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[2602.05687] Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction
Summary
This study investigates how AI, specifically large language models, can enhance the understanding and use of patient-generated health data by healthcare professionals, focusing on cardiac risk reduction.
Why It Matters
As healthcare increasingly relies on patient-generated data from wearables and smartphones, integrating AI tools can improve clinical decision-making. This research highlights the potential benefits and challenges of using AI to assist healthcare professionals in interpreting complex health data, which is crucial for effective patient care.
Key Takeaways
- AI-generated summaries can provide quick overviews of patient data, aiding healthcare professionals.
- Conversational interfaces help bridge data literacy gaps among healthcare providers.
- Concerns about transparency and privacy in AI applications must be addressed.
- The study emphasizes the importance of sociotechnical design in integrating AI into clinical workflows.
- Healthcare professionals recognize the potential of AI but are cautious about overreliance.
Computer Science > Human-Computer Interaction arXiv:2602.05687 (cs) [Submitted on 5 Feb 2026 (v1), last revised 13 Feb 2026 (this version, v4)] Title:Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction Authors:Pavithren V S Pakianathan, Rania Islambouli, Diogo Branco, Albrecht Schmidt, Tiago Guerreiro, Jan David Smeddinck View a PDF of the paper titled Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction, by Pavithren V S Pakianathan and 5 other authors View PDF 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 conversati...
