Artificial Intelligence (AI) Driven Change Management for Work-Life Balance in Nursing Profession
DOI:
https://doi.org/10.55766/sjhsci-2025-02-e04352Keywords:
AI-Driven Change Management, Work-Life Balance, Nursing ProfessionAbstract
This article investigates the application of Artificial Intelligence (AI) technology in driving change management to enhance work-life balance in the nursing profession. Healthcare organizations globally confront critical challenges including nurse burnout, elevated turnover rates, and workforce shortages. Through systematic literature review and analysis of leading institutional case studies, this research examines how AI transforms nursing work environments across three key dimensions: intelligent scheduling systems that reduce overtime by 28%, automated documentation systems saving 1.8 hours per shift, and predictive analytics for burnout prevention with 83% accuracy. The article presents the AI-Driven Change Management (AIDCM) theoretical framework alongside success stories from Mayo Clinic, Singapore General Hospital, and Cleveland Clinic, demonstrating nurse turnover reduction of 18-41% and improved job satisfaction. Additionally, it addresses ethical considerations including data privacy, algorithmic bias prevention, and future directions toward personalized AI and IoT integration. The study reveals three critical success factors: visionary leadership support, participatory design methodologies, and comprehensive ethical frameworks. Case study analysis shows significant improvements in work satisfaction (45% increase), reduced unplanned overtime (34% decrease), and enhanced burnout prevention capabilities. The research also explores emerging trends including personalized AI systems that adapt to individual nurse preferences, IoT-AI convergence for smart work environments, and continuous learning systems for professional development. Findings indicate that AI-driven change management approaches create sustainable nursing work environments while maintaining high-quality patient care standards, positioning AI as an augmentative tool rather than a replacement for human expertise in healthcare delivery.
References
Anderson, R. T., & Taylor, M. K. (2024). Digital capability development in nursing: A systematic approach. Journal of Nursing Education and Practice, 14(3), 45-58.
Chen, L., Wang, S., & Kim, J. H. (2024). Workload management in modern nursing: Technology-enabled solutions. International Journal of Nursing Studies, 142, 104485.
Cheng, L., & Roberts, K. (2023). AI-powered nurse scheduling: Impact on work satisfaction and retention in acute care settings. Journal of Nursing Management, 31(2), 178-192.
Davidson, P., & Wong, S. (2023). Organizational readiness for AI implementation in healthcare: A three-dimensional assessment framework. Health Technology Assessment, 27(3), 215-230.
Foster, J. L., & Kim, S. Y. (2024). Vision-driven change management in healthcare AI adoption. Healthcare Management Forum, 37(2), 89-95.
Haddad, L. M., Annamaraju, P., & Toney-Butler, T. J. (2022). Nursing shortage. StatPearls Publishing.
Hassan, A. M., & Al-Rashid, K. (2024). Technology resilience in AI-dependent healthcare systems. Journal of Healthcare Risk Management, 43(4), 12-24.
Johnson, K., Martinez, C., & Lee, S. (2024). Balancing technology and humanity: The nurse's perspective on AI integration. Nursing Ethics, 31(1), 45-62.
Kumar, R., & Johnson, P. (2022). Impact of AI documentation assistants on nursing workflow and job satisfaction. Journal of Healthcare Informatics, 18(3), 302-317.
Kumar, V., Patel, N., & Singh, R. (2024). IoT-AI convergence in healthcare environments: Opportunities and challenges. Smart Health, 31, 100402.
Lee, H. J., & Park, M. S. (2024). Flexible scheduling models in nursing: A comprehensive review. Journal of Advanced Nursing, 80(4), 1623-1635.
Liu, X., & Zhang, Y. (2024). Personalized AI systems for healthcare workforce management. Artificial Intelligence in Medicine, 148, 102763.
Martinez, A., Thompson, L., & Brown, K. (2024). The ripple effects of work-life imbalance in nursing. Nursing Management, 55(7), 22-29.
Moreno-Jiménez, B., Rodríguez-Carvajal, R., & Hernández, E. G. (2024). Work-life balance in nursing: A multidimensional approach. Applied Psychology: Health and Well-Being, 16(2), 445-467.
Morgan, T., Phillips, J., & Singh, A. (2024). Phased implementation of AI technologies in nursing units: A comparative case study. Health Informatics Journal, 30(1), 87-105.
Morrison, D. K., & Liu, C. (2024). AI-driven change management framework for healthcare organizations. Journal of Healthcare Management, 69(2), 112-128.
Park, S. H., & Lee, K. M. (2024). AI-enabled continuous learning systems for nursing professional development. Nurse Education Today, 134, 106089.
Patel, R., Kumar, S., & Williams, A. (2024). Managing resistance to AI adoption in nursing: Evidence-based strategies. Journal of Nursing Administration, 54(3), 156-163.
Patel, S., & Nguyen, T. (2023). Participatory design approaches for AI integration in nursing practice. International Journal of Medical Informatics, 171, 104918.
Peng, Y., Li, M., & Zhang, W. (2024). Predictive monitoring systems in nursing: Clinical outcomes and workflow implications. Journal of Medical Systems, 48(1), 67.
Ramirez, J., & Taylor, E. (2023). Addressing algorithmic bias in healthcare workforce management AI. Health Affairs, 42(5), 741-749.
Reddy, S., & Nemati, S. (2024). Artificial intelligence in nursing workflow optimization: Current applications and future directions. Computers, Informatics, Nursing, 42(4), 245-253.
Rodriguez, M., & Peters, A. (2023). Proactive burnout prevention: AI-driven approaches to nursing wellness. Journal of Healthcare Leadership, 15, 143-159.
Shah, M. K., Gandrakota, N., & Cimiotti, J. P. (2021). Prevalence of burnout and its relation to turnover in registered nurses. Journal of Advanced Nursing, 77(4), 1674-1684. https://doi.org/10.1001/jamanetworkopen.2020.36469
Singh, P., & Patel, M. (2024). Predictive analytics for nursing workforce wellness: Implementation strategies. Healthcare Analytics, 5, 100298.
Tan, S. L., & Ooi, K. G. (2023). AI-powered documentation systems in nursing: Implementation and outcomes at Singapore General Hospital. Asian Nursing Research, 17(1), 22-31.
Thompson, K. L., & Williams, J. R. (2024). Organizational support systems for nursing work-life balance: A systematic review. Journal of Nursing Management, 32(5), 1456-1468.
Whitaker, J., Jackson, P., & Montgomery, L. (2024). Leadership strategies for AI integration in nursing workforce management: The Mayo Clinic experience. Nursing Administration Quarterly, 48(1), 85-97.
Williams, T., & Chen, H. (2022). Data governance frameworks for AI applications in healthcare. Journal of Healthcare Leadership, 14, 85-97.
Zhang, Y., Thompson, R., & Garcia, M. (2024). Early detection of nurse burnout using machine learning algorithms: A longitudinal study. International Journal of Nursing Studies, 131, 104297.
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