Machine learning to improve the quality of nursing care in open heart intensive care unit: Challenges & opportunities

Open heart intensive care unit (ICU) encounter distinctive challenges due to the critical condition of patients and the intricate nature of required care [1]. Meanwhile, machine learning has emerged as a potent tool to augment decision-making and patient management in this high-stakes setting [2]. This examination delves into how machine learning applications can transform nursing practices, bolster real-time clinical decision-making, and elevate patient outcomes. In this context, research has showcased the effectiveness of machine learning in various facets of ICU management, including real-time monitoring and predictive analytics. For example, machine learning algorithms have effectively anticipated complications like cardiac arrest and acute kidney injury, enabling proactive interventions [3]. Within the realm of open heart ICUs, machine learning models analyze data from continuous monitoring devices to forecast hemodynamic instability [4]. Additionally, machine learning has facilitated personalized care plans by adapting treatments based on individual patient data trends, thereby refining the accuracy of nursing care [5].