Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction

2026-02-05

AI Summary

This study addresses the challenge of effectively utilizing large-scale, heterogeneous patient-generated health data in clinical practice, where time constraints and limited data literacy among healthcare professionals hinder meaningful engagement. To bridge this gap, the authors propose an interactive system that integrates large language model (LLM)-generated summaries with a natural language conversational interface, enabling clinicians to rapidly comprehend and flexibly explore multimodal health data within cardiovascular disease risk reduction scenarios. Embedded into clinical workflows, the system combines automated summarization, dialog-driven interaction, and multimodal visualization. A mixed-methods evaluation involving 16 healthcare professionals demonstrated that AI-generated summaries significantly enhanced data interpretability, while conversational capabilities facilitated adaptive exploration, effectively mitigating disparities in data literacy. The study also identifies critical challenges concerning transparency, privacy preservation, and risks of overreliance on AI assistance.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Individuals are increasingly generating substantial personal health and lifestyle data, e.g. through wearables and smartphones. While such data could transform preventative care, its integration into clinical practice is hindered by its scale, heterogeneity and the time pressure and data literacy of healthcare professionals (HCPs). We explore how large language models (LLMs) can support sensemaking of patient-generated health data (PGHD) with automated summaries and natural language data exploration. Using cardiovascular disease (CVD) risk reduction as a use case, 16 HCPs reviewed multimodal PGHD in a mixed-methods study with a prototype that integrated common charts, LLM-generated summaries, and a conversational interface. Findings show that AI summaries provided quick overviews that anchored exploration, while conversational interaction supported flexible analysis and bridged data-literacy gaps. However, HCPs raised concerns about transparency, privacy, and overreliance. We contribute empirical insights and sociotechnical design implications for integrating AI-driven summarization and conversation into clinical workflows to support PGHD sensemaking.

Problem

• patient-generated health data
• clinical integration
• healthcare professionals
• data literacy
• preventative care

Innovation

• AI-augmented sensemaking
• large language models
• patient-generated health data
• conversational interface
• clinical decision support