An institutional framework to support ethical fair and equitable artificial intelligence augmented care
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01490-9 |
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| _version_ | 1823861578488872960 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Steven Dykstra |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-95cf10f51f844c9987942bcaf377a91f |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-95cf10f51f844c9987942bcaf377a91f2025-02-09T12:55:40ZengNature Portfolionpj Digital Medicine2398-63522025-02-018111110.1038/s41746-025-01490-9An institutional framework to support ethical fair and equitable artificial intelligence augmented careSteven Dykstra0Matthew MacDonald1Rhys Beaudry2Dina Labib3Melanie King4Yuanchao Feng5Jacqueline Flewitt6Jeff Bakal7Bing Lee8Stafford Dean9Marina Gavrilova10Paul W. M. Fedak11James A. White12Department of Cardiac Sciences, Cumming School of Medicine, University of CalgaryDepartment of Cardiac Sciences, Cumming School of Medicine, University of CalgaryDepartment of Cardiac Sciences, Cumming School of Medicine, University of CalgaryDepartment of Cardiac Sciences, Cumming School of Medicine, University of CalgaryDepartment of Cardiac Sciences, Cumming School of Medicine, University of CalgaryDepartment of Cardiac Sciences, Cumming School of Medicine, University of CalgaryDepartment of Cardiac Sciences, Cumming School of Medicine, University of CalgaryAlberta Health ServicesAlberta Health ServicesAlberta Health ServicesDepartment of Computer Science, University of CalgaryDepartment of Cardiac Sciences, Cumming School of Medicine, University of CalgaryDepartment of Cardiac Sciences, Cumming School of Medicine, University of CalgaryAbstract 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.https://doi.org/10.1038/s41746-025-01490-9 |
| spellingShingle | 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 An institutional framework to support ethical fair and equitable artificial intelligence augmented care npj Digital Medicine |
| title | An institutional framework to support ethical fair and equitable artificial intelligence augmented care |
| title_full | An institutional framework to support ethical fair and equitable artificial intelligence augmented care |
| title_fullStr | An institutional framework to support ethical fair and equitable artificial intelligence augmented care |
| title_full_unstemmed | An institutional framework to support ethical fair and equitable artificial intelligence augmented care |
| title_short | An institutional framework to support ethical fair and equitable artificial intelligence augmented care |
| title_sort | institutional framework to support ethical fair and equitable artificial intelligence augmented care |
| url | https://doi.org/10.1038/s41746-025-01490-9 |
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