aceintegration of AI tools into routine clinical practice across diverse health care settings
Source article: Can Artificial Intelligence Deliver in Real-World Health Systems? Early Insights From Augmented Intelligence in Medicine and Healthcare Initiative's 5 Funded Projects
Abstract: Introduction Artificial intelligence (AI) holds tremendous promise to improve clinical decision-making across diagnosis, risk assessment, and patient care. However, most prior work has focused on model development in controlled settings with limited evidence on real-world implementation. The Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI), led by Kaiser Permanente and funded by the Gordon and Betty Moore Foundation, was established to evaluate and support integration of AI tools into routin…
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The Augmented Intelligence in Medicine and Healthcare Initiative, led by Kaiser Permanente and funded by the Gordon and Betty Moore Foundation, funded five projects to move AI beyond controlled model development into routine care. The projects addressed sepsis management, venous thromboembolism risk, diabetic retinopathy screening, cardiac amyloidosis detection, and pediatric asthma risk prediction, with synthesis of implementation processes and lessons learned.
Early results show feasibility is achievable but not automatic, as deployment hinges on EHR integration, handling data complexity, meeting regulatory requirements, and adapting to workflow variation. The findings matter because they shift evidence from model performance to operational requirements like stakeholder engagement and ongoing monitoring, while uncertainty remains about long-term scalability and performance across sites.
- AIM-HI, led by Kaiser Permanente and funded by the Gordon and Betty Moore Foundation, funded 5 projects via national multistage review using structured scoring rubric.
- Projects covered sepsis management, venous thromboembolism risk assessment, diabetic retinopathy screening, cardiac amyloidosis detection, and pediatric asthma risk prediction.
- Cross-project synthesis identified stakeholder engagement, local adaptation, quality assurance, and performance monitoring as common implementation themes.
AI tools for sepsis, VTE, diabetic retinopathy, cardiac amyloidosis, and pediatric asthma were feasibly deployed into routine clinical practice across diverse health care settings.
Real-world implementation was constrained by EHR integration difficulties, data complexity, regulatory requirements, and variation in clinical workflows.
The rundown
AIM-HI funded 5 projects through a national, multistage review process using a structured scoring rubric, spanning sepsis management, venous thromboembolism risk assessment, diabetic retinopathy screening, cardiac amyloidosis detection, and pediatric asthma risk prediction across diverse health care settings.
Authors synthesized findings on implementation processes, noting that success depended on thoughtful integration, strong partnerships with stakeholders, and sustained evaluation and monitoring, while quality assurance and local adaptation were recurring requirements.
Sources
- Peer-reviewedThe Permanente Journal2026-09-16
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