Sensor-based indicators of performance changes between sessions during robotic surgery training

The mental workload of ICU nurses performing human-machine tasks and associated factors: A cross-sectional questionnaire survey

AIMS

To assess the level of mental workload (MWL) of intensive care unit (ICU) nurses in performing different human-machine tasks and examine the predictors of the MWL.

DESIGN

A cross-sectional questionnaire study.

METHODS

Between January and February 2021, data were collected from ICU nurses (n = 427) at nine tertiary hospitals selected from five (east, west, south, north, central) regions in China through an electronic questionnaire, including sociodemographic questions, the National Aeronautics and Space Administration Task Load Index, General Self-Efficacy Scale, Difficulty-assessing Index System of Nursing Operation Technique, and System Usability Scale. Descriptive statistics, t-tests, one-way ANOVA and multiple linear regression models were used.

RESULTS

ICU nurses experienced a medium level of MWL (score 52.04 on a scale of 0-100) while performing human-machine tasks. ICU nurses' MWL was notably higher in conducting first aid and life support tasks (using defibrillators or ventilators). Predictors of MWL were task difficulty, system usability, professional title, age, self-efficacy, ICU category, and willingness to study emerging technology actively. Task difficulty and system usability were the strongest predictors of nearly all typical tasks.

CONCLUSION

ICU nurses experience a medium MWL while performing human-machine tasks, but higher mental, temporal, and effort are perceived compared to physical demands. The MWL varied significantly across different human-machine tasks, among which are significantly higher: first aid and life support and information-based human-machine tasks. Task difficulty and system availability are decisive predictors of MWL.

IMPACT

This is the first study to investigate the level of MWL of ICU nurses performing different representative human-machine tasks and to explore its predictors, which provides a reference for future research. These findings suggest that healthcare organizations should pay attention to the MWL of ICU nurses and develop customized management strategies based on task characteristics to maintain a moderate level of MWL, thus enabling ICU nurses to perform human-machine tasks better.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Eye-Tracking in Physical Human-Robot Interaction: Mental Workload and Performance Prediction

BACKGROUND

In Physical Human-Robot Interaction (pHRI), the need to learn the robot's motor-control dynamics is associated with increased cognitive load. Eye-tracking metrics can help understand the dynamics of fluctuating mental workload over the course of learning.

OBJECTIVE

The aim of this study was to test eye-tracking measures' sensitivity and reliability to variations in task difficulty, as well as their performance-prediction capability, in physical human-robot collaboration tasks involving an industrial robot for object comanipulation.

METHODS

Participants (9M, 9F) learned to coperform a virtual pick-and-place task with a bimanual robot over multiple trials. Joint stiffness of the robot was manipulated to increase motor-coordination demands. The psychometric properties of eye-tracking measures and their ability to predict performance was investigated.

RESULTS

Stationary Gaze Entropy and pupil diameter were the most reliable and sensitive measures of workload associated with changes in task difficulty and learning. Increased task difficulty was more likely to result in a robot-monitoring strategy. Eye-tracking measures were able to predict the occurrence of success or failure in each trial with 70% sensitivity and 71% accuracy.

CONCLUSION

The sensitivity and reliability of eye-tracking measures was acceptable, although values were lower than those observed in cognitive domains. Measures of gaze behaviors indicative of visual monitoring strategies were most sensitive to task difficulty manipulations, and should be explored further for the pHRI domain where motor-control and internal-model formation will likely be strong contributors to workload.

APPLICATION

Future collaborative robots can adapt to human cognitive state and skill-level measured using eye-tracking measures of workload and visual attention.

Using neuroimaging to assess brain activity and areas associated with surgical skills: a systematic review

BACKGROUND

Surgical skills acquisition is under continuous development due to the emergence of new technologies, and there is a need for assessment tools to develop along with these. A range of neuroimaging modalities has been used to map the functional activation of brain networks while surgeons acquire novel surgical skills. These have been proposed as a method to provide a deeper understanding of surgical expertise and offer new possibilities for the personalized training of future surgeons. With studies differing in modalities, outcomes, and surgical skills there is a need for a systematic review of the evidence. This systematic review aims to summarize the current knowledge on the topic and evaluate the potential use of neuroimaging in surgical education.

