Evidence Surveillance Synthesis and Sharing

Evidence Surveillance Synthesis and Sharing facilitates proactive monitoring and aggregation of clinical trial registrations, completions, and reports to support timely systematic review updates.

Key Features:

• Automated Monitoring: Automatically tracks clinical trials as they are registered, completed, and reported to detect evidence relevant to systematic reviews.
• Curated Database Integration: Integrates and consolidates data from bibliographic databases and the ClinicalTrials.gov registry to maintain an aggregated evidence resource.
• Crowd‑sourced Verification and Voting: Collects crowd‑sourced confirmations and votes to verify links between trials and reviews and to nominate trials for inclusion in updates.
• Machine Learning Integration: Employs software agents that perform automated actions such as adding or voting on trials based on user interactions or scheduled updates from external resources.
• Implementation Stack: Implements server‑side components in Python with a PostgreSQL database backend.

Scientific Applications:

• Systematic Review Updates: Assists systematic reviewers in identifying newly registered, completed, or reported trials relevant to existing reviews for update decisions.
• Research Efficiency: Automates monitoring and prioritization tasks to reduce manual screening and streamline the review update workflow.
• Data Integration and Augmentation: Aggregates and augments trial and bibliographic data to provide comprehensive inputs for evidence synthesis and meta‑analysis.

Methodology:

Server‑side architecture developed in Python connected to a PostgreSQL database; automated monitoring of trial registration, completion, and reporting; integration of bibliographic databases and ClinicalTrials.gov; and deployment of machine learning software agents that perform automated actions (for example, adding or voting on trials) based on user inputs or scheduled external updates.