Author Affiliations: Clinical Professor Ad Honorem, School of Nursing, The University of Minnesota, Minneapolis.
J Nurs Adm. 2020 Mar;50(3):125-127. doi: 10.1097/NNA.0000000000000855.
As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on the application of management strategies in health systems. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. Since writing my 1st article for Managing Organizational Complexity in 2004, much has happened to further our understanding of complexity in healthcare systems. The growth of new computational methods in the fields of data science and data analytics has allowed scientists to identify signals or patterns in large complex data sets (big data) that in the past were seemingly hidden. Rather than relying on historical statistical methods to infer outcomes, these advanced methods combined with increased computer processing power allow machines to learn the structure of data and create artificial intelligence (AI). In our ongoing efforts to find solutions for complex healthcare problems, AI is becoming more and more an accepted method. The purpose of this edition of Managing Organizational Complexity is to define AI and machine learning, discuss the recent resurgence of AI, and then provide examples of how AI can provide value to healthcare with an emphasis on nursing.
随着系统随时间的推移而发展，它们自然会变得越来越复杂。复杂系统领域的研究为卫生系统管理策略的应用提供了新的视角。这项研究的大部分内容似乎是系统理论跨学科领域的自然延伸。自 2004 年为《Managing Organizational Complexity》撰写第一篇文章以来，我们对医疗保健系统复杂性的理解有了很大的提高。数据科学和数据分析领域中新的计算方法的发展使得科学家能够识别大数据集中过去似乎隐藏的信号或模式。这些先进的方法与计算机处理能力的提高相结合，使得机器能够学习数据的结构并创建人工智能 (AI)，而不是依赖历史统计方法来推断结果。在我们为解决复杂的医疗保健问题而不断努力的过程中，人工智能越来越被接受为一种方法。本版《Managing Organizational Complexity》的目的是定义人工智能和机器学习，讨论人工智能最近的复兴，然后提供人工智能如何为医疗保健提供价值的示例，重点是护理。
