Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review. Issue 11 (November 2021)

• Record Type: Journal Article
• Authors: Schultz, Mary Anne; Walden, Rachel Lane; Cato, Kenrick; Coviak, Cynthia Peltier; Cruz, Christopher; D'Agostino, Fabio; Douthit, Brian J.; Forbes, Thompson; Gao, Grace; Lee, Mikyoung Angela; Lekan, Deborah; Wieben, Ann; Jeffery, Alvin D.
• Is Part Of: Computers, informatics, nursing. Volume 39:Issue 11(2021)
• Publication Date: 2021-11
• Subjects: Artificial intelligence -- Data analytics -- Nursing research -- Outcome and process assessment; Nursing -- Data processing -- Periodicals; 610.730285
• DOI: 10.1097/CIN.0000000000000705
• Languages: English
• ISSNs: 1538-2931

Abstract

Abstract : 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. Abstract : Supplemental digital content is available in the text.