Sweet Robert M
1 Department of Urology, Kidney Stone Center, University of Washington , Seattle, Washington.
2 WWAMI Institute for Simulation in Healthcare (WISH), University of Washington , Seattle, Washington.
J Endourol. 2017 Apr;31(S1):S69-S75. doi: 10.1089/end.2016.0613. Epub 2016 Dec 22.
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.
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.
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.
Using the CREST process has and will ensure that the systems we create meet the needs of training and assessing endourologic skills.
培训和评估腔内泌尿外科技术面临的挑战推动了新型培训系统的开发。教育与模拟技术研究中心（CREST）已组建了一个团队并采用了一种方法来推动这一开发过程。
应用了逆向设计原则。一个专家小组首先确定了期望的临床和教育成果。随后将这些成果与学习目标联系起来。进行了总体任务解构，并将主要领域归类为主要涉及决策、操作技能或沟通。进行了更详细的认知任务分析，以确定相关解剖结构/组织、指标和错误并确定其优先级。使用数字解剖学家和临床医生根据临床数据集创建了参考解剖结构。三维打印有助于这一过程。在可能的情况下，利用从人体组织获得的数据重新创建合成或虚拟组织的行为和纹理。使用嵌入式传感器/标记和/或基于计算机的系统来促进客观指标的收集。在整个工程开发过程中进行了学习验证和确认。
CREST采用这种方法创建了9个与腔内泌尿外科相关的培训系统。这些系统包括基础腹腔镜技能（BLUS）、膀胱尿道吻合术、肾盂成形术、膀胱镜检查程序、支架置入、硬性和软性输尿管镜检查、绿激光前列腺汽化术（GL Sim）、C形臂引导下经皮穿刺（CAT）、肾切开取石术（NLM）以及血管损伤模型。采用了多种模式，包括“智能”物理模型、虚拟现实、增强现实和视频。已收集到关于这些系统培训和评估的大量有效性证据。CREST正在与国防部共同开发一个基于人体模型的开源模块化平台，该平台将通过通用生理引擎、学习管理系统、标准数据连接器和标准来统一这些以及其他商业任务训练器。
采用CREST流程已经并将确保我们创建的系统满足腔内泌尿外科技术培训和评估的需求。
