An institutional framework to support ethical fair and equitable artificial intelligence augmented care
Coordinated access to multi-domain health data can facilitate the development and implementation of artificial intelligence-augmented clinical decision support (AI-CDS). However, scalable institutional frameworks supporting these activities are lacking. We present the PULSE framework, aimed to establish an integrative and ethically governed ecosystem for the patient-guided, patient-contextualized use of multi-domain health data for AI-augmented care. We describe deliverables related to stakeholder engagement and infrastructure development to support routine engagement of patients for consent-guided data abstraction, pre-processing, and cloud migration to support AI-CDS model development and surveillance. Central focus is placed on the routine collection of social determinants of health and patient self-reported health status to contextualize and evaluate models for fair and equitable use. Inaugural feasibility is reported for over 30,000 consecutively engaged patients. The described framework, conceptually developed to support a multi-site cardiovascular institute, is translatable to other disease domains, offering a validated architecture for use by large-scale tertiary care institutions.
Authors
- Jeffrey A. Bakal (ORCID: https://orcid.org/0000-0002-3658-2554)
- Steven Dykstra (ORCID: https://orcid.org/0000-0001-9042-535X)
- Paul W.M. Fedak (ORCID: https://orcid.org/0000-0003-0658-9381)
- Matthew MacDonald
- Rhys I. Beaudry
- James A. White (ORCID: https://orcid.org/0000-0002-5297-4507)
- Marina L. Gavrilova (ORCID: https://orcid.org/0000-0002-5338-1834)
- B.-H. Lee
- Yuanchao Feng
- Jacqueline Flewitt (ORCID: https://orcid.org/0000-0001-7985-9907)
- Dina Labib (ORCID: https://orcid.org/0000-0001-7108-8554)
- Melanie King
- Stafford Dean
Institutions
- Alberta Health Services (CA)
- University of Calgary (CA)
- Libin Cardiovascular Institute of Alberta (CA)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-02-05
- DOI
- https://doi.org/10.1038/s41746-025-01490-9
- Citations
- 7
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 4.58
Funders
- Alberta Innovates