Stanford Healthcare AI Applied Research Team, Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
J Gen Intern Med. 2021 Apr;36(4):1061-1066. doi: 10.1007/s11606-020-06394-w. Epub 2021 Jan 19.
Despite increasing interest in how artificial intelligence (AI) can augment and improve healthcare delivery, the development of new AI models continues to outpace adoption in existing healthcare processes. Integration is difficult because current approaches separate the development of AI models from the complex healthcare environments in which they are intended to function, resulting in models developed without a clear and compelling use case and not tested or scalable in a clinical setting. We propose that current approaches and traditional research methods do not support successful AI implementation in healthcare and outline a repeatable mixed-methods approach, along with several examples, that facilitates uptake of AI technologies into human-driven healthcare processes. Unlike traditional research, these methods do not seek to control for variation, but rather understand it to learn how a technology will function in practice coupled with user-centered design techniques. This approach, leveraging design thinking and quality improvement methods, aims to increase the adoption of AI in healthcare and prompt further study to understand which methods are most successful for AI implementations.
尽管人们对人工智能 (AI) 如何增强和改善医疗保健服务越来越感兴趣，但新 AI 模型的开发仍然超过了现有医疗保健流程的采用速度。由于当前的方法将 AI 模型的开发与它们旨在运行的复杂医疗保健环境分开，导致开发的模型没有明确和引人注目的用例，并且没有在临床环境中进行测试或扩展，因此集成变得困难。我们提出，当前的方法和传统研究方法不能支持人工智能在医疗保健中的成功实施，并概述了一种可重复的混合方法，以及几个示例，这些方法有助于将人工智能技术纳入以人为驱动的医疗保健流程。与传统研究不同，这些方法不是试图控制变化，而是理解它，以了解技术在实践中如何发挥作用，同时结合以用户为中心的设计技术。这种方法利用设计思维和质量改进方法，旨在增加人工智能在医疗保健中的采用，并促使进一步研究，以了解哪些方法对人工智能实施最成功。
