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; et al. (npj Digital Medicine)
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.