Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
JMIR nursing
Effectiveness of Artificial Intelligence-Based Nursing Interventions for Chronic Illness Care: Umbrella Review
BACKGROUND: Artificial intelligence (AI)-based nursing interventions are increasingly being used to manage chronic illnesses; however, their definitive impact on clinical outcomes remains inconclusive, necessitating a comprehensive evidence synthesis. OBJECTIVE: This umbrella review aimed to synthesize the evidence regarding the effectiveness of AI-based nursing interventions for chronic illness care and their subsequent impact on health care outcomes in clinical settings. METHODS: We conducted an umbrella review and prospectively registered the protocol. A systematic search of 5 electronic databases (PubMed, CINAHL, Cochrane Library, Scopus, and Web of Science) was performed to identify systematic reviews and meta-analyses published in English between 2021 and 2025. The methodological quality of the included studies was evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist. RESULTS: Eight high-quality systematic reviews were included, with machine learning identified as the predominant technology. Three primary outcome domains emerged: predictive, psychosocial, and hospital utilization. Due to measurement heterogeneity, the results were synthesized narratively. Our findings demonstrated that AI-based nursing interventions are effective in predicting adverse clinical events, unplanned hospital utilization, and health care costs. However, evidence regarding psychosocial outcomes remains insufficient. CONCLUSIONS: This review provides systematic evidence supporting the utility of AI in chronic illness management, particularly for improving predictive and utilization outcomes. These findings offer actionable insights for nursing leaders to integrate AI into clinical practice and education. Future research should prioritize rigorous empirical designs to further strengthen the evidence base for AI-driven nursing care.
17 July 2026
Read appraisal →Computers, informatics, nursing : CIN
Bridging the Gap Between Potential and Practice: An Integrative Review of Generative Artificial Intelligence in Nursing
BACKGROUND: Generative artificial intelligence (GAI) has emerged as a transformative tool in health care, particularly with the integration of large language models (LLMs) into electronic health record (EHR) systems. These tools have the potential to enhance clinical decision-making, increase efficiencies, and decrease time in the EHR. However, despite expanding availability, adoption in nursing practice remains limited. This integrative review aimed to synthesize the literature and explore barriers related to the integration of GAI tools into nursing practice. METHODS: We conducted an integrative search of peer-reviewed literature published between November 2022 and July 2025 to examine the current evidence while assessing for methodological quality. The retrieved articles were screened for title, abstract, and full text eligibility. We synthesized themes related to GAI adoption by nurses, focusing on ethical, educational, and workflow-related factors that influence use. RESULTS: A total of 10 studies met criteria including 7 qualitative descriptive design, 2 case reports, and 1 experimental clinical trial. Four key barriers emerged: educational gaps, ethical concerns, data transparency issues, and workflow misalignment contributing to nurses' hesitancy to engage with GAI tools. Recommendations include involvement of nurses in the design, implementation, and evaluation of GAI tools and mandatory AI curricula in nursing education. CONCLUSIONS: Overcoming barriers requires nurse involvement in GAI design efforts, targeted education, and governance models that foster trust and usability. Nurse-centered integration of GAI tools has the potential to advance workforce efficiencies while preserving patient safety.
4 July 2026
Read appraisal →JMIR nursing
Nurses' Experiences Using AI in Clinical Practice: Systematic Review
BACKGROUND: Artificial intelligence (AI) tools are increasingly used in clinical settings, yet most syntheses focus on nurses' attitudes or readiness rather than experiences after direct use in practice. OBJECTIVE: This study aimed to synthesize registered nurses' experiences of using AI in clinical practice and to identify perceived benefits, barriers, and implementation implications. METHODS: We conducted a systematic literature review of empirical studies reporting nurses' experiences of AI use in clinical settings. Searches were performed in CINAHL, Embase, MEDLINE, PsycINFO, and PubMed (last search: September 13, 2023). Moreover, 2 reviewers (AJSS and QZ) independently screened titles, abstracts, and full texts and appraised included studies using the Mixed Methods Appraisal Tool. We used thematic synthesis with a primarily deductive framework based on the Technology Acceptance Model 2, with the addition of facilitating conditions from the unified theory of acceptance and use of technology. RESULTS: In total, 20 studies met the inclusion criteria. Perceived usefulness and facilitating conditions were most frequently reported; nurses described AI as supporting decision-making, workflow efficiency, and confidence when implementation included adequate training, interoperability, and technical infrastructure. Ease of use was closely tied to interface design and training. Job relevance and output quality were rated more positively when nurses described AI as aligning with nursing tasks and as producing interpretable, reliable outputs. Common barriers included usability issues, limited integration into workflows and electronic systems, privacy and trust concerns, and inconsistent or poorly contextualized outputs. Across studies, nurses often described adoption as conditional on organizational readiness and meaningful involvement of nurses in design and implementation. CONCLUSIONS: Nurses' accounts suggest that AI may augment clinical work, but the perceived benefits reported in the included literature were contingent on workflow alignment, usable interfaces, training, and supportive infrastructure. These findings suggest potential value in involving nurses in the co-design and iterative refinement of AI tools, grounded in the consistent evidence that facilitating conditions, such as training, interoperability, and organizational readiness, are the primary determinants of whether AI is experienced positively. Prospective evaluation using nursing-relevant outcomes, such as usability, workflow integration, and trust, is needed to move beyond post hoc experiential accounts and establish what implementation conditions reliably produce benefit.
