Objectives: The aim of this study was to review hospital-based health information system (HIS) studies that used qualitative research methods and evaluate their methodological contexts and implications. In addition, we propose practical guidelines for HIS researchers who plan to use qualitative research methods. Methods: We collected papers published from 2012 to 2022 by searching the PubMed and CINAHL databases. As search keywords, we used specific system terms related to HISs, such as “electronic medical records” and “clinical decision support systems,” linked with their operational terms, such as “implementation” and “adaptation,” and qualitative methodological terms such as “observation” and “in-depth interview.” We finally selected 74 studies that met this review’s inclusion criteria and conducted an analytical review of the selected studies. Results: We analyzed the selected articles according to the following four points: the general characteristics of the selected articles; research design; participant sampling, identification, and recruitment; and data collection, processing, and analysis. This review found methodologically problematic issues regarding researchers’ reflections, participant sampling methods and research accessibility, and data management. Conclusions Reports on the qualitative research process should include descriptions of researchers’ reflections and ethical considerations, which are meaningful for strengthening the rigor and credibility of qualitative research. Based on these discussions, we suggest guidance for conducting ethical, feasible, and reliable qualitative research on HISs in hospital settings.

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.