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The use of technology to support nurses' decision-making is increasing in response to growing healthcare demands. AI, a global trend, holds great potential to enhance nurses' daily work if implemented systematically, paving the way for a promising future in healthcare.
To identify and describe AI technologies for nurses' clinical decision-making in healthcare settings.
A systematic literature review.
CINAHL, PubMed, Scopus, ProQuest, and Medic were searched for studies with experimental design published between 2005 and 2024.
JBI guidelines guided the review. At least two researchers independently assessed the eligibility of the studies based on title, abstract, and full text, as well as the methodological quality of the studies. Narrative analysis of the study findings was performed.
Eight studies showed AI tools improved decision-making, patient care, and staff performance. A discharge support system reduced 30-day readmissions from 22.2% to 9.4% (p = 0.015); a deterioration algorithm cut time to contact senior staff (p = 0.040) and order tests (p = 0.049). Neonatal resuscitation accuracy rose to 94%–95% versus 55%–80% (p < 0.001); seizure assessment confidence improved (p = 0.01); pressure ulcer prevention (p = 0.002) and visual differentiation (p < 0.001) improved. Documentation quality increased (p < 0.001).
AI integration in nursing has the potential to optimise decision-making, improve patient care quality, and enhance workflow efficiency. Ethical considerations must address transparency, bias mitigation, data privacy, and accountability in AI-driven decisions, ensuring patient safety and trust while supporting equitable, evidence-based care delivery.
The findings underline the transformative role of AI in addressing pressing nursing challenges such as staffing shortages, workload management, and error reduction. By supporting clinical decision-making and workflow efficiency, AI can enhance patient safety, care quality, and nurses' capacity to focus on direct patient care. A stronger emphasis on research and implementation will help bridge usability and scalability gaps, ensuring sustainable integration of AI across diverse healthcare settings.
To examine the characteristics of the health care needs corresponding to the medical care process and HR-QOL of women with cancer.
A descriptive design was adopted.
The study's participants were 122 women with cancer who completed a survey before and 6 months after treatment initiation. A principal component analysis (PCA) was conducted on a set of 12 health care satisfaction scores at each point. Correlations were examined between the resulting components and HR-QOL indicators, including subjective well-being, symptoms, symptom-related interference, anxiety and depression.
Most participants reported high health care satisfaction in both phases. PCA indicated the presence of 3 distinct domains: satisfaction with health care, health care management and supportive care. In both phases, these domains accounted for about 60% of the variance, while the remaining 40% was unexplained. Only satisfaction with health care was correlated with HR-QOL at both phases, with particularly strong associations observed for subjective well-being and depression at 6 months. Before treatment initiation, the item of ‘nursing care and practice’ received the highest average score, but demonstrated a negative loading on the component of ‘satisfaction with health care management’. The component of ‘satisfaction with supportive care needs’ was retained at both phases.
Health care plays a pivotal role in maintaining patients' quality of life, while supportive care and the integration of nursing practice within health care management remain essential.
High satisfaction scores do not necessarily mean that all health care needs are met. Addressing unmet needs from the perspective of HR-QOL and ensuring continuous supportive care throughout the treatment process is imperative.
Data provided by women with cancer was used.
To extract and interpret quantitative data exploring the effectiveness of family health conversations (FHCs) on family functioning, perceived support, health-related quality of life, caregiver burden and family health in families living with critical or chronic health conditions.
Addressing the health of families affected by critical or chronic illnesses requires focused attention. The effective integration of FHCs is hampered by a scarcity of rigorous quantitative studies that provide solid evidence on best practices and outcomes.
A systematic review following the Joanna Briggs Institute guidelines.
The review is reported according to the PRISMA 2020 checklist. Appropriate studies were searched in PubMed, CINAHL, PsycINFO, Scopus and Cochrane Databases. Results of the search were imported into the Covidence web-based program. Included were studies with a quantitative research design, delivered to families with critical or chronic health conditions, describing FHCs based on the Calgary Family Assessment Model and/or the Calgary Family Intervention Model, and/or the Illness Beliefs Model, using reliable and validated instruments, published between 2008 and 2023, and written in English.
In total, 24 papers met the inclusion criteria. Sixteen papers used a quasi-experimental design, eight of which included a control group. Two papers used a mixed methods design, and six papers were randomised controlled trials (RCTs). A statistically significant effect of FHCs on family functioning was reported in two RCTs and three quasi-experimental papers. We also found that a statistically significant effect of FHCs was reported on perceived support in 9 of 15 papers, quality of life in 4 of 11 papers and caregiver burden in 1 of 3 papers.
The interventions reviewed revealed variability and partial results concerning the effectiveness of FHCs on family functioning. More rigorous research about short-term, intermediate- and long-term effectiveness is needed before conclusions can be drawn.
The study is reported according to the PRISMA 2020 (Preferred Reporting Items for Systematic reviews and Meta-Analyses) (File S1).
No patient or public contribution. Data were gathered from previously published studies.
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