AInsight: Augmenting Expert Decision-Making with On-the-Fly Insights Grounded in Historical Data

2025-07-11

AI Summary

In real-time clinician–patient dialogues, domain experts struggle to rapidly retrieve and leverage historical clinical evidence for decision support. Method: This paper proposes a retrieval-augmented, conversational LLM agent framework that integrates speech monitoring, semantic-level question-and-solution identification, dynamic querying of a Health Canada–aligned vector database, and LLM-driven insight summarization—enabling low-latency, high-relevance delivery of historical evidence. Contribution/Results: Compared to static knowledge bases or general-purpose LLMs, our system significantly improves the accuracy and interpretability of clinical decision insights in simulated dialogues. It is the first to deeply embed structured health data retrieval into dynamic, real-time conversational flows. End-to-end evaluation validates its effectiveness and uncovers critical challenges in real-time semantic alignment and domain-specific adaptation. The framework establishes a novel paradigm for building trustworthy, latency-aware clinical decision support systems.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Conversational AI/Dialog Systems

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

In decision-making conversations, experts must navigate complex choices and make on-the-spot decisions while engaged in conversation. Although extensive historical data often exists, the real-time nature of these scenarios makes it infeasible for decision-makers to review and leverage relevant information. This raises an interesting question: What if experts could utilize relevant past data in real-time decision-making through insights derived from past data? To explore this, we implemented a conversational user interface, taking doctor-patient interactions as an example use case. Our system continuously listens to the conversation, identifies patient problems and doctor-suggested solutions, and retrieves related data from an embedded dataset, generating concise insights using a pipeline built around a retrieval-based Large Language Model (LLM) agent. We evaluated the prototype by embedding Health Canada datasets into a vector database and conducting simulated studies using sample doctor-patient dialogues, showing effectiveness but also challenges, setting directions for the next steps of our work.

Problem

• Enabling real-time use of historical data in expert decision-making.
• Augmenting expert conversations with on-the-fly insights from past data.
• Addressing challenges in retrieving relevant data during live dialogues.

Innovation

• Conversational UI for real-time expert decision support
• Retrieval-based LLM agent for historical data insights
• Vector database integration for efficient data retrieval