Yuan Luo, Chengsheng Mao, Lazaro N. Sanchez-Pinto, Faraz S. Ahmad, Andrew Naidech, Luke Rasmussen, Jennifer A. Pacheco, Daniel Schneider, Leena B. Mithal, Scott Dresden, Kristi Holmes, Matthew Carson, Sanjiv J. Shah, Seema Khan, Susan Clare, Richard G. Wunderink, Huiping Liu, Theresa Walunas, Lee Cooper, Feng Yue, Firas Wehbe, Deyu Fang, David M. Liebovitz, Michael Markl, Kelly N. Michelson, Susanna A. McColley, Marianne Green, Justin Starren, Ronald T. Ackermann, Richard T. D'Aquila, James Adams, Donald Lloyd-Jones, Rex L. Chisholm, Abel Kho
{"title":"西北大学资源和教育发展倡议，推动整个学习保健系统的人工智能合作","authors":"Yuan Luo, Chengsheng Mao, Lazaro N. Sanchez-Pinto, Faraz S. Ahmad, Andrew Naidech, Luke Rasmussen, Jennifer A. Pacheco, Daniel Schneider, Leena B. Mithal, Scott Dresden, Kristi Holmes, Matthew Carson, Sanjiv J. Shah, Seema Khan, Susan Clare, Richard G. Wunderink, Huiping Liu, Theresa Walunas, Lee Cooper, Feng Yue, Firas Wehbe, Deyu Fang, David M. Liebovitz, Michael Markl, Kelly N. Michelson, Susanna A. McColley, Marianne Green, Justin Starren, Ronald T. Ackermann, Richard T. D'Aquila, James Adams, Donald Lloyd-Jones, Rex L. Chisholm, Abel Kho","doi":"10.1002/lrh2.10417","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Introduction</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 3","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10417","citationCount":"0","resultStr":"{\"title\":\"Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system\",\"authors\":\"Yuan Luo, Chengsheng Mao, Lazaro N. Sanchez-Pinto, Faraz S. Ahmad, Andrew Naidech, Luke Rasmussen, Jennifer A. Pacheco, Daniel Schneider, Leena B. Mithal, Scott Dresden, Kristi Holmes, Matthew Carson, Sanjiv J. Shah, Seema Khan, Susan Clare, Richard G. Wunderink, Huiping Liu, Theresa Walunas, Lee Cooper, Feng Yue, Firas Wehbe, Deyu Fang, David M. Liebovitz, Michael Markl, Kelly N. Michelson, Susanna A. McColley, Marianne Green, Justin Starren, Ronald T. Ackermann, Richard T. D'Aquila, James Adams, Donald Lloyd-Jones, Rex L. Chisholm, Abel Kho\",\"doi\":\"10.1002/lrh2.10417\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Introduction</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Our Collaborative AI in Healthcare initiatives has created valuable educational and practical resources. 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Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system
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.
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