26 June 2026
Read appraisal →The Journal of nursing administration
Virtual Nursing Programs in Acute Care Settings: A Scoping Review of Patient, Nurse, and System-Level Outcomes
OBJECTIVE: To synthesize the literature on the influence of virtual nursing (VN) on patient, nurse, and system-level outcomes in acute care. BACKGROUND: Persistent nursing workforce challenges, including staff shortages and turnover, have been intensified by the COVID-19 pandemic, rising patient acuity, and increasing documentation demands. Many health systems have adopted VN programs, where remote nurses support bedside staff using audiovisual technology. Although these programs are rapidly expanding, evidence of their effectiveness remains limited. METHODS: These authors conducted a scoping review following PRISMA guidelines. Eleven studies reporting patient, nurse, or system-level outcomes were included. RESULTS: Most studies were cross-sectional pilots. Evidence was strongest for nurse and patient satisfaction, with reports of improved discharge efficiency, reduced administrative burden, and higher patient satisfaction. Findings for other outcomes, including safety indicators and financial metrics, were inconsistently reported. CONCLUSIONS: Virtual nursing shows promise for enhancing patient satisfaction and workflow efficiency, but cannot replace investments in sufficient bedside staff and resources.
21 June 2026
Read appraisal →Journal of medical Internet research
Beyond Time Saved: Implementation, Equity, and the Utility Threshold for Nursing AI Scribes
Schwabe et al's pre-post time-motion study of a domain-specific artificial intelligence (AI) speech assistant used by nurses in German long-term care provides one of the few real-world, full-shift evaluations of an AI scribe deployed to a nonphysician workforce, with paired objective observation and self-reported outcomes. This commentary points to the implications of these findings that extend well beyond the time savings headline. The study reports substantial reduction in self-reported documentation time and increased satisfaction with the documentation system, yet workplace satisfaction and the perception that AI scribes are "a good idea to implement" did not improve. Taken together, these findings show three undertheorized issues for AI scribe implementation in nursing and long-term care. First, postimplementation increases in time spent reviewing entries and retrieving information indicate that AI scribes redistribute cognitive effort from authoring to verification, with unknown consequences for satisfaction, mastery, and error detection. Second, the apparent paradox of rising documentation satisfaction alongside falling expectations of AI quality represents user calibration. Third, the substantial equity considerations of automatic speech recognition documentation reflect a broader trend of AI scribe studies that treat equity as a caveat, rather than treating equitable performance as empirically measurable and testable across variations in linguistic styles, dialects, and social linguistic dimensions. To advance the field, the next generation of nursing AI scribe research must treat documentation as a heterogeneous bundle of authoring, reviewing, retrieving, and verifying activities with distinct satisfaction and error profiles; specify and validate end-user-defined anchor utilities, rather than having a narrow focus on diffuse improvement; and treat equity testing and reporting of both automatic speech recognition systems and workforce adoption as standard reporting expectations, rather than caveats.
29 May 2026
Read appraisal →JMIR nursing
Exploring the Impact of Electronic Medical Record-Enabled Versus Paper-Based Systems on the Quality of Nursing Handover: Comparative Case-Study
BACKGROUND: Ineffective clinical handover has the potential to compromise patient safety and quality of care. Standardizing the handover process is a widely adopted improvement strategy intended to reduce failures of information transfer. By enabling real-time access to patient information, electronic medical records (EMRs) could address communication issues inherent to nursing handover. OBJECTIVE: This case study sought to compare the quality of nursing handover occurring at EMR-enabled sites with that occurring at paper-based sites, within a single Australian public health service. METHODS: A comparative case study design was used, using quantitative data collected from observational audits of 60 handovers and posthandover surveys conducted in EMR-enabled and paper-based ward environments. Handover quality was measured through compliance with the organization's Clinical Handover Standard and staff-reported perceptions, enabling comparison between cohorts. RESULTS: Compared with paper-based wards, EMR-enabled wards demonstrated more consistent communication of clinical alerts and risks and fewer interruptions, whereas paper-based wards showed higher rates of bedside handover and patient engagement. CONCLUSIONS: EMR implementation alone does not ensure high-quality nursing handover. EMR interface design and functionality may act as a barrier to bedside handover and patient engagement, and contribute to continued reliance on paper-based artifacts. Targeted EMR design and nursing informatics-led optimization are required to better support nursing handover as a complex and cognitively challenging communication process.
14 May 2026
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