Purpose Based on a literature review of artificial intelligence (AI) applications within nursing tasks, this study delves into the feasibility of employing AI to improve nursing practice in Korea. Methods We used "nursing" and "artificial intelligence" as keywords to search academic databases, resulting in 96 relevant studies from an initial pool of 940.
After a detailed review, 35 studies were selected for analysis based on nursing process stages. Results AI improves nursing assessment by enhancing pain diagnosis, fall detection, and movement monitoring in older adults. It aids nursing diagnosis through clinical decision support, risk prediction, and emergency patient triage. Further, it expedites the creation of precise plans utilizing predictive models in nursing planning. AI also forecasts medication errors and reduces the nursing documentation burden for nursing implementation. Additionally, it manages (re)hospitalization risks by assessing patient risk and prognoses in nursing evaluation. Conclusion AI in Korean nursing can enhance assessment and diagnosis accuracy, promote a prevention-focused paradigm through risk prediction, and ease the burden of nursing practice amidst human resource shortages.
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Purpose This study aimed to develop a discharge nursing service model (DNSM), including one that accounts for the role of discharge nurses and the discharge nursing process for heart failure patients (HFP) with a high readmission rate, and to verify its clinical feasibility. Methods As a methodological study, DNSM was established through a literature review and benchmarking, and the clinical feasibility of the expert group was confirmed through the Delphi technique. Results Through a literature review and the benchmarking, the DNSM for HFP was formulated. The nine core competencies are professional nursing practice, education, counseling, advice, ethical decision-making, research, collaboration, evidence-based practice, and leadership. In addition, fifteen criteria and 42 indicators were added as detailed items. The discharge process comprised five stages and 25 processes identified by analyzing and integrating the content of discharge nursing intervention required for HFP by period-from hospitalization to 30 days after discharge.
Content validity was verified by the twofold application of the Delphi technique, and the average CVI was over 0.92. Conclusion In terms of quality management, developing an efficient system or service is necessary to prevent readmission, and developed DNSM should be continuously revisioned and reinforced through follow-up studies.
PURPOSE The purpose of this study was to suggest agenda priorities for nursing service R&D (Research and Development) related policies development. METHODS Two steps in developing the agendas and priorities were performed in this descriptive study. First, nursing service R&D agendas were extracted through needs assessment of nursing researchers and practitioners. Then, the priority of agendas was set by Analytic Hierarchy Process by ten experts who were representatives of nursing and other healthcare professionals. RESULTS Six core areas and forty-six nursing service R&D agendas were developed. The priority of agendas was different according to the evaluation criteria depending on weight value of nursing services. CONCLUSION In order to select and promote nursing service R&D projects within national healthcare policy, nursing service R&D policy should be proposed with consideration to the importance of the criteria in reflecting characteristics of nursing care. By strengthening R&D capabilities for quality improvement and sensitive awareness of national directions for healthcare R&D policies, nursing service R&D can be appropriately promoted.
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