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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.

Research results reporting the relationships of hospital structural characteristics to inpatient mortality for medical–surgical and Acute Myocardial Infarction patients are inconclusive. Hospital characteristics thought to be significantly associated with mortality are: percentage of board‐certified physicians, the hospital's teaching status, and technological resource availability. Yet studies of these characteristics have yielded mixed results. Only Registered Nurse ratios (measures of Registered Nurse hours per patient days or per patient) have been shown to be consistently (and inversely) associated with mortality. However, far fewer studies have examined the impact of this variable and design weaknesses, including the absence of a conceptual framework to guide the selection of a given study's explanatory variables, have hindered those that have. Further, inconsistencies, indeed clear contradictions, in these studies’ findings (with the exception of nurse ratios) necessitate further analysis of both research methods and interpretation of results. Such analyses must occur to promote advances in research design and methodology in studies of inpatient mortality.

Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

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