Research Platform for Medical Education Informatics

该设备将支持医学教育信息学的研究。我们正在目睹医学教育的革命性变化，利用现代信息学的力量推动了许多进步。传统上，医学实习生在资深医生的监督下对真实的病人进行实践。此外，在技术密集型医学时代，这种模式使患者面临潜在错误的风险。通过医学教育研究，计算机辅助模拟环境已被证明在实践的早期阶段具有上级培训优势。目前大多数医疗模拟器过于简单，只是用人体模型代替病人。 与所要求的设备，我们将进行突破性的研究到新的医疗模拟方法和系统授权的信息学。我们设想，在这些模拟器系统中，物理现实与数字，虚拟或全息体验相结合。微型和非接触式传感器和仪器阵列获得丰富的数字数据流，使我们能够计算客观，准确和一致的技能，性能和能力指标，并为学习者提供即时的定量反馈。然后，在第一次接触患者时，使用类似的仪器测量受训者的表现，而不干扰患者护理。我们将开发培训生数据、成绩和证书的认证管理方法。我们将研究新的分析和机器学习方法，以便从模拟训练和患者接触期间收集的数字感官信息中揭示学员技能和能力的细节。 以前在皇后学院，我们已经开发了Perk Tutor开源软件平台，在原型实验计算机辅助医学教育方法方面提供了卓越的速度和多功能性。我们已经广泛发表了我们的结果使用Perk导师在外科，麻醉学，泌尿科，肿瘤学，急诊医学，胃肠病学，心脏病学和内科。Perk Tutor支持的跨学科合作组合不断增长，证明了对医学教育信息学的需求和兴趣日益增加。在展示了Perk Tutor作为系统集成平台的实力之后，我们要求工具和仪器来支持活动，以吸引更多的NSERC资助的研究人员，他们将调查广泛的计算和工程问题，这些问题是进一步开展医学教育研究的关键。仪器齐全的Perk Tutor研究平台具有巨大的潜力，可以显着改善一系列专业的当代医学教育实践，其中申请人是国际公认的转化临床工程科学家专家。

The equipment will support research in medical education informatics. We are witnessing revolutionary changes in medical education, where much progress has been driven by harnessing the power of modern informatics. Traditionally, medical trainees practice on real patients under the supervision of senior physicians. In addition, to exposing patients to risk by potential errors, in the era of skill-intensive medicine this model is no longer viable. Computer-assisted simulated environments have been proven, through medical education research, to be superior for training in the early stages of practice. Most current medical simulators are overly simplistic, merely replacing the patient with a mannequin. With the equipment requested, we will conduct ground-breaking research into novel medical simulation methods and systems empowered by informatics. We envision that in these simulator systems physical reality is blended with digital, virtual or holographic experiences. Arrays of miniature and non-contact sensors and instruments acquire a rich flow of digital data allowing us to compute objective, accurate and consistent metrics of skills, performance, and competence, and also to provide immediate quantitative feedback to the learner. Then, during first patient encounters, trainee performance is measured using similar instrumentation without interfering with patient care. We will develop approaches for authenticated management of trainee data, grades and certificates. We will investigate novel analytics and machine learning methods to allow to reveal the minutia of trainee skills and competence from digital sensory information collected during simulation training and patient encounters. Previously at Queen’s, we have developed the Perk Tutor open source software platform that offers exceptional speed and versatility in prototyping experimental computer-assisted medical education approaches. We have extensively published our results using Perk Tutor in surgery, anesthesiology, urology, oncology, emergency medicine, gastroenterology, cardiology and internal medicine. The portfolio of our cross-disciplinary collaborations enabled by Perk Tutor is constantly growing, attesting to the increasing need for and interest in medical education informatics. Having demonstrated the prowess of Perk Tutor as a system integration platform, we request tools and instruments to support activities to engage an extended group of NSERC-funded researchers, who will investigate a wide spectrum computing and engineering problems that are key to furthering medical education research. The fully instrumented Perk Tutor research platform holds strong potential to significantly improve contemporary medical education practices in a range of specialties in which the applicants are internationally acknowledged experts as translational clinical engineering scientists.