CDQOps

AI-Powered Clinical Data Quality Automation.

CDQOps is Inductive Quotient’s AI-powered clinical data quality and validation platform designed to improve the speed, consistency, and control of study data review.

It helps teams identify data issues early, automate validation workflows, and strengthen oversight across clinical data management activities.

Built to support modern study execution, CDQOps streamlines edit check management, discrepancy identification, reconciliation support, and data review workflows through intelligent automation and configurable quality rules. By reducing manual effort and improving traceability, it helps clinical teams accelerate data cleaning, improve data reliability, and move faster toward database lock.

Core Capabilities

Built to automate clinical data quality workflows across validation, reconciliation, and review.

AI-Generated Edit Checks

Generate protocol-driven edit checks and validation logic with less manual effort.

SDTM & ADaM Automation

Support standards-ready dataset generation with stronger traceability and consistency.

Cross-System Reconciliation

Identify and resolve discrepancies across EDC and related data sources faster.

Real-Time Data Quality Scoring

Monitor quality trends, outliers, and database lock readiness across study levels.

RBQM Analytics & KRI Generation

Turn quality signals into KRIs and alerts for proactive risk-based oversight.

Multi-EDC Platform Support

Enable consistent data quality operations across major EDC platforms.

Why to choose IQA CDQOps ?

Built to reduce manual effort, improve consistency, and accelerate high-quality clinical data review.

Faster Study Timelines

Accelerate validation, reconciliation, and review workflows to move studies forward with cleaner data and less manual effort.

Accuracy and Consistency by Design

Apply standardized validation, automated checks, and unified quality logic to improve consistency across studies, sites, and systems.

Flexible for Complex Study Environments

Adapt to different study designs, rules, and workflows with scalable automation built for evolving data quality needs.

Multi-EDC Compatibility

Work across major EDC platforms through a flexible connector framework that supports consistent quality operations across environments.

Greater Operational Efficiency

Reduce manual programming, repetitive review cycles, and data management burden through more automated quality workflows.

Quality Scoring Dimensions

Five dimensions that shape the overall clinical data quality score

Completeness

Measure required data population across forms, visits, and study workflows.

Accuracy

Evaluate whether data values align with expected ranges, formats, and business rules.

Consistency

Identify mismatches across forms, visits, and connected data sources.

Timeliness

Track data entry, query response, and review turnaround across study activities.

Conformance

Assess alignment with standards, controlled terminology, and coding requirements.

Ready to Automate Your Data Quality Operations?

See CDQOps generate edit checks from protocol text and auto-resolve queries.

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