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

Brian J. Douthit, Rachel L. Walden, Kenrick Cato, Cynthia P. Coviak, Christopher Cruz, Fabio D'Agostino, Thompson Forbes, Grace Gao, Theresa A. Kapetanovic, Mikyoung A. Lee, Lisiane Pruinelli, Mary A. Schultz, Ann Wieben, Alvin D. Jeffery

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)161-179
Number of pages19
JournalApplied clinical informatics
Volume13
Issue number1
DOIs
StatePublished - Jan 1 2021

Bibliographical note

Funding Information:
This study was funded by the Agency for Healthcare Research and Quality and the Patient-Centered Outcomes Research Institute (grant: K12 HS026395) as well as the resources and use of facilities at the Department of Veterans Affairs, Tennessee Valley Healthcare System.

Publisher Copyright:
© 2022. Thieme. All rights reserved.

Keywords

  • artificial intelligence
  • data analytics
  • nursing research
  • outcome and process assessment

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.
  • Review

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