Sibbald Matt, Zwaan Laura, Yilmaz Yusuf, Lal Sarrah
Department of Medicine, McMaster Education Research Innovation and Theory (MERIT) Program, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
Erasmus Medical Center, Institute of Medical Education Research Rotterdam (iMERR), Rotterdam, The Netherlands.
J Eval Clin Pract. 2024 Feb;30(1):3-8. doi: 10.1111/jep.13730. Epub 2022 Jun 27.
As big data becomes more publicly accessible, artificial intelligence (AI) is increasingly available and applicable to problems around clinical decision-making. Yet the adoption of AI technology in healthcare lags well behind other industries. The gap between what technology could do, and what technology is actually being used for is rapidly widening. While many solutions are proposed to address this gap, clinician resistance to the adoption of AI remains high. To aid with change, we propose facilitating clinician decisions through technology by seamlessly weaving what we call 'invisible AI' into existing clinician workflows, rather than sequencing new steps into clinical processes. We explore evidence from the change management and human factors literature to conceptualize a new approach to AI implementation in health organizations. We discuss challenges and provide recommendations for organizations to employ this strategy.
随着大数据越来越容易被公众获取，人工智能（AI）越来越容易获得并适用于临床决策相关问题。然而，人工智能技术在医疗保健领域的应用远远落后于其他行业。技术能够做到的与实际应用之间的差距正在迅速扩大。虽然提出了许多解决方案来弥合这一差距，但临床医生对采用人工智能的抵触情绪仍然很高。为了推动变革，我们建议通过将我们称之为“隐形人工智能”无缝融入现有的临床医生工作流程，而不是在临床过程中增加新步骤，借助技术来辅助临床医生决策。我们从变革管理和人因学文献中探寻证据，以构思一种在卫生组织中实施人工智能的新方法。我们讨论了挑战，并为组织采用这一策略提供了建议。
