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Importance <p>Surgical robots encounter malfunctions requiring swift resolution, impacting patient safety and surgical efficiency. A Robotic Malfunction Checklist (RMC) may enhance surgical team autonomy reduce stress and intraoperative downtime.</p> Objective <p>To assess the efficacy of a step-by-step RMC for troubleshooting surgical robot errors through a randomized controlled trial simulation.</p> Design <p>An IRB-approved study conducted a needs analysis to identify common robotic errors. A specialized RMC was then developed and tested in a randomized simulation setting, utilizing the da Vinci Xi system.</p> Setting <p>Simulation environment utilizing the da Vinci Xi surgical robot system.</p> Participants <p>Thirty-one participants, including surgery residents and attendings were allocated to either the control (<i>n</i> = 16) or experimental groups (<i>n</i> = 15).</p> Interventions <p>Control group participants addressed errors conventionally, while experimental group members utilized the RMC for troubleshooting.</p> Main outcomes and measures <p>Post-survey data, including NASA Task Load Index (TLX) scores were collected to assess task load and confidence in troubleshooting. Statistical analysis involved the Mann–Whitney <i>U</i> test and Fischer's exact test.</p> Results <p>The experimental group significantly expedited resolution of the third complex error (3.9&#xa0;min; IQR = 5.2; <i>P</i> &lt; 0.01), representing a 43% reduction in resolution time. Task load was notably lower in the experimental group across all TLX domains for error 3 and in 4 out of 5 domains for error 2 (<i>P</i> &lt; .05). Experimental group members expressed increased confidence in troubleshooting in live patient settings if provided with the RMC.</p> Conclusions and relevance <p>ARMC reduced the time to resolve robotic errors and decreased stress load on surgeons. These promising findings advocate for further evaluation and potential implementation of the RMC in live operative settings.</p>

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Robotic malfunction checklist: a guide to operating room safety, efficiency, and surgeon autonomy

  • 期刊论文

  • 发表日期：

    2025年7月22日

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  • Alexis Desir,
  • Kaustubh Gopal,
  • John Norton,
  • Herbert J. Zeh III,
  • Ganesh Sankaranarayanan

摘要

Importance

Surgical robots encounter malfunctions requiring swift resolution, impacting patient safety and surgical efficiency. A Robotic Malfunction Checklist (RMC) may enhance surgical team autonomy reduce stress and intraoperative downtime.

Objective

To assess the efficacy of a step-by-step RMC for troubleshooting surgical robot errors through a randomized controlled trial simulation.

Design

An IRB-approved study conducted a needs analysis to identify common robotic errors. A specialized RMC was then developed and tested in a randomized simulation setting, utilizing the da Vinci Xi system.

Setting

Simulation environment utilizing the da Vinci Xi surgical robot system.

Participants

Thirty-one participants, including surgery residents and attendings were allocated to either the control (n = 16) or experimental groups (n = 15).

Interventions

Control group participants addressed errors conventionally, while experimental group members utilized the RMC for troubleshooting.

Main outcomes and measures

Post-survey data, including NASA Task Load Index (TLX) scores were collected to assess task load and confidence in troubleshooting. Statistical analysis involved the Mann–Whitney U test and Fischer's exact test.

Results

The experimental group significantly expedited resolution of the third complex error (3.9 min; IQR = 5.2; P < 0.01), representing a 43% reduction in resolution time. Task load was notably lower in the experimental group across all TLX domains for error 3 and in 4 out of 5 domains for error 2 (P < .05). Experimental group members expressed increased confidence in troubleshooting in live patient settings if provided with the RMC.

Conclusions and relevance

ARMC reduced the time to resolve robotic errors and decreased stress load on surgeons. These promising findings advocate for further evaluation and potential implementation of the RMC in live operative settings.