InsightFlow: LLM-Driven Synthesis of Patient Narratives for Mental Health into Causal Models

2026-04-14

AI Summary

This study addresses the limitations of manual clinical case conceptualization, which is time-consuming and subject to inter-clinician variability. The authors propose a novel approach that leverages large language models (LLMs) to automatically generate structured causal diagrams from patient–therapist dialogues, conforming to the standardized 5P framework (Presenting problem, Predisposing, Precipitating, Perpetuating, and Protective factors). By integrating LLM-based text understanding with causal graph construction, structural similarity assessment via NetSimile, semantic embedding alignment, and expert evaluation, the method bridges the gap between unstructured narrative discourse and formal clinical reasoning. Experimental results demonstrate that the generated causal graphs achieve structural similarity comparable to inter-expert agreement, exhibit strong semantic alignment, and receive above-moderate ratings from clinicians, particularly in terms of completeness, coherence, and clinical utility.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Clinical case formulation organizes patient symptoms and psychosocial factors into causal models, often using the 5P framework. However, constructing such graphs from therapy transcripts is time consuming and varies across clinicians. We present InsightFlow, an LLM based approach that automatically generates 5P aligned causal graphs from patient-therapist dialogues. Using 46 psychotherapy intake transcripts annotated by clinical experts, we evaluate LLM generated graphs against human formulations using structural (NetSimile), semantic (embedding similarity), and expert rated clinical criteria. The generated graphs show structural similarity comparable to inter annotator agreement and high semantic alignment with human graphs. Expert evaluations rate the outputs as moderately complete, consistent, and clinically useful. While LLM graphs tend to form more interconnected structures compared to the chain like patterns of human graphs, overall complexity and content coverage are similar. These results suggest that LLMs can produce clinically meaningful case formulation graphs within the natural variability of expert practice. InsightFlow highlights the potential of automated causal modeling to augment clinical workflows, with future work needed to improve temporal reasoning and reduce redundancy.

Problem

• case formulation
• causal modeling
• mental health
• clinical variability
• therapy transcripts

Innovation

• LLM-driven causal modeling
• clinical case formulation
• 5P framework
• patient narrative synthesis
• mental health AI