Maicher Kellen, Danforth Douglas, Price Alan, Zimmerman Laura, Wilcox Bruce, Liston Beth, Cronau Holly, Belknap Laurie, Ledford Cynthia, Way David, Post Doug, Macerollo Allison, Rizer Milisa
From the Department of Obstetrics and Gynecology in the College of Medicine (D.D., L.Z.), Advanced Computing Center for the Arts and Design in the College of Arts and Sciences (A.P.), The Ohio State University; Hospital Medicine (B.L.), Family Medicine (H.C., L.B., D.P., A.M., M.R.), General Internal Medicine (C.L.), Emergency Medicine (D.W.), The Ohio State University Wexner Medical Center, Columbus (K.M.); and Brillig Understanding, Inc (B.W.), San Luis Obispo, CA.
Simul Healthc. 2017 Apr;12(2):124-131. doi: 10.1097/SIH.0000000000000195.
Although traditional virtual patient simulations are designed to teach and assess clinical reasoning skills, few employ conversational dialogue with the patients. The virtual standardized patients (VSPs) described herein represent standardized patients that students interview using natural language. Students take histories and develop differential diagnoses of the VSPs as much as they would with standardized or actual patients. The student-VSP interactions are recorded, creating a comprehensive record of questions and the order in which they were asked, which can be analyzed to assess information-gathering skills. Students document the encounter in an electronic medical record created for the VSPs.
The VSP was developed by integrating a dialogue management system (ChatScript) with emotionally responsive 3D characters created in a high-fidelity game engine (Unity). The system was tested with medical students at the Ohio State University College of Medicine. Students are able to take a history of a VSP, develop a differential diagnosis, and document the encounter in the electronic medical record.
Accuracy of the VSP responses ranged from 79% to 86%, depending on the complexity of the case, type of history obtained, and skill of the student. Students were able to accurately develop an appropriate differential diagnosis on the basis of the information provided by the patient during the encounter.
The VSP enables students to practice their history-taking skills before encounters with standardized or actual patients. Future developments will focus on creating an assessment module that will automatically analyze VSP sessions and provide immediate student feedback.
尽管传统的虚拟患者模拟旨在教授和评估临床推理技能，但很少有模拟采用与患者的对话式交流。本文所述的虚拟标准化患者（VSP）代表了学生使用自然语言进行访谈的标准化患者。学生获取VSP的病史并制定鉴别诊断，就如同他们对待标准化患者或实际患者一样。学生与VSP的互动会被记录下来，形成一份关于问题及其提问顺序的全面记录，可用于分析以评估信息收集技能。学生在为VSP创建的电子病历中记录此次问诊情况。
通过将对话管理系统（ChatScript）与在高保真游戏引擎（Unity）中创建的具有情感反应的3D角色相结合来开发VSP。该系统在俄亥俄州立大学医学院对医学生进行了测试。学生能够获取VSP的病史、制定鉴别诊断，并在电子病历中记录此次问诊情况。
VSP回答的准确率在79%至86%之间，具体取决于病例的复杂程度、所获取病史的类型以及学生的技能水平。学生能够根据问诊期间患者提供的信息准确地制定适当的鉴别诊断。
VSP使学生能够在与标准化患者或实际患者接触之前练习其病史采集技能。未来的发展将集中于创建一个评估模块，该模块将自动分析VSP问诊过程并立即向学生提供反馈。
