Artificial Intelligence For Patient Monitoring In Nursing

Abstract

The field of healthcare is changing under the influence of artificial intelligence (AI), which offers new methods of patient monitoring and clinical decision-making assistance. In the case of nurses, the accurate and constant monitoring is of utmost importance to the safety, timely interventions, and quality care. Conventional monitoring is very much dependent on handwritten notes and so this is likely to miss any early signs of deterioration and this results in alarm fatigue and increasing workload. Monitoring based on AI will be able to address these issues by providing real-time data analytics, predict risk, and automated alerts. This review focuses on AI application in nursing patient monitoring. It describes the fundamental AI techniques, such as machine learning, deep learning, computer vision, and wearable-sensors systems, and how they may be applied to monitor vital signs, predict early deterioration, prevent complications, and support the process of decision-making among nurses. It also explains how AI surveillance can affect workflow, safety, and quality care in settings such as ICUs, medical-surgery wards, home care, and long-term care. Ethical, legal and practical concerns like privacy of data, bias, reliability and acceptance by the user are also discussed. The review identifies research gaps and future priorities, including design of AI that is nurse oriented, transparent model, and AI training in nursing education. Overall, AI-based monitoring holds a potential to improve the outcomes and assist nurses in providing care that is safer, more efficient, and patient-centered.

Keywords:

AI, Patient monitoring, Nursing practice, Machine learning, Clinical decision support, Wearable devices, Patient safety, Healthcare technology.