โก Quick Summary
This study explored the behavioral intention of home care nurses in South Korea to adopt digital healthcare technologies, revealing that big data and AI solutions had the highest intention to use. The findings highlight the importance of social influence and effort expectancy in promoting the adoption of these technologies.
๐ Key Details
- ๐ Participants: 180 home care nurses from various settings
- ๐งฉ Technologies assessed: 19 digital health technologies
- โ๏ธ Framework used: Unified Theory of Acceptance and Use of Technology (UTAUT)
- ๐ Key metrics: Behavioral intention rates, odds ratios for predictors
๐ Key Takeaways
- ๐ High intention: 87.2% for big data/AI solutions and 85.6% for AI-assisted care sensors.
- ๐ Negative correlation: Experience and intention were negatively correlated across all technologies.
- ๐ฉโโ๏ธ Group differences: Home hospice nurses had the highest intention at 93.8%.
- ๐ก Effort expectancy: Increased likelihood of adoption (OR = 2.256).
- ๐ Social influence: The strongest predictor of behavioral intention (OR = 2.931).
- ๐ Need for improvement: Enhancing user satisfaction is crucial for successful technology implementation.
๐ Background
As societies age, the demand for effective home care and digital healthcare solutions grows. Understanding the factors that influence healthcare professionals’ willingness to adopt new technologies is essential for improving patient care and operational efficiency. This study focuses on home care nurses, who play a pivotal role in delivering healthcare services in the community.
๐๏ธ Study
Conducted in South Korea, this cross-sectional study surveyed 180 home care nurses using online questionnaires. The research aimed to assess their experiences with and intentions to use various digital health technologies, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT). Key factors evaluated included performance expectancy, effort expectancy, social influence, and attitudes toward digital healthcare.
๐ Results
The results indicated a strong behavioral intention among home care nurses to adopt digital healthcare technologies. Notably, big data and AI solutions emerged as the most favored, with an intention rate of 87.2%. The study also found a significant negative correlation between usage experience and intention, suggesting that greater experience may not necessarily lead to increased willingness to adopt these technologies.
๐ Impact and Implications
The findings of this study have significant implications for the integration of digital healthcare in home care settings. By recognizing the importance of social influence and effort expectancy, healthcare organizations can tailor their training and support systems to enhance adoption rates. This could ultimately lead to improved patient outcomes and more efficient healthcare delivery in an aging society.
๐ฎ Conclusion
This research highlights the strong potential for home care nurses to embrace digital healthcare technologies. However, addressing the negative correlation between experience and intention is crucial. By focusing on reducing perceived effort and enhancing social support, we can foster a more favorable environment for the adoption of these transformative technologies in healthcare.
๐ฌ Your comments
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Factors influencing Home Care Nurses’ Behavioral Intension to Use Digital Healthcare.
Abstract
PURPOSE: Home care and digital healthcare are essential strategies for addressing health challenges in an aging society. This study aims to understand the behavioral intention to use digital healthcare and its influencing factors based on the Unified Theory of Acceptance and Use of Technology (UTAUT).
METHODS: This cross-sectional study surveyed 180 home care nurses in South Korea using online questionnaires. Participants were from hospital-based home care (HHC), home hospice (HH), and community health center-based home care (CHC). The survey assessed their experience with and intention to use 19 digital health technologies and measured key factors based on the Unified Theory of Acceptance and Use of Technology (UTAUT): performance expectancy, effort expectancy, social influence, attitude toward digital healthcare, and concerns.
RESULTS: Among the 19 digital health technologies, big data/artificial intelligence (AI) solutions (87.2%) and AI-assisted care sensors (85.6%) had the highest behavioral intention to use. Usage experience and intention were negatively correlated across all technologies (ฮบ = -0.103 to -0.703, p < 0.001). Behavioral intentions differed significantly between groups (ฯ2 = 6.354, p = .042), with HH nurses reporting the highest intention (93.8%), followed by HHC (87.0%) and CHC (75.0%) nurses. Effort expectancy increased adoption likelihood (odds ratio [OR] = 2.256, 95% confidence interval [CI]: 1.102-4.617, p = .026), while social influence was the strongest predictor (OR = 2.931, 95% CI: 1.152-7.462, p = .024).
CONCLUSIONS: Home care nurses generally showed strong behavioral intentions to adopt digital healthcare. However, the negative association between experience and intention suggests the need to enhance user satisfaction when implementing digital health technologies. Since effort expectancy and social influence significantly affect adoption, reducing perceived effort and strengthening social support are essential for successful integration.
Author: [‘Kwon SH’, ‘Kim YJ’, ‘Park SH’]
Journal: Asian Nurs Res (Korean Soc Nurs Sci)
Citation: Kwon SH, et al. Factors influencing Home Care Nurses’ Behavioral Intension to Use Digital Healthcare. Factors influencing Home Care Nurses’ Behavioral Intension to Use Digital Healthcare. 2025; (unknown volume):(unknown pages). doi: 10.1016/j.anr.2025.10.003