Development of a Nurse Turnover Prediction Model in Korea Using Machine Learning.

Nurse turnover is a critical issue in Korea, as it affects the quality of patient care and increases the financial burden on healthcare systems. To address this problem, this study aimed to develop and evaluate a machine learning-based prediction model for nurse turnover in Korea and analyze factors influencing nurse turnover. The study was conducted in two phases: building the prediction model and evaluating its performance. Three models, namely, decision tree, logistic regression, and random forest were evaluated and compared to build the nurse turnover prediction model. The importance of turnover decision factors was also analyzed. The random forest model showed the highest accuracy of 0.97. The accuracy of turnover prediction within one year was improved to 98.9% with the optimized random forest. Salary was the most important decision factor for nurse turnover. The nurse turnover prediction model developed in this study can efficiently predict nurse turnover in Korea with minimal personnel and cost through machine learning. The model can effectively manage nurse turnover in a cost-effective manner if utilized in hospitals or nursing units.

Analysis of court rulings on involuntary manslaughter or at-fault injury due to professional negligence by pediatric nurses: a systematic content analysis study.

PURPOSE: This study systematically analyzed cases in South Korea wherein nurses were prosecuted for involuntary manslaughter or injury due to professional negligence in pediatric care. METHODS: We analyzed the precedents using the methodology of Hall and Wright (2008) and Austin (2010). Of the 618 cases retrieved from the Supreme Court Decisions Retrieval System in South Korea, we selected the 12 cases in which children were the victims and nurses were the defendants, using a case screening methodology. RESULTS: The most frequent penalty was a fine, and newborns were the most frequent victims. The distribution of cases according to Austin's violation categories was: improper administration of medications (n=5), failure to monitor for and report deterioration (n=4), ineffective communication (n=4), failure to delegate responsibly (n=4), failure to know and follow facility policies and procedures (n=1), and improper use of equipment (n=1). CONCLUSION: To ensure the safety of children, nurses are required to teach and practice a high standard of care. Nursing education programs must improve nurses' awareness of their legal obligations. Nursing organizations and leaders should also work towards enacting effective nursing laws and ensuring that nurses are aware of their legal rights and responsibilities.