases of Construction
Each phase was a complete build cycle: architecture, implementation, verification. The platform grew from a data foundation to a full strategic intelligence system over 9 iterations.
PostgreSQL schema with CDISC SDTM compliance. Compliance audit ledger with immutable event tracking. The data layer everything else stands on.
Executive-level dashboard surfacing trial health, enrollment velocity, and safety posture across the entire portfolio at a glance.
Automated detection of adverse event clusters, disproportionality analysis (PRR, ROR, BCPNN), severity escalation, and signal-to-noise filtering.
Per-site scoring across enrollment, data quality, protocol adherence, and query resolution. Identifies underperforming sites before they impact timelines.
Predictive modeling of enrollment trajectories with Monte Carlo simulation. Protocol amendment impact analysis on retention rates.
What-if analysis engine for protocol amendments, site additions/removals, and enrollment strategy changes. Quantifies downstream impact before decisions are made.
Automated ingestion and validation pipeline for CDISC-compliant trial data. Schema mapping, quality checks, and audit trail generation.
Continuous tracking of FDA, EMA, and PMDA regulatory actions relevant to active trials. Guideline changes mapped to portfolio impact.
Cross-trial pattern analysis with AI governance. Literature synthesis grounded in verified data. Hallucination detection on every generated insight.
Monitoring competing trials in the same therapeutic area. Enrollment velocity comparisons, endpoint analysis, and strategic positioning intelligence.
Six Intelligence Modules
Each module is independently valuable. Together, they form a strategic intelligence layer that no clinical operations team has had access to before.
Portfolio Command Center
Real-time portfolio health across all active trials. Enrollment velocity, safety posture, milestone tracking, and risk indicators on a single surface.
Safety Signal Intelligence
Automated adverse event detection using established pharmacovigilance methods. Disproportionality analysis, temporal clustering, and severity-weighted prioritization.
Site Performance
Multi-dimensional site scoring: enrollment rate, data quality, protocol compliance, query turnaround. Identifies sites that need intervention before they derail timelines.
Enrollment Forecaster
Predictive enrollment modeling with confidence intervals. Monte Carlo simulation of completion dates. Protocol amendment impact quantification.
Scenario Modeler
What-if analysis for strategic decisions. Add sites, modify protocols, change enrollment criteria — see the downstream impact before committing.
AI Governance Pipeline
Every AI-generated insight passes through hallucination detection, source grounding, and confidence scoring. No unverified intelligence reaches the user.
Real Data, Real Scale
This was not built on synthetic data. The system was loaded with 18 real oncology trials and validated against actual clinical outcomes.
| Metric | Value |
|---|---|
| Active Trials | 18 |
| Sites | 189 |
| Subjects | 2,429 |
| Adverse Events | 7,405 |
| Lab Results | 34,224 |
| Signals Detected | 14 (5 critical) |
Compliance Built In
Regulatory compliance is not a feature added after the fact. It is the foundation layer. Every data access is audited, every AI output is governed, every record is immutable.
Three-Layer Intelligence
The architecture separates deterministic computation from pattern recognition from language model reasoning. Each layer is independently testable and independently valuable.
Statistical Analysis & Signal Detection
Traditional pharmacovigilance algorithms, enrollment projections, site scoring. No AI involved. Independently sellable as a standalone analytics product. Every number is reproducible and auditable.
Cross-Trial Correlation
Machine learning models identifying patterns across trials, sites, and therapeutic areas. Enrollment trajectory clustering, adverse event correlation mapping, site performance anomaly detection.
Natural Language Intelligence
Language model reasoning grounded in Layer 1 and Layer 2 outputs. Every AI-generated insight includes source citations, confidence scores, and hallucination detection results. The model can only reason about data that has passed through the deterministic and pattern layers first.
The Application
What follows is the system as built — every screen produced autonomously from the mission specification.
The Portfolio Command Center is where a clinical operations lead starts their day. Trial health, enrollment velocity, safety posture, and milestone status across the entire portfolio — synthesized into a single view that replaces the 45-minute email triage most teams endure every morning.
Safety Signal Intelligence applies established pharmacovigilance methods — PRR, ROR, BCPNN — to detect adverse event clusters that manual review misses. This screen shows 14 detected signals across the portfolio, 5 flagged as critical, with severity-weighted prioritization guiding where attention goes first.
Site Performance Intelligence scores every site across four dimensions: enrollment rate, data quality, protocol adherence, and query resolution time. The sites that will derail your timeline in three months are visible today — if you know where to look.
The Enrollment and Retention Forecaster runs Monte Carlo simulations against current enrollment velocity to project completion dates with confidence intervals. When the answer is no, you see it early enough to act — add sites, adjust criteria, or revise the protocol before the timeline is unrecoverable.
The Scenario Modeler quantifies the downstream impact of strategic decisions before they are made. Add three sites in Germany, tighten the inclusion criteria, extend the observation period — each scenario runs through the same predictive models and shows the cost, timeline, and statistical power implications.
The Competitive Trial Trajectory Tracker monitors trials in the same therapeutic area. Enrollment velocity comparisons, endpoint differences, and strategic positioning intelligence — because the competitive landscape changes what "on track" means for your own program.
The Data Onboarding module handles automated ingestion and validation of CDISC-compliant trial data. Schema mapping, quality gates, and a full audit trail — because the intelligence layer is only as good as the data feeding it.
The Regulatory Landscape Monitor tracks FDA, EMA, and PMDA actions relevant to active trials. Guideline changes, approval decisions, and safety communications are mapped to portfolio impact — so regulatory shifts never catch you off guard.
The Meta-Study Engine synthesizes insights across trials and published literature — but every AI-generated conclusion passes through hallucination detection, source grounding, and confidence scoring before it reaches the user. The narrative view translates quantitative findings into plain language with full citation trails.
What This Means
A strategic intelligence layer for clinical trials — FDA-compliant, loaded with real oncology data, governed by hallucination detection — was built in 12 hours across 9 phases.
Not a mockup. Not a prototype. A working system with 18 real trials, 2,429 subjects, and 14 detected safety signals, reviewed by senior medical researchers for analytical quality in their domain.
The question is no longer whether AI can handle regulated, high-stakes domains. The question is how long organizations will operate without it.