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Review
. 2023 May;14(3):585-593.
doi: 10.1055/a-2088-2893. Epub 2023 May 7.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

Ann M Wieben  1 , Rachel Lane Walden  2 , Bader G Alreshidi  3 , Sophia F Brown  4 , Kenrick Cato  5 , Cynthia Peltier Coviak  6 , Christopher Cruz  7 , Fabio D'Agostino  8 , Brian J Douthit  9 , Thompson H Forbes 3rd  10 , Grace Gao  11 , Steve G Johnson  12 , Mikyoung Angela Lee  13 , Margaret Mullen-Fortino  14 , Jung In Park  15 , Suhyun Park  16 , Lisiane Pruinelli  16 , Anita Reger, Jethrone Role  17 , Marisa Sileo  18 , Mary Anne Schultz  19 , Pankaj Vyas  20 , Alvin D Jeffery  21
Affiliations
Review

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

Ann M Wieben et al. Appl Clin Inform. 2023 May.

Abstract

Objectives: The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools.

Methods: We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework.

Results: Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care.

Conclusion: In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.

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Conflict of interest statement

The authors declare that they have no conflicts of interest in the research.

Figures

Fig. 1
Fig. 1
PRISMA flow diagram depicting the flow of information through the different phases of review. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-analyses.
See this image and copyright information in PMC

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