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

Ann M. Wieben; Rachel Lane Walden; Bader G. Alreshidi; Sophia F. Brown; Kenrick Cato; Cynthia Peltier Coviak; Christopher Cruz; Fabio D'Agostino; Brian J. Douthit; Thompson H. Forbes; Grace Gao; Steve G. Johnson; Mikyoung Angela 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

doi:10.1055/a-2088-2893

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

Ann M. Wieben, Rachel Lane Walden, Bader G. Alreshidi, Sophia F. Brown, Kenrick Cato, Cynthia Peltier Coviak, Christopher Cruz, Fabio D'Agostino, Brian J. Douthit, Thompson H. Forbes, Grace Gao, Steve G. Johnson, Mikyoung Angela Lee, Margaret Mullen-Fortino, Jung In Park, Suhyun Park, Lisiane Pruinelli, Anita Reger, Jethrone Role, Marisa SileoMary Anne Schultz, Pankaj Vyas, Alvin D. Jeffery

Research output: Contribution to journal › Review article › peer-review

1 Scopus citations

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.

Original language	English (US)
Pages (from-to)	585-593
Number of pages	9
Journal	Applied clinical informatics
Volume	14
Issue number	3
DOIs	https://doi.org/10.1055/a-2088-2893
State	Published - Nov 29 2022

Bibliographical note

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© 2023 TumorDiagnostik and Therapie. All rights reserved.

Keywords

data science
deployment
implementation
machine learning
nursing
pilot
prediction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Wieben, A. M., Walden, R. L., Alreshidi, B. G., Brown, S. F., Cato, K., Coviak, C. P., Cruz, C., D'Agostino, F., Douthit, B. J., Forbes, T. H., Gao, G., Johnson, S. G., Lee, M. A., Mullen-Fortino, M., Park, J. I., Park, S., Pruinelli, L., Reger, A., Role, J., ... Jeffery, A. D. (2022). Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature. Applied clinical informatics, 14(3), 585-593. https://doi.org/10.1055/a-2088-2893

Wieben, AM, Walden, RL, Alreshidi, BG, Brown, SF, Cato, K, Coviak, CP, Cruz, C, D'Agostino, F, Douthit, BJ, Forbes, TH, Gao, G, Johnson, SG, Lee, MA, Mullen-Fortino, M, Park, JI, Park, S, Pruinelli, L, Reger, A, Role, J, Sileo, M, Schultz, MA, Vyas, P & Jeffery, AD 2022, 'Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature', Applied clinical informatics, vol. 14, no. 3, pp. 585-593. https://doi.org/10.1055/a-2088-2893

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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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AU - Johnson, Steve G.

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AU - Park, Jung In

AU - Park, Suhyun

AU - Pruinelli, Lisiane

AU - Reger, Anita

AU - Role, Jethrone

AU - Sileo, Marisa

AU - Schultz, Mary Anne

AU - Vyas, Pankaj

AU - Jeffery, Alvin D.

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N2 - 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.

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

Abstract

Bibliographical note

Keywords

UN SDGs

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