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Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach
BACKGROUND: Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529463/ https://www.ncbi.nlm.nih.gov/pubmed/34612839 http://dx.doi.org/10.2196/30940 |
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author | Wiljer, David Salhia, Mohammad Dolatabadi, Elham Dhalla, Azra Gillan, Caitlin Al-Mouaswas, Dalia Jackson, Ethan Waldorf, Jacqueline Mattson, Jane Clare, Megan Lalani, Nadim Charow, Rebecca Balakumar, Sarmini Younus, Sarah Jeyakumar, Tharshini Peteanu, Wanda Tavares, Walter |
author_facet | Wiljer, David Salhia, Mohammad Dolatabadi, Elham Dhalla, Azra Gillan, Caitlin Al-Mouaswas, Dalia Jackson, Ethan Waldorf, Jacqueline Mattson, Jane Clare, Megan Lalani, Nadim Charow, Rebecca Balakumar, Sarmini Younus, Sarah Jeyakumar, Tharshini Peteanu, Wanda Tavares, Walter |
author_sort | Wiljer, David |
collection | PubMed |
description | BACKGROUND: Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today’s health care providers. OBJECTIVE: The aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system. METHODS: To accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature. RESULTS: The environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI. CONCLUSIONS: Technologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/30940 |
format | Online Article Text |
id | pubmed-8529463 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-85294632021-11-09 Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach Wiljer, David Salhia, Mohammad Dolatabadi, Elham Dhalla, Azra Gillan, Caitlin Al-Mouaswas, Dalia Jackson, Ethan Waldorf, Jacqueline Mattson, Jane Clare, Megan Lalani, Nadim Charow, Rebecca Balakumar, Sarmini Younus, Sarah Jeyakumar, Tharshini Peteanu, Wanda Tavares, Walter JMIR Res Protoc Protocol BACKGROUND: Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today’s health care providers. OBJECTIVE: The aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system. METHODS: To accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature. RESULTS: The environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI. CONCLUSIONS: Technologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/30940 JMIR Publications 2021-10-06 /pmc/articles/PMC8529463/ /pubmed/34612839 http://dx.doi.org/10.2196/30940 Text en ©David Wiljer, Mohammad Salhia, Elham Dolatabadi, Azra Dhalla, Caitlin Gillan, Dalia Al-Mouaswas, Ethan Jackson, Jacqueline Waldorf, Jane Mattson, Megan Clare, Nadim Lalani, Rebecca Charow, Sarmini Balakumar, Sarah Younus, Tharshini Jeyakumar, Wanda Peteanu, Walter Tavares. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 06.10.2021. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included. |
spellingShingle | Protocol Wiljer, David Salhia, Mohammad Dolatabadi, Elham Dhalla, Azra Gillan, Caitlin Al-Mouaswas, Dalia Jackson, Ethan Waldorf, Jacqueline Mattson, Jane Clare, Megan Lalani, Nadim Charow, Rebecca Balakumar, Sarmini Younus, Sarah Jeyakumar, Tharshini Peteanu, Wanda Tavares, Walter Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach |
title | Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach |
title_full | Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach |
title_fullStr | Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach |
title_full_unstemmed | Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach |
title_short | Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach |
title_sort | accelerating the appropriate adoption of artificial intelligence in health care: protocol for a multistepped approach |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529463/ https://www.ncbi.nlm.nih.gov/pubmed/34612839 http://dx.doi.org/10.2196/30940 |
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