THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENHANCING CLINICAL NURSING CARE: A SCOPING REVIEW

Abstract

Background: Artificial Intelligence (AI) has reshaped the healthcare industry in recent years. However, little research has been conducted on how nurses utilize AI to deliver better nursing care. Objectives: This review aims to present an overview of how AI benefits nurses in hospital settings. There are three specific review objectives to this: (1) what are the most common AI subfields in healthcare settings; (2) what aspects of nursing care does AI improve; (3) what mechanisms of AI improve nursing care. Design: A scoping review was conducted to systematically scope existing literature and identify key concepts. Methods: Seven electronic databases - CINAHL, Cochrane Library, EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science were searched. Grey literature and reference lists of included articles were also searched. Articles that delineate how AI assists nurses in improving nursing care were included. Results: The five AI subfields used in healthcare settings are machine learning, deep learning, expert systems, fuzzy logic, and natural language processing. These assist nurses in improving monitoring, planning of nursing care plans, diagnoses, and documentation. Machine learning and deep learning normally identifies patterns in data; expert systems identify medical problems through the formation of a knowledge base; fuzzy logic allows better monitoring of numerical data like vitals; natural language processing simplifies documentation processes. Conclusion & Implications: This review informs current nurses and healthcare policymakers on the significance of AI in improving the quality of nursing care. However, due to the nature of current literature, the significance is contingent on the environmental and situational aspects of real-life healthcare settings. This informs future researchers to conduct more clinical trials to maximize the quality of findings.