Geriatric nursing activities are closely related to patient safety; therefore, nurses’ efforts to improve patient safety in geriatric hospitals are important. In the present study, we investigated the relationships between person-centered care practice, patient safety competence, and patient safety during nursing activities in geriatric hospitals. We used the following tools to investigate the factors affecting patient safety during nursing activities: (a) the Korean version of the Person-Centered Care Assessment Tool (P-CAT), (b) the Patient Safety Competence Assessment Tool for Nurses, and (c) the Patient Safety Nursing Activities Assessment Tool for geriatric nurses. The questionnaire survey was completed by 186 geriatric nurses in 12 geriatric hospitals from 1 August to 31 August 2018. We analyzed the survey data using a t-test, one-way ANOVA, Pearson’s correlation coefficient, and multiple regression. We identified patient safety skills (β = 0.417, p < 0.001) and age (β = 0.209, p = 0.035) as key factors that influence patient safety during nursing activities. Therefore, to improve the quality of patient safety during nursing activities conducted by geriatric nurses, it is necessary to develop strategies to improve patient safety skills and expand the pool of competent nurses with clinical experience.

This study compares the expected nurse-to-patient ratio, penalties for violating these regulations, and the laws enacted in the medical and nursing fields in Korea and advanced countries like Germany, Australia, the United States, and Japan. We deployed an integrative-review method and used search terms such as "nursing law," "nurse ratio," "nurse," "nurse staffing," "health," and "staffing" to find articles published in English, Korean, German, or Japanese through CINAHL Plus with Full Text, the Westlaw (International Materials Jurisdiction) site, U.S. government and state sites (federal parliament, National Conference of State Legislatures), and Google Scholar. Compared to medical laws in other advanced countries, Korean laws are quite crude and its nurse-to-patient ratio does not reflect patients' status. Korea also lacks strict penalties for nurse-staffing-ratio violations. Korea requires a strong regulatory apparatus for nurse staffing in healthcare organizations to improve the quality of its healthcare services and patient safety.

Background: A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM) of predicting falls in nursing homes (NHs). Methods: We applied three representative six-ML algorithms to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models. Results: RF was the most accurate model (0.883), followed by the logistic regression model, SVM linear, and polynomial SVM (0.867). Conclusions: RF was a powerful algorithm to discern predictors of falls in NHs. For effective fall management, researchers should consider organizational characteristics as well as personal factors. Recommendations for Future Research: To confirm the superiority of ML in NH research, future studies are required to discern additional potential factors using newly introduced ML methods.

This study aimed to identify the terminologies of NANDA-I, NOC, NIC, and NNN linkages that have been used for nursing home (NH) residents. This study used a retrospective descriptive design. Data accrued from 57 registered nurses (RNs) in 25 Korean NHs. The RNs randomly selected one resident and assessed for applied NANDA-I, NOC, and NIC from the previous 7 days by reviewing nursing charts and records. Finally, the data of 57 residents in 25 NHs were collected. Results: We identified seven NNN linkages: risk for falls–fall prevention behavior–fall prevention; self-care deficit: bathing/hygiene–self-care: activities of daily living (ADL)–self-care assistance: bathing/hygiene; impaired memory–memory–cognitive stimulation; chronic confusion–neurological status: consciousness–medication management; chronic confusion–memory–medication management; impaired walking–mobility–exercise promotion: strength training; and impaired walking–ambulation–exercise promotion: strength training. The identified core NANDA-I, NOC, NIC, and NNN linkages for NH residents from this study provide a scope of practice of RNs working in NHs.