Recent studies exploring the effects of surgical robots on teamwork are revealing challenges not reflected in clinical studies. This study is a sub analysis of observational data collected from 89 procedures utilising the da Vinci systems. Previous analyses had demonstrated interactions between flow disruptions and contextual factors. This study sought a more granular analysis to provide better insight for improvement. Raters sub-classified disruptions, based upon the original notes, grouped according to four operative phases (pre-robot; docking; surgeon on console; undocking; and finish). The need for repeated utterances; additional supplies retrieval; fogging or matter on the endoscope and procedure-specific training were particularly disruptive. Variations across phases reflect differing demands across the operative course. Combined qualitative and quantitative observational methodologies can identify otherwise undocumented sources of process variation and potential failure. Future observational frameworks should attempt to merge human reliability analysis, a priori modelling, and post hoc analyses of observational data. Practioner Summary Robotic surgery introduces new challenges into the operating room. Direct observation was used to classify and identify flow disruptions in order to diagnose problems in need of improvement. This technique complements other error prediction and system diagnostic methods which may not account for the complexity and transparency of health care.

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The current study measured driving performance using the Lane Change Test (LCT) and a Peripheral Detection Task (PDT), to determine if stimulus modality (visual and auditory) and stimulus eccentricity would affect performance on the PDT. The peripheral stimuli were either small white circles or broadband noise bursts located either 13o or 19o to the left or right of the center lane. Participants were instructed to respond with left or right key presses to the stimuli’s relative location while performing the LCT. The cognitive load of participants was also varied, either no load, low load or high load, produced by the n-back task (no n-back, 0-back, 1-or 2-back). The results indicate that higher cognitive load leads to poorer driving performance, increased reaction time of PDTs, and higher subjective workload. This study supports previous research indicating driving performance suffers in response to increased cognitive load due to cognitive or attentional tunneling.

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