AI for Clinical Trial Compliance: How Ideas2IT Improved Data Integrity and Accelerated Submissions

From Data Errors to Regulatory Confidence: A Case Study in Clinical Trial Data Integrity and AI Compliance

One-liner summary:

Ideas2IT built an AI-powered data integrity platform for a global pharmaceutical company, eliminating clinical trial data errors, standardizing outputs to CDISC SDTM, and accelerating regulatory readiness.

The Problem with the Status Quo

Clinical trial data must be accurate, complete, and compliant. This pharmaceutical leader faced increasing pressure from regulatory bodies due to inconsistencies, missing data, and unflagged anomalies that delayed approvals and increased audit risk.

Manual cleanup and spreadsheet workflows could not keep up with the volume or complexity of modern trials. Issues surfaced late during audits or submission prep, causing delays, rework, and revenue loss.

Where the Gaps Were

Key challenges included:

Discrepancies between EDCs, CRFs, and patient registries
Missing data due to input errors, site variability, or subject dropout
Undetected anomalies that surfaced only during audits
Manual QA cycles that consumed analyst time and delayed regulatory submissions
Lack of CDISC SDTM standardization, leading to friction during FDA and EMA reviews

A scalable solution was needed to surface and resolve these issues earlier in the process.

What We Delivered

Ideas2IT designed and deployed a modular AI platform to detect, resolve, and standardize clinical trial data in real time fully aligned with regulatory expectations.

Core Components:

Inconsistency Detection Engine
AI models cross-referenced clinical data across systems, historical patterns, and protocol benchmarks to identify mismatches and route them for review.
Missing Data Recovery
Statistical and contextual inference models flagged missing values and suggested imputation paths, enabling dataset completion without compromising integrity.
Anomaly Detection
Models surfaced outliers, unexpected trends, and protocol deviations across patient cohorts and trial timelines.
CDISC SDTM Standardization
All corrected outputs were structured according to CDISC SDTM, enabling smoother submissions to FDA, EMA, and other global regulators.
Deployment Architecture
Containerized, API-accessible services hosted on AWS, ready to scale across trials and trial phases.

The platform moved quality assurance upstream, making data integrity part of the trial pipeline..

Outcomes We Achieved

Capability	Impact
Data review cycles	Shortened through automated detection and correction
Submission readiness	Improved with CDISC SDTM-aligned outputs
Regulatory compliance	Strengthened with traceable corrections and audit logs
Approval timelines	Accelerated by reducing remediation loops
Analyst productivity	Increased through reduction in manual QA effort

The client can now submit clinical trial data with higher confidence and lower friction resulting in faster regulatory approvals and greater operational efficiency.

Key Takeaways

Manual QA won’t scale. Real-time AI validation reduces bottlenecks and regulatory exposure
Transparent imputation matters. Predicted values must be flagged and traceable
CDISC SDTM should be embedded. Compliance must be native to the pipeline—not a formatting step after the fact
‍Clean data builds trust. Regulators move faster when the data is consistent, reviewable, and audit-ready

Co-create with Ideas2IT

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