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genioux Fact post by Fernando Machuca and Copilot
Introduction:
The article "Mayo Clinic’s Healthy Model for AI Success" from MIT Sloan Management Review provides a comprehensive overview of how the Mayo Clinic, a leading healthcare provider in the U.S., has successfully implemented AI. The article discusses the strategies, innovations, and outcomes of this implementation, offering valuable insights into the potential of AI in healthcare.
genioux GK Nugget:
"The Mayo Clinic's success with AI is attributed to its enablement-focused approach, innovative use cases, and the integration of AI into its administrative and clinical processes." — Fernando Machuca and Copilot
genioux Foundational Fact:
The Mayo Clinic has emerged as one of the most aggressive adopters of AI among U.S. healthcare providers. Its size and long tradition of medical research have facilitated a wide range of AI activities. The organization has developed an infrastructure that enables and facilitates AI development, leading to increased activity. The data and AI team are seen as enablers rather than gatekeepers, fostering a culture of innovation.
The 10 most relevant genioux Facts:
2147%20AI%20in%20Healthcare,%20Lessons%20from%20Mayo%20Clinic%E2%80%99s%20Success%202.png)
- The Mayo Clinic is one of the most aggressive adopters of AI among U.S. healthcare providers.
- The organization's size and long tradition of medical research have facilitated a wide range of AI activities.
- The Mayo Clinic has developed an infrastructure that enables and facilitates AI development.
- The data and AI team are seen as enablers rather than gatekeepers.
- Several new use cases have emerged, including an algorithm identifying certain heart pump problems from echocardiogram readings.
- This algorithm, cleared by the FDA to be marketed as a medical device, can also detect some heart diseases.
- Another innovation is the creation of a new class of AI called hypothesis-driven AI.
- Hypothesis-driven AI may improve the interpretability of AI algorithms for healthcare treatments, particularly for cancer.
- During the COVID-19 pandemic, a machine-learning model was created to forecast the availability of beds in intensive care units.
- This approach was later used to address the capacity to treat RSV in the Children’s Center.
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Conclusion:
The Mayo Clinic's successful implementation of AI serves as a model for other healthcare providers. By fostering a culture of innovation, developing an enabling infrastructure, and integrating AI into administrative and clinical processes, the Mayo Clinic has been able to leverage AI to improve patient care and operational efficiency. This case study underscores the transformative potential of AI in healthcare and offers valuable insights for other organizations embarking on their own AI journeys.
REFERENCE
The g-f GK Article
Thomas H. Davenport and Randy Bean, Mayo Clinic’s Healthy Model for AI Success, MIT Sloan Management Review, March 27, 2024.
ABOUT THE AUTHORS
Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a fellow of the MIT Initiative on the Digital Economy, and senior adviser to the Deloitte Chief Data and Analytics Officer Program. He is coauthor of All in on AI: How Smart Companies Win Big With Artificial Intelligence (Harvard Business Review Press, 2023) and Working With AI: Real Stories of Human-Machine Collaboration (MIT Press, 2022). Randy Bean (@randybeannvp) is an adviser to Fortune 1000 organizations on data and AI leadership. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).
Classical Summary:
The article "Mayo Clinic’s Healthy Model for AI Success" from MIT Sloan Management Review discusses the successful implementation of AI at the Mayo Clinic, a leading healthcare provider in the U.S¹.
The Mayo Clinic has been identified as one of the most aggressive adopters of AI among U.S. healthcare providers¹. The organization's size and long tradition of medical research have facilitated a wide range of AI activities¹.
The Mayo Clinic has developed an infrastructure that enables and facilitates AI development, leading to increased activity¹. The data and AI team are seen as enablers rather than gatekeepers, fostering a culture of innovation¹.
Several new use cases have emerged, including an algorithm that identifies certain heart pump problems from echocardiogram readings, previously only detectable through stress tests¹. This algorithm, cleared by the FDA to be marketed as a medical device, can also detect some heart diseases¹.
Another innovation is the creation of a new class of AI called hypothesis-driven AI, which may improve the interpretability of AI algorithms for healthcare treatments, particularly for cancer¹.
