Healthcare & Life Sciences · use case
Literature Data Extraction
Define the fields once and extract them across the paper set, each value linked to its source passage.
The case
- The problem
- Pulling study design, populations and outcomes from hundreds of papers by hand is slow and inconsistent.
- What DataStruct does
- Define the fields once and extract them across the paper set, each value linked to its source passage.
- What the reviewer gets
- Structured, source-linked study data ready for reviewer checking.
Documents and questions
What goes in, and what you can ask
Typical documents
- Clinical Trial Reports
- Research Papers
- Regulatory Submissions
- Adverse Event Reports
- FDA/EMA Filings
- Study Protocols
- Literature Reviews
Example questions
- “What was the primary endpoint result?”
- “Compare efficacy across studies in this evidence set”
- “What adverse events were reported in Phase III?”
- “Summarise safety signals across all trials”
The boundary
A person makes the call
DataStruct locates and prepares the evidence, links it to its source and states what it could not confirm. A named reviewer confirms, rejects or marks each finding for follow-up, and that decision is attributed and logged.
More in healthcare
- Systematic ReviewsExtract study data across the paper set with source links; screening and synthesis decisions stay with the review team.
- Regulatory SubmissionsRule packs you define for required sections, with source-linked extraction across the dossier for reviewer checking.
- Adverse Event DetectionExtraction of reported adverse events with source links; severity is assigned by rules you define.
Try literature data extraction on your own documents
Book a demo and bring a sample of the documents this review runs on.