Nurses' perceptions of the design, implementation, and adoption of machine learning clinical decision support: A descriptive qualitative study.

INTRODUCTION: The purpose of this study was to explore nurses' perspectives on Machine Learning Clinical Decision Support (ML CDS) design, development, implementation, and adoption. DESIGN: Qualitative descriptive study. METHODS: Nurses (n = 17) participated in semi-structured interviews. Data were transcribed, coded, and analyzed using Thematic analysis methods as described by Braun and Clarke. RESULTS: Four major themes and 14 sub-themes highlight nurses' perspectives on autonomy in decision-making, the influence of prior experience in shaping their preferences for use of novel CDS tools, the need for clarity in why ML CDS is useful in improving practice/outcomes, and their desire to have nursing integrated in design and implementation of these tools. CONCLUSION: This study provided insights into nurse perceptions regarding the utility and usability of ML CDS as well as the influence of previous experiences with technology and CDS, change management strategies needed at the time of implementation of ML CDS, the importance of nurse-perceived engagement in the development process, nurse information needs at the time of ML CDS deployment, and the perceived impact of ML CDS on nurse decision making autonomy. CLINICAL RELEVANCE: This study contributes to the body of knowledge about the use of AI and machine learning (ML) in nursing practice. Through generation of insights drawn from nurses' perspectives, these findings can inform successful design and adoption of ML Clinical Decision Support.

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.

Systematic Review and Meta-analysis of Psychosocial Uncertainty Management Interventions.

BACKGROUND: Psychosocial uncertainty management interventions (UMIs) targeting patients and their family members might help to alleviate the negative influences of illness-related uncertainty, such as diminished quality of life and poor adjustment. OBJECTIVES: The aims of this study were to evaluate the key characteristics of psychosocial UMIs and assess intervention effects on patients' and their family members' short-term and long-term illness-related uncertainty. METHODS: We conducted a systematic review and meta-analysis of psychosocial UMIs published through 2017. We performed a comprehensive electronic search and manual review. The outcome indicator was illness-related uncertainty experienced by patients or their family members. RESULTS: We included 29 studies in the systematic review and 14 studies in the meta-analysis. The main intervention components were information and resource provision, coping skills training, social and emotional support, communication skills, symptom management and self-care, coordination of care, and exercise. Compared to usual care, patients who received UMIs reported less uncertainty immediately after intervention delivery (g = -0.44, 95% confidence interval [CI] [-0.71, -0.16]) and at later follow-up points (g = -0.47, 95% CI [-0.91, -0.03]). Family members who received UMIs also reported less uncertainty immediately after intervention delivery (g = -0.20, 95% CI [-0.33, -0.06]) and at later follow-up points (g = -0.20, 95% CI [-0.36, -0.04]). DISCUSSION: Psychosocial UMIs had small to medium beneficial effects for both patients and their family members. Questions remain regarding what intervention components, modes of delivery, or dosages influence effect size. More rigorously designed randomized controlled trials are needed to validate intervention effects on patients' and family members' uncertainty management.