Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review.

Autor:	Schultz MA; 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)., Walden RL, Cato K, Coviak CP, Cruz C, D'Agostino F, Douthit BJ, Forbes T, Gao G, Lee MA, Lekan D, Wieben A, Jeffery AD
Jazyk:	angličtina
Zdroj:	Computers, informatics, nursing : CIN [Comput Inform Nurs] 2021 May 06; Vol. 39 (11), pp. 654-667. Date of Electronic Publication: 2021 May 06.
DOI:	10.1097/CIN.0000000000000705
Abstrakt:	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. Competing Interests: Dr Jeffery received support for this work from the Agency for Healthcare Research and Quality (AHRQ) and the Patient-Centered Outcomes Research Institute (PCORI) under Award Number K12 HS026395 as well as the resources and use of facilities at the Department of Veterans Affairs, Tennessee Valley Healthcare System. The content is solely the responsibility of the authors and does not necessarily represent the official views of AHRQ, PCORI, the Department of Veterans Affairs, or the US Government. The remaining authors have no conflicts of interest to declare. (Copyright © 2021 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government.)
Databáze:	MEDLINE
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