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 on 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; 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 healthcare. CONCLUSIONS: 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.
ABSTRACT
(1) Background: The five rights of clinical decision support (CDS) are a well-known framework for planning the nuances of CDS, but recent advancements have given us more options to modify the format of the alert. One-size-fits-all assessments fail to capture the nuance of different BestPractice Advisory (BPA) formats. To demonstrate a tailored evaluation methodology, we assessed a BPA after implementation of Storyboard for changes in alert fatigue, behavior influence, and task completion; (2) Methods: Data from 19 weeks before and after implementation were used to evaluate differences in each domain. Individual clinics were evaluated for task completion and compared for changes pre- and post-redesign; (3) Results: The change in format was correlated with an increase in alert fatigue, a decrease in erroneous free text answers, and worsened task completion at a system level. At a local level, however, 14% of clinics had improved task completion; (4) Conclusions: While the change in BPA format was correlated with decreased performance, the changes may have been driven primarily by the COVID-19 pandemic. The framework and metrics proposed can be used in future studies to assess the impact of new CDS formats. Although the changes in this study seemed undesirable in aggregate, some positive changes were observed at the level of individual clinics. Personalized implementations of CDS tools based on local need should be considered.
ABSTRACT
INTRODUCTION: The COVID-19 pandemic has demanded immediate response from healthcare systems around the world. The learning health system (LHS) was created with rapid uptake of the newest evidence in mind, making it essential in the face of a pandemic. The goal of this review is to gain knowledge on the initial impact of the LHS on addressing the COVID-19 pandemic. METHODS: PubMed, Scopus and the Duke University library search tool were used to identify current literature regarding the intersection of the LHS and the COIVD-19 pandemic. Articles were reviewed for their purpose, findings and relation to each component of the LHS. RESULTS: Twelve articles were included in the review. All stages of the LHS were addressed from this sample. Most articles addressed some component of interoperability. Articles that interpreted data unique to COVID-19 and demonstrated specific tools and interventions were least common. CONCLUSIONS: Gaps in interoperability are well known and unlikely to be solved in the coming months. Collaboration between health systems, researchers, governments and professional societies is needed to support a robust LHS which grants the ability to rapidly adapt to global emergencies.