Disruptive Behaviors in an Emergency Department: the Perspective of Physicians and Nurses

Introduction: Disruptive behaviors cause many problems in the workplace, especially in the emergency department (ED).This study was conducted to assess the physician’s and nurse’s perspective toward disruptive behaviors in the emergency department. Methods: In this cross-sectional study a total of 45 physicians and 110 nurses working in the emergency department of five general hospitals in Bojnurd participated. Data were collected using a translated, changed, and validated questionnaire (25 item). The collected data were analyzed by SPSS ver.13 software. Results: Findings showed that physicians gave more importance to nurse-physician relationships in the ED when compared to nurses’ perspective (90% vs. 70%). In this study, 81% of physicians and 52% of nurses exhibited disruptive behaviors. According to the participants these behaviors could result in adverse outcomes, such as stress (97%), job dissatisfaction and can compromise patient safety (53%), quality of care (72%), and errors (70%). Conclusion: Disruptive behaviors could have a negative effects on relationships and collaboration among medical staffs, and on patients’ quality of care as well. It is essential to provide some practical strategies for prevention of these behaviors

Data-driven longitudinal characterization of neonatal health and morbidity

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children's Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/