The purpose of this study was to explore the experience of turnover to a long-term care hospital nurse. Methods: Data were collected using in-depth interviews and analyzed using a Giorgi’s phenomenological approach. The participants were ten nurses who worked on the wards of a long-term care hospital in Busan metropolitan. Results: The results were summarized into five themes and seventeen core meanings. The five themes were ‘A choice based on one's convenience’, ‘Feeling overwhelmed by tasks beyond the scope of nursing duties’, ‘Thinking about turnover from time to time’, ‘An environment where personal growth as a nurse cannot be expected’ and ‘Becoming a nurse for long-term care’. Conclusion: To ensure patient safety and prevent nurse turnover in long-term care hospitals, it is necessary to improve the nursing environment, establish clear job manuals, and create a reasonable salary system.

Purpose: Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin–stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients. Materials and Methods: A total of 524 digital slides were obtained from frozen SLN sections: 297 (56.7%) from Asan Medical Center (AMC) and 227 (43.4%) from Seoul National University Bundang Hospital (SNUBH), South Korea. The slides were divided into training, development, and validation sets, where the development set comprised slides from both institutions and training and validation set included slides from only AMC and SNUBH, respectively. The algorithms were assessed for area under the receiver operating characteristic curve (AUC) and measurement of the longest metastatic tumor diameter. The final total scores were calculated as the mean of the two metrics, and the three teams with AUC values greater than 0.500 were selected for review and analysis in this study. Results: The top three teams showed AUC values of 0.891, 0.809, and 0.736 and major axis prediction scores of 0.525, 0.459, and 0.387 for the validation set. The major factor that lowered the diagnostic accuracy was micro-metastasis. Conclusion In this challenge competition, accurate deep learning algorithms were developed that can be helpful for making a diagnosis on intraoperative SLN biopsy. The clinical utility of this approach was evaluated by including an external validation set from SNUBH.