Enterprise AI Analysis
Factors Associated with Intention to Use Generative AI in Nursing Practice: A Cross-Sectional Study
Generative Artificial Intelligence (GAI) holds significant potential to support healthcare professionals. This study addresses the lack of in-depth understanding regarding how healthcare professionals adopt and utilize GAI in practice. Researchers conducted a cross-sectional online survey with 46 items to assess nurses' intention to use GAI, involving 176 nurses from a tertiary hospital in Seoul, Korea, between March 17 and 28, 2025. The analysis employed descriptive statistics, Mann-Whitney U tests, Kruskal-Wallis H tests, correlation analyses, multiple regression analyses, and an exploratory subgroup analysis.
Executive Impact: Key Findings
The research highlights critical drivers for GAI adoption in nursing, revealing both current utilization patterns and factors influencing future intent.
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Current GAI Adoption by Nurses
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Voluntariness: Highest Impact Factor Coefficient
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Model Explanatory Power (Adjusted R²)
The study found that 61.4% of nurses had no prior GAI experience, with active users primarily focusing on educational material development (50.0%). Voluntariness, performance expectancy, and GAI literacy were identified as the primary drivers of GAI usage intention, while prior experience moderates these influences. These insights underscore the need for tailored strategies to integrate GAI effectively into nursing practice.
Deep Analysis & Enterprise Applications
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Research Framework
GAI Usage Profile
Key Determinants
Subgroup Insights
Enterprise Process Flow: GAI Usage Intention Model
Performance expectancy
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Effort expectancy
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Social influence
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Facilitating conditions
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GAI usage experience
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Voluntariness of use
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Perceived risk
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GAI literacy
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Intention to use GAI
50.0%
Most Common GAI Application by Nurses: Educational Material Development
This highlights an early focus on administrative and supportive tasks for GAI adoption in nursing, potentially due to lower perceived risk compared to direct patient care applications.
| Factor | Correlation (p-value) | Regression Coef (p-value) |
|---|---|---|
| Voluntariness of use | 0.748 (p<.001) | 0.358 (p<.001) |
| Performance expectancy | 0.719 (p<.001) | 0.344 (p<.001) |
| GAI literacy | 0.558 (p<.001) | 0.192 (p=.010) |
| Effort expectancy | 0.509 (p<.001) | 0.047 (p=.422) |
| Perceived risk | 0.411 (p<.001) | 0.100 (p=.113) |
| Social influence | 0.348 (p<.001) | -0.041 (p=.472) |
| Facilitating conditions | 0.255 (p<.001) | 0.048 (p=.339) |
Key Insights: Voluntariness, performance expectancy, and GAI literacy are strong positive predictors of GAI usage intention. Factors like effort expectancy, perceived risk, social influence, and facilitating conditions did not show significant direct associations in the multiple regression model, indicating their influence might be less direct or moderated by other factors in this specific context.
Moderating Role of Experience
Our exploratory subgroup analysis provided a notable finding: the determinants of GAI usage intention appear to evolve with user experience. For nurses who had not yet used GAI, the intention to adopt was influenced by a combination of personal willingness (voluntariness), perceived utility (performance expectancy), and knowledge (GAI literacy).
However, for nurses who had already used GAI, their intention for continued use was predominantly driven by voluntariness. This may imply that once users have direct experience with GAI, its utility is either taken for granted or its value is already internalized, making the decision to continue using it a matter of ingrained personal motivation.
This finding suggests that strategies to promote GAI use in nursing should be tailored: for novices, the focus should be on education and demonstration of the value of GAI, whereas for experienced users, the focus should shift to fostering an environment that supports and reinforces autonomous use.
Calculate Your Enterprise AI ROI
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Estimated Annual Savings
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Estimated Annual Hours Reclaimed
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Strategic Implementation Roadmap
Our phased approach ensures a smooth and effective integration of Generative AI into your nursing practice, maximizing impact and minimizing disruption.
Phase 1: Discovery & Needs Assessment
Comprehensive analysis of current workflows, identification of high-impact GAI application areas, and definition of success metrics tailored to your institution.
Phase 2: Pilot Program & Customization
Development and deployment of a targeted GAI pilot in selected departments, gathering user feedback and refining the system for optimal performance and integration.
Phase 3: Full-Scale Deployment & Training
Seamless integration of GAI solutions across all relevant departments, accompanied by extensive training programs for nurses and staff to ensure high adoption rates and proficiency.
Phase 4: Performance Monitoring & Optimization
Continuous monitoring of GAI system performance, regular updates, and iterative enhancements based on real-world data and evolving clinical needs to sustain long-term value.
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