An institutional framework to support ethical fair and equitable artificial intelligence augmented care

Steven Dykstra; Matthew MacDonald; Rhys Beaudry; Dina Labib; Melanie King; Yuanchao Feng; Jacqueline Flewitt; Jeff Bakal; Bing Lee; Stafford Dean; Marina Gavrilova; Paul W M Fedak; James A White

doi:10.1038/s41746-025-01490-9

An institutional framework to support ethical fair and equitable artificial intelligence augmented care

NPJ Digit Med. 2025 Feb 5;8(1):84. doi: 10.1038/s41746-025-01490-9.

Authors

Steven Dykstra^{1

2}, Matthew MacDonald^{1

2}, Rhys Beaudry^{1

2}, Dina Labib^{1

2}, Melanie King^{1

2}, Yuanchao Feng^{1

2

3}, Jacqueline Flewitt^{1

2}, Jeff Bakal³, Bing Lee³, Stafford Dean³, Marina Gavrilova⁴, Paul W M Fedak^{1

2}, James A White^{5

6

7}

Affiliations

¹ Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
² Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
³ Alberta Health Services, Calgary, AB, Canada.
⁴ Department of Computer Science, University of Calgary, Calgary, AB, Canada.
⁵ Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. jawhit@ucalgary.ca.
⁶ Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. jawhit@ucalgary.ca.
⁷ Department of Diagnostic Imaging, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada. jawhit@ucalgary.ca.

Abstract

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.