Ascent: An Agentic System over the Model Context Protocol for Real-World Clinical Data Analysis
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Merged summary
TL;DR - Ascent is an MCP-based agentic system for answering epidemiological questions from real-world clinical data using medical coding, schema-aware SQL, and cohort-analysis tools. Capable agent models substantially outperform a fixed pipeline, but require more tool calls and longer runtimes.
- Supports both standardized and native clinical-data schemas through a shared Model Context Protocol tool interface.
- Introduces EpiTrap, a dataset testing whether systems avoid recognized pharmacoepidemiological errors.
- Agents improve accuracy over the fixed pipeline by an average of 27 percentage points on native schemas and 20 points on standardized schemas.
- Real-project experience suggests value for feasibility assessment, iterative diagnosis, and expert-guided analysis.
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Ascent: An Agentic System over the Model Context Protocol for Real-World Clinical Data Analysis
Public signals
N/A
TL;DR - Ascent is an MCP-based agentic system for answering epidemiological questions from real-world clinical data using medical coding, schema-aware SQL, and cohort-analysis tools. Capable agent models substantially outperform a fixed pipeline, but require more tool calls and longer runtimes.
- Supports both standardized and native clinical-data schemas through a shared Model Context Protocol tool interface.
- Introduces EpiTrap, a dataset testing whether systems avoid recognized pharmacoepidemiological errors.
- Agents improve accuracy over the fixed pipeline by an average of 27 percentage points on native schemas and 20 points on standardized schemas.
- Real-project experience suggests value for feasibility assessment, iterative diagnosis, and expert-guided analysis.