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.
Subject(s)
COVID-19 , Data Science , Adult , Humans , COVID-19/epidemiology , Delivery of Health CareABSTRACT
A new phylogenetic hypothesis for the Neotropical butterfly genus Hamadryas, based on the combination of a morphological matrix, one mitochondrial (COI) and four nuclear markers (CAD, RpS5, EF1a, and Wingless), is presented. Results from analyses of the molecular evidence are compared with a previously published morphological phylogeny. Molecular data and the analysis of the complete dataset support the monophyly of Hamadryas and most sister groups suggested by morphological data alone. The addition of DNA sequences to the morphological matrix helped define species groups for which no morphological synapomorphies were found. Partitioned Bremer support indicates that COI, CAD, and morphology were consistently in agreement with the combined evidence tree. In contrast, signal from the nuclear markers Rps5, EF1a, and Wingless showed indifference at most levels of the tree, and minor conflict at nodes solving the relationships between species groups. Though resolved, the combined evidence tree shows low resample values, particularly among species groups whose relationships were characterized by short internodes. A reassessment about the pattern of character change for sound production is presented and discussed.
