Exploring Human-AI Interaction with Patient-Generated Health Data Sensemaking for Cardiac Risk Reduction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911246589100032 |
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| author | Pakianathan, Pavithren V S Islambouli, Rania McGowan, Hannah Branco, Diogo Guerreiro, Tiago Smeddinck, Jan David |
| author_facet | Pakianathan, Pavithren V S Islambouli, Rania McGowan, Hannah Branco, Diogo Guerreiro, Tiago Smeddinck, Jan David |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_00936 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Human-AI Interaction with Patient-Generated Health Data Sensemaking for Cardiac Risk Reduction Pakianathan, Pavithren V S Islambouli, Rania McGowan, Hannah Branco, Diogo Guerreiro, Tiago Smeddinck, Jan David Human-Computer Interaction 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. |
| title | Exploring Human-AI Interaction with Patient-Generated Health Data Sensemaking for Cardiac Risk Reduction |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.00936 |