Leveraging machine learning in nursing: innovations, challenges, and ethical insights

Area of Science:

• Nursing Informatics
• Artificial Intelligence in Healthcare
• Digital Health

Background:

• The nursing profession is undergoing transformation with the integration of machine learning (ML).
• Discussions on ML in nursing are crucial for professional advancement.
• This review balances technological innovation with the human-centric nature of nursing.

Purpose of the Study:

• To comprehensively analyze the integration of machine learning (ML) in nursing.
• To explore ML's implications on patient care, nursing practices, and healthcare delivery.
• To highlight current applications, challenges, ethical considerations, and future developments of ML in nursing.

Main Methods:

• A narrative review was conducted using a detailed search strategy.
• Databases searched include PubMed, Embase, MEDLINE, Scopus, and Web of Science.
• Articles published between January 2019 and December 2023 were analyzed and categorized into themes.

Main Results:

• Machine learning significantly enhances patient monitoring, predictive analytics, and preventive care, exemplified by improved sepsis prediction models.
• ML applications in nursing education improve simulation-based training and adaptive learning.
• Operational efficiency is boosted through automated staffing and administrative task reduction, decreasing nurse workload.

Conclusions:

• Machine learning offers transformative potential in nursing for patient care, education, and operational efficiency.
• Significant challenges, including ethical considerations like data privacy and algorithmic bias, require ongoing attention.
• Future directions involve expanding applications, integrating new technologies, and enhancing nursing education through interdisciplinary collaboration.