@inproceedings{v_s_pakianathan_towards_2024,
 abstract = {Patient-generated health data has the potential to benefit health consultations; however, there are also challenges in implementing them into practice. A key challenge is to extract relevant data and allow for effective sensemaking to create actionability for both healthcare providers (HCPs) and patients. Based on a patient-journey model, we explore the use of generative AI to enable personalized data sensemaking to potentially improve shared decision-making between cardiovascular disease (CVD) patients and HCPs during the physical activity planning process in cardiovascular rehabilitation. We discuss open questions around interaction modalities, synchronicity, and patient-HCP social dynamics in the presence of conversational or agentic tools.},
 author = {V S Pakianathan, Pavithren and Fatehi, Alireza and Smeddinck, Jan},
 language = {en},
 pages = {10.18420/muc2024},
 publisher = {Gesellschaft für Informatik e.V.},
 title = {Towards AI Augmented Personalized Data Sensemaking},
 url = {https://dl.gi.de/handle/20.500.12116/44311},
 urldate = {2024-11-13},
 year = {2024}
}