title: Applying artificial neural networks to predict communication risks in the emergency department.
abstract: OBJECTIVE
  To describe the utility of artificial neural networks in predicting communication risks.
  
  
  BACKGROUND
  In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments.
  
  
  METHODS
  A multicentre observational study.
  
  
  METHODS
  Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments.
  
  
  RESULTS
  A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures.
  
  
  CONCLUSIONS
  The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department.
authors.affiliation: Department of Health Sciences, University of Genoa, Italy.
authors.forename: Annamaria
authors.initials: A
authors.lastname: Bagnasco
authors.affiliation: School of Medical and Pharmaceutical Sciences, University of Genoa, Italy.
authors.forename: Anna
authors.initials: A
authors.lastname: Siri
authors.affiliation: Department of Health Sciences, University of Genoa, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Aleo
authors.affiliation: Centre of Excellence for Nursing Scholarship, Rome, Italy.
authors.forename: Gennaro
authors.initials: G
authors.lastname: Rocco
authors.affiliation: Department of Health Sciences, University of Genoa, Italy.
authors.forename: Loredana
authors.initials: L
authors.lastname: Sasso
mesh_terms: D000328:Adult; D000368:Aged; D002983:Clinical Competence / Q000592:standards; D003142:Communication; D016529:Emergency Nursing / Q000458:organization & administration / Q000592:standards; D004636:Emergency Service, Hospital / Q000458:organization & administration*; D005260:Female; D006801:Humans; D007558:Italy; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D012307:Risk Factors
keywords: artificial neural network; communication skills; emergency department; nursing; prediction; risk