Auto Extract Structured Study Data Using AI with Human Verification

Define, collect, and manage extracted data directly alongside your articles and screening decisions.

The Industry Challenge

• Data extraction often occurs outside the review platform.
• Creates disconnected, fragmented workflows.
• Leads to inconsistent data formats across teams.
• Makes it difficult to trace extracted data back to source studies.

How Data Extraction Solves This

• Enables structured data collection within the same environment.
• Keeps extracted data directly linked to source studies.
• Maintains full context within the review workflow.
• Eliminates the need for external tools or disconnected processes.

Capabilities

1. Custom Extraction Forms

Standardize how data is collected across studies. Define extraction questions and fields using text, numeric, or categorical inputs. Ensure consistency across reviewers and studies with a shared structure.

2. Auto-Extract Data

Automatically extract data directly from full-text articles using AI. Automatically extract data to extraction forms using Artificial Intelligence and verified by humans using page hyperlinks and highlighted and extracted text from side-by-side displayed full-texts. This keeps data collection tied directly to source material for easy human verification.

3. Collaborative Extraction

Allow multiple reviewers to extract data independently, maintain blinding when needed, and compare outputs to resolve differences.

4. Dynamic Form Management

Adapt your extraction structure as your review evolves. Add or remove questions and articles at any stage without disrupting progress, enabling flexibility as requirements change.

5. Screening Criteria Integration

Align extraction with study eligibility rules. Apply predefined screening criteria during extraction to ensure consistency between study selection and data capture.

Keep Your Data Structured, Connected, and Ready for Analysis

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