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

Authors:  Ann Wieben, Rachel Walden, Bader G Alreshidi, Sophia Brown, Kenrick Cato, Cynthia Coviak, Christopher Cruz, Fabio D‘Agostino, Brian Douthit, Thompson Forbes, Grace Gao, Steven G Johnson, Mikyoung Lee, Margaret Mullen-Fortino, Jung-In Park, Suhyun Park, Lisiane Pruinelli, Anita Reger, Jethrone Role, Marisa Sileo, Mary Anne Schultz, Pankaj Vyas, Alvin D. Jeffery.

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 on 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; 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 healthcare.

Conclusions: 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.


Data Science and Clinical Analytics
Informatics tools
Machine learning and predictive modeling
Machine Learning, AI, predictive modeling