Dykstra Steven, MacDonald Matthew, Beaudry Rhys, Labib Dina, King Melanie, Feng Yuanchao, Flewitt Jacqueline, Bakal Jeff, Lee Bing, Dean Stafford, Gavrilova Marina, Fedak Paul W M, White James A
Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Libin Cardiovascular Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
NPJ Digit Med. 2025 Feb 5;8(1):84. doi: 10.1038/s41746-025-01490-9.
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.
协调获取多领域健康数据有助于人工智能增强临床决策支持(AI-CDS)的开发与实施。然而,目前缺乏支持这些活动的可扩展机构框架。我们提出了PULSE框架,旨在建立一个综合且符合伦理规范的生态系统,用于以患者为导向、根据患者情况使用多领域健康数据进行人工智能辅助护理。我们描述了与利益相关者参与和基础设施开发相关的可交付成果,以支持患者常规参与同意引导的数据提取、预处理和云迁移,以支持AI-CDS模型的开发和监测。核心重点是常规收集健康的社会决定因素和患者自我报告的健康状况,以便对模型进行背景化处理并评估其公平公正使用情况。报告了连续30000多名参与患者的初步可行性。所描述的框架在概念上是为支持多地点心血管研究所而开发的,可转换到其他疾病领域,为大型三级医疗机构提供了一个经过验证的架构。