METHODS

We conducted a systematic review of neuroimaging studies that mapped functional brain activation while surgeons with different levels of expertise learned and performed technical and non-technical surgical tasks. We included all studies published before July 1st, 2023, in MEDLINE, EMBASE and WEB OF SCIENCE.

RESULTS

38 task-based brain mapping studies were identified, consisting of randomized controlled trials, case-control studies, and observational cohort or cross-sectional studies. The studies employed a wide range of brain mapping modalities, including electroencephalography, functional magnetic resonance imaging, positron emission tomography, and functional near-infrared spectroscopy, activating brain areas involved in the execution and sensorimotor or cognitive control of surgical skills, especially the prefrontal cortex, supplementary motor area, and primary motor area, showing significant changes between novices and experts.

CONCLUSION

Functional neuroimaging can reveal how task-related brain activity reflects technical and non-technical surgical skills. The existing body of work highlights the potential of neuroimaging to link task-related brain activity patterns with the individual level of competency or improvement in performance after training surgical skills. More research is needed to establish its validity and usefulness as an assessment tool.

Surgical Sabermetrics: A Scoping Review of Technology-enhanced Assessment of Nontechnical Skills in the Operating Room

OBJECTIVE

To evaluate the current evidence for surgical sabermetrics: digital methods of assessing surgical nontechnical skills and investigate the implications for enhancing surgical performance.

BACKGROUND

Surgeons need high-quality, objective, and timely feedback to optimize performance and patient safety. Digital tools to assess nontechnical skills have the potential to reduce human bias and aid scalability. However, we do not fully understand which of the myriad of digital metrics of performance assessment have efficacy for surgeons.

METHODS

A systematic review was conducted by searching PubMed, EMBASE, CINAHL, and PSYCINFO databases following PRISMA-ScR guidelines. MeSH terms and keywords included "Assessment," "Surgeons," and "Technology". Eligible studies included a digital assessment of nontechnical skills for surgeons, residents, and/or medical students within an operative context.

RESULTS

From 19,229 articles screened, 81 articles met the inclusion criteria. The studies varied in surgical specialties, settings, and outcome measurements. A total of 122 distinct objective, digital metrics were utilized. Studies digitally measured at least 1 category of surgical nontechnical skill using a single (n=54) or multiple objective measures (n=27). The majority of studies utilized simulation (n=48) over live operative settings (n=32). Surgical Sabermetrics has been demonstrated to be beneficial in measuring cognitive load (n=57), situation awareness (n=24), communication (n=3), teamwork (n=13), and leadership (n=2). No studies measured intraoperative decision-making.

CONCLUSIONS

The literature detailing the intersection between surgical data science and operative nontechnical skills is diverse and growing rapidly. Surgical Sabermetrics may provide a promising modifiable technique to achieve desirable outcomes for both the surgeon and the patient. This study identifies a diverse array of measurements possible with sensor devices and highlights research gaps, including the need for objective assessment of decision-making. Future studies may advance the integration of physiological sensors to provide a holistic assessment of surgical performance.

A Review of Recent Advancements in Sensor-Integrated Medical Tools

A medical tool is a general instrument intended for use in the prevention, diagnosis, and treatment of diseases in humans or other animals. Nowadays, sensors are widely employed in medical tools to analyze or quantify disease-related parameters for the diagnosis and monitoring of patients' diseases. Recent explosive advancements in sensor technologies have extended the integration and application of sensors in medical tools by providing more versatile in vivo sensing capabilities. These unique sensing capabilities, especially for medical tools for surgery or medical treatment, are getting more attention owing to the rapid growth of minimally invasive surgery. In this review, recent advancements in sensor-integrated medical tools are presented, and their necessity, use, and examples are comprehensively introduced. Specifically, medical tools often utilized for medical surgery or treatment, for example, medical needles, catheters, robotic surgery, sutures, endoscopes, and tubes, are covered, and in-depth discussions about the working mechanism used for each sensor-integrated medical tool are provided.

Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking

OBJECTIVE

(1) To assess mental workloads of intensive care unit (ICU) nurses in 12-hour working shifts (days and nights) using eye movement data; (2) to explore the impact of stress on the ocular metrics of nurses performing patient care in the ICU.

