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Applying artificial neural networks to predict communication risks in the emergency department
Author(s) -
Bagnasco Annamaria,
Siri Anna,
Aleo Giuseppe,
Rocco Gennaro,
Sasso Loredana
Publication year - 2015
Publication title -
journal of advanced nursing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 155
eISSN - 1365-2648
pISSN - 0309-2402
DOI - 10.1111/jan.12691
Subject(s) - artificial neural network , computer science , artificial intelligence , terminology , active listening , machine learning , communications system , multilayer perceptron , medical emergency , medicine , psychology , telecommunications , philosophy , linguistics , communication
Abstract Aims 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. Design 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. Conclusion The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department.