Objectives: Nursing documentation consumes approximately 30% of nurses’ professional time, making improvements in efficiency essential for patient safety and workflow optimization. This study compares traditional nursing documentation methods with a generative artificial intelligence (AI)-based system, evaluating its effectiveness in reducing documentation time and ensuring the accuracy of AI-suggested entries. Furthermore, the study aims to assess the system’s impact on overall documentation efficiency and quality.Methods: Forty nurses with a minimum of 6 months of clinical experience participated. In the pre-assessment phase, they documented a nursing scenario using traditional electronic nursing records (ENRs). In the post-assessment phase, they used the SmartENR AI version, developed with OpenAI’s ChatGPT 4.0 API and customized for domestic nursing standards; it supports NANDA, SOAPIE, Focus DAR, and narrative formats. Documentation was evaluated on a 5-point scale for accuracy, comprehensiveness, usability, ease of use, and fluency.Results: Participants averaged 64 months of clinical experience. Traditional documentation required 467.18 ± 314.77 seconds, whereas AI-assisted documentation took 182.68 ± 99.71 seconds, reducing documentation time by approximately 40%. AI-generated documentation received scores of 3.62 ± 1.29 for accuracy, 4.13 ± 1.07 for comprehensiveness, 3.50 ± 0.93 for usability, 4.80 ± 0.61 for ease of use, and 4.50 ± 0.88 for fluency.Conclusions: Generative AI substantially reduces the nursing documentation workload and increases efficiency. Nevertheless, further refinement of AI models is necessary to improve accuracy and ensure seamless integration into clinical practice with minimal manual modifications. This study underscores AI’s potential to improve nursing documentation efficiency and accuracy in future clinical settings.

Aim. To examine the relationships among nursing professionalism, empathy, and clinical decision-making ability and the factors influencing shared decision-making awareness in hemodialysis nurses. Background. Self-management and treatment for hemodialysis patients are essential for maintaining health and life in daily life. In this process, shared decision-making in which patients, nurses, and medical teams participate and make decisions together has a greater impact on the health recovery and improvement of quality of life for hemodialysis patients than for any other chronic disease patients. Methods. A cross-sectional descriptive design was employed. Participants were 145 nurses working in the hemodialysis centers at hospitals in Seoul and Gyeonggi-do. Measures included the general characteristics of study participants, nursing professionalism, empathy, clinical decision-making ability, and shared decision-making awareness. Data were collected from May to July, 2022, and multiple linear regression analysis was used to examine the predictive factors of shared decision-making awareness. Results. The strongest predictor was empathy, followed by clinical decision-making ability and the level of education. The explanatory power of the final regression model was 23. Conclusions. Empathy towards hemodialysis patients was an important factor influencing the shared decision-making awareness in hemodialysis nurses. Implications for Nursing Management. In nursing management, nursing managers or nurses should pay attention to influencing factors to improve the shared decision-making awareness of hemodialysis nurses. Empathy towards hemodialysis patients need to be reinforced to improve the shared decision-making awareness of hemodialysis nurses.

The purpose of this study was to evaluate the reliability and validity of a Korean version of the 20-item COVID-19 phobia tool, which was developed through a translation-reverse translation process. These data were collected from 226 persons using a self-reported questionnaire. Exploratory and confirmatory factor analyses were used to test construct validity. Finally, for 19 out of 20 items, the item-level convergence and differential validity were confirmed. In addition, the reliability and validity of the tool as a whole has been verified. For the subscales, Cronbach’s α was 0.90 for psychological, 0.87 for psychosomatic, 0.86 for economic, and 0.87 for social. Appropriate reliability was confirmed. Correlations between the COVID-19 phobia tool and fear of COVID-19 confirmed validity. The Korean version of the COVID-19 phobia tool is an appropriate scale for measuring the fear of COVID-19 and relevant psychological characteristics. Therefore, future studies in areas such as health and nursing could use this tool as required.

본 연구는 COVID-19에 대한 두려움 측정도구(FCV-19S)를 한국의 실정에 맞게 개발하여 타당도와 신뢰도를 검증하는데 있다. FCV-19S-K의 번역은 번역-역번역 과정을 통하였으며 내용타당도, 문항분석, 구성타당도, 수렴, 판별 및 준거타당도와 신뢰도 검증을 하였다. 데이터는 208명의 일반인을 대상으로 수집하였으며, 요인구조를 탐색하기 위해 탐색적 요인분석을 실시하였으며, 신뢰도를 평가하기 위해 내적 일관성 및 Guttman split-half coeffiicient를 측정하였다. 본 연구결과 FCV-19S-K는 7개 문항으로 최종 확정되었고 Cronbach’s α= .87, Guttman split-half coeffiicient .84로 신뢰성이 확인되었다. 전체 문항은 서로 상관관계가 있었었다. 탐색적 요인분석 결과 2개의 요인으로 산출되었고 2개 요인은 .70~.82의 요인적재량을 보였고 전체 분산의 71.2%를 설명하였다. 2개 요인은 ‘COVID-19에 대한 생리적 반응’ Cronbach’s α= .86과 ‘COVID-19에 대한 정서적 반응’ Cronbach’s α= .81이었으며, 수렴, 판별 준거타당도를 검증하기 위해 FCV-19S-K와 Korean Anxiety Screening Assessment(K-ANX)의 상관관계를 분석결과 FCV-19S-K와 K-ANX는 정적 상관관계를 보였다(r= .56, p<.001). 본 연구를 통해 FCV-19S-K의 타당도와 신뢰도를 검증하였으며 FCV-19S-K가 COVID-19에 대한 두려움을 측정하는데 사용될 수 있다는 근거를 제시하였다. 추후연구에서 FCV-19S-K를 통해 COVID-19로 인한 심리사회적 반응에 대한 연구 및 간호 실무에 대해 기초자료로 활용될 것을 기대한다.