EDC

Clinion EDC is an AI-native electronic data capture platform designed for clinical trials across Phase I to IV. It is used by CROs, sponsors, and biotech organizations to manage study setup, data collection, query resolution, and regulatory reporting. The platform has been deployed in over 300 clinical trials across more than 20 countries, covering more than 300,000 subjects.

The system is built around AI functionality integrated directly into core workflows — including study configuration, CDASH mapping, data review, medical coding, remote source data verification, and custom reporting — rather than added as a supplementary feature. It also integrates with companion modules including RTSM, ePRO, CTMS, eConsent, and eSource within a unified data ecosystem.

Study Setup and Configuration

• Provides a Standardized Global Library of prebuilt, CDASH-compliant, therapeutic area-specific templates to support rapid study configuration.
• Supports codeless study builds with an average go-live timeline of two to four weeks.
• Allows users to duplicate forms and selectively customize edit checks, skip logics, or apply bulk edits without manual programming.
• Reported to reduce operational build costs by up to 50%.

Data Review and Query Management

• Includes an AI Data Review tool that allows data managers to create custom datasets using natural language prompts, without requiring technical or biostats support.
• Enables identification and flagging of data discrepancies directly within the EDC interface.
• Supports bulk query issuance or corrections across all affected subjects in a single action.
• Reported to deliver 40–50% faster time to database lock and 40–50% savings in total data management costs.
• AI-powered edit checks identify errors in real time and are reported to reduce manual errors by up to 30%.

Mid-Study Protocol Amendments

• Allows visits, pages, and fields to be added, edited, or deleted in live studies without study downtime.
• Changes can be applied without vendor assistance, and existing data is preserved through automatic version control.
• Form locks release instantly upon update, and reports always reflect the latest CRF version.
• Reported to deliver 40% savings in time and costs associated with protocol amendments.

Reporting and Data Visualization

• AI Report Generator converts natural language prompts into tailored reports without requiring programming skills.
• Supports scheduling of automated report notifications to stakeholders.
• Maintains full audit trails with version history for all reports.
• Includes a built-in GenAI chart generator supporting bar, pie, scatter, and other chart types.
• Allows reports to be saved, scheduled, emailed, or pushed to third-party tools via secure APIs.
• Reported to be 80–90% faster than manual report generation, with 75–85% reduction in reporting costs.

CDASH Compliance and Variable Mapping

• Users select a study page and send variables to the AI engine to receive annotated CDASH mapping suggestions instantly.
• Suggestions can be reviewed, accepted, customized, or checked against CDASH libraries within a single workflow.
• Reported to reduce coding effort by 40% and study setup time by 20% relative to manual standards-based configuration.

In-Platform AI Support

• CliniBot is an AI conversational assistant trained on Clinion EDC protocol documentation and user manuals.
• Responds to user queries in natural language via chat, guiding users through tasks, clarifying CRF fields, and explaining product features.
• Provides contextual help across modules without requiring external support tickets.
• Intended to reduce support ticket volume and ease the burden on training and help desk teams.

Integrations and Ecosystem

• Integrates with CTMS, RTSM, ePRO, eConsent, eSource, eProtocol, eTMF, and CSR Automation modules.
• Supports real-time data flow across integrated systems to reduce data silos.
• Connects to third-party tools via secure APIs for data export and reporting.

Clinion EDC is compliant with ISO 9001 and ISO 27001 for quality and information security management, and aligns with 21 CFR Part 11, EU Annex 11, and ICH GCP for regulatory data management. System validation follows a risk-based approach aligned with GAMP 5. The platform's Responsible AI framework is structured around principles of accountability, transparency, privacy and security, reliability and safety, and fairness.