TY - JOUR
T1 - Data Science Trends Relevant to Nursing Practice
T2 - A Rapid Review of the 2020 Literature
AU - Douthit, Brian J.
AU - Walden, Rachel L.
AU - Cato, Kenrick
AU - Coviak, Cynthia P.
AU - Cruz, Christopher
AU - D'Agostino, Fabio
AU - Forbes, Thompson
AU - Gao, Grace
AU - Kapetanovic, Theresa A.
AU - Lee, Mikyoung A.
AU - Pruinelli, Lisiane
AU - Schultz, Mary A.
AU - Wieben, Ann
AU - Jeffery, Alvin D.
N1 - Publisher Copyright:
© 2022. Thieme. All rights reserved.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - data analytics
KW - nursing research
KW - outcome and process assessment
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U2 - 10.1055/s-0041-1742218
DO - 10.1055/s-0041-1742218
M3 - Review article
C2 - 35139564
AN - SCOPUS:85124268590
SN - 1869-0327
VL - 13
SP - 161
EP - 179
JO - Applied clinical informatics
JF - Applied clinical informatics
IS - 1
ER -