The CREST Simulation Development Process: Training the Next Generation.

BACKGROUND

The challenges of training and assessing endourologic skill have driven the development of new training systems. The Center for Research in Education and Simulation Technologies (CREST) has developed a team and a methodology to facilitate this development process.

METHODS

Backwards design principles were applied. A panel of experts first defined desired clinical and educational outcomes. Outcomes were subsequently linked to learning objectives. Gross task deconstruction was performed, and the primary domain was classified as primarily involving decision-making, psychomotor skill, or communication. A more detailed cognitive task analysis was performed to elicit and prioritize relevant anatomy/tissues, metrics, and errors. Reference anatomy was created using a digital anatomist and clinician working off of a clinical data set. Three dimensional printing can facilitate this process. When possible, synthetic or virtual tissue behavior and textures were recreated using data derived from human tissue. Embedded sensors/markers and/or computer-based systems were used to facilitate the collection of objective metrics. A learning Verification and validation occurred throughout the engineering development process.

RESULTS

Nine endourology-relevant training systems were created by CREST with this approach. Systems include basic laparoscopic skills (BLUS), vesicourethral anastomosis, pyeloplasty, cystoscopic procedures, stent placement, rigid and flexible ureteroscopy, GreenLight PVP (GL Sim), Percutaneous access with C-arm (CAT), Nephrolithotomy (NLM), and a vascular injury model. Mixed modalities have been used, including "smart" physical models, virtual reality, augmented reality, and video. Substantial validity evidence for training and assessment has been collected on systems. An open source manikin-based modular platform is under development by CREST with the Department of Defense that will unify these and other commercial task trainers through the common physiology engine, learning management system, standard data connectors, and standards.

CONCLUSION

Using the CREST process has and will ensure that the systems we create meet the needs of training and assessing endourologic skills.