Learning health systems · 2024
Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.
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Luo Yuan, Mao Chengsheng, Sanchez-Pinto Lazaro N, Ahmad Faraz S, Naidech Andrew, Rasmussen Luke, Pacheco Jennifer A, Schneider Daniel, Mithal Leena B, Dresden Scott, Holmes Kristi, Carson Matthew, Shah Sanjiv J, Khan Seema, Clare Susan, Wunderink Richard G, Liu Huiping, Walunas Theresa, Cooper Lee, Yue Feng, Wehbe Firas, Fang Deyu, Liebovitz David M, Markl Michael, Michelson Kelly N, McColley Susanna A, Green Marianne, Starren Justin, Ackermann Ronald T, D'Aquila Richard T, Adams James, Lloyd-Jones Donald, Chisholm Rex L, Kho Abel
Аннотация
INTRODUCTION: The rapid development of artificial intelligence (AI) in healthcare has exposed the unmet need for growing a multidisciplinary workforce that can collaborate effectively in the learning health systems. Maximizing the synergy among multiple teams is critical for Collaborative AI in Healthcare.
METHODS: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively.
RESULTS: Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare.
CONCLUSIONS: Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.
METHODS: We have developed a series of data, tools, and educational resources for cultivating the next generation of multidisciplinary workforce for Collaborative AI in Healthcare. We built bulk-natural language processing pipelines to extract structured information from clinical notes and stored them in common data models. We developed multimodal AI/machine learning (ML) tools and tutorials to enrich the toolbox of the multidisciplinary workforce to analyze multimodal healthcare data. We have created a fertile ground to cross-pollinate clinicians and AI scientists and train the next generation of AI health workforce to collaborate effectively.
RESULTS: Our work has democratized access to unstructured health information, AI/ML tools and resources for healthcare, and collaborative education resources. From 2017 to 2022, this has enabled studies in multiple clinical specialties resulting in 68 peer-reviewed publications. In 2022, our cross-discipline efforts converged and institutionalized into the Center for Collaborative AI in Healthcare.
CONCLUSIONS: Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. They have enabled more clinicians, scientists, and hospital administrators to successfully apply AI methods in their daily research and practice, develop closer collaborations, and advanced the institution-level learning health system.