PDF(Pubmed)
Abstract：
BACKGROUND: Artificial intelligence (AI) technologies are expected to \"revolutionise\" healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital.
METHODS: Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework.
RESULTS: Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise. Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients\' digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors\' priorities and the needs and expectations of healthcare organisations and systems.
CONCLUSIONS: Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.
METHODS: Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework.
RESULTS: Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise. Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients\' digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors\' priorities and the needs and expectations of healthcare organisations and systems.
CONCLUSIONS: Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.
摘要：
背景：人工智能（AI）技术有望“彻底改变”医疗保健。然而,尽管他们的承诺，他们在医疗保健组织和系统中的整合仍然有限。这项研究的目的是探索和理解他们在加拿大领先的学术医院整合的系统性挑战和影响。
方法：对29个利益相关者进行了半结构化访谈，这些利益相关者关注组织内大量AI技术的集成（例如，经理,临床医生,研究人员,病人,技术提供商)。使用非收养法收集和分析数据，放弃,放大,传播,可持续发展(NASSS)框架。
结果：在促成因素和条件中，我们的发现强调：支持性的组织文化和领导力，导致连贯的组织创新叙述；高级管理层和前线团队之间的相互信任和透明沟通；冠军的存在，翻译者,以及能够建立桥梁和信任的AI边界扳手；以及吸引技术和临床人才和专业知识的能力。制约因素和障碍包括：人工智能技术价值的对比定义和衡量这种价值的方法；缺乏现实生活和基于背景的证据；不同的患者数字和健康素养能力；组织动态之间的不一致，临床和行政流程，基础设施,和人工智能技术；缺乏涵盖实施的筹资机制，适应,和所需的专业知识；实践变化带来的挑战，新的专业知识开发，和专业身份；缺乏官方专业人士，报销,缺乏上市前和上市后批准的法律和治理框架；人工智能技术的业务和融资模式的多样性；投资者的优先事项与医疗保健组织和系统的需求和期望之间的不一致。
结论：感谢多维NASSS框架，这项研究为从全面的社会技术角度分析医疗保健中的AI技术提供了原始见解和详细的学习基础。我们的发现强调了在当前将AI技术引入临床例程的努力中考虑医疗机构和系统特征的复杂性的重要性。这项研究增加了现有的文献，可以为明智的决策提供信息，负责任,以及这些技术在医疗保健组织和系统中的可持续集成。
方法：对29个利益相关者进行了半结构化访谈，这些利益相关者关注组织内大量AI技术的集成（例如，经理,临床医生,研究人员,病人,技术提供商)。使用非收养法收集和分析数据，放弃,放大,传播,可持续发展(NASSS)框架。
结果：在促成因素和条件中，我们的发现强调：支持性的组织文化和领导力，导致连贯的组织创新叙述；高级管理层和前线团队之间的相互信任和透明沟通；冠军的存在，翻译者,以及能够建立桥梁和信任的AI边界扳手；以及吸引技术和临床人才和专业知识的能力。制约因素和障碍包括：人工智能技术价值的对比定义和衡量这种价值的方法；缺乏现实生活和基于背景的证据；不同的患者数字和健康素养能力；组织动态之间的不一致，临床和行政流程，基础设施,和人工智能技术；缺乏涵盖实施的筹资机制，适应,和所需的专业知识；实践变化带来的挑战，新的专业知识开发，和专业身份；缺乏官方专业人士，报销,缺乏上市前和上市后批准的法律和治理框架；人工智能技术的业务和融资模式的多样性；投资者的优先事项与医疗保健组织和系统的需求和期望之间的不一致。
结论：感谢多维NASSS框架，这项研究为从全面的社会技术角度分析医疗保健中的AI技术提供了原始见解和详细的学习基础。我们的发现强调了在当前将AI技术引入临床例程的努力中考虑医疗机构和系统特征的复杂性的重要性。这项研究增加了现有的文献，可以为明智的决策提供信息，负责任,以及这些技术在医疗保健组织和系统中的可持续集成。
