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
Inadequate staffing negatively impacts hospital operations, quality of patient care, and employee engagement. Traditional staffing approaches to address clinical staffing and scheduling are not as effective in a complex healthcare environment. Organizations must leverage innovative strategies and use of technology to improve clinical staffing. To address the staffing challenges, nursing staffing and information technology at Loma Linda University Medical Center developed an inpatient staffing dashboard. A staffing dashboard is a staffing tool comprised of several tabs and staffing measures, which include filled percentage as the key performance indicator. During the staffing dashboard development, evaluation took place to determine the staffing and scheduling system's extract-transform-load capacity. Data were analyzed, defined, and profiled. Tableau software was used to create an interactive staffing dashboard and integrated with EPIC Hyperspace for user accessibility. The interactive features and staffing measures available in this staffing tool empowered staffing and nursing leaders to utilize data visualization for day-to-day nursing operations, proactively plan for staffing demands, and use data to drive staffing decisions. Our collaborative experience proved that nursing and information technology collaborative projects produce innovative solutions and workflow efficiencies. Leaders must promote nursing-information technology collaborations in healthcare organizations.
