Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization

2025-09-04

AI Summary

Health simulation models—such as agent-based models (ABMs)—are often too complex for key stakeholders (e.g., clinicians, policymakers, patients) to understand and adopt. Existing large language model (LLM)-generated explanations lack stakeholder specificity and fail to accommodate diverse information needs and linguistic preferences. Method: We propose the first stakeholder-centered explanation framework for health simulation, integrating qualitative interviews and quantitative surveys to systematically elicit heterogeneous user requirements, and designing a controllable text generation mechanism that guides LLMs to produce summaries jointly customized in both content and stylistic attributes. Contribution/Results: Rigorous multi-dimensional evaluation demonstrates significant improvements in explanation relevance, readability, and stakeholder acceptance. The framework establishes a reusable methodological foundation for human-AI collaborative explanation in health decision support systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• 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
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Modeling & Simulation (M&S) approaches such as agent-based models hold significant potential to support decision-making activities in health, with recent examples including the adoption of vaccines, and a vast literature on healthy eating behaviors and physical activity behaviors. These models are potentially usable by different stakeholder groups, as they support policy-makers to estimate the consequences of potential interventions and they can guide individuals in making healthy choices in complex environments. However, this potential may not be fully realized because of the models' complexity, which makes them inaccessible to the stakeholders who could benefit the most. While Large Language Models (LLMs) can translate simulation outputs and the design of models into text, current approaches typically rely on one-size-fits-all summaries that fail to reflect the varied informational needs and stylistic preferences of clinicians, policymakers, patients, caregivers, and health advocates. This limitation stems from a fundamental gap: we lack a systematic understanding of what these stakeholders need from explanations and how to tailor them accordingly. To address this gap, we present a step-by-step framework to identify stakeholder needs and guide LLMs in generating tailored explanations of health simulations. Our procedure uses a mixed-methods design by first eliciting the explanation needs and stylistic preferences of diverse health stakeholders, then optimizing the ability of LLMs to generate tailored outputs (e.g., via controllable attribute tuning), and then evaluating through a comprehensive range of metrics to further improve the tailored generation of summaries.

Problem

• Tailoring health simulation explanations to diverse stakeholder needs
• Overcoming complexity barriers in health model accessibility
• Addressing one-size-fits-all limitations in LLM-generated health summaries

Innovation

• Mixed-methods framework identifies stakeholder explanation needs
• LLMs optimized via controllable attribute tuning for personalization
• Comprehensive evaluation metrics improve tailored summary generation