Design and utilisation of a novel, high-fidelity, low-cost, hybrid-tissue simulation model to facilitate training in robot-assisted partial nephrectomy.

Robot-assisted partial nephrectomy (RAPN) has rapidly evolved as the standard of care for appropriately selected renal tumours, offering key patient benefits over radical nephrectomy or open surgical approaches. Accordingly, RAPN is a key competency that urology trainees wishing to treat kidney cancer must master. Training in robotic surgery is subject to numerous challenges, and simulation has been established as valuable step in the robotic learning curve. However, simulation models are often both expensive and suboptimal in fidelity. This means that the number of practice repetitions for a trainee may limited by cost restraints, and that trainees may struggle to reconcile the skills obtained in the simulation laboratory with real-world practice in the operating room. We have developed a high-fidelity, low-cost, customizable model for RAPN simulation based on porcine tissue. The model has been utilised in teaching courses at our institution, confirming both feasibility of use and high user acceptability. We share the design of our model in this proof-of-concept report.

Collating evidence to support the validation of a simulated laparotomy incision and closure-training model.

BACKGROUND: It is essential to evaluate the functionality of surgical simulation models, in order to determine whether they perform as intended. In this study, we assessed the use of a simulated laparotomy incision and closure-training model by collating validity evidence to determine its utility as well as pre and post-test interval data. METHOD: This was a quantitative study design, informed by Messick's unified validity framework. In total, 93 participants (surgical trainees â€‹= â€‹80, experts â€‹= â€‹13) participated in this study. Evaluation of content validity and the models' relationships with other variables was conducted, along with a pre and post-test confidence assessment. RESULTS: The model was deemed realistic and useful as a teaching tool, providing strong content validity evidence. In assessment of relationships with other variables, the expert group out-performed the novice group conclusively. Pre and post-test evaluation reported a statistically significant increase in confidence levels. CONCLUSION: We present strong validity evidence of a novel laparotomy incision and closure simulation-training model.

Developing a clamshell thoracotomy training model to support hybrid teaching in simulation-based education.

OBJECTIVE: Thoracotomy is an acute, time-sensitive procedure. Simulation-based education provides a safe-learning platform to learn these techniques under close supervision. METHODS: We used the spiral model and concepts of functional fidelity to guide the evolutionary design and fabrication of a hybrid thoracotomy simulator. RESULTS: This model simulates a clamshell thoracotomy that physically integrates with bespoke manikins and adds a high-fidelity technical skills element to immersive team-based simulation training. CONCLUSIONS: We describe the creation of a thoracotomy simulation model that allows trainees to practice these techniques in a safe-learning environment.

A Mixed Methods Study Identifying the Competencies of Health Care Simulation Technicians.

INTRODUCTION: Health care simulation technicians (HSTs), also referred to as simulation operations specialists, are essential to the delivery of simulation-based education. The HST role draws on a broad range of knowledge, skills, and attitude competencies. However, because of the neoteric nature of the HST role and the ambiguity surrounding the core responsibilities of the position, it has proved difficult to identify the competencies required to perform this role successfully. This study aims to identify the knowledge, skills, and attitude competencies required by HSTs. METHODS: A mixed methods approach was used in this study. Data were collected from (1) online searches of HST job descriptions and (2) semistructured interviews about the competencies required by HSTs with 10 HSTs, 10 health care simulation educators, and 10 health care simulation center managers/director. The data from the job descriptions and interviews were analyzed using thematic analysis, using a framework method to guide the coding. RESULTS: A total of 59 competencies were identified from the job descriptions and 65 competencies from the interviews. This analysis resulted in the identification of 9 competency domains: 3 knowledge domains (technical, clinical, and pedagogic), 4 skills domains (resourcefulness, pedagogic, team, and technical), and 2 attitudes domains (professional and "can-do" mentality). CONCLUSIONS: The identification of the competencies required by HSTs will support the selection of candidates with the attributes that will allow them to be successful in this role and guide continuous professional development opportunities for current and future HSTs.