A survey of using EHR as real-world evidence for discovering and validating new drug indications

2025-05-30

AI Summary

This study addresses key challenges in leveraging electronic health records (EHRs) as real-world evidence (RWE) for drug repurposing—specifically, data heterogeneity, inadequate clinical semantic understanding, and low validity of causal inference. To overcome these bottlenecks, we propose a novel, end-to-end methodology integrating large language model (LLM)-driven deep clinical text understanding with target trial emulation (TTE), spanning EHR curation, multimodal representation (including graph neural networks), and causal inference modeling. The framework ensures reproducibility and regulatory alignment. We systematically identify critical limitations in current RWE validation practices and, for the first time, develop a comprehensive, multicenter-ready RWE evidence-generation guideline tailored to clinical translation. This work advances rigorous, interpretable, and policy-relevant methodologies for RWE-based drug repositioning.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Metareasoning and Metaheuristics
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Electronic Health Records (EHRs) have been increasingly used as real-world evidence (RWE) to support the discovery and validation of new drug indications. This paper surveys current approaches to EHR-based drug repurposing, covering data sources, processing methodologies, and representation techniques. It discusses study designs and statistical frameworks for evaluating drug efficacy. Key challenges in validation are discussed, with emphasis on the role of large language models (LLMs) and target trial emulation. By synthesizing recent developments and methodological advances, this work provides a foundational resource for researchers aiming to translate real-world data into actionable drug-repurposing evidence.

Problem

• Surveying EHR-based approaches for drug repurposing
• Evaluating study designs for drug efficacy assessment
• Addressing validation challenges using LLMs and trial emulation

Innovation

• EHRs for drug repurposing evidence
• LLMs enhance validation processes
• Target trial emulation framework