The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings

Abstract

Research and development of artificial intelligence (AI)-based technologies systems in healthcare has increased over the past decade, highlighting the strong potential of AI to improve the quality of nursing care. To meet the new demands for nursing care, it is necessary that AI be integrated into nursing science and healthcare setting, especially in nursing care. The current challenge is to transform this expanded set of technology into clinical benefits for patients, through more advanced, accurate, practical, effective, efficient, and economical and personalized care. Along with the potential positive outcomes, AI technology also has unintended consequences that have the potential to negatively impact and adversely affect the nursing profession and the primary purpose of nursing practice in healthcare system. This aimed to explore and discuss the impact of applying AI in nursing science and healthcare system to provide approximate nursing care. Some of the impacts that can be evaluated and seen today in the context of using AI technology systems in the scope of nursing and healthcare are expanding access to quality medical care, improving medical records, and improving the quality of services. The use of AI technology systems also has some issues, such as bias and algorithms, which are drawbacks that need to be considered when evaluating the accuracy of the displayed results.

A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What’s Next?

“…These positive effects underscore the advantages and the pressing need for integrating AI technology systems in healthcare, with the aim of optimizing healthcare services for greater efficiency and effectiveness. 22 …”

“… 49 , 50 The review results showed that AI could be incorporated into EHR in terms of recording, organizing, and managing EHR data for optimal use, reducing time and effort, and increasing the quality of care. 22 For instance, one of the examples was the implementation of a voice-based AI system that provided real-time assistance for medical record input during the triage process in the emergency department. This system was trained on speech-to-text data and could reduce the triage time and improve the efficiency of emergency care.…”

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation

“…[24] To date, little AI in healthcare research has considered real-world scenarios or real-world effect on outcomes. [25] Uses of AI in nursing span from virtual assistants to patient monitoring and predictive modeling. [26] AI can also contribute by helping to streamline workflows and analyze vast amounts of data for evidence-based recommendations, helping nurses make more informed decisions about patient care.…”

Advancing Health Equity Through Artificial Intelligence: An Educational Framework for Preparing Nurses in Clinical Practice and Research

“…By analyzing extensive datasets, ML-driven analytics provides insights that can identify health risks, refine diagnoses and treatments, and prevent adverse health events (Chilla, 2023). This technology is increasingly used in nursing for patient monitoring, chronic disease management, and emergency preparedness (Hwang et al, 2022;Pailaha, 2023). These developments highlight the significant impact of ML-driven predictive analytics on health care and its potential to revolutionize nursing practice.…”