BACKGROUND

Prior studies have employed workload scoring systems or accelerometer data to assess ICU nurses' workload. This is the first naturalistic attempt to explore nurses' mental workload using eye movement data.

METHODS

Tobii Pro Glasses 2 eye-tracking and Empatica E4 devices were used to collect eye movement and physiological data from 15 nurses during 12-hour shifts (252 observation hours). We used mixed-effect models and an ordinal regression model with a random effect to analyze the changes in eye movement metrics during high stress episodes.

RESULTS

While the cadence and characteristics of nurse workload can vary between day shift and night shift, no significant difference in eye movement values was detected. However, eye movement metrics showed that the initial handoff period of nursing shifts has a higher mental workload compared with other times. Analysis of ocular metrics showed that stress is positively associated with an increase in number of eye fixations and gaze entropy, but negatively correlated with the duration of saccades and pupil diameter.

CONCLUSION

Eye-tracking technology can be used to assess the temporal variation of stress and associated changes with mental workload in the ICU environment. A real-time system could be developed for monitoring stress and workload for intervention development.

Measures of performance and proficiency in robotic assisted surgery: a systematic review

Robotic assisted surgery (RAS) has seen a global rise in adoption. Despite this, there is not a standardised training curricula nor a standardised measure of performance. We performed a systematic review across the surgical specialties in RAS and evaluated tools used to assess surgeons' technical performance. Using the PRISMA 2020 guidelines, Pubmed, Embase and the Cochrane Library were searched systematically for full texts published on or after January 2020-January 2022. Observational studies and RCTs were included; review articles and systematic reviews were excluded. The papers' quality and bias score were assessed using the Newcastle Ottawa Score for the observational studies and Cochrane Risk Tool for the RCTs. The initial search yielded 1189 papers of which 72 fit the eligibility criteria. 27 unique performance metrics were identified. Global assessments were the most common tool of assessment (n = 13); the most used was GEARS (Global Evaluative Assessment of Robotic Skills). 11 metrics (42%) were objective tools of performance. Automated performance metrics (APMs) were the most widely used objective metrics whilst the remaining (n = 15, 58%) were subjective. The results demonstrate variation in tools used to assess technical performance in RAS. A large proportion of the metrics are subjective measures which increases the risk of bias amongst users. A standardised objective metric which measures all domains of technical performance from global to cognitive is required. The metric should be applicable to all RAS procedures and easily implementable. Automated performance metrics (APMs) have demonstrated promise in their wide use of accurate measures.

Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review

BACKGROUND

There is a need to standardize training in robotic surgery, including objective assessment for accreditation. This systematic review aimed to identify objective tools for technical skills assessment, providing evaluation statuses to guide research and inform implementation into training curricula.

METHODS

A systematic literature search was conducted in accordance with the PRISMA guidelines. Ovid Embase/Medline, PubMed and Web of Science were searched. Inclusion criterion: robotic surgery technical skills tools. Exclusion criteria: non-technical, laparoscopy or open skills only. Manual tools and automated performance metrics (APMs) were analysed using Messick's concept of validity and the Oxford Centre of Evidence-Based Medicine (OCEBM) Levels of Evidence and Recommendation (LoR). A bespoke tool analysed artificial intelligence (AI) studies. The Modified Downs-Black checklist was used to assess risk of bias.

RESULTS

Two hundred and forty-seven studies were analysed, identifying: 8 global rating scales, 26 procedure-/task-specific tools, 3 main error-based methods, 10 simulators, 28 studies analysing APMs and 53 AI studies. Global Evaluative Assessment of Robotic Skills and the da Vinci Skills Simulator were the most evaluated tools at LoR 1 (OCEBM). Three procedure-specific tools, 3 error-based methods and 1 non-simulator APMs reached LoR 2. AI models estimated outcomes (skill or clinical), demonstrating superior accuracy rates in the laboratory with 60 per cent of methods reporting accuracies over 90 per cent, compared to real surgery ranging from 67 to 100 per cent.

CONCLUSIONS

Manual and automated assessment tools for robotic surgery are not well validated and require further evaluation before use in accreditation processes.PROSPERO: registration ID CRD42022304901.

