Abstract
Surgical simulation is increasingly being recognised for the considerable benefits it can confer to medical training and assessment. Simulation training has been applied widely to the majority of medical and surgical disciplines and is supported by a large body of evidence. Robotic surgery, as an emerging speciality, has been enthusiastically adopted by both surgeons and patients. To ensure the safe and successful implementation of this new technology, evidence-based and effective training programmes need to be developed. Robotic surgery offers the unique opportunity to embed the principles of simulation based education within the new training programmes. The aim of this work, entitled the Multi-Institutional Validation and Assessment of Training Modalities in Robotic Surgery (The MARS) Project is to identify, assess and validate the key components required for the delivery of effective robotic surgical simulation.Each potential component of the robotic simulation curriculum was addressed separately. Initially a comprehensive review of current simulation practices was conducted to inform the rest of the work. Uniquely the review focussed on the process and structure of simulation training as reported in the published literature in contrast to the outcomes based reviews undertaken previously. All the principle modalities of simulation training applicable to robotic surgery and their potential role within a robotic surgical curriculum were then addressed. The role of virtual reality (VR) training was explored for both basic and procedural training. VR is widely used for basic surgical skills training but this training remains largely ad hoc. To date no structured VR simulation training programmes have been developed for robotic surgery. Deliberate practice is widely be recognised for its important role in training, greatly enhancing skill acquisition. Incorporating these principles, I devised a robotic VR basic skills training programme employing formal benchmark scores. Procedural VR simulation represents an emerging simulation modality that offers the opportunity for more advanced skills training. As part of this work, I undertook the first validation assessment of procedural VR training for robotic surgery. Dry lab training is another common approach to basic skills training. In comparison to other surgical techniques, dry lab training is less commonly used in robotic surgery. To address this I completed a comparative analysis of dry lab and basic VR training to determine whether either technique offers any advantages. The key modalities for advanced, procedural surgical skills training are human cadaver and live animal wet lab training. Yet neither form of simulation have been comprehensively evaluated for robotic surgery. Studies of the role of both modalities for robotic training were undertaken. Given the absence of human cadaveric training for robotic training, I developed the first UK training programme and evaluated the outcomes from five courses run between 2015 and 2017. To assess the role of live animal training, I conducted a survey of outcomes following training at the Minimal Invasiv Udviklings Center, Aalborg University Hospital, Denmark. Alongside technical skills training, non technical skills (NTS) are of critical importance to all surgical training. As part of this work, I created the first NTS rating system specifically for robotic surgery. Finally mental imagery (MI) training, widely used in competitive sports as an adjunct to training, remains under-utilised in surgery. Prior studies have demonstrated that potential for MI in surgery. I developed a MI programme for robotic surgery and completed an RCT evaluating its role for basic technical and non technical skills training. The results and conclusions from these studies supporter further recommendations on a structure for simulation-based robotic surgical training. Further work will be required to validate the effectiveness of the training curriculum.
| Date of Award | 1 Apr 2021 |
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| Original language | English |
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| Supervisor | Prokar Dasgupta (Supervisor) & Kamran Ahmed (Supervisor) |