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Towards AI Augmented Personalized Data Sensemaking

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dc.contributor.authorV S Pakianathan, Pavithren
dc.contributor.authorFatehi, Alireza
dc.contributor.authorSmeddinck, Jan
dc.date.accessioned2024-08-21T11:08:38Z
dc.date.available2024-08-21T11:08:38Z
dc.date.issued2024
dc.description.abstractPatient-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.en
dc.identifier.doi10.18420/muc2024-mci-ws05-182
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/44311
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2024 - Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.rightshttp://purl.org/eprint/accessRights/RestrictedAccess
dc.rights.urihttp://purl.org/eprint/accessRights/RestrictedAccess
dc.titleTowards AI Augmented Personalized Data Sensemakingen
dc.typeText/Workshop Paper
gi.conference.date1.-4. September 2024
gi.conference.locationKarlsruhe
gi.conference.sessiontitleMCI-WS05: AI and Health: Using Digital Twins to Foster Healthy Behavior

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