Utilizing Eye Tracking to Assess Medical Student Non-Technical Performance During Scenario-Based Simulation: Results of a Pilot Study

Background

Non-technical skills (NTS) are essential for safe surgical patient management. However, assessing NTS involves observer-based ratings, which can introduce bias. Eye tracking (ET) has been proposed as an effective method to capture NTS. The purpose of the current study was to determine if ET metrics are associated with NTS performance.

Methods

Participants wore a mobile ET system and participated in two patient care simulations, where they managed a deteriorating patient. The scenarios featured several challenges to leadership, which were evaluated using a 4-point Likert scale. NTS were evaluated by trained raters using the Non-Technical Skills for Surgeons (NOTSS) scale. ET metrics included percentage of fixations and visits on areas of interest.

Results

Ten medical students participated. Average visit duration on the patient was negatively correlated with participants' communication and leadership. Average visit duration on the patient's intravenous access was negatively correlated with participants' decision making and situation awareness.

Conclusions

Our preliminary data suggests that visual attention on the patient was negatively associated with NTS and may indicate poor comprehension of the patient's status due to heightened cognitive load. In future work, researchers and educators should consider using ET to objectively evaluate and provide feedback on their NTS.

Relating process and outcome metrics for meaningful and interpretable cannulation skill assessment: A machine learning paradigm

BACKGROUND AND OBJECTIVES

The quality of healthcare delivery depends directly on the skills of clinicians. For patients on hemodialysis, medical errors or injuries caused during cannulation can lead to adverse outcomes, including potential death. To promote objective skill assessment and effective training, we present a machine learning approach, which utilizes a highly-sensorized cannulation simulator and a set of objective process and outcome metrics.

METHODS

In this study, 52 clinicians were recruited to perform a set of pre-defined cannulation tasks on the simulator. Based on data collected by sensors during their task performance, the feature space was then constructed based on force, motion, and infrared sensor data. Following this, three machine learning models- support vector machine (SVM), support vector regression (SVR), and elastic net (EN)- were constructed to relate the feature space to objective outcome metrics. Our models utilize classification based on the conventional skill classification labels as well as a new method that represents skill on a continuum.

RESULTS

With less than 5% of trials misplaced by two classes, the SVM model was effective in predicting skill based on the feature space. In addition, the SVR model effectively places both skill and outcome on a fine-grained continuum (versus discrete divisions) that is representative of reality. As importantly, the elastic net model enabled the identification of a set of process metrics that highly impact outcomes of the cannulation task, including smoothness of motion, needle angles, and pinch forces.

CONCLUSIONS

The proposed cannulation simulator, paired with machine learning assessment, demonstrates definite advantages over current cannulation training practices. The methods presented here can be adopted to drastically increase the effectiveness of skill assessment and training, thereby potentially improving clinical outcomes of hemodialysis treatment.

In robot we trust? The effect of emotional expressions and contextual cues on anthropomorphic trustworthiness

Following the evolution of technology and its application in various daily contexts, social robots work as an advanced artificial intelligence (AI) system to interact with humans. However, limited research has been done to discuss the role of emotional expressions and contextual cues in influencing anthropomorphic trustworthiness, especially from the design perspective. To address this research gap, the current study designed a specific robot prototype and conducted two lab experiments to explore the effect of emotional expressions and contextual cues on trustworthiness via a combination of subjective ratings and physiological measures. Results showed that: 1) positive (vs. negative) emotional expressions enjoyed a higher level of anthropomorphic trustworthiness and visual attention; 2) regulatory fit was expanded in parasocial interaction and worked as a prime to activate anthropomorphic trustworthiness for social robots. Theoretical contributions and design implications were also discussed in this study.

Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills

Previous studies in robotic-assisted surgery (RAS) have studied cognitive workload by modulating surgical task difficulty, and many of these studies have relied on self-reported workload measurements. However, contributors to and their effects on cognitive workload are complex and may not be sufficiently summarized by changes in task difficulty alone. This study aims to understand how multi-task requirement contributes to the prediction of cognitive load in RAS under different task difficulties. Multimodal physiological signals (EEG, eye-tracking, HRV) were collected as university students performed simulated RAS tasks consisting of two types of surgical task difficulty under three different multi-task requirement levels. EEG spectral analysis was sensitive enough to distinguish the degree of cognitive workload under both surgical conditions (surgical task difficulty/multi-task requirement). In addition, eye-tracking measurements showed differences under both conditions, but significant differences of HRV were observed in only multi-task requirement conditions. Multimodal-based neural network models have achieved up to 79% accuracy for both surgical conditions.

