How Does Robot-Assisted Laparoscopic Surgery Impact Pain and Burnout Among Minimally Invasive Surgeons? A Survey Study

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Abstract

Pain is experienced by 77.8% and 53.8% of surgeons performing Traditional Laparoscopic Surgery (TLS) and Robot-Assisted Laparoscopic Surgery (RALS), respectively. Contributing factors include equipment design issues and lacking ergonomic knowledge among surgeons. This study investigated the burnout and discomfort experienced by laparoscopic surgeons. A survey composed of the Nordic Musculoskeletal Questionnaire (NMQ) and the Copenhagen Burnout Inventory (CBI) was distributed via social media groups of laparoscopic surgeons between October 2020 and January 2021. Results were analyzed in Microsoft Excel and RStudio. In total, 164 responses were provided by surgeons practicing TLS, 48 of whom also practiced RALS. Intraoperative pain was experienced by 91.5% and 75% of TLS and RALS surgeons, respectively. The main sites of complaint were the neck for RALS (35.4%) and the shoulders for TLS (64.6%). Average burnout scores from the CBI were between 25.8 and 36.5 out of 100 for the three dimensions of burnout. Surgeons practicing both RALS and TLS experienced discomfort at more anatomic sites in total and greater work-related burnout than those who only practiced TLS. In this study, RALS was associated with a lower prevalence of pain than TLS for all anatomic sites, although physical complaints were alarmingly common for both modalities. On average, respondents were experiencing low levels of burnout. The neck symptoms, increased multisite discomfort, and higher work-related burnout among RALS surgeons suggest the need to investigate console design and cumulative workload experienced by RALS surgeons across modalities.

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Hislop, J., Hensman, C., Isaksson, M., Tirosh, O., & McCormick, J. (2022). How Does Robot-Assisted Laparoscopic Surgery Impact Pain and Burnout Among Minimally Invasive Surgeons? A Survey Study. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13320 LNCS, pp. 54–66). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-06018-2_4

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