Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.

Autor:	Douthit BJ; Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States., Walden RL; Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States., Cato K; Department of Emergency Medicine, Columbia University School of Nursing, New York, New York, United States., Coviak CP; Professor Emerita of Nursing, Grand Valley State University, Allendale, Michigan, United States., Cruz C; Global Health Technology and Informatics, Chevron, San Ramon, California, United States., D'Agostino F; Department of Medicine and Surgery, Saint Camillus International University of Health Sciences, Rome, Italy., Forbes T; College of Nursing, East Carolina University, Greenville, North California, United States., Gao G; Department of Nursing, St Catherine University, Saint Paul, Minnesota, United States., Kapetanovic TA; College of Nursing, East Carolina University, Greenville, North California, United States., Lee MA; College of Nursing, Texas Woman's University, Denton, Texas, United States., Pruinelli L; School of Nursing, University of Minnesota, Minneapolis, Minnesota, United States., Schultz MA; Department of Nursing, California State University, San Bernardino, California, United States., Wieben A; School of Nursing, University of Wisconsin-Madison, Wisconsin, United States., Jeffery AD; School of Nursing, Vanderbilt University; Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, Tennessee, United States.
Jazyk:	angličtina
Zdroj:	Applied clinical informatics [Appl Clin Inform] 2022 Jan; Vol. 13 (1), pp. 161-179. Date of Electronic Publication: 2022 Feb 09.
DOI:	10.1055/s-0041-1742218
Abstrakt:	Background: 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. Objectives: 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. Methods: 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. Results: 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. Conclusion: 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. Competing Interests: None declared. (Thieme. All rights reserved.)
Databáze:	MEDLINE
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