SCoPE: Planning for Hybrid Querying over Clinical Trial Data

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Science & Research — Health & Medical Research, Mathematics & Computational Sciences · Depth: Expert, quick

Summary

SCoPE (Structured Clinical hybrid Planning for Evidence retrieval in clinical trials) is a multi-LLM planner-based framework designed to extract complex attributes from clinical trial data that are not directly available as columns. It addresses the challenge of inferring details like drug class or outcome type from visible content by decomposing tasks into row selection, structured planning, and execution. This explicit planning approach reduces ambiguity compared to direct LLM prompting. Evaluated on 1,500 hybrid reasoning questions over oncology clinical-trial tables, SCoPE demonstrated improved accuracy for reasoning-based questions and a better accuracy-efficiency tradeoff than baselines like zero-shot, few-shot, chain-of-thought, TableGPT2, BlendSQL, and EHRAgent.

Key takeaway

For NLP Engineers or Research Scientists developing solutions for clinical data extraction, SCoPE's multi-LLM planner-based framework offers a robust alternative. Its explicit decomposition of tasks into planning and execution improves accuracy for reasoning-based questions and provides a stronger accuracy-efficiency tradeoff than current agentic baselines. Consider adopting similar planner-based decomposition strategies to enhance the reliability and performance of your clinical trial data analysis workflows.

Key insights

Explicit multi-LLM planning significantly improves accuracy and efficiency for complex clinical trial data querying.

Principles

• Clinical trial reasoning is a distinct table understanding problem.
• Explicit planning reduces ambiguity in LLM-based data extraction.
• Decomposition improves accuracy-efficiency tradeoff for complex queries.

Method

SCoPE decomposes hybrid querying into row selection, structured planning, and execution, explicitly defining source fields, reasoning rules, and output constraints before answer generation.

In practice

• Apply multi-LLM planning to infer attributes from unstructured clinical data.
• Decompose complex data extraction into explicit sub-tasks.

Topics

• Clinical Trial Data
• Hybrid Querying
• Large Language Models
• Multi-LLM Planning
• Data Extraction
• Systematic Reviews
• Oncology

Best for: AI Scientist, Research Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.