Using Task-Evoked Pupillary Response to Predict Clinical Performance during a Simulation Training

Training in healthcare skills can be affected by trainees' workload when completing a task. Due to cognitive processing demands being negatively correlated to clinical performance, assessing mental workload through objective measures is crucial. This study aimed to investigate task-evoked changes in pupil size as reliable markers of mental workload and clinical performance. A sample of 49 nursing students participated in a cardiac arrest simulation-based practice. Measurements of cognitive demands (NASA-Task Load Index), physiological parameters (blood pressure, oxygen saturation, and heart rate), and pupil responses (minimum, maximum, and difference diameters) throughout revealed statistically significant differences according to performance scores. The analysis of a multiple regression model produced a statistically significant pattern between pupil diameter differences and heart rate, systolic blood pressure, workload, and performance (R² = 0.280; F (6, 41) = 2.660; p < 0.028; d = 2.042). Findings suggest that pupil variations are promising markers to complement physiological metrics for predicting mental workload and clinical performance in medical practice.

Effect of time-delay on lunar sampling tele-operations: Evidences from cardiac, ocular and behavioral measures

The purpose of this study is to quantify performance in human-robot interaction under time-delay conditions in a lunar tele-operations sampling task, by testing the hypothesis that an increase of time-delay would lead to higher perceived workload and lower human performance in human-robotic integrated operations. Tele-operation is key in the exploration of the Moon, and allows for robotic elements to be controlled from orbital infrastructure and other planetary bodies such as the Earth. Considering that future missions aim to control rovers (amongst others for sampling tasks) from Earth (delay: 3s), the Gateway (delay: 0.5s) and the Moon (delay: 0s), control under the time-delay conditions for these locations must be studied. Time-delay can affect performance, and understanding the performance means that mission operations can be planned bottom-up, which benefits both the preparation of the crew and the design of rovers. An experiment was conducted with 18 engineers who were assigned to control a robotic arm under three time-delay conditions, representing the three control locations. Several metrics were derived from cardiac, ocular, subjective and behavioral measures. The analyses disclosed that the large time-delay condition statistically increased the perceived workload, the time to complete the mission and decreased heart rate variability compared to the other conditions. However, no effect of time-delay was found on attentional and executive abilities. The metrics proved to be effective in the study of performance quantification in human-robot interaction for tele-operations in lunar control scenarios. This approach can be implemented for a larger range of robotic activities, such as tele-operated driving.

The Measurement of Cognitive Workload in Surgery Using Pupil Metrics: A Systematic Review and Narrative Analysis

INTRODUCTION

Increased cognitive workload (CWL) is a well-established entity that can impair surgical performance and increase the likelihood of surgical error. The use of pupil and gaze tracking data is increasingly being used to measure CWL objectively in surgery. The aim of this review is to summarize and synthesize the existing evidence that surrounds this.

METHODS

A systematic review was undertaken in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A search of OVID MEDLINE, IEEE Xplore, Web of Science, Google Scholar, APA PsychINFO, and EMBASE was conducted for articles published in English between 1990 and January 2021. In total, 6791 articles were screened and 32 full-text articles were selected based on the inclusion criteria. A narrative analysis was undertaken in view of the heterogeneity of studies.

RESULTS

Seventy-eight percent of selected studies were deemed high quality. The most frequent surgical environment and task studied was surgical simulation (75%) and performance of laparoscopic skills (56%) respectively. The results demonstrated that the current literature can be broadly categorized into pupil, blink, and gaze metrics used in the assessment of CWL. These can be further categorized according to their use in the context of CWL: (1) direct measurement of CWL (n = 16), (2) determination of expertise level (n = 14), and (3) predictors of performance (n = 2).

CONCLUSIONS

Eye-tracking data provide a wealth of information; however, there is marked study heterogeneity. Pupil diameter and gaze entropy demonstrate promise in CWL assessment. Future work will entail the use of artificial intelligence in the form of deep learning and the use of a multisensor platform to accurately measure CWL.

