A. D. Desir1, J. Norton1, K. Gopal1, G. Sankaranarayanan1, H. Zeh1 1University Of Texas Southwestern Medical Center, Surgery, Dallas, TX, USA
Introduction: Robotic surgery systems are susceptible to malfunctions, leading to intra-operative downtime which necessitates the need for preparedness in troubleshooting errors. Currently, there is limited focus on this topic among general surgeons. Specific steps for troubleshooting such malfunctions are poorly documented, primarily relying on case reports. This study addresses this gap by investigating the effectiveness of a robotic malfunction checklist through a randomized-controlled trial simulation.
Methods: Participants included 31 general surgery residents and attendings tasked with resolving three robotic malfunctions on the Da Vinci Xi system at our simulation center. Malfunctions were selected based on needs analysis, common IntuitiveTM call logs , and observations from the OR Blackbox. Of the chosen tasks, Task 1 and Task 2 were deemed “simple” tasks, while Task 3 was deemed “complex” by expert opinion. Participants were randomly stratified to either the control group or the experimental group (using the checklist). Time to resolve each malfunction was recorded, and participants completed a post-survey which included the NASA-TLX scale and clinical applicability questions. Mann-Whitney U test and Fischer’s Exact test were used to evaluate for continuous and categorical variables respectively.
Results: For Task 3, the experimental was significantly faster in resolving the error when compared to control counterparts (5.1 (IQR:2.3-5.8), p<0.01), with an 43% reduction in resolution time when using the guide. The experimental group experienced significantly lower task load across all 5 domains (mental, effort, temporal, physical, frustration) when addressing the Task 3 malfunctions and similarly experienced significantly lower task load across 4/5 domains for Task 2 (p<.05). Furthermore, when polled, all experimental members either “strongly agreed” or “agreed” that that they would feel comfortable troubleshooting all 3 tasks in a live patient operating room setting. When compared to the control group, the experimental group was significantly more confident in completing all three tasks in a live setting (p<.05).
Conclusion: Our results from the robotic malfunction checklist simulation demonstrates the guide’s potential to enhance surgeon autonomy, improve patient safety, and reduce intraoperative downtime, particularly for more complex robotic malfunctions. The encouraging outcomes of our research propel the advancement to the subsequent phase, where we will examine the effects of implementing this guide in the operating room.
