CDQOps

CDQOps is a clinical data quality and validation platform developed by Inductive Quotient. It is designed to improve the speed, consistency, and control of study data review across clinical data management activities. The platform targets clinical teams looking to identify data issues earlier, automate validation workflows, and strengthen oversight throughout the data lifecycle, from edit check management through to database lock.

CDQOps applies AI-driven automation to discrepancy identification, reconciliation support, edit check generation, and data review workflows. It uses configurable quality rules and intelligent automation to reduce manual programming effort, improve traceability, and support more reliable data cleaning processes.

Core Capabilities

• AI-Generated Edit Checks: Generates protocol-driven edit checks and validation logic with reduced manual effort.
• SDTM and ADaM Automation: Supports standards-ready dataset generation with improved traceability and consistency.
• Cross-System Reconciliation: Identifies and resolves discrepancies across EDC systems and related data sources.
• Real-Time Data Quality Scoring: Monitors quality trends, outliers, and database lock readiness across study levels.
• RBQM Analytics and KRI Generation: Converts quality signals into key risk indicators and alerts to support risk-based quality management oversight.
• Multi-EDC Platform Support: Enables consistent data quality operations across major EDC platforms through a flexible connector framework.

Quality Scoring Dimensions

CDQOps evaluates clinical data quality across five defined dimensions that contribute to an overall quality score:

• Completeness: Measures required data population across forms, visits, and study workflows.
• Accuracy: Evaluates whether data values align with expected ranges, formats, and business rules.
• Consistency: Identifies mismatches across forms, visits, and connected data sources.
• Timeliness: Tracks data entry, query response, and review turnaround across study activities.
• Conformance: Assesses alignment with standards, controlled terminology, and coding requirements.

Design Considerations and Workflow Benefits

• Accelerates validation, reconciliation, and review workflows to reduce time to database lock.
• Applies standardized validation logic and automated checks to improve consistency across studies, sites, and systems.
• Adapts to different study designs, rules, and workflows through scalable automation intended for evolving data quality needs.
• Supports multi-EDC environments via a connector framework that maintains consistent quality operations across platforms.
• Reduces manual programming, repetitive review cycles, and overall data management burden through automated quality workflows.