Navigating Challenges in the Integration of Artificial Intelligence in Nursing Practice: An Integrative Literature Review

Abstract

BACKGROUND: The integration of artificial intelligence (AI) in nursing practice holds promise for enhancing patient care but is hindered by significant challenges. METHOD: This literature reviews synthesized research from 2015 to 2024, examining barriers nurses face in adopting AI technologies. A comprehensive search across multiple databases identified relevant articles, leading to the inclusion of 19 studies for thematic analysis. RESULTS: The analysis revealed nine key challenges: a lack of training and education, resistance to change, data privacy and security concerns, integration issues with existing systems, high costs, limited access to technology, and regulatory and ethical dilemmas. These interconnected obstacles collectively impact the effective integration of AI in nursing practice. DISCUSSION: Addressing these challenges is crucial for leveraging AI's full potential to improve patient outcomes. As organizations implement AI solutions, they must prioritize targeted training, infrastructure upgrades, and ethical guidelines to foster an adaptive and compliant nursing workforce. CONCLUSION: Proactively navigating these barriers is essential for optimizing AI integration in nursing, ultimately enhancing the quality of care and operational efficiency. IMPLICATIONS FOR NURSING AND HEALTH POLICY: The integration of AI in nursing practice necessitates enhanced training and education to equip nurses with the skills needed for effective use, fostering a culture of adaptability to technological advancements. Additionally, healthcare policies must prioritize investments in robust cybersecurity and clear regulatory frameworks to ensure ethical AI implementation while safeguarding patient data and enhancing care delivery.

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