Feasibility of EEG to monitor cognitive performance during venous cannulation: EEG Distracted Intravenous Access (E-DIVA)

Lowe, D. A. , James, S. A., Lloyd, A. and Clegg, G. R. (2016) Feasibility of EEG to monitor cognitive performance during venous cannulation: EEG Distracted Intravenous Access (E-DIVA). BMJ Simulation and Technology Enhanced Learning, 2(3), pp. 68-72. (doi: 10.1136/bmjstel-2015-000082)

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Background: The feasibility study aims to evaluate the use of EEG in measuring workload during a simulated intravenous cannulation task. Cognitive workload is strongly linked to performance, but current methods to assess workload are unreliable. The paper presents the use of EEG to compare the cognitive workload between an expert and novice group completing a simple clinical task. Methods: 2 groups of volunteers (10 final year medical students and 10 emergency medicine consultants) were invited to take part in the study. Each participant was asked to perform 3 components of the simulation protocol: intravenous cannulation, a simple arithmetic test and finally these tasks combined. Error rate, speed of task completion and an EEG-based measure of cognitive workload were recorded for each element. Results: EEG cognitive workload during the combined cannulation and arithmetic task is significantly greater in novice participants when compared with expert operators performing the same task combination. EEG workload mean measured for novice and experts was 0.62 and 0.54, respectively (p=0.001, 95% CI 0.09 to 0.30). There was no significant difference between novice and expert EEG workload when the tasks were performed individually. Conclusions: EEG provides the opportunity to monitor and analyse the impact of cognitive load on clinical performance. Despite the significant challenges in set up and protocol design, there is a potential to develop educational interventions to optimise clinician's awareness of cognitive load. In addition, it may enable the use of metrics to monitor the impact of different interventions and select those that optimise clinical performance.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Lowe, Dr David
Authors: Lowe, D. A., James, S. A., Lloyd, A., and Clegg, G. R.
College/School:College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
Journal Name:BMJ Simulation and Technology Enhanced Learning
Publisher:BMJ Publishing Group
ISSN (Online):2056-6697
Published Online:01 June 2016

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