Measuring mental workload during the performance of advanced laparoscopic tasks

  • Zheng B
  • Cassera M
  • Martinec D
 et al. 
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INTRODUCTION: Mental workload is a finite resource and is increased while learning new tasks and performing complex tasks. Measurement of a surgeon's mental workload may therefore be an indication of expertise. We hypothesized that surgeons who were expert at laparoscopic suturing would have more spare mental resources to perform a secondary task, compared with surgeons who had just started to learn suturing. METHODS: Standardized suturing tasks were performed on a bench-top model. Twelve junior residents (novices) and nine fellows and attending surgeons (experts) were instructed to perform as many sutures as possible in 6 min. An adjacent monitor was placed 15 degrees off axis to the first and randomly displayed 30 true visual signals among 90 false ones. Participants were required to identify the true signals while continuing to suture. Laparoscopic sutures were evaluated using the Fundamentals of Laparoscopic Surgery (FLS) scoring system. The secondary (visual detection) task was evaluated by calculating the rate of missed true signals or detection of false signals. RESULTS: Experts completed significantly more secure sutures (6 +/- 2) than novices (3 +/- 1; p = 0.001). The suture performance score was 50 +/- 20 for experts, significantly higher than for novices (29 +/- 10; p = 0.005). The rate for detecting visual signals was higher for experts (98%) compared with for novices (93%; p = 0.041). CONCLUSION: Practice develops automaticity, which reduces the mental workload and allows surgeons to have sufficient spare mental resources to attend to a secondary task. Visual detection provides a simple and reliable way to assess mental workload and situation awareness abilities of surgeons during skills training, and may be an indirect measure of expertise.

Author-supplied keywords

  • Assessment
  • Mental workload
  • Secondary task
  • Surgical competence
  • Surgical education
  • Visual detection

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