Virtual Reality Simulator Enhances Ergonomics Skills for Neurosurgeons

This paper aims to assess the needs of neurosurgical training in order to strategize the future plans for simulation and rehearsal. The project main objective is to investigate the ability virtual reality to enhance the training.An online questionnaire has been conducted among surgeons practicing in different countries across the globe. The study shows significant differences in rehearsal methods and surgical teaching methods practiced by the respondents. Among respondents, 90% did believe that virtual reality technology can serve surgical training, and almost all respondents agreed that there is a gap in the existing neurosurgical training in terms of operating room ergonomics. Adequate education on surgical ergonomics might lead to an improvement in the outcomes for both surgeon and patient. The contribution of the paper is two fold. From one side investigates the new requirements for the enhancement of Neurosurgenos’ training and adoption on Virtual Reality Simulator. From the other side contributes to the body of knowledge related to the required Ergonomics skills.

Exploring the roles of artificial intelligence in surgical education: A scoping review

BACKGROUND

Technology-enhanced teaching and learning, including Artificial Intelligence (AI) applications, has started to evolve in surgical education. Hence, the purpose of this scoping review is to explore the current and future roles of AI in surgical education.

METHODS

Nine bibliographic databases were searched from January 2010 to January 2021. Full-text articles were included if they focused on AI in surgical education.

RESULTS

Out of 14,008 unique sources of evidence, 93 were included. Out of 93, 84 were conducted in the simulation setting, and 89 targeted technical skills. Fifty-six studies focused on skills assessment/classification, and 36 used multiple AI techniques. Also, increasing sample size, having balanced data, and using AI to provide feedback were major future directions mentioned by authors.

CONCLUSIONS

AI can help optimize the education of trainees and our results can help educators and researchers identify areas that need further investigation.

A systematic review on artificial intelligence in robot-assisted surgery

BACKGROUND

Despite the extensive published literature on the significant potential of artificial intelligence (AI) there are no reports on its efficacy in improving patient safety in robot-assisted surgery (RAS). The purposes of this work are to systematically review the published literature on AI in RAS, and to identify and discuss current limitations and challenges.

MATERIALS AND METHODS

A literature search was conducted on PubMed, Web of Science, Scopus, and IEEExplore according to PRISMA 2020 statement. Eligible articles were peer-review studies published in English language from January 1, 2016 to December 31, 2020. Amstar 2 was used for quality assessment. Risk of bias was evaluated with the Newcastle Ottawa Quality assessment tool. Data of the studies were visually presented in tables using SPIDER tool.

RESULTS

Thirty-five publications, representing 3436 patients, met the search criteria and were included in the analysis. The selected reports concern: motion analysis (n = 17), urology (n = 12), gynecology (n = 1), other specialties (n = 1), training (n = 3), and tissue retraction (n = 1). Precision for surgical tools detection varied from 76.0% to 90.6%. Mean absolute error on prediction of urinary continence after robot-assisted radical prostatectomy (RARP) ranged from 85.9 to 134.7 days. Accuracy on prediction of length of stay after RARP was 88.5%. Accuracy on recognition of the next surgical task during robot-assisted partial nephrectomy (RAPN) achieved 75.7%.

CONCLUSION

The reviewed studies were of low quality. The findings are limited by the small size of the datasets. Comparison between studies on the same topic was restricted due to algorithms and datasets heterogeneity. There is no proof that currently AI can identify the critical tasks of RAS operations, which determine patient outcome. There is an urgent need for studies on large datasets and external validation of the AI algorithms used. Furthermore, the results should be transparent and meaningful to surgeons, enabling them to inform patients in layman's words.

REGISTRATION

Review Registry Unique Identifying Number: reviewregistry1225.

Simulation-Based Training in Robotic Surgery: Contemporary and Future Methods

While robotic surgery has grown in popularity and scope over the past decade, there is a persistent need for simulation-based training as surgeons adapt from the working at the bedside to the immersive and multisensory tasks at the console. From dry laboratory to virtual reality (VR) environments, simulation can be used to train surgeons in basic tasks, complex operative steps, and coordination of whole operations with members of the entire operating room (OR) staff. By integrating simulation into mentored training programs, surgeons can reduce the number of cases required to master a complex operation. Future VR based simulation will become essential to the adaptation of the surgical workforce to new technologies and adoption of emerging robotic platforms. Ultimately, robotic simulation will set standards for credentialing of new surgeons.