Do surgeon non-technical skills correlate with teamwork-related outcomes during robot-assisted surgery?

Intraoperative workload of the surgeon in robot-assisted radical prostatectomy: a systematic review

In the present study, we aimed to systematically evaluate the current evidence regarding the intraoperative workload of surgeons performing robot-assisted radical prostatectomy (RARP) for prostate cancer. A systematic search was carried out in the PubMed-MEDLINE and Web of Science databases through April 2024 using the following search terms: "workload AND robot assisted radical prostatectomy", "workload AND robotic radical prostatectomy", "task load AND robotic radical prostatectomy", "task load AND robot assisted radical prostatectomy" and "NASA-TLX AND robot assisted radical prostatectomy" by combining population, intervention, comparison, and outcome (PICO) terms, following preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. We therefore selected studies that included patients with prostate cancer (P) who underwent robotic radical prostatectomy (I) and reported a workload/task load questionnaire (C) to assess the intraoperative workload/task load of the surgeon performing robot-assisted radical prostatectomy (O). A total of 11 studies were identified. The surgeon's workload during RARP was assessed using the National Aeronautics and Space Administration task load index (NASA-TLX) and/or the surgery task load index (SURG-TLX) in the studies. Total NASA-TLX scores of the studies ranged from 22.7 ± 3.2 to 62.0 ± 6.4. Mental and physical demands, flow interruptions, surgeon experience, the use of single or multiple ports, and the relationship between the surgeon and other staff in the operating theater may play a role in the intraoperative workload of the console surgeon. The studies we reviewed suggest that RARP offers an acceptable workload for the console surgeon despite its mental demands.

Assessment and application of non-technical skills in robotic-assisted surgery: a systematic review

BACKGROUND

Undeniably, robotic-assisted surgery (RAS) has become very popular in recent decades, but it has introduced challenges to the workflow of the surgical team. Non-technical skills (NTS) have received less emphasis than technical skills in training and assessment. The systematic review aimed to update the evidence on the role of NTS in robotic surgery, specifically focusing on evaluating assessment tools and their utilisation in training and surgical education in robotic surgery.

METHODS

A systematic literature search of PubMed, PsycINFO, MEDLINE, and EMBASE was conducted to identify primary articles on NTS in RAS. Messick's validity framework and the Modified Medical Education Research Study Quality Instrument were utilised to evaluate the quality of the validity evidence of the abstracted articles.

RESULTS

Seventeen studies were eligible for the final analysis. Communication, environmental factors, anticipation and teamwork were key NTS for RAS. Team-related factors such as ambient noise and chatter, inconveniences due to repeated requests during the procedure and constraints due to poor design of the operating room may harm patient safety during RAS. Three novel rater-based scoring systems and one sensor-based method for assessing NTS in RAS were identified. Anticipation by the team to predict and execute the next move before an explicit verbal command improved the surgeon's situational awareness.

CONCLUSION

This systematic review highlighted the paucity of reporting on non-technical skills in robotic surgery with only three bespoke objective assessment tools being identified. Communication, environmental factors, anticipation, and teamwork are the key non-technical skills reported in robotic surgery, and further research is required to investigate their benefits to improve patient safety during robotic surgery.

Next in Surgical Data Science: Autonomous Non-Technical Skill Assessment in Minimally Invasive Surgery Training

Background: It is well understood that surgical skills largely define patient outcomes both in Minimally Invasive Surgery (MIS) and Robot-Assisted MIS (RAMIS). Non-technical surgical skills, including stress and distraction resilience, decision-making and situation awareness also contribute significantly. Autonomous, technologically supported objective skill assessment can be efficient tools to improve patient outcomes without the need to involve expert surgeon reviewers. However, autonomous non-technical skill assessments are unstandardized and open for more research. Recently, Surgical Data Science (SDS) has become able to improve the quality of interventional healthcare with big data and data processing techniques (capture, organization, analysis and modeling of data). SDS techniques can also help to achieve autonomous non-technical surgical skill assessments. Methods: An MIS training experiment is introduced to autonomously assess non-technical skills and to analyse the workload based on sensory data (video image and force) and a self-rating questionnaire (SURG-TLX). A sensorized surgical skill training phantom and adjacent training workflow were designed to simulate a complicated Laparoscopic Cholecystectomy task; the dissection of the cholecyst’s peritonial layer and the safe clip application on the cystic artery in an uncomfortable environment. A total of 20 training sessions were recorded from 7 subjects (3 non-medicals, 2 residents, 1 expert surgeon and 1 expert MIS surgeon). Workload and learning curves were studied via SURG-TLX. For autonomous non-technical skill assessment, video image data with tracked instruments based on Channel and Spatial Reliability Tracker (CSRT) and force data were utilized. An autonomous time series classification was achieved by a Fully Convolutional Neural Network (FCN), where the class labels were provided by SURG-TLX. Results: With unpaired t-tests, significant differences were found between the two groups (medical professionals and control) in certain workload components (mental demands, physical demands, and situational stress, p<0.0001, 95% confidence interval, p<0.05 for task complexity). With paired t-tests, the learning curves of the trials were also studied; the task complexity resulted in a significant difference between the first and the second trials. Autonomous non-technical skill classification was based on the FCN by applying the tool trajectories and force data as input. This resulted in a high accuracy (85%) on temporal demands classification based on the z component of the used forces and 75% accuracy for classifying mental demands/situational stress with the x component of the used forces validated with Leave One Out Cross-Validation. Conclusions: Non-technical skills and workload components can be classified autonomously based on measured training data. SDS can be effective via automated non-technical skill assessment.

Non-Technical Skill Assessment and Mental Load Evaluation in Robot-Assisted Minimally Invasive Surgery

BACKGROUND: Sensor technologies and data collection practices are changing and improving quality metrics across various domains. Surgical skill assessment in Robot-Assisted Minimally Invasive Surgery (RAMIS) is essential for training and quality assurance. The mental workload on the surgeon (such as time criticality, task complexity, distractions) and non-technical surgical skills (including situational awareness, decision making, stress resilience, communication, leadership) may directly influence the clinical outcome of the surgery. METHODS: A literature search in PubMed, Scopus and PsycNet databases was conducted for relevant scientific publications. The standard PRISMA method was followed to filter the search results, including non-technical skill assessment and mental/cognitive load and workload estimation in RAMIS. Publications related to traditional manual Minimally Invasive Surgery were excluded, and also the usability studies on the surgical tools were not assessed. RESULTS: 50 relevant publications were identified for non-technical skill assessment and mental load and workload estimation in the domain of RAMIS. The identified assessment techniques ranged from self-rating questionnaires and expert ratings to autonomous techniques, citing their most important benefits and disadvantages. CONCLUSIONS: Despite the systematic research, only a limited number of articles was found, indicating that non-technical skill and mental load assessment in RAMIS is not a well-studied area. Workload assessment and soft skill measurement do not constitute part of the regular clinical training and practice yet. Meanwhile, the importance of the research domain is clear based on the publicly available surgical error statistics. Questionnaires and expert-rating techniques are widely employed in traditional surgical skill assessment; nevertheless, recent technological development in sensors and Internet of Things-type devices show that skill assessment approaches in RAMIS can be much more profound employing automated solutions. Measurements and especially big data type analysis may introduce more objectivity and transparency to this critical domain as well. SIGNIFICANCE: Non-technical skill assessment and mental load evaluation in Robot-Assisted Minimally Invasive Surgery is not a well-studied area yet; while the importance of this domain from the clinical outcome's point of view is clearly indicated by the available surgical error statistics.