On the administrative side, during the COVID-19 pandemic, a machine learning model was created to forecast the availability of beds in intensive care units¹. This approach was later used to address capacity to treat RSV in the Children’s Center¹.
In conclusion, the Mayo Clinic's success with AI is attributed to its enablement-focused approach, innovative use cases, and the integration of AI into its administrative and clinical processes¹.
Source: Conversation with Bing, 3/30/2024
(1) Mayo Clinic’s Healthy Model for AI Success - MIT Sloan Management Review. https://sloanreview.mit.edu/article/mayo-clinics-healthy-model-for-ai-success/.
(2) AI-Based Innovations at Mayo Clinic - MIT Sloan Management Review. https://sloanreview.mit.edu/article/ai-based-innovations-at-mayo-clinic/.
(3) MIT Sloan Management Review. https://sloanreview.mit.edu/.
(4) Getty Images. https://www.gettyimages.com/detail/news-photo/general-views-of-the-mayo-clinic-sports-medicine-building-news-photo/1228365907.
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Thomas H. Davenport
Thomas Hayes "Tom" Davenport, Jr. (born October 17, 1954) is an American academic and author specializing in analytics, business process innovation, knowledge management, and artificial intelligence¹. He is currently the President’s Distinguished Professor in Information Technology and Management at Babson College, a Fellow of the MIT Initiative on the Digital Economy, Co-founder of the International Institute for Analytics, and a Senior Advisor to Deloitte Analytics¹.
Davenport has written, coauthored, or edited twenty books, including the first books on analytical competition, business process reengineering and achieving value from enterprise systems, and the best seller, Working Knowledge (with Larry Prusak), on knowledge management¹. He has written more than one hundred articles for such publications as Harvard Business Review, MIT Sloan Management Review, California Management Review, the Financial Times, and many other publications¹.
In 2003, Davenport was named one of the world’s 'Top 25 Consultants' by Consulting magazine, and in 2005 was named one of the world’s top three analysts of business and technology by readers of Optimize magazine¹. In 2012 he was named one of the world's "Top 50 Business School Professors" by Poets and Quants and Fortune Magazine¹.
Davenport initially trained as a sociologist, with a BA in Sociology from Trinity University in 1976, a Master's degree in Sociology from Harvard in 1979, and a Ph.D. in Sociology from Harvard in 1980¹. After graduating with a Ph.D. in Sociology, Davenport worked as an academic before being offered a research and consulting job at Index by James Champy¹. Davenport became Director of research at Index, studying business process improvement¹.
Source: Conversation with Bing, 3/30/2024
(1) Thomas H. Davenport - Wikipedia. https://en.wikipedia.org/wiki/Thomas_H._Davenport.
(2) Analytics 3.0 - Harvard Business Review. https://hbr.org/2013/12/analytics-30.
(3) Beyond Automation - Harvard Business Review. https://hbr.org/2015/06/beyond-automation.
(4) Keep Up with Your Quants - Harvard Business Review. https://hbr.org/2013/07/keep-up-with-your-quants.
Randy Bean
Randy Bean is a recognized thought leader, author, speaker, and Innovation Fellow in the field of data and AI-driven business leadership¹². He is the founder and former CEO of NewVantage Partners, a data, analytics, and AI advisory firm that served Fortune 1000 clients until its acquisition by Wavestone in 2021¹.
Bean is the author of the bestselling book "Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI" and is a regular contributor to Forbes, Harvard Business Review, and MIT Sloan Management Review¹. His expertise in data and analytics has made him a sought-after consultant, helping companies across industries unlock new opportunities and drive growth².
In addition to his professional achievements, Bean serves on several industry advisory boards and is a globally recognized speaker on the topic of data and AI-driven business leadership¹. He is a graduate of Washington University in St. Louis¹.
Source: Conversation with Bing, 3/30/2024
(1) About the Author — Fail Fast, Learn Faster. https://www.failfastlearnfaster.org/about-copy.
(2) Randy Bean - Wikitia. https://wikitia.com/wiki/Randy_Bean.
(3) Interview with Randy Bean: Winning the Race to Insights. https://infotrust.com/articles/winning-the-race-to-insights-interview-with-randy-bean-ceo-of-newvantage-partners/.
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