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To assess healthcare professionals' digital health competence and its associated factors.
Cross-sectional study.
The study was conducted from October 2023 to April 2024 among healthcare professionals in Italy, using convenience and snowball sampling. The questionnaire included four sections assessing: (i) socio-demographic and work-related characteristics; (ii) use of digital solutions as part of work and in free time, and communication channels to counsel clients in work; and DigiHealthCom and DigiComInf instruments including measurements of (iii) digital health competence and (iv) managerial, organisational and collegiality factors. K-means cluster analysis was employed to identify clusters of digital health competence; descriptive statistics to summarise characteristics and ANOVA and Chi-square tests to assess cluster differences.
Among 301 healthcare professionals, the majority were nurses (n = 287, 95.3%). Three clusters were identified: cluster 1 showing the lowest, cluster 2 moderate and cluster 3 the highest digital health competence. Most participants (n = 193, 64.1%) belonged to cluster 3. Despite their proficiency, clusters 2 and 3 scored significantly lower on ethical competence. Least digitally competent professionals had significantly higher work experience, while the most competent reported stronger support from management, organisation, and colleagues. Communication channels for counselling clients and digital device use, both at work and during free time, were predominantly traditional technologies.
Educational programmes and organisational policies prioritising digital health competence development are needed to advance digital transition and equity in the healthcare workforce.
Greater emphasis should be placed on the ethical aspects, with interventions tailored to healthcare professionals' digital health competence. Training and policies involving managers and colleagues, such as mentoring and distributed leadership, could help bridge the digital divide. Alongside traditional devices, the adoption of advanced technologies should be promoted.
This study adheres to the STROBE checklist.
None.
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.
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