An Innovative Model to Predict Pediatric Emergency Department Return Visits.

OBJECTIVES: Return visit (RV) to the emergency department (ED) is considered a benchmarking clinical indicator for health care quality. The purpose of this study was to develop a predictive model for early readmission risk in pediatric EDs comparing the performances of 2 learning machine algorithms. METHODS: A retrospective study based on all children younger than 15 years spontaneously returning within 120 hours after discharge was conducted in an Italian university children's hospital between October 2012 and April 2013. Two predictive models, artificial neural network (ANN) and classification tree (CT), were used. Accuracy, specificity, and sensitivity were assessed. RESULTS: A total of 28,341 patient records were evaluated. Among them, 626 patients returned to the ED within 120 hours after their initial visit. Comparing ANN and CT, our analysis has shown that CT is the best model to predict RVs. The CT model showed an overall accuracy of 81%, slightly lower than the one achieved by the ANN (91.3%), but CT outperformed ANN with regard to sensitivity (79.8% vs 6.9%, respectively). The specificity was similar for the 2 models (CT, 97% vs ANN, 98.3%). In addition, the time of arrival and discharge along with the priority code assigned in triage, age, and diagnosis play a pivotal role to identify patients at high risk of RVs. CONCLUSIONS: These models provide a promising predictive tool for supporting the ED staff in preventing unnecessary RVs.

The electronic cigarette phenomenon: prevalence, knowledge and opinion of health care professionals in Italy.

BACKGROUND: Electronic cigarette smoking is spreading among health care professionals. E-cigarette smoke effects on health are not known, especially long-term effects. AIM: The aim of this study was to investigate the phenomenon of electronic cigarettes as regards smoking habits, knowledge and opinions of health care professionals. METHODS: A multicentre cross-sectional descriptive study was conducted by administering an online questionnaire to all the health care professionals employed in two hospitals. RESULTS: The population included 800 employees. More than half (66.8%) of respondents believed the e-cigarette is potentially harmful and capable of attracting young people to smoking and 38.8% of respondents believed that it can serve to stop smoking. The male gender was statistically associated with tobacco and e-cigarette smoking (p=0.034). The electronic cigarette was smoked little at the work place. The population studied did not have any specific knowledge about e-cigarettes and asked for specific training; the population knew the ban on the sale of e-cigarettes to underaged and emphasized the importance of specific management guidelines. CONCLUSIONS: The results of the study show the predominantly negative opinion of health professionals concerning the use of electronic cigarette. Moreover, the study results contributed to an improvement of the smoking policies in the hospitals studied.