The Digital Evolution in Healthcare: What Does It Mean for Nursing?

The advancement of huge data sets in healthcare has spawned unique insights into care provision. Formerly known as the "Digital Revolution," now more commonly and correctly termed an "evolution," the deployment of Big Data continues to inspire implications for how care is provided, evaluated, and experienced. This commentary reviews foremost developments that offer direction for acquisition of digital competencies by nurses.

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.

Setting the Research Agenda for Nursing Administration and Leadership Science: A Delphi Study.

OBJECTIVE: The aim of this study was to identify and prioritize research topics for nursing administration and leadership science. BACKGROUND: Nursing administration and leadership research priorities should provide a framework for building the science needed to inform practice. METHODS: The Association for Leadership Science in Nursing (ALSN) and American Organization for Nursing Leadership (AONL) Foundation (AONL-F) for Nursing Leadership and Education collaborated on a Delphi study. Initial input on research priority items were received from ALSN and AONL members. National experts participated in a 3-round Delphi study. RESULTS: Top-ranked priorities included: 1) nurses' health, well-being, resiliency, and safety in the workplace; 2) developing and managing a nursing workforce to meet current and future healthcare needs; 3) healthy work/practice environments for direct care nurses; 4) healthy work/ practice environments for nurse leaders; 5) quantification of nursing's value across the healthcare delivery system; and 6) nurse leader development and essential competencies. CONCLUSIONS: Researchers and funders should use these priorities to guide future studies.

Diffusion of Precision Health Into a Baccalaureate Nursing Curriculum.

BACKGROUND: Precision health (PH) and precision medicine are emerging approaches to health care promising more individualized care for health consumers. This improved type of care management uses innovation in science and technology to accurately identify diseases, treatments, and environmental influences to provide effective and efficient care. Multiple industries are supporting this venture, including nursing. METHOD: To respond to the national call to integrate PH in nursing curricula, a small urban university in Southern California proposed to integrate concepts of PH into six select courses in the baccalaureate curriculum. RESULTS: This curriculum revision launched in fall 2020; it was the first time PH concepts were introduced to Bachelor of Science in Nursing students in the department of nursing. Student outcomes will be measured using the nine competencies developed. CONCLUSION: Nurse educators shape future practice. It is incumbent upon them to adopt the opportunities for transformation presented by the emergent phenomenon of PH. Only then will students be prepared with the knowledge, skills, and attitudes foundational for precise care. [J Nurs Educ. 2021;60(2):107-110.].

Learning from mistakes in a simulated nursing leadership laboratory.

Human patient simulation in nursing education has become an accepted and expected form of pedagogy. Research on the use of human patient simulation to evaluate student performance, however, is still at an early stage. The vast majority of these sources report the unit of analysis as the nurse-patient dyad (one nurse-one patient) situated in an infrequently occurring, high-risk, or costly event such as a code blue, and the literature reveals little evidence on the efficacy of the use of simulation for the care of multiple patients. The teaching innovation, discussed herein, involving a simulation, used a leadership scenario of a routine day in an acute-care hospital unit. The aim of the project was to provide a high-fidelity simulation of the competing demands on a nurse's time and attention while caring for multiple patients. Working as a team, using principles of prioritization, delegation, scope of practice, and communication, senior baccalaureate nursing students assumed the various roles of interdisciplinary team members as they moved through staged sequences of changing patient and unit conditions. This was followed by debriefing session that prompted the students to identify their errors in judgment, including sending the wrong patient to the operating room, failing to rescue a patient, and failing to delegate critical tasks to other nursing team members.