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

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.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review.

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Revision of the Osteoporosis Knowledge Test: Reliability and Validity.

The purpose of this study was to revise the Osteoporosis Knowledge Test (OKT) and evaluate its reliability and validity. The original OKT, developed in the early 1990 s, needed updating based on current research. A convenience sample of 105 adults completed the draft revised OKT. A subsample (n = 27) completed the questionnaire 2 weeks later to determine stability. The sample was recruited from diverse sites in western and northern Michigan over a year. The 32-item Revised OKT (2012) demonstrated internal consistency (total scale Kuder-Richardson-20 = .85, Nutrition subscale = .83, and Exercise subscale = .81). Test-retest analysis resulted in a Pearson correlation coefficient of .87. Validity was evaluated by content validity. Questions were examined for difficulty, effectiveness of distracters, and discrimination. In addition, measures of point-biserial, internal consistency and stability were determined. The Revised OKT (2012) is a comprehensive instrument reflecting current research and assesses osteoporosis knowledge of adults.