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

Author(s):  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

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.

Citation:    Douthit BJ, Walden RL, Cato K, Coviak CP, Cruz C, D'Agostino F, Forbes T, Gao G, Kapetanovic TA, Lee MA, Pruinelli L, Schultz MA, Wieben A, Jeffery AD. Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature. Appl Clin Inform. 2022 Jan;13(1):161-179. doi: 10.1055/s-0041-1742218. Epub 2022 Feb 9. PMID: 35139564; PMCID: PMC8828453.

Data Science and Clinical Analytics
Sabas 21 care components
Clinical decision support
Informatics Research Methods – NI, Medical BioMed
Machine learning and predictive modeling
Machine Learning, AI, predictive modeling
Literature Review