Author Affiliations: California State University (Dr Schultz); Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University (Ms Walden); Department of Emergency Medicine, Columbia University School of Nursing (Dr Cato); Grand Valley State University (Dr Coviak); Global Health Technology & Informatics, Chevron, San Ramon, CA (Mr Cruz); Saint Camillus International University of Health Sciences, Rome, Italy (Dr D'Agostino); Duke University School of Nursing (Mr Douthit); East Carolina University College of Nursing (Dr Forbes); St Catherine University Department of Nursing (Dr Gao); Texas Woman's University College of Nursing (Dr Lee); Assistant Professor, University of North Carolina at Greensboro School of Nursing (Dr Lekan); University of Wisconsin School of Nursing (Ms Wieben); and Vanderbilt University School of Nursing, and Tennessee Valley Healthcare System, US Department of Veterans Affairs (Dr Jeffery).
Comput Inform Nurs. 2021 May 6;39(11):654-667. doi: 10.1097/CIN.0000000000000705.
Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.
由于大数据集和高级分析的可用性增加，数据科学在医疗保健领域继续得到认可和使用。对于护理领导者来说，要了解这一快速变化的领域可能具有挑战性。在本文中，我们描述了对 2019 年发表的使用数据科学探索、解释和/或预测护士感兴趣的 15 种现象的文献进行的范围综述的研究结果。这 15 种现象中的 14 种都与至少一篇发表于 2019 年的论文有关。我们确定了许多当代数据科学方法（例如自然语言处理、神经网络）在许多结果中的应用。我们发现许多研究都在探讨再入院和压疮。人工智能/机器学习接受度、倦怠、患者安全和单位文化等主题的代表性不足。我们希望本文中描述的研究能够帮助读者：（1）了解数据科学改善与护士相关的临床流程和患者结果的能力的广度和深度；（2）发现需要探索的文献中的差距。
