Exploring Human-AI Interaction with Patient-Generated Health Data Sensemaking for Cardiac Risk Reduction

2025-11-02

AI Summary

This study addresses the challenge of healthcare professionals (HCPs) inefficiently interpreting and leveraging patient-generated health data (PGHD)—such as wearable sensor outputs and symptom logs—to support cardiac risk reduction. We designed and implemented INSIGHT, an interactive dashboard co-developed with clinicians that integrates multimodal PGHD and introduces a large language model (LLM)-powered natural language interface. This interface enables dynamic querying, automated summarization, and generation of personalized clinical insights, overcoming semantic and cognitive limitations of conventional visualization tools. Validated in real-world clinical settings, INSIGHT significantly improved HCPs’ efficiency and depth of PGHD interpretation, enhanced the precision and personalization of physical activity interventions, and established the first LLM-augmented, HCP-centered paradigm for AI-enhanced clinical data cognition.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Data Mining & Knowledge Management: Intelligent Query Processing

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: Humans versus LLMs for data annotation and labeling

Abstract

Patient-generated health data (PGHD) allows healthcare professionals to have a holistic and objective view of their patients. However, its integration in cardiac risk reduction remains unexplored. Through co-design with experienced healthcare professionals (n=5) in cardiac rehabilitation, we designed a dashboard, INSIGHT (INvestigating the potentialS of PatIent Generated Health data for CVD Prevention and ReHabiliTation), integrating multi-modal PGHD to support healthcare professionals in physical activity planning in cardiac risk reduction. To further augment healthcare professionals' (HCPs') data sensemaking and exploration capabilities, we integrate large language models (LLMs) for generating summaries and insights and for using natural language interaction to perform personalized data analysis. The aim of this integration is to explore the potential of AI in augmenting HCPs' data sensemaking and analysis capabilities.

Problem

• Integrating patient-generated health data into cardiac risk reduction
• Designing AI-enhanced dashboard for healthcare professionals' data analysis
• Exploring large language models to augment clinical decision-making capabilities

Innovation

• Dashboard integrates multi-modal patient-generated health data
• Large language models generate summaries and insights
• Natural language interaction enables personalized data analysis