Testing a Novel Design Framework for Patient-Facing Machine Learning–Based Predictions of Heart Failure Decompensation
The prototype demonstrated high levels of patient comprehension, appropriate risk perception, and behavioral intention, suggesting its viability for empowering patients with actionable health insights.
Authors
- Meghan Reading Turchioe (ORCID: https://orcid.org/0000-0002-6264-6320)
- Stephanie Lai
- David J. Slotwiner (ORCID: https://orcid.org/0000-0002-4130-0419)
- Viktoria Averina
- Nicholas Yee
- Afra Shamnath
- So Hyeon Bang
- Julie Lindmark
- Jessica Federak
Institutions
- Cornell University (US)
- Columbia University Irving Medical Center (US)
- Boston Scientific (United States) (US)
- Weill Cornell Medicine (US)
- Columbia University (US)
Publication Details
- Journal
- JACC Advances
- Published
- 2025-09-04
- DOI
- https://doi.org/10.1016/j.jacadv.2025.102171
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Boston Scientific Corporation
- National Institute of Nursing Research