Currently submitted to: JMIR Formative Research
Date Submitted: Jul 8, 2026
Open Peer Review Period: Jul 8, 2026 - Sep 2, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Exploring the Potential of Generative Artificial Intelligence to Support the Sensemaking of Patient-Generated Health Data by Healthcare Professionals in Cardiac Risk Reduction
ABSTRACT
Background:
Individuals are increasingly generating substantial personal health and lifestyle data, e.g. through wearables and smartphones. While such patient-generated health data (PGHD) 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).
Objective:
We explore how large language models (LLMs) can support healthcare professionals’ sensemaking of PGHD with automated summaries and natural language data exploration.
Methods:
Using cardiovascular disease (CVD) risk reduction as a use case, 16 HCPs reviewed synthetic multimodal PGHD in an exploratory qualitative study with a clinical dashboard prototype that integrated charts, LLM-generated summaries, and a conversational interface. The study was complemented by standardized measures of workload, usability, confidence and trust in AI summaries.
Results:
AI summaries provided quick overviews that anchored data exploration, while conversational interaction supported flexible analysis and bridged data-literacy gaps. However, HCPs raised concerns about transparency, privacy, and overreliance.
Conclusions:
We contribute early empirical insights and sociotechnical design implications for integrating AI LLMs into clinical workflows to support PGHD sensemaking and decision-making.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.