Center for Public Health Research (CreSP), Université de Montréal, Montreal, Canada.
Department of Health Management, Evaluation and Policy, École de Santé Publique de l'Université de Montréal, Montreal, Canada.
J Health Organ Manag. 2020 Dec 3;ahead-of-print(ahead-of-print). doi: 10.1108/JHOM-03-2020-0074.
Artificial intelligence (AI) raises many expectations regarding its ability to profoundly transform health care delivery. There is an abundant literature on the technical performance of AI applications in many clinical fields (e.g. radiology, ophthalmology). This article aims to bring forward the importance of studying organizational readiness to integrate AI into health care delivery.
DESIGN/METHODOLOGY/APPROACH: The reflection is based on our experience in digital health technologies, diffusion of innovations and healthcare organizations and systems. It provides insights into why and how organizational readiness should be carefully considered.
As an important step to ensure successful integration of AI and avoid unnecessary investments and costly failures, better consideration should be given to: (1) Needs and added-value assessment; (2) Workplace readiness: stakeholder acceptance and engagement; (3) Technology-organization alignment assessment and (4) Business plan: financing and investments. In summary, decision-makers and technology promoters should better address the complexity of AI and understand the systemic challenges raised by its implementation in healthcare organizations and systems.
ORIGINALITY/VALUE: Few studies have focused on the organizational issues raised by the integration of AI into clinical routine. The current context is marked by a perplexing gap between the willingness of decision-makers and technology promoters to capitalize on AI applications to improve health care delivery and the reality on the ground, where it is difficult to initiate the changes needed to realize their full benefits while avoiding their negative impacts.
人工智能(AI)在深刻改变医疗服务方面的能力引发了人们的诸多期待。有大量文献探讨了 AI 应用在许多临床领域(如放射科、眼科)的技术性能。本文旨在强调研究将 AI 融入医疗服务提供的组织准备的重要性。
设计/方法/途径:这一反思基于我们在数字健康技术、创新传播以及医疗保健组织和系统方面的经验。它深入探讨了为什么以及如何应仔细考虑组织准备情况。
作为确保 AI 成功整合并避免不必要投资和昂贵失败的重要步骤,应更好地考虑以下因素:(1)需求和附加值评估;(2)工作场所准备情况:利益相关者的接受度和参与度;(3)技术-组织一致性评估;(4)商业计划:融资和投资。总之,决策者和技术推动者应更好地应对 AI 带来的复杂性,并理解其在医疗保健组织和系统中实施所带来的系统性挑战。
原创性/价值:很少有研究关注将 AI 融入临床常规所引发的组织问题。当前的情况是,决策者和技术推动者愿意利用 AI 应用来改善医疗服务提供,但现实情况却令人困惑,难以启动必要的变革,以充分发挥其优势,同时避免其负面影响。