Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States.
Appl Clin Inform. 2022 Jan;13(1):161-179. doi: 10.1055/s-0041-1742218. Epub 2022 Feb 9.
The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.
This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.
We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.
Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.
This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.
“数据科学”一词涵盖了多种方法，其中许多方法被认为是前沿的，并被用于影响全球的护理流程。护理是一门应用科学，也是临床和行政领域医疗保健系统的关键学科，这使得该专业越来越受到数据科学最新进展的影响。更大的信息学社区应该了解护理和数据科学交叉领域的当前趋势，因为护理实践的发展具有跨专业的影响。
本研究旨在总结数据科学文献中与护理相关的患者结局和临床流程的最新（2020 年日历年）研究和应用。
我们对文献进行了快速回顾，以确定 2020 年期间发表的相关研究。我们探讨了以下 16 个主题：（1）人工智能/机器学习的可信度和接受度，（2）倦怠，（3）复杂护理（门诊），（4）急诊就诊，（5）跌倒，（6）医院获得性感染，（7）医疗保健利用和成本，（8）住院，（9）院内死亡率，（10）住院时间，（11）疼痛，（12）患者安全，（13）压疮，（14）再入院，（15）人员配备，以及（16）单位文化。
在 16589 篇文章中，有 244 篇被纳入综述。所有主题都有 2020 年发表的文献代表，从 1 篇文章到 59 篇文章不等。文献中代表了许多当代数据科学方法，包括使用机器学习、神经网络和自然语言处理。
本综述提供了 2020 年与护理实践相关的数据科学趋势概述。对这类文献的研究对于监测数据科学在护理实践中的影响状态非常重要。
