Healthcare Transformation Through AI: Improving Patient Care and Nursing Efficiency based on Systematic Review

Abstract

Background: The opportunities and challenges for artificial intelligence integration in patient care and nursing healthcare are explored. While AI can improve diagnostics, support decision making and streamline nursing workflows, inconsistent adoption, data privacy concerns and lack of training slow its pace. The impact of AI on patient safety and nursing efficiency is explored for clinical settings in this review.
Aim: This review seeks to evaluate how AI technologies have improved quality of patient care and nursing efficiency, point out barriers to further adoption and provide strategies that enhance AI integration into healthcare.
Method: Studies conducted between 2020 and 2024 were searched systematically in PubMed, Scopus, Web of Science, Google Scholar, and IEEE Xplore. By using a multi-stage screening process meeting PRISMA guidelines, ten studies that fulfilled the inclusion criteria were identified. Data extraction efforts centered in AI diagnostics, decision making, workflow improvements, and patient safety.
Results: AI has helped positively on patient care by making it better in diagnostic accuracy, decision making and nursing workflows. Nevertheless, data privacy, bias, or a lack of staff training are still impediments. The results highlight a requirement for ongoing education, robust data protection and clearer rules to capitalize on the advantages of AI.
Conclusion: To utilize AI in the healthcare sector, we must adopt a multi facet approach to integrate AI in optimization of diagnostics, to increase nursing efficiency and improving patient safety among other related areas. To successfully adopt AI, its training has to be strengthened, its protocols standardized, and privacy issues addressed.