Davenport Thomas H, Glaser John P
Operations and Information Management Division, Babson College, Wellesley, MA 02457 USA.
Harvard Medical School, Boston, MA 02115 USA.
Discov Health Syst. 2022;1(1):4. doi: 10.1007/s44250-022-00004-8. Epub 2022 Oct 31.
Artificial intelligence applications are prevalent in the research lab and in startups, but relatively few have found their way into healthcare provider organizations. Adoption of AI innovations in consumer and business domains is typically much faster. While such delays are frustrating to those who believe in the potential of AI to transform healthcare, they are largely inherent in the structure and function of provider organizations. This article reviews the factors that govern adoption and explains why adoption has taken place at a slow pace. Research sources for the article include interviews with provider executives, healthcare IT professors and consultants, and AI vendor executives. The article considers differential speed of adoption in clinical vs. administrative applications, regulatory approval issues, reimbursement and return on investments in healthcare AI, data sources and integration with electronic health record systems, the need for clinical education, issues involving fit with clinical workflows, and ethical considerations. It concludes with a discussion of how provider organizations can successfully plan for organizational deployment.
人工智能应用在研究实验室和初创企业中很普遍，但相对较少进入医疗服务机构。在消费和商业领域，人工智能创新的采用通常要快得多。虽然这种延迟让那些相信人工智能有潜力改变医疗保健的人感到沮丧，但它们在很大程度上是医疗服务机构的结构和功能所固有的。本文回顾了影响采用的因素，并解释了采用速度缓慢的原因。本文的研究来源包括对医疗服务机构高管、医疗保健信息技术教授和顾问以及人工智能供应商高管的访谈。文章考虑了临床应用与管理应用中采用速度的差异、监管审批问题、医疗保健人工智能的报销和投资回报、数据来源以及与电子健康记录系统的集成、临床教育的必要性、与临床工作流程适配的问题以及伦理考量。文章最后讨论了医疗服务机构如何成功规划组织部署。
